TY - JOUR
T1 - An accurate and efficient implicit thermal network method for the steady-state temperature field
AU - Zhan, Ziquan
AU - Fang, Bin
AU - Wan, Shaoke
AU - Bai, Yu
AU - Hong, Jun
AU - Li, Xiaohu
N1 - Publisher Copyright:
© IMechE 2023.
PY - 2024/3
Y1 - 2024/3
N2 - To calculate the temperature value accurately and efficiently, an implicit thermal network method (TNM) is developed in this study. The main idea of the method is the conversion of such time-varying observed variables as the thermal resistance and the heat source into latent variables to build the implicit thermal equilibrium equation. In the implicit TNM, the steady-state temperature is taken as an independent variable, then parameters related to the steady-state temperature can be expressed as the function of the independent variable. On this basis, implicit thermal equilibrium equations can be constructed. Finally, the steady-state temperature field can be obtained by Newton’s method in optimization. To validate the performance and effectiveness of the implicit TNM, case studies and the sensitivity analysis of algorithms are conducted. The result reveals that the implicit TNM outperforms conventional TNMs, the steady-state TNM and the transient TNM, in computation accuracy, computing time and allocated memory.
AB - To calculate the temperature value accurately and efficiently, an implicit thermal network method (TNM) is developed in this study. The main idea of the method is the conversion of such time-varying observed variables as the thermal resistance and the heat source into latent variables to build the implicit thermal equilibrium equation. In the implicit TNM, the steady-state temperature is taken as an independent variable, then parameters related to the steady-state temperature can be expressed as the function of the independent variable. On this basis, implicit thermal equilibrium equations can be constructed. Finally, the steady-state temperature field can be obtained by Newton’s method in optimization. To validate the performance and effectiveness of the implicit TNM, case studies and the sensitivity analysis of algorithms are conducted. The result reveals that the implicit TNM outperforms conventional TNMs, the steady-state TNM and the transient TNM, in computation accuracy, computing time and allocated memory.
KW - Thermal network method
KW - implicit thermal network method
KW - sensitivity analysis
KW - spindle-bearing system
KW - steady-state temperature field
UR - https://www.scopus.com/pages/publications/85165440771
U2 - 10.1177/09544062231187791
DO - 10.1177/09544062231187791
M3 - 文章
AN - SCOPUS:85165440771
SN - 0954-4062
VL - 238
SP - 1800
EP - 1810
JO - Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
JF - Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
IS - 5
ER -