TY - GEN
T1 - A Taguchi-LHS-RSM Double-Staged Approach for Design Optimization of Self-Ventilated Cooling Systems Utilized in PMSMs
AU - Zhu, Gaojia
AU - Xu, Zeyuan
AU - Zhang, Fengyu
AU - Gerada, Chris
AU - Gerada, David
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Thermal management is critical for high-power-density permanent magnet synchronous machines (PMSMs). To realize the efficient and effective design optimization of self-ventilated air cooling systems for PMSMs, in this paper, a Taguchi-preconditioned Latin hypercube sampling (LHS) and response surface method (RSM) combined approach is proposed. First, as the optimization variable numbers are mostly large when considering simultaneously the heat-sink and air-fan influences, the Taguchi method is employed to decouple the variables based on comprehensive fluidic-thermal calculations. The values of the less interconnected variables are optimized directly in this stage while the two most interconnected ones are selected and optimized in the next stage. Secondly, LHS is used to generate the sample points of the two variables with the values centered at the initially optimized results in the first stage. The RSM surrogate model is established with the LHS points, and the finally optimized values are then decided based on analytical analysis of the surrogate model. The proposed double-staged method is utilized in optimizing the self-ventilation system of a 2000-rpm outer rotor PMSM, and the optimization effectiveness is validated by comparing the cooling efficiency and effectiveness of the results at each optimization stage.
AB - Thermal management is critical for high-power-density permanent magnet synchronous machines (PMSMs). To realize the efficient and effective design optimization of self-ventilated air cooling systems for PMSMs, in this paper, a Taguchi-preconditioned Latin hypercube sampling (LHS) and response surface method (RSM) combined approach is proposed. First, as the optimization variable numbers are mostly large when considering simultaneously the heat-sink and air-fan influences, the Taguchi method is employed to decouple the variables based on comprehensive fluidic-thermal calculations. The values of the less interconnected variables are optimized directly in this stage while the two most interconnected ones are selected and optimized in the next stage. Secondly, LHS is used to generate the sample points of the two variables with the values centered at the initially optimized results in the first stage. The RSM surrogate model is established with the LHS points, and the finally optimized values are then decided based on analytical analysis of the surrogate model. The proposed double-staged method is utilized in optimizing the self-ventilation system of a 2000-rpm outer rotor PMSM, and the optimization effectiveness is validated by comparing the cooling efficiency and effectiveness of the results at each optimization stage.
KW - Latin hypercube sampling (LHS)
KW - Taguchi method
KW - permanent magnet synchronous machine (PMSM)
KW - response surface method (RSM)
KW - self-ventilated cooling system
UR - http://www.scopus.com/inward/record.url?scp=85182344171&partnerID=8YFLogxK
U2 - 10.1109/ICEMS59686.2023.10344616
DO - 10.1109/ICEMS59686.2023.10344616
M3 - Conference contribution
AN - SCOPUS:85182344171
T3 - 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023
SP - 2169
EP - 2174
BT - 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Conference on Electrical Machines and Systems, ICEMS 2023
Y2 - 5 November 2023 through 8 November 2023
ER -