Engineering What's Ahead
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Multiphysics simulation gives organizations nearly superhuman abilities to understand how their products are working – and how to improve them. This keynote presentation highlights several real-world stories of companies using simulation to not only change the way their products are development, but to transform the way they do business.
ENGINEERING SIMULATION – Still much to offer, huge potential to discover
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The evolution of the simulation in Ferrari in the last two decades, the present status – the level reached and how could be or must be. The evolution of the simulation in the future to cope with the challenge Ferrari has to face.
Microsoft’s Partner Strategy: Ubiquitous Cloud, Digital Twins, Autonomous Vehicles and more
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The world was already changing at a breathtaking pace, and the global pandemic has only accelerated digital transformation trends that were starting to ramp up. Microsoft’s CVP of Cloud and AI, Uli Homann, will have a discussion with Matt Zack, Ansys VP of Business Development, about Microsoft’s approach to meeting the world’s needs during this crisis and how they are building the framework that will enable a faster recovery for everybody. Uli and Matt will discuss how supporting a vibrant partner ecosystem is important to Microsoft and leads to exciting joint technology developments such as fully cloud-enabled simulation, physics-based digital twins, and advances in virtual testing for autonomous vehicles.
Pikes Peak, Nürburgring-Nordschleife, Goodwood, Tianmen Mountain – the fully electric Volkswagen ID.R has made his mark in the history books of Motorsport
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On 24 June 2018, just 250 days after the project started, the ID.R completed the route up to the famous Pikes Peak in a time of 7:57.148 minutes. In doing so, he smashed the previous record by more than 16 seconds. This success of innovative technology was only made possible by fast and courageous decisions and exceptional engineering methods – including Ansys´ simulation services which played a key role in the thermal management of the battery system. In 2019 the success story continued: In a time of 6:05.336 minutes, the further developed ID.R set the fastest lap by an electric race car on the Nürburgring-Nordschleife. Just four weeks later, the ID.R broke the 20-year-old Formula 1 record on the Goodwood Hillclimb at the Festival of Speed in a time of 39.90 seconds. The ID.R was once again optimized: improvements to the battery management and the downsizing of the lithium-ion batteries lead to increased efficiency and reduced weight. In September 2019 the ID.R embarked on a voyage to China to conquer the world-famous road at the Tianmen Mountain. The 10.906-kilometre road climbs roughly 1,100 metres via 99 hairpin bends – and in the hands of French motorsport ace Romain Dumas, the ID.R completed the road in 7:38.585 minutes to set the first official record. In his keynote Volkswagen Motorsport Director Sven Smeets will give a deep and personal insight into the ID.R´s unique success story, the crucial role of Ansys as a technical partner and the influence of Motorsport for road car development.
Digitizing the 21st Century Oil and Gas Industry: Transformation of the Value Chain from the Reservoir to the Commercial Use to the Climate’s Remediation
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Digitization of the Oil and Gas industry is ongoing in segments. What has yet to happen, but will occur with increasing momentum, is the industry’s total transformation utilizing the tools of intelligence, software, technology and innovation to fully rationalize and integrate the segments within companies, from the operating units to the enterprise, to achieve breakthroughs only heretofore imagined. Such transformation includes the following: optimizing information, simplifying complexity, accelerating outcomes, delivering cross-organization operational efficiency, increasing productivity and output while reducing costs, re-imagining waste management, combating global warming, advancing technical frontiers and maximizing human capacity. Looking back from 2050 the industry will be recognizable for its continuous realization of the benefits of technology across the operations and functions applied to its ever-improving business model and its sustainable future.
Convergence of massive data and compute
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Traditionally, there has been a division of labor between computers and humans where all forms of number crunching and bit manipulations are left to computers; whereas, intelligent decision-making is left to us humans. We are now at the cusp of a major transformation that can disrupt this balance. Primary trigger for this comes from availability of massive data and compute, coupled with algorithmic advancement in reverse engineering good enough data-driven models for complex engineering and decision-making problems. Often these models are highly approximate, yet good enough proxy for what has traditionally taken us a long time to compute through traditional first-principle simulations. This talk aims to highlight opportunities ahead of us for enabling a new class of such applications and services, and their system level implications.
Affordable Healthcare through Digital Innovation
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GE Healthcare has been working on enabling affordable healthcare through multiple products specifically designed targeting affordability, apart from quality and reach. This means we adopt to most modern technology practices including using simulations in product design cycle as against traditional way of build-test as one example. Most devices being used in critical care settings, requires high availability and reliability. Apart from this, usability is another important aspect considering staff burnout as another major challenge. In this session we cover how our approach of leveraging simulation and analytics helps build robust products by picking an example requirement around user experience. This approach also enables Digital Twin. Another important aspect in care delivery is digitised surveillance and workflow digitization and monitoring, all these generate lot of data and also new possibilities. This talk will cover the journey, challenges in data and how we convert them into actionable insights using our Edison platform with an example virtual care solution application MURAL.
Porsche AG: Formula E, More Than a Tech Lab
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Importance of e-mobility for Porsche - Meaning of Formula E to Porsche - The role of simulation in R&D for the 99X Electric power train - Technology transfer from motorsport to series type cars - more than just a quote at Porsche
COVID-19 and Me: How the Global Pandemic Changed the Way we Work Forever
Presented By:
Jacqueline de Rojas, President, techUK, President at digileaders, Co-Chair at Institute of Coding
Rachel Neaman, Technology Leader, Non-Executive Director, Leadership Mentor and Coach, Neaman Consulting
Kay Oswald, President of International, SmileDirectClub
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Business leaders from across industry sectors share the changes they have had to make to survive the corona virus lock down, and what new digital practices they will be keeping in place when social distancing is no longer a necessity.
Multiphysics Simulation
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From fundamental science, Multiphysics simulation to fluids mechanics, Ansys enables Baker Hughes to deliver breakthrough technologies, products and processes to transform the energy industry.
Accelerating the Autonomous Vehicle Revolution with A 100+ year History of Innovation
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There is a fundamental connection between innovations on the racetrack and real-world improvements on the highway. With the advent of autonomous vehicles Indianapolis Motor Speedway (IMS) continues to embrace its 100+ years historic role as a catalyst for the next generation of vehicle technologies by launching the Indy Autonomous Challenge (IAC). The IAC that builds upon the DARPA Grand Challenges of 2004-05 that helped create the modern autonomous vehicle industry, is a $1.5 million prize competition among 37 universities from around the world who will compete in the world’s first head-to-head highspeed race of autonomous vehicles around the oval of the famed Indianapolis Motor Speedway. The talk will review how the IAC can advance technology that can speed the commercialization of fully autonomous vehicles and deployments of advanced driver-assistance systems (ADAS).
When Life Gives you Lemons: Digital Transformation in a Post-COVID-19 World
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As the world recovers from the devastating economic and cultural impact of social distancing measures, Kate Russell proposes a fresh view of the business landscape ahead. A technology reporter and digital transformation evangelist for 25 years, Kate shares the unique opportunity she sees rising from the ashes of global disaster, and how we can use what we have learned to take a huge leap forward for true digital transformation.
Ansys Autonomy: An Introduction
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A Simulation Tool Chain for Verification and Validation of L3 and Higher Level Autonomous Vehicles
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Autonomous vehicle developers realize that moving from L2 to L3-L4 requires a technological quantum leap. Ensuring safety of automated driving systems is of paramount importance, as is achieving fastest time-to-market in this disruptive industry. Simulation is a known accelerator of product innovation and indispensable in the development of autonomous vehicles. BMW and Ansys are collaborating on joint development of a simulation tool chain for virtually testing and validating automated driving systems. The simulation tool chain will support BMW’s autonomous engineering efforts in areas including drive analytics, scenario creation and variation, closed-loop simulation, result analytics, tool chain validation and data lineage.
Thermal Cameras for Safer Cars
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Azure for Autonomous Vehicle Development
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Microsoft’s approach and offerings for Autonomous Vehicle solutions designed on Azure to accelerate development, validation, compliance and productization of highly automated and self-driving solutions that improve safety and meet consumers' evolving expectations. Enable industry leading partners like Ansys to provide critical workloads such as simulation on top of our global, secure, hyper-scale and best in class autonomous development platform for our customers.
From POC to Production: Functional Safety Considerations for Your Embedded Software
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You’ve finally finished your proof of concept (PoC) system. Now you face the even harder task of taking your concept into production. This session will look at functional safety considerations and how to streamline your certification processes to get your PoC into production more efficiently. We will discuss how software safety constructs such as separation and isolation, freedom from interference, safe communications and safe algorithm development can be achieved with the right combination of operating system and tools. We’ll also explore a case study in which this was achieved in an active automotive safety system.
Autonomous Safety in Sight
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This presentation will provide an overview of key ideas and practical application of 4600 for self-driving cars. It will showcase the key principles for building a safety case and applying this standard, including the scope, from fault models to safety culture. It will also discuss how the 4600 approach complements other safety standards and how metrics and feedback loops tie everything together. Lastly, find out how to join the community already contributing to the next version!
2020 State of Automated Driving
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The primary engineering challenge in developing ADAS and Autonomous Vehicles is ensuring that they will operate safely under all situations encountered. Since they are likely to encounter many millions of different driving situations through out the course of their operation, engineering of ADAS and AVs must comprehensively address the design and validation of their software and hardware components over millions of driving scenarios. This is a massive task involving thousands of engineers working collaboratively at OEMs as well as Tier 1 and 2 suppliers. Ansys Autonomy is a comprehensive tool chain that provides tools and infrastructure for expediting the design and validation of all aspects of an automated driving system including software - perception, localization, planning, controls, automated driving features, supplemental software and integration software - as well as hardware - sensors, electronics, human-machine-interfaces, and vehicle aspects. Ansys Autonomy provides extensive capabilities for software-in-loop simulation, hardware-in-loop simulation, driver-in-loop simulation, scenario definition and massive parametric variation, test planning, data analytics, coverage analysis, model based software development with certified auto-code generation, high-fidelity physics based simulation of radar, lidar, camera and other sensors, and functional safety, SOTIF and cybersecurity analysis. This talk will present aspects of Ansys Autonomy along with specific case studies.
Towards Homologation of Sensors, Sensor Fusion and Automated Driving Function: The Role of High Fidelity Environment Modeling
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Advanced driver assistance systems (ADAS), Highly Automated Driving Functions (HAD) and Autonomous Driving (AD) provide among comfort to the driver also a great potential for future mobility and tends to increase traffic and car safety. All ADAS, HAD and AD functions and especially those associated with high safety levels, require paradigm-changing approaches for the homologation: some ADAS and AD functions require up to about 200 million km of real drive testing for the qualification. This amount of real drive testing is not feasible for any OEM and therefore there is a strong need for a mixed test strategy where the performed real drive tests take credit from a virtual campaign evaluation. Such a combined real and virtual test strategy could reduce the necessary efforts for the qualification of a given function development and its validation. To support virtual testing, the triangle of the driver, the vehicle and the environment has to be modeled for simulation. The interface between the vehicle and the environment, i.e. a sensor that is a device to transform physical information into electrical signals, is of crucial importance for the ADAS, HAD and AD function, because it replaces step by step the perception of the driver in the car. This presentation gives a survey about the challenges of the modeling of sensors and their interaction with the fusion strategy and the automated driving functions and the crucial role of high fidelity environment models in order to get confidence in the simulations as a part for the homologation. The presentations lists also first insights in results of the environment modeling of the city of Kempten / Allgäu
The Role of Simulation in Digital Transformation
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Digital Transformation has already played a critical role in reducing cost, accelerating time to market, and improving product quality. However, Digital Transformation has the potential to unlock trillions of dollars across all industries and simulation, as there is an even larger opportunity ahead as simulation finds its way from the traditional analysis phase, to be used pervasively across the product lifecycle. This talk will give an overview of the opportunities and set the stage for the Digital Transformation track at Simulation World
Advanced Simulation in Process Industry
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Advanced simulation techniques have gained wide acceptance by the process industry, particularly in oil & gas sector, power plants, petrochemical plants and fertilizer units. Simulation is being used to improve the existing designs to achieve higher efficiency, for troubleshooting, for visualization of flow in equipment and for minimising the experimentation cycle and Time-to-Market (TTM) for new products - enabling faster delivery. L&T Heavy Engineering manufactures and supplies custom designed critical equipment & piping to process industries. L&T extensively uses advanced simulation techniques viz. Computational Fluid Dynamics (CFD), Structural Stress Evaluation (FEA) & Piping Stress analysis in the design and manufacture of equipment. Flows found in process industries are predominantly multi-phase flows where the flowing material is composed of two or more distinct phases, which may be either fluid or solid. Compared to single phase flows of liquids and gases, understanding the behavior of multi-phase flows is considerably more difficult and is very critical from equipment & piping design point of view. CFD analysis plays a vital role in developing this understanding. Examples of equipment handling multi-phase flows where CFD analysis has been used are Waste Heat Boilers, kettle type reboilers, nuclear steam generators, power plant condensers and Reactors & Regenerators in Fluidized Catalytic Cracking Units. FEA of various process equipment components are majorly performed in line with ASME design by analysis method. Analysis are performed to evaluate stress in complex components like large tubesheets, local load evaluation for nozzle to shell junctions etc. Non-linearity in material & large size adds to the complexity. Fatigue analysis, fracture & buckling analysis have become routine practices. Fluid-Structure Interaction and Flow Induced Vibration analysis are mandatory part of design these days. Lately, advanced simulation techniques are also used during manufacturing for increasing the efficiency of production and for achieving First-Time-Right. Simulation of forming process helps in optimizing the manufacturing sequence resulting in higher productivity.
