Engineering & Physics > AI


topology is selected with the ML ensuring a manufacturable shape has been selected.” Likewise, Ansys has expanded its


Altair’s digital twin solutions use simulation, machine learning, and artificial intelligence to create virtual representations of physical assets


multiphysics capabilities “beyond automotive structure to meet challenges involving electromagnetics, controls, functional safety, reliability, materials intelligence, and more,” Tang said. “Beyond Ansys multiphysics simulation capabilities, we have streamlined workflows, design automation and optimisation, high-performance computing (HPC), and cloud-enabled solutions to assist engineering teams.” Whatever the underlying technology that’s sitting under the hood, it’s clear that a range of tools are now vital to streamline the design and development of tomorrow’s vehicles – and that machine learning and optimisation will continue to play a significant role, going forward. SCW


e-motor’s unique personality, reducing the amount of data to collect for calibration. This further accelerates the calibration process and reduces the overall time required to generate accurate calibration maps that tell the e-motor and inverter how to function under certain conditions.


Can you tell us a little more about the technology behind the Optimization Engine? The technology fuel that powers the Optimization Engine is Secondmind Active Learning, which intelligently automates the process of data acquisition, modelling, analysis and experimental design to achieve optimisation objectives faster. Traditional design of experiments (DoE) approaches are manual and linear, with engineers spending a lot of time upfront planning and acquiring more data than they need before running the experiment. This brute force approach more often than not results in a suboptimal outcome and the process needs to be repeated many times. As an integral element of the Optimization Engine, Secondmind Active Learning significantly reduces the upfront preparation time and effort by giving the engineer a simple template to define


the parameters, constraints and objective for a given optimisation session, and with a small sample of data, Active Learning algorithms intelligently design and run smaller experiments based on data from the targeted regions of interest deemed important enough to collect. Knowledge of the problem


increases with each iteration and, if left to run in a fully automated fashion, the optimisation objective is typically reached in half the time of existing DoE tools with results being as good or better. If engineers prefer a deeper level of engagement, they can participate by leveraging their domain knowledge in the process – knowledge not captured in the data, but potentially vital to achieving successful results.


Can you tell us more about your System Design and Calibration products? Why have you focused on these? The broad design and calibration phases of vehicle development are the most complex, time- consuming, and costly and this is where we believed we could make the biggest initial impact. Our System Design and Calibration solutions offer capabilities that are unique


to each development phase, application and engineering end user. At its core however, the Optimization Engine is designed to be system and application agnostic, offering flexibility in optimising system, subsystem and component-level designs, and in addition to the calibration of any number of vehicle control systems without the need for bespoke software development. Secondmind for Calibration


both modelling accuracy and less time on test benches has resulted in projections of significantly less prototype fabrication costs in future new vehicle programs. Secondmind for System Design reduces design and simulation time, and error correction costs helping design engineers discover more design options and make better system configuration choices.


‘By quickly identifying optimal design spaces, engineers can explore and innovate with better choices’ Gary Brotman, CEO, Secondmind


helps calibration engineers design high-value control strategies and produce high- precision calibration maps for complex systems like e-motors, internal combustion engines, and hybrid systems. A key capability of the Calibration solution is the intelligent automation of experiments to more quickly and easily generate calibration maps. Car makers like Mazda are using Secondmind for Calibration to halve calibration time using just 20% of the data they would otherwise need, and


By quickly identifying optimal design spaces, engineers can explore and innovate with better choices than they would have otherwise had. Multiple engineers contributing to the design of complex vehicle systems are also empowered to experiment and make design trade-offs without worrying about team or component- specific dependencies, resulting in efficient parallel planning that improves collaboration and ensures development schedules remain on track. SCW


Summer 2023 Scientific Computing World 25


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