Electrical Machine Loss Distribution and Thermal Parameter Identification through Experimentally Informed Virtual Prototyping

Dominic J North; Suzie M Collins; Nick Simpson; Phil Mellor

doi:10.1109/ECCE.2019.8912683

Electrical Machine Loss Distribution and Thermal Parameter Identification through Experimentally Informed Virtual Prototyping

Dominic J North, Suzie M Collins, Nick Simpson, Phil Mellor

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

6 Citations (Scopus)

189 Downloads (Pure)

Abstract

Precise loss predictions inform key decisions in the design of high performance electrical machines. Detailed understanding and effective management of operational losses are critical to maximising power density and operational life. Imprecise loss predictions can stem from lack of accurate material and interface data. A method is presented to identify key thermal parameters and AC loss distributions, without construction of a full scale prototype. Using a 3D Virtual Prototype of a machine subassembly, calibrated by experimental data, key parameters such as the conductor-slot thermal conductivity are extracted and implications on machine thermal performance and build quality are inferred.

Original language	English
Title of host publication	2019 IEEE Energy Conversion Congress and Exposition (ECCE)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	4853-4859
Number of pages	7
ISBN (Electronic)	978-1-7281-0395-2
DOIs	https://doi.org/10.1109/ECCE.2019.8912683
Publication status	Published - 28 Nov 2019
Event	ECCE 2019: IEEE Energy Conversion Congress & Expo. - Baltimore, United States Duration: 29 Sept 2019 → …

Publication series

Name	Energy Conversion Congress and Exposition, ECCE, IEEE
Publisher	IEEE
ISSN (Electronic)	2329-3748

Conference

Conference	ECCE 2019
Abbreviated title	ECCE 2019
Country/Territory	United States
City	Baltimore
Period	29/09/19 → …

Keywords

PM electrical machine
thermal analysis
machine sub-assembly
model calibration
virtual prototype

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10.1109/ECCE.2019.8912683

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Accepted author manuscript, 6.84 MB

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North, D. J., Collins, S. M., Simpson, N., & Mellor, P. (2019). Electrical Machine Loss Distribution and Thermal Parameter Identification through Experimentally Informed Virtual Prototyping. In 2019 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 4853-4859). (Energy Conversion Congress and Exposition, ECCE, IEEE). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ECCE.2019.8912683

North, Dominic J ; Collins, Suzie M ; Simpson, Nick et al. / Electrical Machine Loss Distribution and Thermal Parameter Identification through Experimentally Informed Virtual Prototyping. 2019 IEEE Energy Conversion Congress and Exposition (ECCE). Institute of Electrical and Electronics Engineers (IEEE), 2019. pp. 4853-4859 (Energy Conversion Congress and Exposition, ECCE, IEEE).

@inproceedings{4d98665a97ec47cd97c260de4ce26ad6,

title = "Electrical Machine Loss Distribution and Thermal Parameter Identification through Experimentally Informed Virtual Prototyping",

abstract = "Precise loss predictions inform key decisions in the design of high performance electrical machines. Detailed understanding and effective management of operational losses are critical to maximising power density and operational life. Imprecise loss predictions can stem from lack of accurate material and interface data. A method is presented to identify key thermal parameters and AC loss distributions, without construction of a full scale prototype. Using a 3D Virtual Prototype of a machine subassembly, calibrated by experimental data, key parameters such as the conductor-slot thermal conductivity are extracted and implications on machine thermal performance and build quality are inferred.",

keywords = "PM electrical machine, thermal analysis, machine sub-assembly, model calibration, virtual prototype",

author = "North, \{Dominic J\} and Collins, \{Suzie M\} and Nick Simpson and Phil Mellor",

year = "2019",

month = nov,

day = "28",

doi = "10.1109/ECCE.2019.8912683",

language = "English",

series = "Energy Conversion Congress and Exposition, ECCE, IEEE",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "4853--4859",

booktitle = "2019 IEEE Energy Conversion Congress and Exposition (ECCE)",

address = "United States",

note = "ECCE 2019 : IEEE Energy Conversion Congress \& Expo., ECCE 2019 ; Conference date: 29-09-2019",

}

North, DJ, Collins, SM, Simpson, N & Mellor, P 2019, Electrical Machine Loss Distribution and Thermal Parameter Identification through Experimentally Informed Virtual Prototyping. in 2019 IEEE Energy Conversion Congress and Exposition (ECCE). Energy Conversion Congress and Exposition, ECCE, IEEE, Institute of Electrical and Electronics Engineers (IEEE), pp. 4853-4859, ECCE 2019, Baltimore, Maryland, United States, 29/09/19. https://doi.org/10.1109/ECCE.2019.8912683

Electrical Machine Loss Distribution and Thermal Parameter Identification through Experimentally Informed Virtual Prototyping. / North, Dominic J; Collins, Suzie M; Simpson, Nick et al.
2019 IEEE Energy Conversion Congress and Exposition (ECCE). Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 4853-4859 (Energy Conversion Congress and Exposition, ECCE, IEEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Electrical Machine Loss Distribution and Thermal Parameter Identification through Experimentally Informed Virtual Prototyping

AU - North, Dominic J

AU - Collins, Suzie M

AU - Simpson, Nick

AU - Mellor, Phil

PY - 2019/11/28

Y1 - 2019/11/28

N2 - Precise loss predictions inform key decisions in the design of high performance electrical machines. Detailed understanding and effective management of operational losses are critical to maximising power density and operational life. Imprecise loss predictions can stem from lack of accurate material and interface data. A method is presented to identify key thermal parameters and AC loss distributions, without construction of a full scale prototype. Using a 3D Virtual Prototype of a machine subassembly, calibrated by experimental data, key parameters such as the conductor-slot thermal conductivity are extracted and implications on machine thermal performance and build quality are inferred.

AB - Precise loss predictions inform key decisions in the design of high performance electrical machines. Detailed understanding and effective management of operational losses are critical to maximising power density and operational life. Imprecise loss predictions can stem from lack of accurate material and interface data. A method is presented to identify key thermal parameters and AC loss distributions, without construction of a full scale prototype. Using a 3D Virtual Prototype of a machine subassembly, calibrated by experimental data, key parameters such as the conductor-slot thermal conductivity are extracted and implications on machine thermal performance and build quality are inferred.

KW - PM electrical machine

KW - thermal analysis

KW - machine sub-assembly

KW - model calibration

KW - virtual prototype

U2 - 10.1109/ECCE.2019.8912683

DO - 10.1109/ECCE.2019.8912683

M3 - Conference Contribution (Conference Proceeding)

T3 - Energy Conversion Congress and Exposition, ECCE, IEEE

SP - 4853

EP - 4859

BT - 2019 IEEE Energy Conversion Congress and Exposition (ECCE)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - ECCE 2019

Y2 - 29 September 2019

ER -

North DJ, Collins SM, Simpson N , Mellor P. Electrical Machine Loss Distribution and Thermal Parameter Identification through Experimentally Informed Virtual Prototyping. In 2019 IEEE Energy Conversion Congress and Exposition (ECCE). Institute of Electrical and Electronics Engineers (IEEE). 2019. p. 4853-4859. (Energy Conversion Congress and Exposition, ECCE, IEEE). doi: 10.1109/ECCE.2019.8912683

Electrical Machine Loss Distribution and Thermal Parameter Identification through Experimentally Informed Virtual Prototyping

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