A novel way to determine transient heat flux based on GBDT machine learning algorithm. (November 2021)

• Record Type: Journal Article
• Authors: Wu, Weimin; Wang, Jianxiang; Huang, Yaosong; Zhao, Huanyu; Wang, Xiaotian
• Is Part Of: International journal of heat and mass transfer. Volume 179(2021)
• Publication Date: 2021-11
• Subjects: Transient heat flux measurement -- Machine learning -- Gradient boosting decision tree -- Artificial neural network; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2021.121746
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: Transient heat flux can be predicted with machine learning algorithm. The material temperature dependent parameters are not sensitive to the model. The temperature changing trending is a necessary input of the model. The predict model response time can achieve less than 1s with high accuracy. Abstract: The traditional transient heat flux measuring instrument has harsh working conditions and high cost, which is not conducive to promoting the wide range of applications in the engineering manufacturing area. A novel transient heat flux measuring method based on Gradient Boosting Decision Tree (GBDT) algorithm is proposed in this paper. The new measurement method consists of a thermal receptor, a thermocouple, and a data processing device. The system can measure the heat flux absorbed by the thermal receptor through collecting the temperature tendency. This turns the complex problem of heat measurement into a simple problem of temperature measurement, which can greatly reduce the cost of measuring equipment. In this work, numerical simulation was also concerned, and related experiments were designed to verify the feasibility of the method. The GBDT method has demonstrated significant predictive accuracy in both cases. The relative errors of the model trained by the simulation data and the experimental data are 0.8% and 2.9%, respectively. The relative error of GBDT measurement method is less than 5.99% and the response time is 0.3s, which can meet most engineering needs. This work has broad application scenarios and commercial prospects.