Level Up Your Simulations

In 2022 R2, the Structures products deliver new features that provide improved efficiency, innovation, and robustness. Product integration continues to be a driving force to enable users to expand and broaden their scope of simulations. Specifically, customers in the aerospace, high tech, and automotive industry will now be able to conduct more advanced closed-loop reliability workflows. Ansys LS-DYNA includes improved integrations with other Ansys tools, plus Ansys optiSLang and Ansys Composite PrePost, as well as new multi-scale co-simulation possibilities to analyze global structural response without compromising accuracy. Highlights will include: • Ansys Composite PrepPost (ACP) is now integrated with Workbench LS-DYNA to enable users to analyze composite structures for explicit analyses with layered structures.  This workflow supports shell models and assemblies.  • New integrations between optiSLang Pro and LS-OPT Pro allows users to extend their design optimization analyses by leveraging technology from the two robust optimization tools. These integrations include additional capabilities such as MOP (metamodel of optimal prognosis) that allow users to automatically identify key parameters, saving valuable time and resources. • Patent pending technology, multi-scale co-simulation, introduces a way for LS-DYNA users to include meso-scale effects from joints with macro-scale structures to analyze the global structural response and simultaneously capture failure responses at meso-scale without compromising accuracy.

Drop impact can cause severe damage to portable and consumer devices, including interconnect breakage, battery separation and display damage. Therefore, drop impact performance is an important concern for the design of these products. However, it is very expensive and time-consuming to conduct drop tests to experimentally detect the failure mechanisms and identify their drop behaviors. That is why finite element simulation is key to accurately capture drop impact behavior early in the design phase of the device.    Here, we focus on providing a chain of simulation software tools to predict the drop impact behavior of devices with components made of injection molded short fiber reinforced plastics (for instance casings of electronics devices). The workflow shown here combines Moldex3D to simulate the injection molding process and Ansys Workbench LS-DYNA to simulate the drop impact. Local fiber orientations, weld lines, and residual stresses predicted by Moldex3D can be easily imported into the Workbench Mechanical application, where they are mapped onto the structural model and combined with an orientation-dependent anisotropic nonlinear material model. This workflow enables you to easily and accurately simulate multiple drop test configurations to better understand product integrity and identify critical regions.

Engineering layered composites involves complex definitions that include numerous layers, materials, thicknesses, and orientations. Ansys Composite PrepPost (ACP) provides all necessary functionalities for the analysis of layered composite structures.  Through simulations of composites in LS-DYNA, you can analyze these parts with respect to crash and impact. The workflow between ACP and LS-DYNA has been improved for various areas in recent releases: it now supports t he transfer of assemblies of composites shell models as *ELEMENT_SHELL and solid models as *ELEMENT_TSHELLfrom ACP to LS-DYNA.  Besides exporting composites models to  a native LS-DYNA environment, support for workflows towards Ansys WB LS-DYNA has been released from Ansys version 2022 R2, increasing the easiness of users to realize simulations of composites using LS-DYNA solvers within the familiar interface if Ansys Mechanical.  The study of a bird strike against a composites windblade assembly will be used in this presentation to highlight the new capabilities of ACP towards native Ansys workflows using LS-DYNA solvers.

"Although CAE has built a strong reputation as a verification, troubleshooting and analysis tool for industrial applications, there is an increasing need for multi-scale multi-physics analysis including new materials and processes, for which current software packages are still missing. Short-fiber-reinforced composites (SFRC) are high-performance engineering materials for structural applications in the automotive and electronics industries. Typically, SFRC are manufactured by injection molding, which induces heterogeneous microstructures across different length scales, and the resulting nonlinear anisotropic behaviors are challenging to predict by conventional micromechanical methods. In this work, we present a data-driven multiscale modeling framework that integrates the injection molding simulation, material homogenization, and a mechanistic machine learning model referred to as Deep Material Network (DMN) [1]. The physics-embedded DMN learns the microscale material morphologies hidden in Representative Volume Elements (RVE) [2] of composites through offline training, and the trained network predicts nonlinear material behaviors at a computational speed orders-of-magnitude faster than the high-fidelity RVE simulation. Through transfer learning [3] of different material microstructures, a unified DMN database is created to cover a full range of SFRC geometric descriptors. Based on DMN, we have developed a highly accurate and efficient concurrent multiscale simulation approach for modeling injection-molded SFRC structures [4], where microstructural distributions predicted by Moldex3D are imported into LS-DYNA through the LS-PrePost mapping function. Released in 2022 LS-DYNA R14 and LS-Prepost 4.10, this machine learning-based multiscale simulation framework is quite promising for the design and analysis of composite structures.  REFERENCES [1] Liu, Z. and Wu, C. T. Exploring the 3D architectures of deep material network in data-driven multiscale mechanics. Journal of the Mechanics and Physics of Solids. 127, 20-46. (2019). https://doi.org/10.1016/j.jmps.2019.03.004  [2] Wei, H., Lyu, D., Hu, W. and Wu, C.T., RVE analysis in LS-DYNA for high-fidelity multiscale material modeling. FEA Information Engineering Solutions. (2021). 7, 37-47. (2021). http://www.feapublications.com/2021/07_FEAIES_July_2021.pdf  [3] Liu, Z., Wei, H., Huang, T. and Wu, C. T. Intelligent multiscale simulation based on process-guided composite database. arXiv preprint (2020). https://arxiv.org/abs/2003.09491  [4] Wei, H., Wu, C.T., Lyu, D., Hu, W., Rouet, F.H., Zhang, K., Ho, P., Oura, H., Nishi, M., Naito, T. and Shen, L. Multiscale simulation of short-fiber-reinforced composites: from computational homogenization to mechanistic machine learning in LS-DYNA. 13th European LS-DYNA Conference Proceedings. (2021). https://www.dynalook.com/conferences/13th-european-ls-dyna-conference-2021/composites/wei_ansys_lst.pdf"