Material Data Management in a Digital Environment
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Digital material data management can dramatically help the development of more reliable design and manufacturing processes.
Engineering and Digital Development through Demanding Times
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Lennox as a manufacturer of high-end airconditioning and refrigeration products, are experts at adding value and comfort to everyone’s life through several products. Whilst, our focus through the years has been aimed at innovation in launching state of the art product lines coupled with strong material cost reduction programs, we have survived many tough times in the recent past. While uncertainty prevails in the market even now, during these tough times, we as a strong technology driven organization, we are prepared in tackling the unknown more stronget than ever. Started very early, on the digital transformation through various global partnerships, we are prepared for the new demand for harder virtual collaboration and new ways of product development. With increase in constraints on physical testing based validation, Ansys based tools help us drive our virual prototyping approaches accelerate faster. The focus of this presentation is to cover some of the exigent situations we faced in the recent times and to touch a few real-time examples of accelerated product development with the aid of Ansys tools and partnership.
How Companies Drive Democratization of Simulation with Discovery Live
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Companies today understand the value of simulation. The next step is to multiply that value by means of democratization. Some are already on that track some start their journey these days. It is very informative to gain insights what a company like Endress and Hauser has achieved over the last years and what kind of setting maximizes the value for them. This includes the user support as well as the implementation of the best fitting simulation technology at different user groups. All of that follows a global simulation strategy.
The COVID Factor: Prospects for Autonomous Cars
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The COVID Factor: Prospects for Autonomous Cars
US Air Force Digital Campaign
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An AFMC (+SMC) coordinated effort to move activities of our enterprise to modern digital capabilities and processes. Defined by six (6) lines of effort: integrated IT infrastructure; integrated models and tools; standards, data and architectures; lifecycle strategies and processes; policy and guidance; and workforce and culture. The desired end state is a collaborative integrated digital environment which guides, orchestrates, and delivers the means for each individual across the AFMC enterprise to access the data, functions and elements needed to do a his/her job in a purely digital manner--all functions, not just engineering.
Simulation Verification of Automotive Millimeter Wave Radar
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Mingliang Gao, Pre-research Director, Radar system Engineer and Senior RF expert, Beijing Autoroad Technology Co., Ltd.
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1. What are the breakthrough technologies of second-generation 77GHz mid-range radar 2. The road to product verification and mass production 3. AEB's demands for radar and test data 4. Integration of SAR and FMCW technologies
Virtual Simulations Lead to Real Victories in Elite Cycling
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Computer simulation, carefully validated with wind tunnel testing, is a main contributor towards fair play and prestigious victories in elite cycling. Simulation allows us to visualize airflows and aerodynamic effects that are in reality are invisible. Sometimes the results of those simulations confirm common sense, sometimes they confirm intuition or negate intuition, sometimes they reveal previously unknown and also unexpected effects and cause a shock in the field. In this presentation, we will present some examples in each category. We will also demonstrate how simulation has contributed to some remarkable achievements in elite cycling in the past year.
Simulation-Based Digital Twin with Ansys Twin Builder
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Predictive and prescriptive maintenance are key promises of the Internet of Things. Simulation-Based Digital Twins are critically important to making this promise a reality. Simulation-based digital twins are helping companies better analyze machines in real-world operating conditions, allowing them to make informed decisions, improving their performance far above what is possible today. In this talk, we will show how ANSYS Twin Builder, in combination with popular out partners help our customers to quickly realize the promise of predictive and prescriptive maintenance.
Improve time to market through enhanced virtual Commissioning with Rockwell Automation and Ansys
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Learn how ANSYS can help bring higher levels of fidelity when virtual commissioning industrial assets using Rockwell Automation Emulate 3D. Discover how automation project risk and time-to-market can be reduced through the ANSYS & Rockwell Automation Partnership
Detailed 3D simulations to enable digital twin development and validation
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GDTech is an engineering service company that is partner of Ansys for the modelling, simulation et validation of Digital Twins to be developed in Ansys Twin Builder. GDTech has a long experience in detailed multi-physics simulation, such as structural mechanics, CFD, EMAG, acoustics, as well as Model Based System Engineering. This is the reason why GDTech has identified Ansys Twin Builder as the ideal environment where all our competences can converge and will provide maximum value to customers, helping them to develop their digital twins; furthermore, model reduction techniques are also one of our field of expertise. This presentation will illustrate GDTech background in CFD simulation with Fluent, in fast transient dynamics with LS-Dyna, and model reduction techniques in structural mechanics, including DOE and optimization capabilities, which are relevant for the construction of Digital Twins.
Digital Twin for high-voltage electric motors, coupled rotor-dynamics & electro-mechanics system simulation
Presented By:
Philipp Rauh (Siemens) & Johannes Einzinger (ANSYS), Simulation Engineer & Lead Application Engineer, Siemens Large Drive Applications & ANSYS Germany
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All over the world, scientist and engineers are searching for ways to shape the future. Digital transformation is one major aspect. Connected devices will communicate and interact with each other. In order to achieve this, the physical and functional properties of devices must be known. Behavioral models of devices (“digital twins”), are essential to provide this information. Siemens and Ansys are working on a technology and complete workflow to generate such twins of devices to simulate and predict their behavior. The foundation is state space models (CMS) for each part, generated independently and finally assembled on system level. A CMS analysis is performed on each part, and related sub space matrices for mass, damping, gyroscopic and stiffness, are extracted and arranged in state space representation, including parameter dependencies like rotation speed required for rotor dynamics. State space formulations for each component are combined in a system simulation. Each component “digital twin” can be coupled with other twins, and further elements like boundary conditions defined. This enables designers and engineers to describe the interaction of all components in different physics domains, ultimately modeling a whole plant application to simulate and predict its behavior on certain excitations or loads, in its real operating environment. The holistic approach allows to solve with required accuracy – close to full FEA – in seconds instead of hours. Siemens LDA is expecting significant value add by this novel “numerical twin” technology. Finally, the assembled components, the twin and environment are transferred to an encapsulated *.twin file, representing the whole application. Utilizing an API to generate the runtime, this *.twin file is implemented in Siemens IIoT cloud for HV motors, SiDrive IQ, and consuming time series data of its real-world twin for predicting its behavior.
Extending Azure Digital Twins with Ansys Twin Builder
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Basak will present how Ansys Twin Builder and Azure Digital Twins teams collaborated to extend Azure Digital Twins platform with Ansys Twin Builder; integrating simulation based digital twins with IoT data. The session will cover industry use cases as well as the reference solution architecture.
Leveraging a transformative Digital Twin Ecosystem to Improve Product Operations
Presented By:
Eric Bantegnie, VP and GM, S&PBU, Ansys
Sam George, CVP of Azure IoT, Microsoft
Vatsan Govindrajan, Global Head of PLM and Engineering, SAP
Tom O’Reilly, VP, Global Business Development, Rockwell Automation
Steve Dertien, CTO, PTC
Kenneth Wong, Digital Engineering (Moderator)
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Predictive and preventive maintenance are a key promise of Digital Twins. Challenges to adoption relate to the heterogeneity of the various systems involved. By building a strong partner ecosystem, Ansys is easing our customers' journey to value. In this panel, the panelists will outline their Digital Twin capabilities, why they chose to partner with Ansys and share some successful outcomes through customer examples.
Enhance Monitoring & Service Applications with Digital Twin Simulation
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Industrial companies are feeling more pressure than ever before to deliver results and react to constantly changing market landscapes. Competitive and disruptive threats are reshaping product and service expectations to demand higher quality, lower costs and greater flexibility. With service revenues often growing faster than new product sales and new types of business models for service emerging, innovations in service operations are required. To address these business challenges, companies are applying technology and creating digital twins to enable remote monitoring, improved diagnostics and predictive maintenance applications. In this session, we will discuss how digital simulation models can enhance digital twin applications to optimize service operations.
The Digital Twin Consortium
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The Digital Twin Consortium is forming with the objective of influencing the direction of digital twin technology development, driving best practices for digital twin usage and defining requirements for new digital twin interoperability and portability standards, especially in markets that underuse the technology and across vertical markets. As one of four founding members, Ansys is playing a key role. This talk will provide an overview of the consortium and our goals, with a focus on the central role that simulation has to play towards the success of Digital Twins.
Integration of simulation-based digital twins across design-to-operate businesses processes from SAP
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The session will explain how product-centric companies in discrete industries can create digital products through IoT-enabled digital twins from ANSYS. Examples will include closed-loop engineering scenarios to showcase SAP’s integration excellence across the design-to-operate value chain.
Four Pillars of Electrification
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Electrification will drive future mobility and providers are pivoting to invest heavily in electric vehicles (EVs). The race is on to capture market share. Manufacturers must totally re-engineer their vehicles to satisfy driving range and battery life, safety, and cost, requiring an unprecedented amount of innovation in a short time. Only simulation enables you to accelerate EV development and win the race to market. In this talk, we will show how engineers design each for the four ‘pillars’ of electrification: battery, power electronics, electric motors, and electric powertrain integration. Batteries need to be designed at the electrochemical level, and integrated battery pack thermal-mechanical considerations must be accounted for to ensure reliability and safety. Power from the battery must be managed by power-control circuitry and then be distributed via busbars and wiring harnesses that do not radiate electrical noise. Electric machine design centers around efficiency, quiet operation, and thermal reliability across a range of speed and loading conditions. Finally, full system integration of the electric powertrain must be achieved with system-level analysis with the embedded controller software and the vehicle. Industry examples will be highlighted that show how high-performance computing (HPC) can be leveraged to fully explore designs, how system simulation linked to 3D electromagnetic simulation can optimize the drive and machine, and how companies combine product simulation with hardware emulation to build drive systems before any prototypes have been built.
Aerospace Platform Electromagnetic Environmental Effects Virtual Test Environment
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Aerospace Platform Electromagnetic Environmental Effects Virtual Test Environment A representative electromagnetic (EM) model of an aerospace platform is essential for effective system design and analysis. Effective models of systems aid in robust design for electromagnetic compatibility (EMC) to real world environments. Testing on individual boxes is typically performed during the development stages. However testing of the integrated system, ex. entire vehicle, occurs late in the program, at a time when major changes in design are costly to schedule and budget. Simulation allows for end to end designs to be assessed earlier in the design processes. This ensure confidence in the system-level compatibility in a development or sustainment program. The following discussion describes the steps to build a system level virtual test environment to support lightning, EMC, radio frequency interference (RFI), and electromagnetic environmental effects (E3) design and verification. This workflow and process is demonstrated using the electromagnetic simulation environment of ANSYS EMA3D Cable. The main topics include: -Virtual Electromagnetic Testing Fundamentals -Capturing Platform Details and Configuration Control -Cable Modeling and Thin Material Properties -Interpreting Results and Optimizing the Design -Validation
How Continental Automotive is integrating Ansys Sherlock software into their Design Process
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The use of electronics is ever increasing in automotive applications. New innovations such as active and passive safety systems, electric propulsion, and semi and fully autonomous vehicles have all contributed to this increase. Automotive designers, however, must still adhere to the size and packaging constraints to ensure vehicle size and weight does not increase. Because of this, there has been a push to make electronic components and packages smaller, while increasing performance. One example of a company facing these demands is Continental Automotive, who designs and manufactures printed circuit board assemblies (PCBAs) for automotive electrification and autonomous vehicle applications. They have seen increased use of ball grid array (BGA) components and High-Density Interconnect (HDI) FR4 boards in their PCBAs, where components are tightly placed on both sides of the PCB to ensure the most efficient use of the board space. For a company manufacturing PCBAs required to perform in various extreme field conditions, it is critically important to ensure reliability by understanding how different designs can affect solder fatigue. Each variable must be tested to determine what influence it has on solder fatigue and thus reliability of the board. Continental has been using ANSYS Sherlock, Icepak, and Workbench to predict solder fatigue due to system-level effects such as thermomechanical, shock, and vibration influences early in the design process.
An Electro-thermal Coupled Model for a 48V Li-on Battery Pack Using Reduced Order Thermal Model
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In this presentation, an electro-thermal coupled battery model for an A123 liquid-cooled 48V Li-on battery pack is developed. The electrical part in the coupled model uses the equivalent circuit model (ECM) approach due to its speed and accuracy. The thermal part in the coupled model uses the reduced order model (ROM) approach. Compared with the common thermal network approach, the ROM approach demonstrates higher level accuracy and convenience. The coupled model, as part of BMS, is then simulated in Matlab Simulink. The capability of the coupled model on developing derate function in battery state of power (SoP) algorithm is demonstrated. Simulation results show that current derate caused by battery overheating is accurately captured under a dynamic drive cycle current profile. Such a ROM based electro-thermal coupled model proves to be a powerful tool for BMS development.
