Design optimization of a short-term duty electrical machine for extreme environment

Arumugam, Puvaneswaran and Amankwah, Emmanuel K. and Walker, Adam and Gerada, C. (2017) Design optimization of a short-term duty electrical machine for extreme environment. IEEE Transactions on Industrial Electronics, 64 (12). pp. 9784-9794. ISSN 1557-9948

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This paper presents design optimisation of a short term duty electrical machine for extreme environments of high temperature and high altitudes. For such extreme environmental conditions of above 80⁰C and altitudes of 30km, thermal loading limits are a critical consideration in machines, especially if high power density and high efficiency are to be achieved. The influence of different material on the performance of such machines is investigated. Also the effect of different slot and pole combinations are studied for machines used for such extreme operating conditions but with short duty. In the research, A Non-dominated Sorting Genetic Algorithm (NSGAII) considering an analytical electromagnetic model, structural and thermal model together with Finite Element (FE) methods are used to optimise the design of the machine for such environments achieving high efficiencies and high power density with relatively minimal computational time. The adopted thermal model is then validated through experiments and then implemented within the Genetic Algorithm (GA). It is shown that, generally, the designs are thermally limited where the pole numbers are limited by volt-amps drawn from the converter. The design consisting of a high slot number allows for improving the current loading and thus, significant weight reduction can be achieved.

Item Type: Article
Additional Information: P. Arumugam, E. Amankwah, A. Walker and C. Gerada, "Design Optimization of a Short-Term Duty Electrical Machine for Extreme Environment," in IEEE Transactions on Industrial Electronics, vol. 64, no. 12, pp. 9784-9794, Dec. 2017. doi: 10.1109/TIE.2017.2711555 c2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Extreme Environment, optimisation, genetic algorithm, short-duty, thermal management
Schools/Departments: University of Nottingham, UK > Faculty of Engineering > Department of Electrical and Electronic Engineering
Identification Number: 10.1109/TIE.2017.2711555
Depositing User: Burns, Rebecca
Date Deposited: 13 Mar 2018 08:37
Last Modified: 13 Mar 2018 10:01

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