Can I read Deep Learning-based Prediction of Key Performance Indicators for Electrical Machine on EtoBox?

Deep Learning-based Prediction of Key Performance Indicators for Electrical Machine by Parekh, Vivek; Flore, Dominik; Schöps, Sebastian is a scholarly article available to read on EtoBox.

What is Deep Learning-based Prediction of Key Performance Indicators for Electrical Machine about?

The design of an electrical machine can be quantified and evaluated by Key Performance Indicators (KPIs) such as maximum torque, critical field strength, costs of active parts, sound power, etc. Generally, cross-domain tool-chains are used to optimize all the KPIs from different domains (multi-objective optimization) by varying the given input parameters in the largest possible design space. This optimization process involves magneto-static finite element simulation to obtain these decisive KPIs. It makes the whole process a vehemently time-consuming computational task that counts on the availability of resources with the involvement of high computational cost. In this paper, a data-aided, deep learning-based meta-model is employed to predict the KPIs of an electrical machine quickly and with high accuracy to accelerate the full optimization process and reduce its computational costs. The focus is on analyzing various forms of input data that serve as a geometry representation of the machine. Namely, these are the cross-section image of the electrical machine that allows a very general description of the geometry relating to different topologies and the classical way with scalar pa

Author

Parekh, Vivek; Flore, Dominik; Schöps, Sebastian

Published

2020

Language

EN