Simulations for EMC concept of On Board Battery Chargers
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The work is based on Metasystem approach in the e mobility industry for the development, design and optimization of On-board battery chargers (OBCs) for Battery Electric Vehicles (BEV) and Plug-In Hybrid Electric Vehicles (PHEV), with a particular attention on the fulfilment of the Electromagnetic Compatibility (EMC) performances. Car-makers are pushing towards extreme and challenging production lead times, so it is often mandatory that early prototypes behave similarly to the final product sold with the car. In order to fulfill Electromagnetic EMC requirements, simulations can help the EMC design engineer to speed-up the optimization phase by limiting the number of time consuming tests in anechoic chambers. Our work shows how is possible to use the Ansys Electronics suite, mainly Ansys Circuit, Ansys Maxwell and Ansys SIwave for EMC purposes, applied to a three-phase 22kW – 800V battery charger, with a power density of about 2.5kW/l, suitable for high-end cars.
Use of AI for Optimization of Induction Machines designed using Ansys Maxwell
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In the coming years, the EVs are poised to take off in a big way. One of the major challenges to address is to develop an EV technology that is less reliant on imports. In this direction, we at e-mobility lab, IITG, have been working towards advance induction machines, instead of PM motors, for use in EVs. To aid and reduce the time to design and optimization of these machines the lab uses AI and Ansys. In this talk, we will be presenting the core ideas of how this is done and the benefits that one can get.
Cable Emissions/Immunity Analysis for Automotive Applications
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In the automotive industry there are certain problems that tend to be discovered during testing, either of components or at the vehicle level, driving late changes and added cost. This presentation aims to illustrate simulation methods that can identify these problems and solutions much earlier in the design process, saving many hours of time and thousands of dollars of cost. In particular this presentation will focus on simulating cable harnesses and antennas. Among the examples discussed will include: the impact of radiated immunity testing on poorly twisted cables (as per ISO 11451-1 and ISO 11451-2); the emissions from high voltage cables that might impact the vehicle’s on board antennas (as per CISPR 25); and analysis of co-located antennas supporting different transmit and receive functions (e.g. Bluetooth and over the air communications). Together these cases will illustrate a broad capability to use simulation to achieve overall EMC compliance on even the most complicated vehicle architectures.
Progress of e-Motors and Power Electronics Technology in Automotive Electrification
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Full abstract coming soon...
Multiphysics electric motor models for system engineering using Ansys Motor-CAD and a standardised FMU interface
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In the rapidly developing world of electrification, increasingly, components need to be designed and optimised a part of the wider system. This challenge requires component level models to be shared and integrated, often across departments or even different organisations. This session will look at how advanced multi-physics motor models can be used in a system simulation to analyse the interaction between components and assess overall system level behaviour. The various techniques available in Motor-CAD for exporting and co-simulating different electromagnetic, control, loss and thermal models are discussed. It is shown how new capability in Motor-CAD enables a standardised FMU interface to be used to effectively share and co-simulate accurate and fast solving Motor-CAD Multiphysics models into a TwinBuilder or Simulink environment.
Ansys Electric Machine Design and Analysis Simulation Platform
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As part of industry’s Electrification initiatives, Ansys provides a comprehensive design and analysis simulation platform for electric machines. This enables engineering teams to take their customer’s requirements and simulate concepts and designs that reduce the build and test cycle, and reduces time to market. Consideration of magnetic, thermal, stress, and NVH performance early in the design cycle allows increases in performance and reduction in cost.
A Review of HPC Technologies in Ansys HFSS
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Along with accuracy and reliability of results, speed of simulation is of primary importance to all users of HFSS. To deliver faster simulations HFSS is constantly being improved for faster simulation speeds including enhancements that leverage multiple compute cores and nodes. In 1999 the HFSS team first introduced multi-core matrix solving and from that point forward has implemented various HPC technologies, leveraging both shared and distributed compute resources, to increase the speed and scalability of HFSS simulations. This presentation will review the HPC technologies available in HFSS including techniques such as matrix multi-processing, for both shared and distributed memory, and parallel frequency points for rapid extraction of SYZ parameter models. It will then discuss the ground-breaking domain decomposition method (DDM) introduced over ten years ago in HFSS v12 which enabled HFSS simulations to scale up by orders of magnitude. The presentation follows DDM’s evolution starting with the hybrid FEM-MoM FEBI boundary condition onto true 3D multi-domain FEM-MoM allowing analysis of extremely large-scale systems such as antenna integration on platform and on through DDM’s most recent enhancement for modeling large antenna arrays like those being implemented for 5G mmWave applications. Wrapping up, it will discuss simulating design points in parallel along with multi-level HPC that leverages in one simulation the full panoply of the HFSS HPC technologies. Finally, it will conclude with an overview of the recent enablement of HFSS on Ansys Cloud.
Overview of Ansys HFSS Solver Technology
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Ansys HFSS solvers have been developed and fine tuned over the last 30 years. Many think of HFSS as only the Finite Element Method (FEM) but in reality we offer a number of complimentary techniques such as Integral Equations (IE), Shooting Bouncing Rays (SBR, and Transient DGTD. The robustness of its FEM solver is unquestioned in industry and has earned it the gold standard reputation for accuracy. In addition, the solver engine has been optimized for speed and capacity using several techniques that include a Distributed Matrix Solver. The most dramatic performance impact comes from is its well-known automatic adaptive meshing technology typically attributed to its gold standard accuracy. The goal of adaptive meshing is to provide an accurate mesh with minimum number of mesh elements that can be solved much faster than a uniform mesh which is dense everywhere. HFSS adaptive meshing technology has been verified by its large user base ever since its first release in 1990. Another less known key HFSS solver technology is its use of transfinite elements which strongly contribute to its highly accurate S-parameter extractions while enabling faster simulations. A more recent exploitation of the transfinite element method in HFSS has made it possible to solve S-parameters faster and with much less memory when fields are not required. During this presentation we will discuss some of the HFSS FEM foundational technologies along with a broad overview of the various HFSS solver offerings such as the transient and SBR+ solvers. In addition, we will highlight the solver enhancements in HPC over the last 10+ years. The HPC offerings makes it possible to solve huge problems very efficiently by taking advantage of distributed computing with access to 1000s of cores and many TBs of memory.
Best practices for maximizing HFSS performance during Package, PCB and Connector simulations
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Higher speeds, denser layouts, and tighter design constraints demand usage of full-wave 3D electromagnetic field solvers for complex ECAD (electrical CAD) and MCAD (mechanical CAD) designs. With recent advancements in usability, solver technology, and high-performance compute (HPC) engineers can easily exploit HFSS’ speed and capacity to ensure signal and power-integrity constraints within their designs are met. Attend the first presentation in this track to see just how easy it is to setup and solve complex package, PCB, and connector designs within HFSS. In this presentation you will see how engineers can use HFSS to design high-speed signal lines along with the power delivery network for a full package design. We will demonstrate HFSS’ speed and capacity by modeling the signal integrity of an entire PC system that consists of 8 IC packages mounted on a DIMM card that is connected to a motherboard. Visualize currents flowing from the package through the board and the connector revealing design flaws using full fidelity and coupled electromagnetics. Come be amazed at what can be accomplished for these large layouts in full 3D HFSS!
Learn about new HFSS simulation technologies that solve complex IC layout designs in hours versus days!
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For the second presentation in this track, we will focus on the smaller, more complex world of integrated circuit (IC) design. Higher data rates combined with low supply voltage present new signal integrity (SI) and power integrity (PI) challenges, such as the design of 3D interposers with through-silicon vias (TSVs). Ansys offers two workflows for fast 3D HFSS extraction to ensure SI and PI in complex IC design: 1) ECADXplorer designed for typical electromagnetics engineers comfortable working in the Electronics Desktop environment and 2) the newly introduced RaptorH product, for RFIC designers who are experienced working in IC layout environments. GDSII is the industry standard file format for IC layouts and file sizes are growing as more detail is packed into smaller footprints. As the first step to extraction is translation of these GDSII files, Ansys has introduced ECADXplorer to facilitate the import and simplification of these complex GDSII-based designs for fast 3D HFSS extraction. Attend this presentation to learn about new tools and workflows that make 3D HFSS extraction of complex IC-based designs simple, fast and accurate.
Not Your Dad’s Power Integrity Analysis
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Advanced FinFET process technologies make possible ultra-low supply voltages and very high switching speeds. But for these very big, low-power designs to work reliably in the presence of thermal hotspots and highly variable switching activities requires careful attention to the design of the power distribution network - and there is no longer margin for over-design. As the technical constraints become more acute, the designs are also becoming very much larger with tens of billions of electrical nodes in a power supply network. RedHawk-SC achieves the unprecedented speed and capacity to analyze these huge designs by implementing the signoff algorithms on the Ansys® SeaScape™ platform - a highly parallelized, elastic compute database structure derived from big-data machine learning architectures, but optimized for electronic design.
Reducing Your Project Risk in a Time of Great Change
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Today, we are living in a time of significant and accelerating change in the world of microelectronic design. The newest silicon process technologies are hugely expensive and are driving a rapid shift to the Multiphysics world of 3D integrated packaging technology. This shift to Multiphysics is happening as the Cloud, AI, and machine learning are changing the face of entire industry sectors; from mobile communications, to autonomous vehicles, to the nature of compute architectures themselves. Risk is all around us, and launching a new chip project carries unavoidable elements of technical and business risk. But we are not powerless. There are proven strategies that shed risk and give you more predictability and more control. That is what Ansys Semiconductor simulation products are all about. Come share our vision on how Ansys’s deep and broad technology can help you can avoid unnecessary risk and get the best, most accurate, and most reliable information at every stage of your chip project - from RTL ,to full chip layout, to integrated 3D systems.
Elastic Compute Scalable Design Methodologies for Next-Generation FPGAs
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Next-generation field programmable gate arrays (FPGAs) for 5G, AI, automotive, cloud and data center applications are getting bigger, faster and more complex. With the market’s continuous demand for higher performance and lower power products, FPGA designers strive hard to achieve stringent power, performance, area and reliability goals to stay ahead of the game. Traditional electronic design automation (EDA) techniques for full-chip critical path timing analysis and power integrity signoff cannot meet the capacity, performance and accuracy requirements for these complex FPGAs. Productivity and project schedules are negatively impacted as a result. In this session, FPGA inventor Xilinx discusses the many applications for its innovative elastic compute scalable design methodologies.
Designing High-Speed Memories for the Edge Without Falling Over the Edge
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As Artificial Intelligence and Machine Learning increasingly move from the cloud to the edge, they need more than just algorithms: They also need infrastructure to enable other things like analog sensing, high-speed analog-digital conversion, processing, storage, and communication. Several use cases of edge compute - like autonomous vehicles and industrial applications – also require higher performance and reliability with lower power consumption. This trend is driving an increasing number of FinFET design starts to include significant amounts of analog, high-speed interfaces and more embedded or DRAM/flash memory. However, moving to advanced process nodes implies higher sensitivity to temperature variation, which impacts the EM/ESD reliability of these high-speed interfaces. This talk will review the extensive capabilities Ansys® Totem provides to analyze high-speed memories and mixed-signal FinFET designs, and how it can lead to faster design closure.
Top Electromagnetic Coupling Issues to Watch Out for in High Frequency Silicon Design
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As system frequencies and clock speeds increase, unplanned/unwanted electric and magnetic field interactions between on-chip elements as well as chip layers to package layers lead to real chip failures and present significant design and verification challenges. This presentation focuses on the most significant electromagnetic coupling issues that plague high-speed design, and will describe mitigation and prevention strategies and how they can be implemented in a design flow. This presentation draws on the experience of hundreds of customers using Ansys’s comprehensive suite of electromagnetic simulation tools on a wide array of design types – from single device simulations to full 3DIC system designs.
AI accelerated Scientific Computing & Engineering
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Simulations are pervasive in every domain of science and engineering, but are constrained by long computational times, tedious setup effort and technical expertise. Neural networks not only accelerate simulations that can be solved by traditional solvers, but also simplify simulation setup and address problems not solvable using traditional solvers. NVIDIA SimNet is a a Physics Informed Neural Networks (PINNs) toolkit for students and researchers who are either looking to get started with AI-driven physics simulations or are looking to leverage a powerful, existing framework to implement their domain knowledge to solve complex nonlinear physics problems with real-world applications.
