Puvaneswaran Arumugam
Design optimization of a short-term duty electrical machine for extreme environment
Arumugam, Puvaneswaran; Amankwah, Emmanuel K.; Walker, Adam; Gerada, Chris
Authors
Emmanuel K. Amankwah
Dr ADAM WALKER Adam.WalkerEEE@nottingham.ac.uk
ASSISTANT PROFESSOR
Professor CHRISTOPHER GERADA CHRIS.GERADA@NOTTINGHAM.AC.UK
PROFESSOR OF ELECTRICAL MACHINES
Abstract
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.
Citation
Arumugam, P., Amankwah, E. K., Walker, A., & Gerada, C. (2017). Design optimization of a short-term duty electrical machine for extreme environment. IEEE Transactions on Industrial Electronics, 64(12), 9784-9794. https://doi.org/10.1109/TIE.2017.2711555
Journal Article Type | Article |
---|---|
Acceptance Date | May 5, 2017 |
Online Publication Date | Jun 2, 2017 |
Publication Date | Dec 1, 2017 |
Deposit Date | Mar 13, 2018 |
Publicly Available Date | Mar 13, 2018 |
Journal | IEEE Transactions on Industrial Electronics |
Print ISSN | 0278-0046 |
Electronic ISSN | 1557-9948 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 64 |
Issue | 12 |
Pages | 9784-9794 |
DOI | https://doi.org/10.1109/TIE.2017.2711555 |
Keywords | Extreme Environment, optimisation, genetic algorithm, short-duty, thermal management |
Public URL | https://nottingham-repository.worktribe.com/output/864143 |
Publisher URL | http://ieeexplore.ieee.org/document/7938395/ |
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. |
Contract Date | Mar 13, 2018 |
Files
Design Optimisation of a Short Term Duty Electrical Machine for Extreme Environment.pdf
(764 Kb)
PDF
You might also like
Airgap Length Analysis of a 350kW PM-Assisted Syn-Rel Machine for Heavy Duty EV Traction
(2022)
Journal Article
A Multiport Power Electronics Converter for Hybrid Traction Applications
(2021)
Journal Article
Influence of Airgap Length on Performance of High Power PM-Assisted Syn-Rel Machines
(2020)
Presentation / Conference Contribution
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search