Pouch cells are extensively used in mobile electronic devices such as smartphones and laptops due to space constraints. The packaging material for pouch cells consists of an outer layer of polyamide (PA) or polyethylene terephthalate (PET), middle layer of aluminum film and an inner layer of adhesive like polypropylene (PP). The outer layer provides protection against mechanical deformation and the inner layer of polypropylene offers chemical protection to the cell. These layers are joined together by application of heat and pressure. The adhesive, which has a low melting point of 140-160 deg C, melts under the application of heat, leading to reduction in thickness of the adhesive layer. It is important to understand the flow behavior polypropylene due to heat and pressure and final thickness of the polypropylene layer, since it affects the chemical protection of the Li-ion cell. The adhesive undergoes phase change during hot pressing and shows elastic deformation in solid phase and viscous deformation in molten phase – which can be quite challenging to model using CFD tools.  Structured Arbitrary Lagrangian Eulerian (S-ALE) solver in LS-DYNA can be used to simulate flow problems, by considering a moving domain approach. In addition, the ALE fluid domain can easily be coupled to the structural solver. The Thermal – S-ALE coupling was added recently, and this new feature allows users to perform coupled thermal, structural, and fluid flow analysis using a single model within LS-DYNA, reducing the preprocessing effort needed and need for data exchange between different tools.  The current presentation demonstrates the complete workflow used to perform coupled structural, S-ALE and thermal analysis of hot pressing of the pouch cell assembly in LS-DYNA. The phase change of polypropylene during the hot-pressing process is modeled using a material model, which considers both viscous flow as well as elastic deformation. The temperature-dependence of mechanical properties is characterized using experimental data. This workflow is being used by our customer to understand the effect of process variables (pressure, temperature, processing time etc.) on the flow of adhesive (polypropylene) and its thickness at the end of the pressing stage. The presentation will include results of the hot-pressing analysis in LS-DYNA and how this approach can be applied to polymer flow and adhesion problems.

Engineers are more and more forced to deliver better and more robust designs in less time. In same moment the complexity of simulations increases. Other topics (e.g. costs) need to be directly involved into analysis. Thus, many parameters need to be considered. This fact makes it hard to understand the interrelation and to get an optimal design. Traditional manual approaches are not applicable anymore. We will show on practical examples how to overcome this with use of the existing tools. It will be presented, that with the help of those techniques engineer saves time for repetitive work, get insights through machine learning algorithms and get more time for the physical problem. At the end we can show how this work can be given to non-simulation experts (e.g. designers).