ANSYS Mechanical Performance on a 1500 cores Skylake Cluster
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Ansys Mechanical Performance on a 1500 cores Skylake Cluster Herbert Güttler, MicroConsult GmbH, Bernstadt, Germany The solder joints that connect the integrated circuit (IC) to the printed circuit board (PCB) are subject to failure due to mechanical stresses during thermal cycling caused primarily by thermal mismatch. When we started doing this class of simulations a decade ago, a typical simulation would take weeks to conclude. Over the years, with the help of software enhancements and new hardware capabilities, these runtimes could be reduced down to a few hours. The largest speedups could be achieved running models consisting only of solids and adding contacts would usually degrade performance and scaling. However, by adding features like contact small sliding or and contact splitting that became available during the latest ANSYS Mechanical releases those limitations could be largely overcome, especially at high core counts. We will report on results from actual real world problems that have been performed using up to 1500 Xeon Skylake cores on MicroConsult’s HPC cluster. MicroConsult is a high performance computing partner to ANSYS and works closely together with the ANSYS solver team in Canonsburg.
Computational Modeling of Hypersonic Flows
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Full abstract coming soon...
Multi-objective free-shape optimization of a heat sink by means of the Fluent Adjoint Solver
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With the ongoing trend towards increasing power density, conventional liquid-cooled pin-fin heat sinks for the power electronics of an electric vehicle are reaching their limits.The use of adjoint-based free-shape optimization is expected to result in significantly more powerful cooler designs, while at the same time increasing the efficiency of the optimization process. Adjoint methods allow to consider any number of design parameters and thus open up completely new design spaces. This makes it possible to identify designs that are significantly closer to the physical limit and enable higher power densities in power electronics for electric vehicles.
The geometry of typical pin-fin heat sinks with up to 3000 individual pins is very detailed and the flow scenario is in the transitional range between laminar and turbulent flow. Therefore, an adequate choice of both the turbulence modeling (LES vs. RANS) and the considered geometric cut-out is crucial to enable a systematic optimization.
By combining systematic parameter studies and efficient, gradient-based multi-objective free-shape optimization methods based on the shape sensitivities from the Fluent Adjoint Solver, it is possible to significantly improve the cooling performance of these heat sinks without increasing their pressure drop.
Predicting Thermo-mechanical Fatigue Life in Exhaust Manifolds
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Exhaust manifolds are subject to large temperature swings and high maximum temperatures, which can lead to thermo-mechanical fatigue (TMF) failures. On the surface, TMF appears difficult to predict due to nonlinear manifold material behavior at high temperatures (creep, plasticity, oxidation). However, life predictions are further complicated by there being three analysis components in any TMF process: a transient thermal analysis, a structural analysis, and failure modeling. Each analysis component relies on the prior component - if any link in the chain is inaccurate, the whole process does not work. This presentation delves into what is required to successfully perform each analysis component to more accurately predict failures.
Adaptivity in Implicit Nonlinear Mechanical Analysis
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Focusing on mesh nonlinear adaptivity, this paper is also going to talk about how to change element types, dimension and other control parameters during solutions. The applications to some industrial problems are to be demonstrated.
Adjoint Methods and Optimization Technology
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For more than a decade Ansys adjoint solver technology in Fluent has grown in strength to emerge as a refined toolkit for shape optimization. The current adjoint solver, in combination with a suite of supporting tools, can optimize the performance of many types of fluid system of engineering interest for a wide range of user-selected objectives. Problems of compressible and incompressible fluid flow and heat transfer, including conjugate heat transfer, porous media and rotating flows can be addressed. The key to this success is a robust adjoint solver that can solve small and large problems and has a broad scope of supported physics, numerics and boundary conditions. In addition, custom-crafted mesh morphing tools and single and multi-objective constrained optimization algorithms are available. These tools are integrated into a flexible yet straightforward workflow.
Comprehensive Li-ion Battery Solutions in Ansys Fluent
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In this presentation, a suite of comprehensive Li-ion battery solutions in ANSYS FLUENT are presented.
Fatigue Crack Growth - A New Paradigm
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Present paper present a recent development, ANSYS SMART (Separating, Morphing, Adaptive and Remeshing Technology) crack growth simulation framework, for automatic, robust and seamless simulation of complex crack growth. The framework is essential for large scale crack growth simulation. Validation with standard CCT specimen has shown very good agreement with analytic solution. Several examples are presented to demonstrate the automatic simulation of crack growth with mixed-mode fracture in focus for the specific application.
Gas Turbine Simulation Overview
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This talk describes the Ansys end-to-end gas turbine solution with its streamlined workflow that allows the design and optimization of the next generation of gas turbines. We will highlight Ansys multiphysics capabilities and the expansion of gas turbine design capacities through process automation, cycle compression, predictive maintenance, and knowledge management.
Large Scale Computing in Ansys Simulation Products
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Ansys products employ various software architectures and targeted HPC methods to reach a high degree of performance and scalability on High Performance Computing systems. This presentation will provide an overview of these methods and their positive impact via benchmarks and applications to industrial strength problems.
Machine Learning Based Radar Perception for Autonomous Vehicles Using Full Physics Simulation
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Safety critical systems in Advanced Driver Assistance Systems (ADAS) depend on multiple sensors to perceive the environment in which they operate. Radar sensors provide many advantages and complementary capabilities to other available sensors but are not without their own shortcomings. Performance of radar perception algorithms still pose many challenges, one of which is in object detection and classification. In order to increase redundancy in ADAS, the ability for a radar system to detect and classify objects independent of other sensors is desirable. In this paper, an investigation of a machine learning based radar perception algorithm for object detection is implemented, along with a novel, automated workflow for generating large-scale virtual datasets used for training and testing. Physics-based electromagnetic simulation of a complex scattering environment is used to create the virtual dataset. Objects are classified and localized within Doppler-Range results using a single channel 77 GHz FMCW radar system. Utilizing a fully convolutional network, the radar perception model is trained and tested. The training is performed using a wide range of environments and traffic scenarios. Model inference is tested on completely new environments and traffic scenarios. These simulated radar returns are highly scalable and offer an efficient method for dataset generation. These virtual datasets facilitate a simple method of introducing variability in training data, corner case evaluation and root cause analysis, among other advantages.
Machine Learning Initiatives at ANSYS
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A summary of machine learning initiatives at ANSYS. A context of the history, a plan of execution and some updates on recent work.
Mesh Morphing and Its Application at ANSYS
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Mesh morphing enables engineers to explore similar variations of a problem quickly at a low cost by offering easy mesh shape changes. Progresses made in efficient and accurate computing mesh morphing field, improving user experience in setting up mesh morphing controls and applying the developed technologies will be presented.
Parallel Volume Meshing
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Size of CAE problems have steadily increased over time. Mesh generation has increasingly become a bottleneck due to largely serial software. We will present a distributed parallel solution to unstructured volume meshing.
Recent progress in CAD Workflows for Topology Optimization and Model Deformation
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This presentation highlights two recent methods to automatically convert faceted simulation results into CAD bodies. The first one shows how to automatically generate a CAD body from topology optimization and preserving boundary condition faces. The second one applies the shape change of a displacement result to the original CAD body without making topology changes.
Recent progress in Geometry Modeling: Hybrid Modeling for Automation in SpaceClaim
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On how the parameter modeler in SpaceClaim can be used as a object-oriented, visual programming environment.
Turbulent Times: Challenges in CFD
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The presentation will provide an insight into the challenges posed by the resolution of turbulence in CFD. It will show that turbulence is the central driving factor for CFD and will detail the level of computing power which will be required with different strategies for handling turbulence. Strategies for achieving optimal performance on highly massive CPU/GPU hardware will be highlighted.
Ansys HPC/Cloud Solutions for Scalability
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This presentation will cover Ansys' approach to a single, integrated user experience and set of services for running simulations The approach spans workstation, on-premise HPC and public/private hybrid cloud. The presentation will cover the architecture, functionality and integration with Ansys products.
Ansys Long-Term Technology Strategy
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Simulation has advanced considerably in the last 50 years, from finite element analysis to the study of photonics. What will the next 50 years bring? This keynote presentation will look at the future of simulation, including the impact of artificial intelligence/machine learning, a multiphysics platform and high-performance computing.
Supercomputing on the Cloud: How Azure Drives Rapid Innovation and Deeper Insight
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Ansys users have very exacting and challenging requirements and want to reap the benefits of supercomputing on the cloud. Meeting their demands and Microsoft’s commitment to being the cost performance leader for simulation requires aggressive investments in servers specifically designed for HPC and software tools to facilitate the orchestration of both jobs and clusters.
Ericsson's Vision & Thoughts on the Future of 5G
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The past, present and future of mobile networking, and how convergence of 5G, edge computing and AI/ML changing the industry landscape, and how it is relevant to simulation/emulation efforts moving forward.
Can simulation and digital twin keep up with “C.A.S.E.”?
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The mobility mega trends represented by C.A.S.E.* are a challenge to the world of automotive engineering as the variety, complexity and interaction of technologies continues to grow. Today we want to look at the range and extent of modelling as it is currently used across the many different systems in the automotive landscape and in Marelli’s portfolio specifically, and also at the evolution of these techniques in the future.
As economics and development cycles continue to push down costs and lead times, and electrification and connectivity demand a paradigm shift towards a holistic, integrated approach to vehicle design and resource management, we ask the question: how will the tools of the future provide the power, accuracy and interoperability required to keep up with C.A.S.E.?
*Connected, Autonomous, Shared, Electric
Impact on the cloud on simulation
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The performance of Artificial Intelligence algorithms depends highly on the quality of labeled data available for training. The lack of cost-effective, high quality training data is impactful to challenging business segments such as Government, Space, and automotive. In this talk we’ll discuss Microsoft Azure’s capabilities for generating synthetic data for the most challenging industries as well as an overview of the impact of the cloud across multiple security levels.
Focus on Photonics: The State of the Art
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As Moore’s Law becomes more and more challenged in electronics, photonics is stepping up as a favored solution to advanced innovations. For example, in data transmission, photonics is becoming relevant, then prevalent, and finally dominant at shorter and shorter distances. Today, telecommunications delivered over kilometers to your home and business travel via fiber optics, an application dominated by photonics. Now photonics has moved into the data center, and soon on chip. Autonomous vehicles will depend on photonics for sensing and detection via LiDAR. Photonics is a key enabler of quantum computing. This talk will provide an overview of photonics’ key impact on today’s and tomorrow’s innovations, along with the advantages and challenges of photonics.
A View from the CTO Office: Simulation-Based Product Innovation in the Digital World
Presented By:
Sujeet Chand, CTO Rockwell Automation
Suresh Venkatarayalu, CTO Honeywell
Mallik Tatipamula, CTO Ericsson
Sudhi Bangalore, Global Vice President, Industry 4.0, Stanley Black & Decker
Ed Abbo, President and CTO, C3.ai
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Companies in industries as diverse as high-tech and semiconductors, aerospace and defense, automotive, industrial, and energy use simulation to engineer and test products completely in the digital domain without the need for costly and error-prone physical prototypes and experimentation. We call this approach simulation-based product innovation. Simulation enable these companies to drive top-line revenue by designing better products, and bringing them to market faster and with higher quality, and bottom-line cost savings by reducing the cost of R&D. These companies can innovate and solve incredibly complex challenges in areas like 5G, autonomy, electrification and the industrial internet of things. In this panel, we will discuss the role of simulation in the process of designing, analyzing, manufacturing and operating products in various industries. The panelists will share real challenges that these companies have faced and opportunities using digital technologies (AI/ML, big data analytics, IOT, 5G, Cloud, Mobility). Digital Twins are now being used in the design, analysis, manufacturing and operations phase of products in various industries. The panel will discuss how data analytics and simulation are being integrated to create Digital Twins and what value they are providing to various customers. Please join our panelists as they address these and other related topics.
Simulation Accelerating Storage Innovation
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State of the art simulation methods are critical to growing the data-sphere as architectures expand on the backbone of hyperscale clouds and expand further into the edge. Seagate in particular is laser focused on using simulation for multiple areas of engineering to accelerate innovation for product delivery and research. Verticals including autonomous vehicles, telco networks, AI/ML applications, smart manufacturing, and healthcare are all driving massive storage growth opportunities. We have prioritized our engineers to capitalize on the powers of simulation in order to mature magnetic recording density, improve our manufacturing efficiency, design state of the art electronics and mechanics, evaluate new materials and most importantly accelerate the time to learning to keep us at the cutting edge for these growing ecosystems.
Transformational Impact of Simulation for Industrial Applications
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The convergence of IT and OT and the availability of scalable compute resources from servers to the cloud, is unlocking exciting new business value from simulation across the entire industrial lifecycle of design, operations, and maintenance. In today’s Connected Enterprises, simulation can run concurrently with the real-time environment to realize productivity and optimization that was not possible before. We will highlight the transformational impact of simulation and highlight a few industrial use cases.
Maintenance, repair and overhaul (MRO) in the light of digitalization
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Full abstract coming soon...
Role of Simulation in Defending the America's Cup
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Technology and Innovation is at the heart of a success. This presentation focusses on the use of Ansys Simulation to design and develop the next generation of Emirates Team New Zealand Yacht to defend the 36th America’s Cup Challenge.
Design to Operate - Intelligent Product Development for Intelligent Products
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Digitization is driving massive changes for the entire enterprise. While digitization of business processes was one major driver in the past, now the products are getting digitized as well, which in turn drives product complexity and enables more digitization of business processes along the entire value chain from idea through engineering and manufacturing to sales and service. Learn in this presentation, how SAP closes the loop to connect your enterprise to your products and customers and how you can boost the experience your customers have with your products.
