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System Simulation Mapping Complex Physical Behavior in Real Time

Source: Altair | Translated by AI 2 min Reading Time

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Complex system behavior can be efficiently mapped with neural networks that reproduce the system behavior as reduced order models (ROMs). This speeds up system simulations significantly, freeing up time for new ideas, for example.

Reduced order models are useful for integrating 3D simulations into a computationally more efficient environment for system-level investigations.(Image: Altair, Gorodenkoff/Shutterstock.com)
Reduced order models are useful for integrating 3D simulations into a computationally more efficient environment for system-level investigations.
(Image: Altair, Gorodenkoff/Shutterstock.com)

Many control systems—especially those supporting autonomous driving— require complex, realistic system models. Physical simulations, together with AI-driven modeling at the system level, can provide realistic synthetic data to capture the interactions between the machine and the material, terrain, and subsoil.

Digital Twin Optimizes Product Development

A current customer example is an application at CNH Industrial. Here, Altair's AI-powered simulation tools enabled a powerful digital twin that accelerates and optimizes product development through rapid predictions. "Thanks to Altair's innovative solutions for virtual product development, we were able to create a digital twin of our machine that helps us gain a better understanding of machine behavior and shorten time to market," said Giuseppe Gullo, FEA Design Analysis Engineer, CNH Industrial. "The AI algorithm maps complex effects and accelerates the delivery of system responses for an efficient overall view. This has streamlined validation, reduced computation costs, and optimized our development process."

The Technology Behind It: AI Module Generates Reduced Order Models

Reduced order models (ROMs) are useful for integrating detailed 3D simulations into a computationally more efficient 1D environment for system-level studies. Simulation tools like Altair EDEM or Altair CFD enable detailed investigations of time-varying and nonlinear systems. Due to the long runtime of the simulations, the analysis usually focuses on one component or subsystem. However, in the case of a complete system simulation, it is often sufficient to reduce the behavior of the components to their interaction with the overall system, in order to shorten the solver's runtime while still obtaining sufficiently accurate results.

Simulations Train ROM

By using Altair romAI for artificial intelligence, 3D simulations can be used as training data for creating dynamic ROMs. Only a few 3D simulation runs are required, as this approach needs less training data than traditional data-driven methods. romAI can work with any solver and provides highly accurate results when operating within the training range, and is even stable and useful when extrapolating outside the range. The same machine learning method can also be used for system identification when starting from test data.

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