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Bridging Physics and Machine Learning: NVH Optimization in the Early Stages of Electric Motor Design
WATCH WEBINAR RECORDING
The electric motor is a key source of noise in an electric powertrain. Effective Noise, Vibration, and Harshness (NVH) management requires advanced and integrated simulation techniques to identify and mitigate noise and vibration early in the design process. This ensures that electric motors not only deliver optimal performance but also enhance the driving experience with smooth and quiet operation.
In this webinar, we explored how the integration of physics-based simulations and machine learning can optimize NVH at the early stages of electric motor design.
GT-FEMAG
E-Motors today consume approximately 45% of the global electric power. A meaningful impact to a greener tomorrow can be made by making these E-Machines more efficient and by ensuring that they are optimized for the end-use application.
To enable this GT-FEMAG offers a unique solution for motor designers to optimize the electromagnetics, thermal, mechanical design; and for system integration engineers to analyze the e-motor performance by digitally integrating the motor and system models.