Ansys Autonomy in Practice
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This presentation illustrates the Ansys Autonomy solution through a simple Automated Emergency Braking (AEB) example. The solution is first introduced, presenting the Autonomy toolchain as an implementation of the Safety First for Automated Driving paradigm (SaFAD) which is based on achieving a combination of Safety by design and Safety by validation. Ansys Autonomy implements Safety by design through a consistent use of Model-Based System and Software Engineering (MBSE) practices where the system is designed as succession of systems and software models, exercising full traceability, powerful analyses and verification techniques, and certified automatic code generation from models. Ansys Autonomy implements Safety by validation through its comprehensive simulation toolchain that allows a combination of thousands of simulation runs with a high degree of flexibility in the level of fidelity of the sensors simulation, from ideal sensors, to stochastic sensors, to physics-based sensors, together with a link to road driving and drive analytics. The combination of design models and simulation is illustrated through the AEB example as we go through the description of the Ansys Autonomy solution.
Applications of Optical Simulation in Comfort and Driving Assistance Systems – Intuitive controls and Vision Systems
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Intuitive controls and Driver assistance systems in Mobility Industry are mandated to meet regulatory requirements in order to ensure functional safety and ease of use. In this presentation, the focus is on applications of Optical simulation in providing objective and supplementary artifacts in conforming to the respective regulations. Intuitive controls for example Products like switches need optical illumination studies to predict reflections and light leakage. The critical validation requirements are Luminance level, Contrast, Luminance uniformity and color levels. The simulation methodology using ANSYS SPEOS OPTICS LIGHT MODELLING involves Assignment of material, physical properties to the parts under consideration, creating representation of symbols (if any), characterization of light sources (example LEDs), application of optical properties, characterization of measurement surfaces and finally performing the calculations. Vision systems that include exterior and interior Cameras utilize optical simulation to predict Field of Vision and Driver monitoring respectively. Field of vision requirements for Exterior cameras are dictated by numerous international regulations like NHTSA, FMVSS, EC and UNECE along with specific local adaptations needed in specific geographies for the bollards. The Simulation methodology includes characterization of Field of View Scene with appropriate ambient physical/ light conditions, Vehicle outline with appropriate of number of cameras with positions, appropriate Camera Lens, measurement locations and finally performing the calculations.
Combining ANSYS VRXPERIENCE & NI to solve ADAS/AD HIL validation
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From SAE Level 3+, V&V engineers are facing large increase of on-road testing and HIL testing of ADAS/AD functions. ANSYS and NI offer a streamlined Development Process in order to smoothly progress from virtual simulation (MIL/SIL) toward hardware or vehicle in the loop (HIL/VIL) simulation. In this session we will provide an overview and demonstration of the integration between ANSYS VRXPERIENCE and NI’s platform for ADAS/AD Hardware in the loop.
Leveraging Ansys Tools for Developing Certifiable (DO-178C) Avionics Intelligent Agents Embedded in Autonomous UAVs
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Introduction: Unmanned Aerial Systems (UAS) market, types of Unmanned Aerial Vehicles (UAV), civil and military applications, key enablers for UAVs autonomy. Part 1 – Avionics Adaptive and Learning Agent (AALA): Avionics Adaptive and Learning Agents (AALA) are an enabler for UAVs reaching higher levels of autonomy. AALAs bring computational intelligence to their aircraft host systems, adapt to novel situations they encounter in their complex air space environment in order to accomplish the mission and learn from their experience to improve their performance with Machine Learning algorithms. Part 2 – AALAs designed with a Cognitive Architecture: AALAs under development at SkyAngels are designed as per a Cognitive Architecture. Cognitive Architecture allows transposing human or animal cognitive capabilities: learning, perception, memory, decision making, action… to software agents to enable them reaching some levels of General Artificial Intelligence. Soar has been selected for the project. Part 3 : AALAs DO-178C Certification Challenges: AALAs providing computational intelligence to autonomous UAVs, aimed at navigating in non-segregated airspace, need to follow DO-178C certification path. This represents software enginering challenges related to adaptability and learning requirements specification and verification, algorithms convergence, deterministic behavior and absence of emergent comportments. Those AALAs should also resist to cyberattacks and thus compliance against DO-326A and DO-356 is sought. Part 4 – Leveraging Ansys Tools for developing DO-178C certifiable AALAs: AALAs development relies on Ansys SCADE Suite as a qualified tool (DO-330) with a certified source code generator. Software is modelled with SysML state machine and data flow Diagrams (DO-331). Scade6, the underlying formal notation language is verified with model checkers (DO-333). SCADE Architect is used to model avionic systems following a Y-chart approach. Medini covers safety and cybersecurity analysis. Twin Builder is used for software simulations. Scade Vision will be adapted to UAVs cameras and air-to-air radar edge cases. VRXExperience will be transposed from automotive for system simulations before UAV prototypes flight testing. Conclusion and future works
Virtual ADAS Validation Using Physics-Based Camera HiL Testing
Presented By:
Vaclav Trnka, SKODA Auto, Loukas Rentzos, Ansys, Maximer Vaclav, SKODA Auto
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Full abstract coming soon...
Common Simulation Capabilities to Accelerate Digital Transformation
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Simulation is a critical component of digital transformation as companies seek to accelerate innovation, drive down cost and get to market faster. Maximizing its impact requires a number of core capabilities. This presentation will share five capabilities that are consistently identified by our customers and provide an overview of how Ansys strategy is delivering against them.
Digital Transformation to support the NNSA’s Stockpile Stewardship Program
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Lawrence Livermore National Laboratory works to establish a science-based understanding of nuclear weapons performance to assess the safety, security, and effectiveness of the stockpile as part of the DOE’s National Nuclear Security Administration. Enhanced data management strategies are a means to a higher quality, enduring archive for data generated in support of our Nation’s Stockpile Stewardship Program. Data management encompasses a disciplined approach to metadata, which tracks provenance and provides traceability from raw data products to analytic results, as well as effective curation to ensure long term data access and security. Together metadata and curation support data discovery, repeatability, attribution, improved quality, collaboration, and transparency. LLNL is developing modern, flexible solutions to help meet our rigorous data management needs, ensuring data assets are available to engineers and analysts that rely on this data to support/defend critical design decisions today and into the future. This talk will focus on the unique challenges facing the NNSA’s modernization process with an emphasis on the management of materials intelligence data throughout the product lifecycle, Materials 4.0. The NNSA has worked with GRANTA for over 20 years on this mission.
Paving the way to the Next-Generation Virtual Lung Model for Personalized Pulmonary Healthcare
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Nowadays, “personalized medicine” is starting to replace the current “one size fits all” approach. The goal is to have the right drug with the right dose for the right patient at the right time and location. An example of personalized pulmonary healthcare planning is the targeted pulmonary drug delivery methodology. However, traditional in vitro and in vivo studies are limited and not sufficient for the personalized treatment plan development purpose. Specifically, due to the invasive nature and imaging limitations, animal studies and clinical tests are lack of operational flexibility and will not be able to provide insightful high-resolution patient-specific data. Therefore, alternative methods should be developed to conquer these bottlenecks. Models based on the computational fluid-particle dynamics (CFPD) method play a critical role in exploring alternate study designs and provide high-resolution data in the noninvasive, cost-effective, and time-saving manner. The in silico methodologies can fill the knowledge gap due to the deficiency of the traditional in vitro and in vivo methods, as well as make breakthroughs to pave the way to establish a reliable and efficient numerical investigation framework for pulmonary healthcare on a patient-specific level. CFPD models can provide high-resolution local dosimetry of inhaled aerosols to address the public health concern, i.e., “What type of inhaled aerosols deposits where at what surface concentrations in the patient-specific respiratory system under what operational conditions?” In this presentation, the speaker will discuss the research progress and challenges on creating the individualized digital twin for in silico pulmonary healthcare planning, with details on how to use computational fluid-particle dynamics to simulate inhaled aerosol transport, deposition, and translocation in human respiratory systems. The presentation will cover: (1) Reconstruction of patient-specific whole-lung model from CT/MRI scanned data; (2) Prediction the toxicants and drug chemicals translocations in human body; (3) Inter-subject variability studies to generate statistically robust numerical analyses, i.e., CFPD simulation with “error bars”; and (4) Establishment of an elastic lung model; (5) Applications of the virtual lung model on pulmonary drug targeted delivery and occupational exposure health risk assessments.
Accelerating Product Development Through Digital Transformation
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Garrett is leading Tier-1 supplier of Turbocharger for nearly every major global automaker; average launch rate of 100 new applications annually spanning, gas, diesel, natural gas, electric and fuel cell powertrains. Even in this disruption due to more electrified powertrain predicting life of Turbocharger remains key. Predicting life through complex Thermo Mechanical Fatigue analysis requires better understanding of material model, integration of multiple tools and functions covering the entire Digital thread. Garrett vision is to bring in more AI/ML in the process through data mining of the huge data generated over the years, faster material characterization from test data, improving efficiency through process integration and data management (SPDM) leading to faster time to market and reduce cost. This talk will cover the digital transformation Garrett has undertaken from traditional way of designing to data driven (test and simulation) designing to be ahead of the market.
CFD-DEM coupled Simulation of Hair Flow Inside a Cyclonic Device
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In this work, the modeling of hair flowing in a cyclonic device was accomplished by the DEM-CFD integration between Rocky® and ANSYS Fluent®. The hair strands were modeled using Rocky’s flexible fiber particle, based on bonded sphero-cylinders, which enables the simulation of a large number of fibers, depicting behaviors such as flexibility, deformation, and inter-fiber interaction as well as the effect of fluids on fibers through coupled simulation with ANSYS Fluent®. A new drag law, developed for long slender fibers immersed in turbulent flows was implemented in order to better predict the correct particle/flow behavior by taking into account the particle shape and its alignment with the flow field. The numerical results were compared to experimental data and showed good agreement, proving this approach as useful tool for evaluating new designs and operational conditions, reducing the cost involved with new prototypes and experimental testing.
Analysis Process for Predicting Thermo-mechanical Fatigue Life
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Many components are subject to large temperature swings and extremely high maximum temperatures, which can lead to thermo-mechanical fatigue (TMF) failures. On the surface, TMF appears difficult to predict due to nonlinear material behavior at high temperatures (creep, plasticity, oxidation). However, life predictions are further complicated by there being three analysis segments in any TMF process: a transient thermal analysis, a structural analysis, and damage modeling. Each analysis segment relies on the prior segment - if any link in the chain is inaccurate, the whole process does not work. This presentation delves into what is required to successfully perform each analysis segment to more accurately predict failures. An exhaust manifold is used to illustrate this process.
Automotive shape aerodynamic performance optimization based on Adjoint solution
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Due to the increasingly stringent environmental regulations all around the world confronted by exhaust emission and energy consumption, improving fuel economy has been the top priority for most automotive manufacturers. In this context, the basic process for vehicle shape development has evolved into optimizing the design to achieve better aerodynamic characteristics, especially drag reduction. Of all the optimization approaches, the gradient- based adjoint method has currently received extensive attention for its high efficiency in calculating the objective sensitivity with respect to geometry parameters, which is the first and foremost step for subsequent shape modification. This paper evaluates the effectiveness of adjoint method for aerodynamic optimization of a production vehicle, which indicates more extensive and promising application of this approach in the early stage of vehicle development for its high efficiency as well as strong robustness. [ Keyword ] Vehicle configuration optimization, adjoint method, drag reduction, numerical simulation, wind tunnel test
Best Practices for Industrial Flows and Turbomachinery
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CFD has an increasingly important role in the design or analysis process. While computational models are capable of a great accuracy, the human effort and computational cost may unnecessarily strain resources or place CFD beyond practical requirements. Understanding and managing uncertainty is key to successful and quality CFD simulations, whether you are using CFD for preliminary design or detailed analysis purposes. This presentation aims to inform practitioners on sources of uncertainty in CFD, particularly those which are under their control, and implement best practices so that simulation and uncertainty level targets can be managed and quantified, ensuring the quality your process requires. Motivation: Help practitioners improve the quality and impact of CFD in their design process. Key learning objectives: sources of error in CFD analysis, practical methods for appropriately managing CFD errors, and scoping CFD to match the needs at various stages of the design process
Learn How to Speed Up Your Simulations with More Powerful Hardware
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Shorter time-to-market through reducing design and development time is often cited as one of the key benefits of simulation. For many businesses, engineers accept slow performance of key engineering applications due to lack of appropriate hardware and/or IT expertise. Product designers may even constrain the size and quantity of simulations to reduce turn around wait times. Realistic simulation can push the need for compute capacity beyond a workstation toward a high-performance computing (HPC) system. This presentation will demonstrate how joint Ansys, HPE and Intel® programs can help engineers to overcome these challenges. With the HPC Benchmark Program, you can see the increase in speed using an HPC system on your own simulation model. Experts will help you evaluate your existing capabilities and compare them to a small HPC system. The HPC cluster appliance program provides an ideal out-of-the-box, plug-and-simulate, externally managed HPC cluster solution. The solution includes an appliance that is optimized for, and preconfigured with Ansys simulation and job-management software. In addition, this presentation will provide some standard ANSYS Fluent, ANSYS CFX and ANSYS Mechanical benchmark results for 2020R1 release on the latest Intel Xeon based HPE Apollo 2000 Gen10 cluster.