Yong Guo, Dandan Lyu, Yuxi Xie, Haoyan Wei and C. T. Wu  Computational and Multiscale Mechanics Group, Livermore Software Technology, Ansys   ABSTRACT Multi-stage process simulation of metal fabrication followed by machining is of great importance in part performance and tool design to manufacturers in modern product design and analysis. During the metal machining processes, the material properties are highly sensitive to the polycrystalline microstructure changes induced by different metal fabrication processes, such as forging and extrusion. However, this microstructure-induced location-dependent metal material response is multiscale in nature and often neglected in the state-of-the-art metal machining simulation due to the high computational cost, thus strongly affecting the solution accuracy. In conventional metal machining analysis, the Smoothed Particle Galerkin (SPG) method recently developed in LS-DYNA has shown a superior capability over finite element methods by capturing the desired physics in material failure and separation evolution [1, 2], but it is still challenging to incorporate the microstructure-based process-dependent heterogeneous metal material properties in the simulation with an efficient manner.  In this study, a data-driven machine learning method [3] named Crystal Plasticity Deep Material Network (CP-DMN) is presented to restore the accuracy of the metal machining simulation. The proposed CP-DMN is created in an offline stage with training datasets based on polycrystalline RVE with different grain orientations. In the on-line prediction, the trained CP-DMN with a finite-strain rate-dependent crystal plasticity model is implemented in the SPG simulation framework to capture the multiscale machining effect and material deformation in metals. Additionally, since different numerical methods and discretizations are used in forging and machining analyses, a mapping procedure is needed to transfer the CP-DMN predicted residual stress data from the finite element integration points in the forging simulation to SPG particles in the machining simulation. Several numerical examples are studied to compare the accuracy of different mapping algorithms and demonstrate the performance of the proposed computational method with comparisons to the conventional methods.  REFERENCES [1] C.T. Wu, T.Q. Bui, Y.C. Wu, T.L. Luo, M. Wang, C.C. Liao, P.Y. Chen, Y.S. Lai, Numerical and experimental validation of a particle Galerkin method for metal grinding simulation, Computational Mechanics, 61, 365-383, 2018. [2] X.F. Pan, C.T. Wu, W. Hu, Y.C. Wu, A momentum-consistent stabilization algorithm for Lagrangian particle methods in the thermo-mechanical friction drilling analysis, Computational Mechanics, 64, 625–644, 2019. [3] Z. Liu and C.T. Wu, Exploring the 3D architectures of deep material network in data-driven multiscale mechanics, Journal of the Mechanics and Physics of Solids, 127, 20-46, 2019.

Hydroacoustics is the study of sound under water. Underwater sound waves can come from a variety of human activities and travel long distances, leading to increased noise pollution and a negative impact on the ecosystem. These sound waves can travel such long distances because water is much denser than air, which requires a different modeling approach compared to aeroacoustics simulations. This presentation will cover how Ansys Fluent can be used to accurately model the sound generation and propagation of a marine propeller using the Ffowcs Williams-Hawkings (FW-H) model to compute and analyze sound pressure levels (SPLs) at a variety of receiver locations.

Generating a high-quality CFD mesh can be a cumbersome and time-consuming process, especially when using complex geometries. Sometimes, there may be areas where there is too much refinement resulting in longer solve times or too little refinement resulting in less accurate results. Adaptive meshing is a method of refining the mesh based on the solution. Adaptive meshing in Ansys Fluent enables you to refine and/or coarsen a mesh based on numerical solution data to eliminate the need for excessively fine meshes or operating condition specific meshes. This presentation will provide an overview of adaptive meshing, show a few examples of how this technology has been used in aerospace and combustion applications and finally perform a live demo on how to use the streamlined workflow with predefined adaption criteria in Fluent.

As cutting-edge Aquatics Designers, Planners, and Engineers, the Water Technology, Inc. (WTI) team is constantly looking for new ways to turn technological advancements into useful industry tools for improved aquatic facility quality and design.  Our team has turned to ANSYS Fluent to further develop our simulation technologies in the Aquatics Engineering field.    This presentation will flow through the process and development that leads to the adoption of Computational Fluid Dynamics (CFD) technology by our internal and external teams, the unique applications we commonly use CFD to simulate, and the results that have allowed WTI to maintain the advantage as the technological thought leaders in the design of waterparks and aquatic facilities of all shapes and sizes throughout the world. “Dive in” with Jen as she explores the evolution of lazy river design from simple rule of thumb slow rivers to the use of CFD modeling to push the envelope further into fast rivers with action channels and unique flow paths. Dive Deeper into the verification of river CFD results as WTI redirected a river in the field post-analysis and ventured on site with a velocity meter to confirm analysis.  While entertainment rivers were a natural starting point for the aquatics industry to dip their toe into CFD technology, there’s no stopping the power of CFD in aquatics and WTI is poised to harness the power of Ansys to enhance water quality and flow in swimming pools and water attractions throughout the world.

