TY - JOUR
T1 - A quantitative method of calculating transient nonlinear heat partition coefficient between clutch friction discs with deep learning
AU - Zhang, Peng
AU - Zheng, Changsong
AU - Xiong, Cenbo
AU - Ma, Biao
AU - Yu, Liang
AU - Luo, Dengming
N1 - Publisher Copyright:
© IMechE 2024.
PY - 2024
Y1 - 2024
N2 - In the current clutch temperature field study, the generally used constant heat partition coefficient tends to overestimate the separator disc temperature and underestimate the friction disc temperature. Although some researchers have found the characteristics of the time-varying heat partition coefficient, a suitable method is still needed to apply it to temperature calculations. This study provides a quantitative method for the application of the transient nonlinear heat partition coefficient to temperature calculations. The finite difference method is adopted to figure out the time-varying curve of the heat partition coefficient by coupling the contact temperature of the friction components. The numerical results show that the heat partition coefficient is independent of rotation speed with three stages: initial value, rapid time-varying, and steady-state. Different from the analytical method, we apply a deep learning method to train the quantisation function to characterise these three stages, avoiding complex formula derivation. As a result, the quantitative function can characterise the time-varying heat partition coefficient accurately, with an average error of 0.19%, 3.05% and 0.62% for the inert, time-varying, and steady-state stages, respectively. In addition, the accuracy of applying the quantisation function in temperature simulation is verified by friction experiments, and the error is less than 8%. This is superior to the results of solving the temperature field by a constant heat partition coefficient.
AB - In the current clutch temperature field study, the generally used constant heat partition coefficient tends to overestimate the separator disc temperature and underestimate the friction disc temperature. Although some researchers have found the characteristics of the time-varying heat partition coefficient, a suitable method is still needed to apply it to temperature calculations. This study provides a quantitative method for the application of the transient nonlinear heat partition coefficient to temperature calculations. The finite difference method is adopted to figure out the time-varying curve of the heat partition coefficient by coupling the contact temperature of the friction components. The numerical results show that the heat partition coefficient is independent of rotation speed with three stages: initial value, rapid time-varying, and steady-state. Different from the analytical method, we apply a deep learning method to train the quantisation function to characterise these three stages, avoiding complex formula derivation. As a result, the quantitative function can characterise the time-varying heat partition coefficient accurately, with an average error of 0.19%, 3.05% and 0.62% for the inert, time-varying, and steady-state stages, respectively. In addition, the accuracy of applying the quantisation function in temperature simulation is verified by friction experiments, and the error is less than 8%. This is superior to the results of solving the temperature field by a constant heat partition coefficient.
KW - Clutch
KW - deep learning
KW - heat partition coefficient
KW - quantification function
KW - temperature field
UR - http://www.scopus.com/inward/record.url?scp=85193747423&partnerID=8YFLogxK
U2 - 10.1177/09544070241247876
DO - 10.1177/09544070241247876
M3 - Article
AN - SCOPUS:85193747423
SN - 0954-4070
JO - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
JF - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
ER -