Metal Additive Manufacturing & Defense Electronics: A Discovery Live Success Story
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We will present the use of the ANSYS suite of product to design and validate the redesign of Embedded Defense Electronics Products with Metal Additive Manufacturing & Generative Design processes
Optimizing Materials Selection at Electrolux for Food Preservation
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Preserving food is one of Electrolux core competences. Making a wide range of components and products, gives them the opportunity and the challenge to innovate with different types of materials. They need to carefully choose those materials that meet the engineering and business requirements; and to ensure these requirements are met and innovations keep coming at a fast space, Electrolux considers materials issues early in design. This best practice contributes to improve unnecessary and costly iterations later in the engineering process, reducing cost and ultimately time-to-market. In this presentation Francesco Clementi, Foam and Plastic Expert, at Electrolux Food Preservation in Italy, will share how Ansys Selector helps him make informed material decisions at product conception.
Simulation for Future - with Customized Solutions Leading
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My Calendar, My App – in everyday life, individual digital solutions are often found that make our lives easier and help us to have time for the important things.
Taking Simulation from the Microchip to the Mission
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Within the digital engineering enterprise, mission problems are broken down into executable pieces such as component development, system integration, and operational usage. Complex systems come together in a system-of-systems architecture and must operate in a coordinated way across multiple domains in order to achieve the intended mission outcomes. Digital Mission Engineering connects the design of these components and systems to operational outcomes by extending digital engineering and MBSE technologies, standards, and practices to the mission level. Traditional systems engineering methods are often disconnected from one stage of the lifecycle to another causing significant investment in tool reinvention and model recreation which leads to inconsistency in simulations and lengthy development timelines. Since these models are rarely connected to the mission environment and how disparate systems collaborate to achieve objectives, many problems are not discovered until very late in the lifecycle. As system requirements are becoming more complex and development cycles are moving much faster, traditional methods will not keep up. Executing model-based systems-of-systems interactions in a consistent, physics-based environment, such as AGI’s Systems Tool Kit, creates the true Mission Digital Twin. As models evolve throughout the lifecycle they can be reevaluated in the common mission environment along with other systems, assessing the impact of those components on the overall mission objectives. Through Digital Mission Engineering (DME), we are now capable of quickly evaluating the overall mission impact of the smallest change to any component. By leveraging the ANSYS technology stack, DME enables customers to rapidly “dial-up” the fidelity of their simulation environment to evolve, optimize and validate operational performance requirements of their system designs via an integrated Digital Twin prototype. As the system is deployed into operations, this simulation capability continues to evolve and predict performance as the operational Digital Twin. Integrating the mission environment and operational objectives into the Digital Thread early and throughout the entire product lifecycle provides significant value. Using Digital Mission Engineering, we are able to identify critical issues much earlier in the project timeline, providing a stronger ability to positively influence the project outcomes and deliver better solutions in faster cycles.
Workflow for predicting chain whine in a T-case using Ansys Motion
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Chain Sprocket excitation is considered as a major source of noise from a Transfer case in 4H range.
Unlocking your Competitive Advantage
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Changes in technology, manufacturing process, engineered materials and customer preference have set the stage for unparalleled disruption within Manufacturing. These market drivers are further exacerbated by fluctuations in demand, tariffs and trade policies, not to mention shortages in new skills needed within our workforce. In this session, we’ll demonstrate how leading companies are embracing this disruption to secure a competitive advantage by enabling collaborative product development and manufacturing across multi-disciplinary skillsets, supply chains and an inconsistent global infrastructure.
Condition Based Monitoring (CBM) Using Digital Twin Concept in Oil and Gas
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Condition based monitoring is used in a lot of different equipment these days. Being able to predict what will happen to a component days or weeks in advance can prevent catastrophic disasters and also save millions of dollars. Combining Condition Based Monitoring (CBM) with digital twin capabilities will enable oil and gas companies to answer a lot of “what-if” questions without running expensive tests. Implementing a physic-based digital twin combined with sensor data and machine learning algorithms will provide significant insight for any complex equipment.
Nonlinear reduced order model for life assessment of a digital twin
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The increasing decentralization and integration of renewables into the energy market has led to new challenges for gas turbine OEMs and energy producers. For both, the requirements regarding flexibility in operation, optimized service downtimes and long-term operation are increasing. As a result, gas turbines are either exposed to a more complex load scenarios or, to optimize the lifetime consumption of core components, less conservative but plant-specific load transients and location-specific environmental conditions must be considered. Continuous online processing of actual operation using high-fidelity FEA requires huge computational effort, particularly considering a fleet of hundreds of engines and hence fast, and accurate surrogate models are needed. Within the concept of digital twins, the non-linear reduced order model (ROM) technique promises such a solution to obtain local temperatures and stresses required for component lifing models considering non-linear boundary conditions and material data. Such a ROM, implemented within Ansys, can be built into the system control system as part of the digital twin and made available to the customer or service-contractor for further decisions on operation and outage planning. This presentation covers a first example of a non-linear ROM of a turbine rotor disk, created using Ansys Dynamic ROM Builder with data from a transient thermal-structural FE model. Time-varying temperatures and heat transfer coefficients, calculated at 28 regions in an upstream whole-engine model, were applied as thermal loads. Temperatures and strains were measured at discrete points to generate training data for the ROM. Input compression was used to reduce the 58 input signals to 10 significant modes. A non-linear ROM was produced that gave good predictions on both static and dynamic operating phases during representative drive cycles. ROM results can be fed into a downstream lifing model to give in-service life prediction in a digital twin.
Introduction to Digital Twins
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Full abstract coming soon...
Simulation based Digital Twins for predictive maintenance, optimal operation and new business models
Presented By:
Teresa Alberts (ITficient) & Christof Gebhardt (CADFEM), CEO & Head of Digital Twin Innovation Lab
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The digital twin is the virtual image of a specific asset that accompanies its physical counterpart throughout its life. Its virtual sensors provide information especially in situations where plain sensor data is not sufficient. Various types of degradation such as cavitation, abrasion, corrosion, fouling or fatigue can be detected and projected into the future. For operators, the digital twin serves to ensure availability, implements predictive maintenance and optimizes operation in terms of performance, operating time or operating costs. From the manufacturer's point of view, digital twins offer new business models through digital services such as recommendations-as-a-service, maintenance-as-a-service and machine-as-a-service. The implementation of digital twins is shown from both perspectives - operator and manufacturer: For a leading european operator of hydro power plants, the digital twin provides extended insights in the current and expected hydro power plant state. Based on this information, he is able to see not only the actual state but as well to balance the mode of operation in the sweet spot between revenue and maintenance costs. In the context of oil & gas applications, a leading manufacturer of mission critical equipment implements digital twins to offer new digital services for his customers based on virtual sensors. The integration in the SAP infrastructure provides a compelling customer experience and effective business processes. See as well: https://tinyurl.com/y8rkn9jj
5G Design Innovation Through Simulation
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Modern electromagnetic simulation is founded on the vision that all electronic design is fundamentally based on Maxwell’s Equations, thus solving them directly would one day become the basis for the highest performance design. That day is today. In this 5G Anchor presentation, we will highlight the promise of the coming 5G revolution, as well as spotlight some of the significant challenges that accompany this leap in communications technology. You will see that superior design can be delivered using advanced engineering simulation and high-performance computing leading to advantage for both large corporations and small start-ups. During the course of our 5G track, you will see what our customers, technology solution partners and Ansys field engineers are doing to apply best-in-class solutions to address the 5G design and analysis challenges from the scale from transistors to cities.
Ansys Multiphysics for 5G: Chips to Cities
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5G is positioned to be much larger than simply the next-generation mobile network. 5G will offer a significant increase and performance and efficiency over the previous mobile networks, and this new performance will allow for new, larger applications to flourish along with next-gen mobile to mobile communications. Because of its proposed ability to handle multi-Gbps data rates, 5G will usher in the rise of the machines. 5G will become the backbone for big data transfer that will allow for applications such as smart cities, autonomous vehicles, industrial electrification, next-generation medical, and much more to become a reality. However, for 5G to become a reality, the communication channels must be reliable and optimized. Ansys offers the special ability to simulate and predict the operations of equipment that will be used to make 5G networks a reality, such as new data centers, EDGE network systems, mmWave antennas and system designs, next-gen semiconductor applications, and more. In this presentation, you will learn how you can deliver innovative designs for 5G using physics-based simulation. Ansys can help engineers design, study and solidify next-generation communications channels because only Ansys offers broadband, multiphysics simulation capabilities that stretch from chips to cities.
Modeling and Simulating the Coupled Effect Between Antenna Arrays and Nonlinear RF Front Ends in Modern Communication Systems
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AMCAD VISION is an advanced RF/microwave circuit and system modeling solution for accurate system design. This presentation highlights the use of VISION models to take into account the interaction between power amplifiers and antenna array. VISION offers a unique platform that provides accurate behavioral models for High Power Amplifier accounting for nonlinearities, inter-block mismatches, and memory effects. From antenna designed in ANSYS Electronics desktop, VISION is able to predict the magnitude and phase distribution considering complex load-pull effects from HPA/Antenna interaction.
Recent Advances in 3D EM Component Models
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The combination of circuit simulation, equivalent-circuit models, and planar electromagnetic (EM) simulation of a PCB layout is typically sufficient for designs in which components are not packed into a compact layout. However, once components are placed near each other, this approach no longer captures all the interactions that will be present in the fabricated design. For these scenarios, 3D simulation is required to accurately predict a circuit’s performance. However, while 3D simulations are extremely powerful, they require a high degree of expertise and an in-depth knowledge of the components used in the simulation. This session explains when 3D simulations may be required rather than planar EM simulations. In addition, an alternative 3D-simulation method for capacitors is discussed. This approach involves “brick-model” elements that can capture the effects of component coupling in the simulation. Several 3D-simulation-based design examples are demonstrated that incorporate highly accurate Modelithics 3D models. The design methods presented in this session can be applied to 5G applications and much more.
Simulation Support of 5G Over-The-Air (OTA) Test
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New frequency bands and dense integration of 5G antenna products forbid traditional subsystem level conducting test leaving developers and manufacturers with Over-The-Air (OTA) test as the only viable way. ANSYS Electronics Desktop simulation can be an important part of the design of test setup and assurance of best return on investment (RoI) before building a costly test facility.
Perspective on Assurance-Enabled Microelectronics for 5G
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The Office of Secretary of Defense (OSD) has prioritized microelectronics as a top technology initiative to enable future warfighting capability. Sanctioned by the OSD, the Air Force and other branch services are executing upon the Trusted and Assured Microelectronics Program to secure the supply chain and ensure successful foundational knowledge for microelectronic deployment in applications like 5G, autonomy, AI, and signals intelligence. Mr. Orlando will provide his perspective on assurance-enabled microelectronics for the 5G markets.
RF Synthetics – A virtual wireless globe
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The airwaves are becoming increasingly complex to navigate with the growth of 5G, and proliferation of low earth orbit satellite industry. In this talk we’ll discuss how we are leveraging Ansys tools at cloud scale, to build a completely virtual wireless globe that will enable AI for 5G, expansion of military and commercial spectrum sharing, and the ability to virtually fly satellite RF payloads.
A microwave filter design platform for 5G and mmWave requirements that features an AI-optimized tuning system
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With direct integration into Ansys HFSS, SynMatrix offers one of the world’s first AI-based tuning optimization systems with an advanced space mapping workflow. The integrated HFSS-SynMatrix design workflow for filter design improves simulation performance, obtains more accurate and faster design outputs, and also helps to avoid physical production rework. This session will review the design issues introduced with mmWave components, introduce a microwave filter design platform called SynMatrix, and review use cases to help illustrate how improved design and simulation tools can help meet those design challenges.
Novel Method to Model Emissions through small Gaps in Housings
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Due to bolt pre-tension the parts of a housing will gape by up to a few microns. Those gaps are often weak points for EMI shielding and affect the shielding effectiveness of the housing in an adverse way. For housings of electric drives, it is crucial to evaluate the effect of the bolts, their positions and their pre-tension on the shielding effectiveness. Simulation is the best way to gain understanding, evaluate and plan in an early stage, especially when the housings are big, prototyping is expensive and takes a lot of time.