"Ansys has pushed the envelope of high-performance computing for years, and this year we are setting a new standard for the use of GPUs in CFD with the introduction of a native multi-GPU solver in Ansys Fluent. GPUs provide many benefits for CFD simulations, including reducing simulation solve time, hardware costs, and power consumption. This new solver, known as the Live-GX solver, has been architected from the ground up to run natively on GPUs and provide Fluent users the power and flexibility to run on a laptop or workstation with a single GPU or scale up to a multi-GPU server.  Attend this presentation to learn how you can leverage GPUs for your CFD simulations and learn more about the new native GPU solver in Fluent, including: • How to run the GPU solver in Fluent • Supported solver capabilities • Validation and performance benchmarks"

The coating of pharmaceutical tablets to achieve desired properties is accomplished via a common film coating unit operation. Typically, this unit operation is achieved by coating tablets in a rotating drum, which is equipped with multiple nozzles that spray this coating on to the tablets. Along with such spraying operations, the tablets are also exposed to an environment where hot air is blown onto them to dry the obtained coating. Physically, this combination of tablet bed flows, coupled with spray coating and drying represents a complex process, the effectiveness of which is characterized by the inter-tablet coating uniformity as a function of the number of revolutions of the drum. De-risking the performance of the coater at different scales of process development through experimental evaluations is time and resource intensive, although it is of paramount importance. In order to enable a more efficient process design, we have developed a state-of-the-art digital twin of the tablet coating process. Our digital twin uses a novel first-principles model by combining Computational Fluid Dynamics to simulate spray coating and air flow, and Discrete Element Method to simulate tablet motion. This provides us with unprecedented levels of precision in virtual design, optimization and scale-up, enabling us to study coaters from laboratory scale to commercial scale. Using this digital twin, we have evaluated the effect of tablet loading, drum rotation speeds and tablet types, and we have computed several measures to characterize the performance of the coater. We have then used the result of these models to guide our scale-up decisions. This digital twin is at the forefront of our digitalization initiatives in process development and extends our predictive capabilities, enabling us to reduce developmental time and resources.

PyAnsys is a set of technologies that allows users to interface with Ansys products through Python. The PyAnsys project is Ansys’ commitment to open source where we provide Python libraries that expose our technologies into the Python ecosystem. PyFluent is a newly released Python package allowing users to interface with Ansys Fluent to automate processes, build custom workflow, craft customized solutions and much more.    This presentation will provide an introduction to what PyFluent is, cover the capabilities, and include a demonstration of PyFluent in action. Topics covered will include the following:    • Using Fluent within or alongside other Python environments • Access all Fluent text user interface (TUI) commands for meshing, solving and post-processing • Empower automation utilizing the Settings API (beta) • Extract field data and use with standard Python tools

"As electronic components continue to get smaller, thinner and lighter, thermal management simulation is critical to ensure reliability and performance of sensitive and heat producing components. For some thermal management simulations, more advanced physics capabilities may be required to simulate more than just the cooling. Ansys offers a workflow to perform advanced electronics cooling analyses that involve complex physics capabilities like condensation, evaporation and more. Attend this presentation to learn how to perform advanced electronics cooling analyses, ans  • Extend the strength of thermal management analysis to advanced physics that Fluent offers • Investigate evaporation and condensation effects in LED headlamps and automotive sensor assemblies where multiphase flow modeling is required • Import a case from Icepak classic or AEDT and setup a printed circuit board (PCB) analysis in Fluent"

Cameras and sensors on autonomous vehicles must reliably detect pedestrians and other physical objects ― and trigger an appropriate reaction from critical systems. Adverse weather conditions like rain, fog and snow can confuse visual cameras and impact the performance of lidar, radar and other conventional sensor technologies. One solution to rapidly evaluate the performance of these systems in different weather scenarios is by coupling CFD and optical simulation. This presentation will detail the workflow to assess camera and lidar performance under a moderate rainfall using Ansys Fluent and Ansys Speos. Fluent is used to simulate primary rain as well as water droplets generated by other sources such as side splash from the tires, or splashes and droplets generated by other vehicles on the road. The position and size of the droplets predicted from Fluent are then imported into Speos to generate the water droplets in the scene via the 3D texture feature so that its impact can be tested on a camera sensor and associated perception algorithm.

Over the past few years, Ansys has invested heavily in the advancement of our CFD simulation solutions for all flight regimes, from low subsonic all the way into the hypersonic regime. We have added important physical models, improved the robustness of the solution, and streamlined the relevant workflows to create an unparalleled working environment in which engineering productivity is of primary importance.    Attend this presentation to learn how you can benefit from all the latest aerospace advancements, including:  Workflow, physical and solver improvements for more robust and efficient simulations A dedicated workspace in Fluent that tailors the user interface to external aerodynamics simulations and streamlines the workflow Industry validations and benchmarks from the subsonic to the hypersonic regime

Battery thermal management is critical to improve performance, efficiency and safety. For batteries to operate safely and efficiently, temperature variations on a cell-to-cell level need to be maintained below a certain level. Maintaining these variations becomes even more challenging for pack-level simulations, because battery packs can contain hundreds or even thousands of cells.  This presentation will explain how to efficiently setup and simulate battery pack thermal management simulations using Ansys Fluent. Because battery packs are comprised of many modules, the setup and simulation process for an individual module will be explained. Then a demonstration showing how to use Fluent’s battery pack builder, which streamlines the process of converting a single module into a pack, will be shown.

