CFD optimisation of the thermal design for a vented electrical machine
Bersch, Kevin; Connor, Peter H.; Eastwick, Carol N.; Galea, Michael; Rolston, Rob
Peter H. Connor
Carol N. Eastwick
MICHAEL GALEA Michael.Galea@nottingham.ac.uk
Professor of Electrical Machines and Drives
Optimisation algorithms hold the potential to dramatically reduce computational time whilst ensuring the optimal solution is found. Within this paper, the feasibility of using this novel approach on complex 3-D Computational Fluid Dynamics models, which are required for thermal management of electrical machines, is proven. A model of a simplified generator is parameterised with the aim of minimising the peak stator temperature by varying the axial location of a single stator vent. By generating a single parameterised case, and automating the optimisation, the simulations are run independently after initial setup, hence reducing both computational and user time. Locating a vent in the optimal position reduced the peak stator temperature by 9.4 K. A sensitivity study linking peak temperature to vent position has been carried out developing a polynomial relationship between them for the aforementioned geometry. Mass flow and pressure distribution in the vent have been analysed in detail.
|Publication Date||Apr 21, 2017|
|Peer Reviewed||Peer Reviewed|
|Book Title||2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)|
|APA6 Citation||Bersch, K., Connor, P. H., Eastwick, C. N., Galea, M., & Rolston, R. (2017). CFD optimisation of the thermal design for a vented electrical machine. In 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD). https://doi.org/10.1109/WEMDCD.2017.7947721|
|Keywords||Alternator, CFD, Computational fluid dynamics, Cooling, Electrical machine, Stator vent, Synchronous generator, Optimization, Thermal management|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf|
|Additional Information||ISBN 978-1-5090-5853-2.
© 2017 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.
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
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