S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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S&M2586 Research Paper of Special Issue

Multi-objective Optimal Parameter Design of Nanofilters and Heat Sink Fins in Train Traction Motor Drive System

Jian-Long Kuo

(Received December 29, 2020; Accepted April 1, 2021)

Keywords: multi-objective optimal design, heat sink modules, heat dissipation, intelligent maintenance, train traction motor drive system

In this paper, the multi-objective parameter design of the heat sink modules of the converter and inverter in a train traction motor drive system was studied. The high-power insulated gate bipolar transistor (IGBT) devices embedded in converter and inverter modules usually generate considerable heat due to pulse width modulation (PWM) switching behavior. An appropriate heat dissipation system should be designed to reduce the temperature rise of the devices and to ensure their safe operation. The response surface method (RSM) was used to build a statistical model of the studied problem. The optimal design for the heat dissipation system is to minimize the objective functions of both the inlet temperature and outlet temperature of the heat sink modules. The three major control factors selected for the RSM are the mileage of nanofilter usage, the nanofilter type, and the limited current of the inverter. Since it is difficult to find an appropriate mathematical model associated with the three control factors that is based on physical principles, the optimal design of experiments (DOE) technique was used to describe the relation among the three control factors. In order to obtain the optimal inlet temperature and outlet temperature of the heat sink modules, multi-objective optimal design of the heat sink modules was addressed. The multiple performance characteristics index (MPCI) method was used to combine the two objective functions into one integrated index. To solve the nonlinear statistical model, orthogonal particle swarm optimization was used to efficiently find the optimal solution. Results showed that the obtained optimal solution provided the lowest inlet temperature and lowest outlet temperature for the heat sink modules in the train traction motor drive system. The statistical model can also be uploaded onto a cloud server to provide an effective cloud model. The optimal parameter design of the heat sink modules in the train traction motor drive system can be applied to provide effective information for an intelligent maintenance system (IMS) of the heat sink modules. The developed IMS is expected to increase the availability and reliability of nanofilters and heat sink fins using internet of things (IoT) and Industry 4.0 techniques.

Corresponding author: Jian-Long Kuo




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