Long Huang^1,2,3,*, Junjia Zou³, Baoqing Liu¹, Zhijiang Jin^1,2, Jinyuan Qian¹

Frontiers in Heat and Mass Transfer, Vol.22, No.6, pp. 1613-1643, 2024, DOI:10.32604/fhmt.2024.058231 - 19 December 2024

Abstract This study explores the effectiveness of machine learning models in predicting the air-side performance of microchannel heat exchangers. The data were generated by experimentally validated Computational Fluid Dynamics (CFD) simulations of air-to-water microchannel heat exchangers. A distinctive aspect of this research is the comparative analysis of four diverse machine learning algorithms: Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), and Gaussian Process Regression (GPR). These models are adeptly applied to predict air-side heat transfer performance with high precision, with ANN and GPR exhibiting notably superior accuracy. Additionally, this research further delves into… More >

Qiang Wang, Zhen Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 443-462, 2022, DOI:10.32604/cmes.2022.015717 - 29 November 2021

Abstract Micromotors are widely used in cell operation, drug delivery and environmental decontamination due to their small size, low energy consumption and large propelling power. Compared to traditional Janus micromotor, the shell Janus micromotor has better motion performance. However, the structural optimization of its motion performance is still unclear. The main factor restricting the motion performance of shell Janus micromotors is the drag forces. In the current work, theoretical analysis and numerical simulation were applied to analyze the drag forces of shell Janus micromotors. This study aims to design the optimum structure of shell Janus micromotors More >