Ansys has long provided leading multi-physics simulation solutions to address the electrical, thermal, mechanical, and reliability concerns that arise during the electronics design process. While these solutions have provided our users with great insights into how their devices will respond in the field and in the hands of consumers, it has also required advanced workflows that often lead to multiple engineers of different disciplines working with diverse sets of simulation data. This can cause challenges in data hand-offs, data transparency and workflow collaboration between engineering teams. Without a streamlined project management system in place, communication and analyses can break down, resulting in deadline and design delays and negative business impact. In this session, you will learn how the implementation of Ansys Minerva for electronics simulation workflows can provide substantial value to our users, including managing simulation data within a single platform, providing a birds-eye view of the simulation stage and enabling informed design decisions before your product reaches the market.

There are a number of factors that impact printed circuit board and packaging reliability, including temperature cycling, shock and drop, power and signal integrity, random and harmonic vibration and more. Ansys has developed a comprehensive solution that allows our users to analyze for each of these failures using a multi-tool, multi-physics workflow. In 2022 R2, our developers have made substantial updates to this solution, including a more customizable and robust parts database, more accurate time-to-failure (TtF) predictions and a more advanced closed loop reliability workflow that allows users to seamlessly integrate products together all the way through pre- and post-processing. Attend this session to learn about our latest software updates and improvements to your workflows.

The development of modern printed circuit boards can be challenging due to the need to overcome coupled electrical, thermal, and mechanical reliability concerns. In this presentation, we will discuss multiple issues that electrical and mechanical engineers must address during printed circuit board design and how advanced physics-based simulation tools can be used to optimize performance, prevent potential failures, and predict reliability.

In the electronics industry, product designs are getting more complicated while deadlines are shortening—requiring engineers to design better products faster. To give our users the best tools to manage these demands, Ansys has developed customizable interfaces and automation capabilities to improve workflows and streamline the design process. In this session, we will showcase practical examples of how to use the new APIs available within Ansys Sherlock, our PCB reliability prediction tool, for more accurate physics and customized analyses. Additionally, we will showcase how pairing Sherlock with Ansys optiSLang, our design optimization tool, allows users to optimize designs in less time, perform more robust reliability studies and reduce overall turnaround time.

Board Level Reliability Testing (BLRT) encompasses a wide range of environmental stress tests that evaluate the robustness of a semiconductor package once soldered to a printed circuit board (PCB). These tests often focus on solder joint reliability under thermal and mechanical loads and include test programs like accelerated thermal cycling, vibration exposure, drop testing, and others. However, these test programs often require 6 months or more to complete and cost hundreds of thousands of dollars. The proactive use of simulation can be a helpful tool to design for reliability (DfR) and improve overall BLRT robustness. This session will cover examples of how to use simulation to predict test performance and evaluate design decisions that can improve the likelihood of passing qualification on the first attempt.

Noise Vibration and Harshness (NVH) is an important design criterion for many applications where electric machines are involved from medical devices, to electrified mobility and household appliances. Due to complexity and the nonlinear coupled physics governing the magnetic and structural behavior of these systems, combined with various noise sources, the level of acoustic noise in applications is not easy to predict without proper modeling and simulation. In this presentation we will show you how to leverage Ansys tools solutions to accurately predict the acoustic noise generated by electric machines. NVH performance consideration for a permanent magnet machine from concept design to sound prediction will be presented. Tools Ansys solutions and workflows used to design a quiet electric machine will be demonstrated step by step.

The sound a product makes directly impacts the user experience. Whether positive or negative, sound reinforces product and brand identity. The difficulty of predicting acoustics means that a single engineering mistake can prove costly. At Ansys, we've developed a set of Noise, Vibration and Harshness (NVH) solutions that address sound quality and design through simulation and post-processing. In this session, we will showcase how acoustics engineers can use a number of Ansys software tools, including Ansys Mechanical, Fluent, LS-DYNA, Maxwell, Motion and Sound, to analyze the noise of numerous products, including heavy machinery, industrial equipment, automotives, airplanes, trains, sporting equipment and much more. No matter the industry or application, ensuring you get the sound right is critical to a positive customer and user experience.

