OS Numerical Optimization: Machine Learning based Reduced Order Model for Full Temperature Prediction (Case Study: Power electronics study)
Time
Tuesday, 5. November 2024
15:15 - 16:45
Location
F426
Organizer
S. Volkwein
Speaker:
Vishwas Kulkani
On 5th November 2024 at 15:15, Vishwas Kulkani from the Virtual Vehicle Research GmbH will give a talk.
Abstract: The talk elaborates machine learning methods to develop a new reduced order model (ROM) approach to be able to efficiently predict the full temperature field of power electronics component cooling system. The approach is demonstrated for a simplified use case of a powertrain inverter. The ROM is based on the different combinations of reduction and approximation methods which is applied on the simplified case of the electric vehicle inverter. The reduction methods, namely proper orthogonal decomposition (POD) or auto encoders (AE), writes the whole evolution of the data in a lower dimension while the approximation methods, i.e. the regression/interpolation methods, finds the optimal coefficient to correlate the lower dimension with higher one. Error analysis had been perform to examine the combinations of reduction and approximation methods to develop a ROM to compute the results fast in a reduced parametric space while retaining its accuracy. The resultant model was capable of predicting the temperature field of solid and fluid regions accurately within few minutes.