Materials Selection: The Missing Link for Optimal Product Design
Your business success is intimately linked with the success of your products. As a result, you invest huge amounts of time and money in optimizing your product development processes. You optimize your product design by providing your product designers with the latest CAD tools. You optimize your product performance by giving your analysts the latest simulation tools. But what do you do to optimize your choice of materials?
Using Materials Data for Faster, Cheaper, More Repeatable Additive Manufacturing
Using an open ecosystem product, such as Ansys Granta MI for Additive Manufacturing (AM), delivers the required flexibility to overcome the various obstacles that a typical AM build will introduce.
Ansys SPEOS Enterprise
Ansys SPEOS Enterprise performs visual ergonomic reviews for perceived quality, safety and comfort. It also virtually simulates visual aspect, reflection, visibility and information legibility observed in real-world vehicle environments.
ANSYS SPEOS Optical Sensor Test Add-On
The Ansys SPEOS Optical Sensor Test provides a built-in environment for easily evaluating the impact of different design versions (low/high-end) on sensor perception and global product compliance early in the design phase.
Ansys SPEOS Pro
The Ansys SPEOS Pro package contains all the core functionalities to simulate the photometric performance — intensity, illuminance and luminance — of lighting systems.
Ansys SPEOS Premium
The Ansys SPEOS Premium package contains all functionalities of the Ansys SPEOS Pro package, including one solver and four built- in high-performance computing (HPC) tasks. It has additional advanced analytics for photometry and radiometry, spectral intensity, illuminance and luminance.
Granta Materials Data
Materials experts, designers, engineers and simulation experts need top quality up-to-date technical, environmental and economic properties of materials – metals, plastics, composites, ceramics and more. Such data informs critical decisions in design and materials selection and substitution. Additionally, it helps meet environmental and restricted substance regulations. Ansys Granta collaborates with leading data providers to maintain an unrivalled, diverse catalog of materials reference data, combined with flexible materials selection and data management software.
How to Use Trace Reinforcements to Optimize PCB Models
The increasing complexity of 21st century electronics continues to drive demand for comprehensive multiphysics simulations. Simulation helps engineers determine potential failure risks before physical prototyping, making it one of the most beneficial and cost-effective solutions for electronics manufacturers. However, there are often significant modeling challenges associated with the increased layout complexity of printed circuit boards (PCBs), including finer density traces and intricate routing structures. Fortunately, there are a number of modeling techniques that exist that allow for more complex and accurate PCB models. These techniques range from homogeneous effective material properties to trace mapping to detailed trace modeling. In this webinar we will focus on a trace modeling technique called trace reinforcements. In the trace reinforcement method, copper traces are modeled with 1D or 2D elements that are embedded within 3D structural elements. This strategy allows for the inclusion of detailed trace geometry features in a highly complex PCB model without the unreasonable body/element counts that come with a standard 3D body trace modeling approach. This webinar will discuss when to use trace reinforcement modeling, the accuracy and efficiency the methodology offers, and how to use Ansys Sherlock to automate the creation of a trace reinforcement model.
High-Fidelity Radar Driving Scenario Simulations
Application Field: ADAS and Autonomous Driving Advanced driver assistance systems (ADAS) are at the core of automotive safety and will serve as the enabling technologies for autonomous vehicles. In this webinar we explore a high-resolution MIMO radar system simulation in realistic driving scenarios. A full physics-based radar scene corner case is modeled to obtain high-fidelity range-Doppler maps. We investigate the effects of inclined roads on late pedestrian detection as well as the effects of construction metal plate radar-returns on false target identification. 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. Speaker: Amazir Moknache, Senior Application Engineer, Ansys
Micro-Doppler Simulation for Auto Radar Applications
Application Field: ADAS and Autonomous Driving Multiple input, multiple output (MIMO) radar enables “4D imaging” for driver-assistance and automated driving systems. Angular position and range can be determined using triangulation and propagation delay from the sensor to the target. The fourth dimension is the target trajectory (i.e., velocity) which can be extracted from the Doppler frequency shift. Additional information resulting from relative motion within the moving target coordinate system can also be determined. For example, the vibrating mudflaps on a truck, the periodic leg motion of a person riding a bicycle and the rotation of wheels on a vehicle all exhibit characteristic “micro-Doppler” signatures. This webinar will describe the application of HFSS SBR+ to accurately model the physical interaction of radar with dynamic objects to predict radar performance and capture the micro-Doppler effect. We will describe how to create and modify dynamic objects exhibiting motion within the local frame of reference and provide an outlook on the application of machine learning for target classification. Speaker: Hen Leibovich, Application Engineer II, Ansys
A Simple, Easy Trick to Model a Battery Module/Pack Using Ansys Fluent
With the growth of the electric vehicle market, the demand for smaller and lighter batteries with greater capacity has never been higher. A simple, comprehensive option for a lithium-ion (Li-ion) cell, based on a multiscale, multidimensional (MSMD) battery modeling methodology, has been developed in Ansys Fluent to study the cell-to-cell temperature variation or maximum temperature in the module. This option helps you to investigate the cell-to-cell temperature variation and maximum temperature in an Li-ion battery module/pack much more easily. This webinar reveals the details of this option and demonstrates an example of testing MSMD models with and without this option. What you will learn Get a tour of the Fluent battery modeling options. Learn how to model the battery module or pack in a much simpler way. Learn how to validate the model. Speaker Seeta Gunti , Senior Technical Support Engineer
Ensure 5G Systems Integrity by Using Multiphysics Analysis of Chips, Packages and Systems
The evolution of 5G systems is rooted in the consistently increasing need for more data. Whether the application is future medical systems, autonomous vehicles, smart cities, AR/VR, IoT, or standard mobile communications systems, all require an ever-increasing amount of data. For 5G systems to reliably work, the physical data pathways must be well understood and reliably designed. Whether the pathway is in the chip or the wireless channel, large amounts of data must seamlessly flow unimpeded. This presentation will discuss the various data paths in 5G systems and how Multiphysics analysis can help design these paths to allow for maximum data integrity.