The way a vehicle sounds, whether it’s the chassis, infotainment system or drivetrain can have a profound impact on the consumer and buying experience—making understanding potential issues for noise, vibration and harshness (NVH) early in the design stage critical. Many Automotive OEMs want to anticipate noise, vibration and harshness (NVH) issues early, however this can require numerous prototypes and physical testing, which can be costly and time consuming. In this session, we will showcase how using a combination of computer-aided engineering (CAE) files and NVH measurements allows acoustics engineers to attain real-time playback and evaluation within a driving simulator. We will showcase a workflow using IPG CarMaker simulations and Ansys Sound post-processing tools to understand how vehicle noise will impact human perception. This workflow saves time on NVH troubleshooting and target setting, eliminates over-engineering costs, and allows companies to differentiate from the competition with an NVH-customer centric approach.

Many companies are investing or currently using drones for package delivery, military reconaissance reconnaissance, emergency services, environmental monitoring, media coverage and much more. As drone use increases in urban and suburban areas, engineers are turning to simulation to reduce the noise they create. By analyzing how their sound affects human perception (based on metrics such as loudness, tonality, sharpness and articulation index), acoustics engineers can determine what will likely annoy the human ear and how to optimize the product design for improved sound quality. In this session, we will showcase a workflow involving Ansys Sound to create a realistic 3D sound of a drone flyover based on simulation or measurement data of individual propellers.

Consumer electronic products have traditionally used both permanent and electromagnets for a wide range of applications from storing data on hard drives, use in speakers to scanners, etc. However, in the recent past, there has been a growing trend in the usage of simple magnets in many modern high-tech devices such as phones, tablets, and laptops for connecting the many accessories such as writing stylus and keyboards. Additionally, the magnets used in such products have only been getting stronger and have been observed to influence the performance of PCBs. Primarily, the Lorentz forces generated inside the traces of a PCB due to the presence of such a huge number of magnets can potentially introduce vibrations and even some acoustic noise in the device. In this work, a coupled multi-physics workflow is presented where we start by calculating the Lorentz forces in Maxwell and map these body forces onto the PCB to calculate the harmonic response (vibrations) and acoustic noise using Ansys Mechanical. The Lorentz force calculations utilize some of the latest technology introduced in the recent Maxwell releases such as the Link Mesher etc. Recommendations on effective mapping techniques from an electromagnetic mesh to a structural mesh have also been discussed. Lastly, we present a comparative study around the usefulness of using all the PCB modelling techniques (Trace Mapping, Trace Reinforcements, and Trace Modelling techniques) that Ansys has to offer in studying such a multi-physics problem.

Ansys Discovery leverages the direct modeling capabilities to create and modify any type of geometry. The interactive nature of the tool is great for many processes, but there are cases where repeatability and automation are key. By combining the easy of use with script recording and python scripting capabilities advanced workflows can have the best of both worlds. This introduction to scripting inside of Discovery will give you a hands on demonstration of how to get started as well as some previews of advanced workflows solved with Discovery scripting.

The electric vehicle market is growing rapidly, and with the push to reduce component volumes, lower costs, and reduce weight without sacrificing performance or reliability, the challenges associated with thermal management increase for electric motors. Thermal constraints place significant limitations on how electric motors ultimately perform, and as thermal management improves, there will be a direct trade-off between motor performance, efficiency, cost, and the sizing of electric motors to operate within the thermal constraints. All these trends demand more effective thermal management systems, solutions, and materials.  Designing an electric motor requires a comprehensive understanding of the impacts that different technologies can have on motor mass, performance, and cost — ideally at an early stage in the design process when design choices are still open, and the cost of change is low. At this stage of the design process there is typically no 3D CAD available, only some concepts and requirements, with the goal to look at all variations and produce a subset of design criteria. Ansys Motor-CAD is the world leading tool for exploring concept design trade-offs of electrical motors and is seen by many as the single source of truth for the electric motor design. Ansys Motor-CAD lumped parameter thermal network provides the temperature of motor components in steady-state and transient operating conditions and accurately model thermal behaviour within seconds of calculation.  There are certain cases where a CFD analysis can be used alongside Ansys Motor-CAD, for example to verify assumptions with 3D physics bases simulations and to extend beyond the templates of Motor-CAD. In these cases, a dedicated workflow is required to export the cooling geometry and data into CFD and import the results back into Ansys Motor-CAD. Ansys Discovery provides a unique platform for simulation-driven design combining instant physics simulation and interactive geometry modelling in a single easy-to-use experience. Ansys Fluent is the industry-leading fluid simulation software known for its advanced physics modelling capabilities including multiphase methods for oil spray cooling simulations. What you will learn: We will present a novel workflow coupling Ansys Motor-CAD via Ansys Discovery all the way to Ansys Fluent for concept stage simulation of electric motor thermal management enabling faster, more reliable, and more efficient designs to ensure motor protection and performance.

