On Simulation Knowledge Acquisition Using Gaussian Processes for the Design of Electric Motors

Dworschak F, Tüchsen J, Pinto D, Pop AC, Schleich B, Wartzack S (2019)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2019

Pages Range: 737-742

Conference Proceedings Title: Procedia CIRP Volume 84

Event location: Póvoa de Varzim

DOI: 10.1016/j.procir.2019.04.266

Open Access Link: https://www.sciencedirect.com/science/article/pii/S2212827119309138#!

Abstract

In the automotive industry, high efforts are made to design high-efficiency, low-cost, requirement-optimized electric drives. Traditionally, time-consuming and expensive 3D finite element analyses are used to assess the effects of different design parameters from various engineering disciplines such as the electromagnetics, heat transfer, and mechanics. Often, to decrease the calculation efforts, scalable, physics-based reduced order models are used. For setting up and interpreting such reduced order models, simulation experts employ implicit knowledge by applying heuristic correction factors. This paper proposes a novel method to formalizing such implicit simulation knowledge using Gaussian Processes. Furthermore, the successful application of the method to a case study of electric motors is reported by validating the aforementioned correction factors for different motor designs.

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APA:

Dworschak, F., Tüchsen, J., Pinto, D., Pop, A.-C., Schleich, B., & Wartzack, S. (2019). On Simulation Knowledge Acquisition Using Gaussian Processes for the Design of Electric Motors. In Putnik, Goran D. (Eds.), Procedia CIRP Volume 84 (pp. 737-742). Póvoa de Varzim, PT.

MLA:

Dworschak, Fabian, et al. "On Simulation Knowledge Acquisition Using Gaussian Processes for the Design of Electric Motors." Proceedings of the 29th CIRP Design 2019, Póvoa de Varzim Ed. Putnik, Goran D., 2019. 737-742.

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