HFSS Theater Curtain Call
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Ansys HFSS Comprehensive Radar Solutions for Autonomous Vehicles
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Advanced driver assistance systems (ADAS) are at the core of automotive safety and will serve as the enabling technologies for autonomous vehicles. Ansys HFSS solvers have a direct application in the design and testing of the sensors required. In this presentation, we will demonstrate how HFSS can be used to develop vehicle to everything (V2X) systems, automotive radar antenna, and radar returns of traffic scenarios. Using hybrid solver techniques, such as FEM, IE, and SBR+, the installed antenna performance will be evaluated on electrically large platforms. V2X simulation studies will reveal how multipath propagation can lead to reduction in received signal strength between communicating vehicles. Finally, a high-resolution MIMO radar system will be simulated in a realistic driving scenario. The radar returns will be processed into Range-Doppler and Angle of Arrival maps. Additionally, micro-doppler signatures by vehicles and pedestrians will be studied using full-physics simulation.
Design Innovation of Handheld 5G Antenna Systems with HFSS Simulation
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Great opportunities exist for those engineering the next generation of 5G-compatible portable communication devices. With the promise of significantly higher data bandwidth, combined with low latency, these devices will pose significant challenges in design and optimization for the 5G reality. Handheld devices engineered for the 5G network must co-exist with 4G and 3G system antenna systems, adding more antennas to operate at more bands, inside of a form factor that remains largely unchanged over previous generations. Furthermore, antenna systems for higher frequency bands will employ phased-array technology, using more antennas to dynamically focus radiated energy in beamforming. Concentrating radiated energy poses requirements to validate specific absorption rate (SAR) and power density (PD) levels to satisfy human safety, regulatory and compliance standards. Developing these antenna systems and performing radiated emissions testing with build-test-validate methods at each design iteration for 5G mobile devices will not meet challenging product release timelines and price targets. Mobile devices move through difficult physical environments (the “physical channel”) that become challenging at near-6 GHz and much more complex at the mm-wave 5G bands. Higher frequency bands provide higher data bandwidth potential, but wireless propagation is reduced, and multi-path and diffraction effects become more pronounced. For this reason, MIMO beamforming techniques anticipated in 5G base stations will shape optimal radiation patterns based on the channel state information (CSI). CSI is based on the physical channel response between the mobile device and the base station and depends on the locations of the antennas and the physical structures in the environment. Choosing an optimal location for a base station will depend on optimal channel states afforded by the placement, which in turn depends on the possible gain and radiation pattern control provided in the mobile device. Ansys HFSS provides the mobile systems engineer with abilities to simulate and optimize antenna designs to account for antenna-to-antenna, package, and human body interaction. Virtual compliance testing capabilities in HFSS enable breakthrough capabilities in virtual compliance testing, catching costly compliance failures well before the physical prototype. Modeling the physical environment (channel) response between the base station and the mobile device enables an end-to-end simulation capability for virtually testing microcell and nanocell placements throughout a city or other extended setting—without installed RF heads and conducting costly OTA site testing. In this presentation, you’ll see how Ansys HFSS helps you meet design and regulatory compliance testing challenges and tests your devices in a high-fidelity virtual environment setting.
EMI/EMC Workflows in HFSS
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Simulation of EMI/EMC may require analysis of complex systems, including many aspects of signal integrity, power integrity, and RF. This presentation introduces automated workflows and templates available in HFSS for EMI/EMC simulations. Allowing users to easily identify design flaws that can lead to potential EMI issues, providing a unique capability to quickly build a full virtual compliance environment. These integrated workflows avoid repetitive design iterations and costly recurrent EMC certification tests. Multiple solvers intended to address diverse electromagnetic problems, as well as the circuit simulators in HFSS, help engineers assess the overall performance of their electrical devices and create interference-free designs. Several examples will be presented, highlighting how exclusive HFSS technologies such as hybrid solvers, robust meshing algorithms, system solvers, and cable modeling techniques are used to model complex real-world EMI/EMC challenges.
The Next Generation of Phased Array Simulation with HFSS
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Ansys has developed a breakthrough technology, known as 3D Component Domain Decomposition Method (3D Comp DDM), that enables the accurate and efficient simulation of antenna arrays. Whether solving complex, electrically large antenna arrays, or relatively simple antenna arrays, this technology enables fast simulation without compromising on accuracy. Based on the proven HFSS solver and gold standard in simulation, this capability is the next generation of solver technology. 3D Comp DDM enhances the simulation process offering a robust and scalable solution for modeling realistic arrays while capturing finite array truncation effects. This patented, non-conformal finite element technique enables the solver to not only exploit repeated geometry, but also enables the use of multiple, different unit cells to build the full array geometry. This allows for consideration of more complex arrays, which could include edge treatments or radome effects to be fully accounted leading to an accurate characterization of array-radome assemblies. In this presentation, this 3D Comp DDM workflow for analysis of phased array antennas will be demonstrated.
Novel RTL Power Regression and Minimization Workflow for Mobile GPU Cores
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RTL power has been widely adopted in the industry for early feedback, especially for mobile chips. It is also a growing trend to analyze power on real benchmarks for realistic power. By using the streaming flow of emulator+power tools, we are able to efficiently analyze power with much faster turnaround time. A power regression and minimization framework is built up with the streaming flow. On top of that, we developed a nova feature-based multi-window flow. A big benchmark is divided into dozens of windows. This can provide a more detailed power break down per ‘function’ and get pseudo-time-based power.
Designing Large-Scale Silicon Photonics Integrated Circuits through PDK Compnent Library
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Analog Photonics creates the photonic Process Design Kits (PDKs) for AIM, the United States’ flagship manufacturing facility for photonic IC’s. Similar to semiconductor PDKs, photonic PDKs provide the essential basics for photonic ICs. PDKs are critical for innovation and commercialization of photonic technology. This talk will survey the state of the art of photonic design and applications.
Thermal Issues and Solutions for 3D ICs: Latest Updates and Future Prospect
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It is well-known that thermal issues have been the most prominent roadblock hindering the mainstream acceptance of 3D IC technology. This has called for a concerted effort from all parts of the community: architects, circuit designers, electronic design automation (EDA) vendors, foundries, and packaging houses. In this talk, we present our latest research accomplishments from circuit design and EDA perspectives. Specifically, we provide detailed thermal analysis results of commercial-grade 3D IC designs targeting a wide range of applications. We analyze the root causes of their thermal hotspots and their impact on power, performance, and area (PPA). Next, we discuss which set of thermal-aware design and EDA solutions have been useful to mitigate thermal hotspot issues. We conclude with our thoughts on the need for multi-disciplinary and multi-faceted efforts. This project is currently being funded by DARPA, Arm, and Ansys.
All things 3D-IC: Taking the Headache out of Managing Multiphysics Co-design for a 3D-Chip-Package-System
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Every stage of a 3DIC design – from chip to package to PCB to system – involves careful attention to ensure reliability in the face of thermal, power, electromagnetic, and mechanical constraints, and interactions. But the design stages are often disjoint and fragmented with limited visibility from one abstraction level to the other. Chip-package co-design is a vital part of 3DIC design and has become a critical requirement for modern system design. Ansys 3DIC analysis platform provides a smooth and well-thought-out workflow to easily pass the right information between chip, package, and board tools for a seamless early-design to sign-off methodology for thermal, power & signal integrity. With automatically generated chip abstractions and powerful analysis engines at all levels, the Ansys platform lets designers focus on the relevant issues at every stage without having to accept compromising simplifications or unmanageable complexity.
A C-P-S Simulation Technique of Power-Noise Side Channel Leakage in Cryptographic Integrated Circuits
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Cryptographic algorithms are vulnerable to implementation attacks on side-channel (SC) leakage information. This paper introduces an efficient simulation technique of SC leakage at the full IC chip level. Tool chains and modeling flows will be explained in detail. The whole power delivery network (PDN) including Si substrate is captured in a chip power model (CPM) and then integrated in a chip-package-system board (CPS) model. The proposed technique was demonstrated with an advanced encryption standard (AES) Si test chip for SC leakage evaluation using correlation power analysis (CPA) over 1000 different plain texts through power delivery and Si substrate combined networks.
Is Your Chip Green Yet? Steps to Power-Efficient RTL Design
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Less is more. If COVID-19 has taught us one thing, it is to make do with less. As semiconductor designs increase in complexity to cater to the insatiable need for more compute power, designers are constantly faced with improving Watt per Hertz targets. Optimal power consumption prolongs battery life, improves thermal performance, and leads to a green chip and a green earth! Early visibility to chip power provides a critical boost for high-impact and timely design decisions versus traditional gate level power sign-off. Designs competing on power or with energy efficiency concerns have not only adopted but expanded RTL power methodologies to analyze real application scenarios ranging 100s of milliseconds. Join this session to learn best practices in use by leading semiconductor companies to analyze, debug and reduce power
Developing the Next-Generation Engineering Curriculum to Democratize Simulations
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Many engineering professors recognize the benefits of integrating simulations into the undergraduate curriculum such as connecting physics to real-life applications, supporting project-based learning and helping students acquire skills sought by industry. However, they struggle with how to incorporate simulations into their courses without taking away from the teaching of physics. At Cornell, I have developed an approach that blends the teaching of physics and simulations using just-in-time, problem-based learning. This approach seamlessly integrates the teaching of theory with the hands-on use of ANSYS Mechanical and Fluent. It has become clear from these efforts that ANSYS can serve not only as a problem-solving platform but also as a visual learning platform that helps students master physics better than through a pure textbook approach. My approach has been implemented in over 10 Cornell engineering courses as well as in a free “massive open online course” or MOOC at edx.org which has an enrollment exceeding 160,000 people from universities and industry. My MOOC has helped thousands of people to not only learn practical simulations in a hands-on manner but also physics through simulations. It has opened a pathway towards the democratization of simulation whereby all engineering graduates would be able to deploy simulations reliably. The MOOC demonstrates how to shift the paradigm in engineering education by embracing two disruptive technologies: simulation and online learning. Considering that now most faculty have experience with online learning due to Covid-19, this is the moment for industry to push universities to leave behind a dated, theory-heavy education model and move towards the next-generation engineering curriculum in which simulation becomes the standard to teach physics on-demand. This would benefit a large number of undergraduate programs and ensure students graduate with real-world skills, empowered and excited to solve practical problems.
The US Exascale Computing Project Software Stack: Why It Matters to You
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The US will deliver its Exascale systems (capable of a billion-billion operations per second) over the next couple of years, enabling a new generation of application codes to deliver fundamentally new simulation and analysis results. At the same time, the underlying tools and libraries that enable these application codes to achieve success will have a much broader impact, enabling improved performance for countless other applications, even those targeted toward workstations and clusters that contain accelerator devices. In this presentation we give a brief overview of the Exascale Computing Project (ECP), then some details about the ECP Software Technology portfolio. We discuss the challenges of preparing for Exascale platforms, which include preparations for accelerated architectures, and highlight opportunities for interaction with US industry. While GPU accelerators have been available for some years, the emergence of new accelerated devices brings new opportunities and challenges. We will discuss ECP efforts to realize performance and portability across devices from Nvidia, AMD and Intel.
Additive Manufacturing and Materials Intelligence
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As Additive Manufacturing (AM) technology evolves, better understanding of the data generated during AM projects becomes vital to realizing its potential. This applies both to empirical data, from production and testing, and to data generated by the various simulation packages that help users to understand process parameters and reduce “trial & error”. Simulation results can help to optimize and calibrate your process. Testing validates simulation and ensures that final parts meet design requirements. You need to capture and use data from both in order to speed your AM development process and certify products at lower cost. GRANTA MI for Additive Manufacturing is focussed on these 3 things; 1)Traceability and capture of data across AM Value Chain 2)Efficient data analytics to enable the technical decision-making process 3)Integration between AM technology and simulation solutions
Creating World Class Designs in Record Time with Photonic Inverse Design’
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Photonic Inverse Design is changing the game for photonics designers, driving the transition to commercial applications. The innovative technique changes the design methodology from a manual, iterative and lengthy process, to an automated, simulation focused methodology that is producing designs that are better than the best published designs in hours, instead of weeks or months. Ansys Lumerical Photonic Inverse Design uses Lumerical's open source Python API to drive the indiustry leading FDTD simulation. The adotopn of Photonic Inverse Design is doing for photonic design what logic synthesis did for IC design, raising the level of abstraction that designers design at and generating better, larger designs.
Data-driven Fast Static On-chip Thermal Solver
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Accurate prediction of on-chip temperature distribution becomes important for the performance and reliability of upcoming 5G, automotive, and AI chip-package-systems. In particular, a large thermal gradient (the temperature variation across a chip) accelerates electromigration and aging, and also impacts design performance and power. Furthermore, there are usually Tmax (maximum temperature) constraints on junctions of a chip, skin temperature concerns for mobile devices or wearables, and important placement considerations of on-chip thermal sensors for use in dynamic voltage and frequency scaling. However, obtaining an accurate and detailed thermal gradient on-chip is very time-consuming using the finite element method (FEM) or computational fluid dynamics (CFD) technology. Furthermore, there are many different functional scenarios for various applications that users need to identify possible Tmax locations on-chip. Therefore, there is an urgent need in the industry to provide a fast, yet accurate on-chip thermal solution in a chip-package-system or more complicated 3DIC design, which may include multiple chips. This paper proposes a method to use a data-driven DNN-based thermal solver that can be 100-1000x faster depending on the size of the chip compared to traditional FEM-based thermal solvers with the same level of accuracy.