Ansys Discovery is a product that combines the power of Live Physics simulation with tried and tested Ansys Mechanical and Fluent solvers in a modern, easy to use interface. Thanks to its simplicity and automated workflows, any engineer can perform routine structural, fluid flow and thermal simulation early in the design process, leading to significantly reduced product development times. New in 2022 R2 we have porous media capabilities that solve in seconds. Want to see how key engineering quantities of an air box filter can be simulated and visualized in Ansys Discovery? Join our session to see how it is done!

o What you will learn:  How Ansys Discovery enables design exploration for antenna ideation  How to quickly evaluate antenna performance in a complex environment  How Ansys Discovery’s robust FDTD solver can simulate performance with minimal geometry clean-up  How Ansys Discovery can link with Ansys HFSS to establish a “Gold standard” workflow

"o Learn how Ansys Discovery’s real-time topology optimization can shift how you think about structural design through conceptual iterations  What you will learn: • How Ansys Discovery provides design exploration through structural optimization and delivers detailed insight into product performance. • How engineers can use topology optimization to easily produce early conceptual designs • How topology optimization capabilities account for manufacturing constraints and can apply to multiple load cases and physics, including modal and structural simulations. • How designer-level tools can provide engineers with upfront guidance to make better design decisions"

Curious on how topology optimization is leveraged in a real-world application?  How about understanding how to manufacture a generative designed part?  This session will cover all of the above, showcasing the final light weighted design, results on optimal performance for a valve under static and dynamic loading conditions, and reducing costs to manufacture.

All simulations start with geometry.  In order to converge on a solution as quickly as possible it’s often useful to abstract or simplify a model to remove unnecessary details.  While this can sometimes seem like a daunting task, this presentation will provide some useful best practices that can be taken and used so simplify both large and small assemblies down to simpler elements in the new Discovery application.

As engineers attempt to solve more challenging problems than ever before, the need to effective technical communication, collaboration and transparency between teams has never been more important. Creating engineering best practices to ensure companywide knowledge sharing has become paramount.  This presentation explores how you can leverage Ansys Discovery to automate your upfront simulation results through seamless report generation, saving result scenes and exporting 2D and 3D images to ensure you are capturing your simulation in the best way possible.

The success of every major technology trend depends on electronics reliability. As engineers developing electrical and electronic systems face increased power demands, as well as heightened packaging constraints, the need for liquid cooling systems to adequately manage temperatures grows greater. Best practices for ensuring and predicting reliability require comprehensive multiphysics simulations earlier in the product development process.  This webinar explores how you can leverage Ansys Discovery’s real-time physics simulation and harness the power of the Ansys Fluent inside Discovery to determine if your “best design” holds true to your performance goals. Discover how to conduct thermal management and CFD simulations in seconds using real-time physics. And also leverage the Fluent solver within Ansys Discovery to perform a fluid-solid CHT simulation to precisely predict temperature distribution and fluid pressure drop.

"Ansys Discovery 2022 R2 continues to expand simulation capabilities and ease of use for every engineer to unlock innovation and increase productivity throughout the product development process.  Specifically, customers in aerospace and defense, electronics, and the automotive industry will further their reach of simulation through accelerated associative workflows for bolted connections, the addition of porous media modeling in Live Physics and the availability of high-frequency electromagnetics within Discovery. In addition, more analysts can now benefit from Ansys Discovery’s geometry preprocessing workflows, award-winning Live physics, and interactive user experience. • Bolted structures – Modeling bolted assemblies is now ridiculously easy. A new bolted connection tool detects mating bolt holes, matches sizing to industry standards, suppresses bolt geometry, and creates an associative physics connection that supports pre-tensioning between parts all in an automated single-click workflow. Additionally, Named Selections can now be used as physics references enabling a fully associative workflow from CAD to Discovery. • Flow control devices & antenna ideation – Porous media modeling is now supported in Discovery’s Live Physics enabling fast, easy-to-use simulation of filters and other resistive flows. Additionally, high frequency electromagnetics modeling has been added for initial concept evaluation and placement of basic antennas before more detailed study in Ansys HFSS. • Electronics cooling – Revamped tools for Icepak model simplification are now a part of Discovery offering an accelerated workflow for electronics cooling applications. Automated small feature and logo detection have also been added making it fast and easy to speed up your simulations and reduce issues through model simplification."

