Engineering & Physics > AI
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as well as different vehicle types to improve performance and reduce costs. Royston Jones, global head of
automotive at Altair, said: “Engineers will need to adopt optimisation tools – if they don’t their competitor OEMs will. Optimisation fits the artificial intelligence (AI) narrative. Traditionally, engineering has been data poor, so optimisation technology has been key.”
Jones added: “In the future, we will be data rich and that data will come from everywhere (for example: the field, warranty, physical testing, manufacturing etc) including massive amounts of synthetic (simulation) data. Engineers will become more conversant with data analytics techniques to provide increased insight into the product since the product complexity and refinement level will continue to increase.”
There are now a range of machine learning tools coming online to address these design challenges. Cambridge-based start-up Secondmind, for example, uses a specific branch of machine learning to help optimise the development of vehicles (see the ‘Intelligent Engineering’ box). Altair’s digital twin solutions also use simulation, machine learning, and artificial intelligence to create virtual representations of physical assets. Jones explained: “Optimisation has been in Altair’s DNA for well over two decades and recently, with the ability to generate large volumes of synthetic (simulation) data we can efficiently utilise machine learning (ML).” Jones said: “In Altair e-Motor Director, we
utilise optimisation and ML technologies to ensure that from a vast array of motor family permutations the correct e-Motor
E-Motor Director lets users easily define a broad design space for a single baseline concept, where they can then copy, paste, and change design concepts to build a DOE database with design information
Intelligent engineering
Overcoming automotive challenges
S
econdmind is focused on using model-based design and optimisation
solutions to overcome the many challenges today’s automotive engineers face. Scientific Computing World caught up with the company’s chief executive officer, Gary Brotman, to find out more about the Secondmind’s machine learning approach and work in the automotive industry.
How is Secondmind addressing the challenges that today’s automotive engineers face and what makes it different from other simulation solutions? The challenge of virtually achieving design and performance reliability through simulations that translate to validity and effectiveness in real-world scenarios, an already difficult exercise, is made even harder by the growing glut of data bogging down the
design process. As a result, existing vehicle design and development technologies used to build complex models for simulating and optimising new components, systems and materials are struggling to keep pace at a time when they are needed the most. The Secondmind Optimization Engine powers our cloud-based solutions for vehicle system design and control system calibration. And, because it’s cloud-native, it has the ability to continuously optimise the performance of complex systems throughout the vehicle lifecycle. The Optimization Engine is designed to solve the most difficult engineering problems in automotive and address the shortcomings of other AI-based solutions, by enabling intelligent, automated experiments, modelling of physical and virtual data, and providing engineers with better choices in
24 Scientific Computing World Summer 2023
Gary Brotman, CEO of Secondmind: ‘Existing vehicle design and development technologies … are struggling to keep pace’
design simulation and testing. The result is higher precision prototype designs, faster and more accurate performance optimisation, and less rework throughout the design and development process. Most importantly,
Secondmind slashes data dependencies by up to 80 percent to facilitate design and performance optimisation of high-dimensional problems more efficiently and accurately. A good example of powertrain optimisation is the calibration of the e-motor and inverter pair, one of the most critical subsystems in electric vehicles. Calibration involves optimising myriad parameters and constraints to achieve optimal performance, such as effective
use of energy from the battery, in as little time as possible. The engineer is particularly
interested in the rotor temperature because the motor’s magnetic field decreases as the magnet heats up, so a wide range of measurements is required. However, during the testing process the rotor temperature rises and the engineer must wait for it to cool and return to a stable state before they can take the next measurement, resulting in many lost hours. Secondmind incorporates domain knowledge into its machine learning models in order to understand more precisely the physics of the e-motor. These models quickly learn and adapt to the
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