Deliver Engineering Grade Light Simulation to Studio Designers with Ansys and Autodesk
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Autodesk and ANSYS combined forces to create a joint workflow for automotive companies that combines Autodesk’s automotive 3D visualization and virtual prototyping software with ANSYS' physics-based lighting simulation solutions. The combination of Autodesk VRED and ANSYS VRXPERIENCE Light Simulation delivers physics-based engineering-grade light simulation to studio designers. The unique solution empowers the design studio with accurate lighting to enhance photorealistic visualizations, up to the simulation of highly complex optical components such as light guides. Join our webinar and Discover VRXPERIENCE Light Simulation to: •Easily identify lighting quality issues resulting from the optical engineering process •Run Ansys SPEOS-based ray file simulation that is compatible with High-Performance Computing (HPC) Clusters •Explore of a wider range of design possibilities •Bridge the gap between engineering and design in the long run by creating a consistent shared data stream, ensuring data consistency
Designing Next Generation Materials for Optimal Processing and Performance using Integrated Computational Materials Engineering
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Integrated Computational Materials Engineering (ICME) avails a set of computational tools based on fundamental understanding of Materials Science and Engineering principles. This further enables engineers to obtain and optimize the process-structure-property-performance relationships for further exploitation in design, processing and assuring performance of functional parts and products for the industry. Recently, Ansys became active in the area of ICME by leveraging its existing tools as well as building new tools and engaging in research projects within and outside the company. Ansys is developing two variants of tools, the first being a set of tools by which material information could be streamlined into the performance simulation ecosystem at Ansys and the second being another set by which existing simulation tools at Ansys could be used for generating materials information. In order to realize the above-mentioned possibilities, several action items have been pursued. First, the existing CALculation of PHAse Diagram (CALPHAD) techniques have been extended as a function of cooling rates to account for high thermal gradient and cooling rate processes ranging from Casting to Additive Manufacturing (AM) technologies and hybrid technologies such as Castforging. Second, the existing Ansys tools have been shown to simulate existing materials characterization scenarios such as conventional hardness testing, crystal plasticity based mechanical property estimations based on experimental and simulated microstructural evolutions for a variety of cooling rates, AM powder bed heat transfer models and the effect of such heat transfer on delineating design methodologies for optimal processing, performance and combinations of those. Third, a real time simulation methodology would be showcased where instead of combining and collecting data from sensors for validation of simulations, an AM based case study would be shown where the rhs weak form in Finite Element has been computed using sensor data leading to real-time simulation outcomes in the volume which further leads to better computation on evolution of nonlinear material properties as well as paves the way for simulation assisted feedback control systems in AM in the future. The same methodology has been also extended to NISAR earth interior displacement simulations based on surface data from simulated NASA inputs. Fourth, our current ongoing research partnerships in the area of ICME will be explored in greater details alongwith our current ongoing efforts in ICME based understanding of disease causing species and their interactions with nanomedicine.
Dynamic Thermal Management with AEDT Icepak 2020R2
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Today's mobile electronics like cell phones encounter a diverse application workloads. For example, someone using a cell phone can be browsing for 10 minutes, then watch a movie but may be interrupted by an incoming call etc. These activities put a varying load on the CPU and its subsequent heat dissipation characteristics and temperature imbalances among the different cores. This in turn leads to increased cooling costs and reduced reliability. Dynamic thermal management capability in AEDT Icepak allows users to model the transient temperature response based on user-specified CPU workload profiles. The temperature response under various workload scenario can provide guidance on CPU power management, active/passing cooling design etc. Machine learning and AI can use these temperature responses to construct reduced order models (ROM) that can eventually provide real-time temperature response prediction.
Engineering Post-Simulation Analysis in a Web-Centric World
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Answering engineering questions using modern simulation tools requires the integration of an wide array of data. Sharing actionable knowledge from this growing set of data is increasingly difficult. We present Ansys Nexus, a web-centric tool that embraces the explosion of data to provide interactive and shareable reports that summarize engineering data from a wide set of sources. Nexus has been successfully deployed standalone or integrated into other Ansys tools like Fluent, Workbench, and EnSight.
How Extended Reality Changes Visualization of and Interaction with Simulation results
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In this presentation, we will start from non-realtime Predictive simulation and which problem can be solved especially predicting the final appearance of any product. We will then jump into real-time applications especially talking about what AR/VR and GPU rendering can bring to perceived quality analysis.
Material Designer – Multi-Scale Simulation Made Easy
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Material characterization leads to a significant effort in many companies. This is particularly true if the material can be varied in some way, be it on purpose or not. A classic example are composite materials. There the user can combine different fibers and matrix systems leading to different composite materials. In addition, also the fiber volume fraction plays a key role with respect to stiffness and strength of the resulting material. However, we are not limited to classical composites. For instance, the infill structures in additive manufacturing can also be treated as (meta-)material to ensure an efficient simulation. Also in this case, you have some choices that influence the macroscopic material behavior: you can choose between different (lattice) structures and you can vary the relative density. Experimental testing of all combinations would be inefficient. This is where ANSYS Material Designer aims to come into play. The goal is to provide you with an environment where you can easily investigate a certain microstructure, obtain corresponding macroscopic properties, use them in a macroscopic simulation, and identify how the macroscopic loading impacts the microstructure. In addition, you could also try to optimize the design of the microstructure or the way key parameters of the microstructure vary over the macroscopic part. This all allows to reduce the amount of experimental testing and replace it by virtual material testing, thus obtaining results quicker and cheaper.
Materials Data for Simulation
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This presentation details how accurate and consistent material definitions and properties are important for multiphysics simulations, and how Ansys Materials solutions can provide them.
Multiphysics simulation solution for complex SoC and power management IC
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Multiphysics simulations solution for IC designs
Ingraining Simulation into our DNA
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Cummins has been on a 20+ year journey of driving simulation methods into our product development processes and deliver products that are right the first time and robust to all our customers’ uses. There are several key factors behind this journey that helped ingrain simulation into our DNA and provide an advantage for our company and customers.
Multi-Scale Electromagnetic Modeling for Antenna Applications
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This talk will present finite element methods (FEMs) for multiscale modeling in electromagnetics. The presentation will first go over few key technologies in traditional FEM for tackling multiscale problem. A domain decomposition method is then combined with state-of-art FEM to permit original problem decomposed into multiple disjoint or contacting domains where each domain can be meshed separately.
Patient Specific Real Time Coupled System and 3D Hemodynamic enabled by Reduced Order Modeling
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Cardiovascular disease is currently a major cause of death, and a prevalence of the disease continues to increase globally. Cardiovascular disease includes coronary artery disease, stroke, and pathologies of large arteries. For example, coarctation, aneurysm, or aortic dissections of large arteries present a high risk of mortality. Computer models of large arteries have been developed to help understand the physics of the arteries induced by resident pathologies. Patient-specific 3D model simulations of large arteries can provide useful diagnostic and evaluative information for cardiologists, cardiac surgeons, or other clinicians. For example, patient-specific simulations can non-invasively provide clinicians with relevant data like the velocity of the blood, wall pressure, or wall shear stress of large arteries. Such data allows for the diagnosis of disease and supports surgical or treatment options. Such a 3D models must be embedded inside a system simulation of a peripheral vasculature to provide the necessary unsteady boundary conditions. However, those coupled simulation have always been difficult to setup, converge and maintain for a sufficiently large lapse of time to enable real diagnostic. Moreover, the calculation time require for such embedded 3D simulations are not compatible with a daily practice in clinic. In this paper, we will show both a methodology to successfully compute the aortic arch hemodynamics but also a methodology to build a dynamic Reduce Order Model (ROM) which will enable the derivation of a patient specific model and a real-time usage. The methodology can be described in 6 steps Step 1: A 3D geometry of the aortic arch is extracted from the MRI scan, CT scan, or echocardiogram data obtained for the vascular system of a patient. Step 2: A steady state mass flow rate sweep is done in order to compute a response surface of the pressure drops as function of the pressure at inlet and mass flow rate at every outlet using ANSYS DesignXplorer Step 3: Replace the 3D simulation in the whole vascular system simulation in ANSYS TwinBuilder to generate coherent multiple scenarios for the boundary conditions Step 4: Launch multiple 3D unsteady calculations inside ANSYS Fluent based on that scenarios Step 5: Use the scenario to build a Dynamic ROM of the Full 3D velocity, wall pressure and wall shear stress using TwinBuilder Dynamic ROM Builder Step 6: Optimize the parameters of the vascular system in ANSYS TwinBuilder simulation to retrieve velocity fields results from 2D or 4D MRI scans Finally, TwinBuilder provides a vascular system model that predicts the effect of certain factors like therapeutic substances or exercise on the vascular system of the patient in real-time giving the surgeons an unprecedented insight into the patient health.
Process Integration and Design Optimization (PIDO) - the glue and the driver of virtual prototyping
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For digitalization of virtual prototyping, simulation process integration, parametric modelling and design optimization need to go hand in hand and customer ask for an open and vendor neutral process integration platform – the main driver behind the dynardo acquisition in 2019. Dynardo’s optiSLang PIDO software tools enables customer to integrate ANSYS as well as any third party parametric simulation models from different physical disciplines, to build simulation workflows to be used for calibration, optimization and robust design. After a short introduction of simulation workflow building capabilities applications of model calibration, optimization and robust design at different industries are shown.
Process Parameter Optimization for Metal Additive Manufacturing through Simulation
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ANSYS Additive Science enables users to optimize their machine process parameters via prediction of meltpool dimensions, porosity and microstructure. This can dramatically reduce the time and cost needed to develop new process parameters for metal additive manufacturing machines.
Real-time lidar simulation
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Lidar is one of the main autonomous driving sensors used for obstacle and vehicle detection. We present a physics-based simulation of different types of lidars. The outputs, generated in real time, are point clouds enriched with intensity data and raw waveforms. The implementation leverages GPU acceleration for ray tracing and post-processing.
Recent Technology Development for Real-Time Automotive Radar Sensor Simulation Using SBR Ray Tracing
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Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV) increasingly rely on radar sensors for awareness of the dynamic traffic and pedestrian scene in all-weather conditions. With the ongoing aggressive development of millimeter wave radars and their deployment in ADAS/AV systems, there exists a pronounced need for high-fidelity, real-time prediction of their range-Doppler imagery when interrogating complex traffic scenarios. These are large scale electromagnetic (EM) scattering problems where the scenes span tens of thousands of wavelengths, placing them far beyond the reach of matrix-based EM solvers that depend upon sub-wavelength meshing. Such problems are, however, well suited to the ray-tracing methodology of HFSS-SBR+. Modern automotive radars generate imagery at 10 – 30 frames per second. With each frame, the radar issues and observes hundreds of chirps over many combinations of antennas, each capturing hundreds of frequency samples over a 200 – 1000 MHz bandwidth. For this reason, and in spite of its otherwise large-scale capabilities, a standard multi-core implementation of traditional SBR (shooting-and-bouncing rays) method remains roughly one million times too slow. Through a combination of breakthrough algorithmic and hardware accelerations recently developed at ANSYS, we have bridged this performance chasm while retaining the full physics-benefits of SBR. This presentation covers accelerated Doppler processing (ADP) and the key concepts of putting the entire SBR + ADP pipeline on the GPU to realize a composite acceleration factor of over one million and deliver radar simulation rates that, in many cases, meet or exceed the frame rate of actual radar hardware.
ROM capabilities within Twin Builder product's line
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Reduced Order Model (ROM) is a simplification of high-fidelity model that preserves essential behavior and dominant effects, for the purpose of reducing solution time and storage capacity required for the more complex model. As such, it is a key capability to build a winning digital twin strategy. In this presentation, we will present ROM capabilities exposed in Ansys Twin Builder product’s line.
Scade Hybrid
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SCADE Suite is a development environment dedicated to the software design of safety critical systems. It is built around a data-flow synchronous language called Scade, that, as most of the programming languages allows to describe discrete systems. In collaboration with academia, we have proposed a way to extend the language with Ordinary Differential Equations (ODEs) to model continuous and hybrid (continuous and discrete) systems. The approach is based on two key ideas: •divide: ensure a clean separation of discrete and continuous parts of the model •recycle: reuse existing language and compiler architecture as much as possible. The presentation will introduce the language and the main results of the related research work. We will highlight some advantages of our language-based approach compared to Simulink.
Time Decomposition Method for Transient Electromagnetic Field Simulation
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The transient electromagnetic field simulation of electrical machines and electronic transformers with nonlinear materials is usually very time-consuming, since it requires NM matrix solutions with N the number of time steps and M the average number of nonlinear iterations. Parallel computing can be applied to the matrix assembling and matrix solving at each time step to cut down simulation time. However, its parallel scalability is limited. In order to gain much better parallel scalability, a novel HPC technology was developed in terms of the domain decomposition along time-axis, namely time decomposition method (TDM) to solve all time steps or a subdivision of entire time steps simultaneously, instead of solving a transient problem one time step by one time step. This innovation of HPC technology has been granted by US patent office in May 2019.