"Ansys 2022 R2 continues to expand toolsets for Ansys Additive Manufacturing users, streamlining your workflows among design, simulation, and manufacturing.  In addition, every Ansys Mechanical user can now identify and minimize risk for builder errors and ensure high-quality parts through process simulation for metal powder bed fusion (PBF), directed energy deposition (DED), and sintering (SLS).  What you will learn:  1. Workflow improvements – Automatic Distortion Compensation, the ability to optimize a distortion compensated model within a simple automated workflow, which improves first time print success. 2. Accuracy enhancements – Consolidating additive technologies to include Laser Power Bed Fusion (LPBF) scan pattern effects in Workbench Additive.  With the consolidation of additive technologies, all users of the Ansys suite of additive tools now can rapidly increase the fidelity of simulation, capturing localized strain variations in each deposition layer.  Scan pattern variations are particularly important in thin wall structures and rapidly repeating laser exposure strategies.  3. Speed performance – Introducing a Machine Learning (ML) model for predicting thermal strain that can result in 10X time savings.  Users can now easily explore the benefits of varying process setup parameters such as power, speed, and scan vector hatching by accessing the full fidelity of capturing process parameter and scan orientation effects without consuming compute resources for long periods of time.    4. Expanded capabilities – More manufacturing possibilities through Metal Binder Jet, predicting large distortion and shrinkage during the furnace sintering process; as well as expanding the process simulation portfolio with the ability to identify, compensate and optimize the additive manufacturing process."

Part quality is largely governed by process parameters applied in the metal laser powder bed fusion process (PBF-LB/M). To produce defect-free quality parts using PBF-LB/M, process parameter optimization is a highly time and cost-consuming journey, which often requires experienced process engineers to iteratively implement extensive trial-and-error experimentations based on sophisticated design of experiments. In this talk, we present a simulation-experimentation hybrid printing parameter optimization workflow, which aims at improving build efficiency while maintaining a near full density of the as-built part for a 316L stainless steel PBF-LB/M build. By implementing this hybrid workflow guided by Ansys Additive Science’s single bead melt pool and porosity simulation outputs correlated with Reliability Engineering Services physical analysis of microstructure, physical experimentation could possibly be reduced at least 50%, thus cutting down the R&D turnover time. The resulting optimized 316L stainless steel process parameters deliver a 65.6% increase in throughput per layer in the PBF-LB/M process.

"Additive Manufacturing (AM) of metal parts has become broadly accessible in the last decade. However, most industry sectors still have uncertainty when it comes to AM adoption for production. This is especially true when it comes to new unconventional design paradigms that can be only manufactured with technologies such as AM. The use of simulation & data management for capturing end-end AM data can reduce the risk of failure while accelerating the product development process for these next-generation products.   What you will learn: 1. The presentation will focus on a novel heat exchanger design that was developed in partnership between nTopology, Ansys, Synopsys, EOS, and North Star Imaging (NSI). The specific heat exchanger design for aerospace applications (Fuel Cooled Oil Cooler) leverages properties of triply periodic minimal surfaces (TPMS) to generate a highly efficient single component design to replace existing plate and tube exchanger designs.  2. We will also discuss how to efficiently capture and manage AM Data using Ansys Granta MI. Design simulations for fluid and thermal simulations were used to verify and optimize the performance of the new design. The resulting design is 80% lighter yet more efficient than comparable traditional designs.  3. Additive Manufacturing process simulations (coupled thermal-structural) were used to optimize the process so that the residual stresses and the support requirements were minimized or eliminated, altogether. The successful build of the optimized design was scanned for potential build failures or porosity using CT.  4. The simulation of the as-built geometry (from CT scan) is useful in validating the performance of the printed part. Additionally, the mechanical integrity of the as-built designs for structural integrity."

"In recent years, laser metal powder bed fusion (L-MPBF) has proven that it’s capable of amazing capabilities that lead to extraordinary designs that would be almost impossible to manufacture with traditional manufacturing methodologies.  What you will learn: 1. How easy is to model additive manufacturing processes using finite element analysis?  2. What can be predicted and what level of computational effort is required?  3. Workflows to deal with high residual stresses, warped components, part failures and aborted jobs 4. Addressing post-processing steps which apply after printing: cutoff steps, heat treatments and distortion compensation workflows."

Sheet metal forming simulation has greatly evolved over the last 25 years. Simulation of the entire deep drawing process including trimming, flanging and springback is a standard procedure at most automotive OEMs and tool shops. Using LS-DYNA, one of the most accurate solvers for sheet metal forming, still needs some expert knowledge, which makes it difficult to use for method engineers in their day-to-day work. Therefore, Ansys is developing a dedicated application for sheet metal forming simulation, “Ansys Forming”.  Ansys Forming is an easy-to-use GUI for setting up and analyzing a sheet metal forming simulation without expert knowledge of LS-DYNA.  This presentation will demonstrate the current features of Ansys Forming and the new LS-DYNA solver enhancements for sheet metal forming.