A novel way to determine transient heat flux based on GBDT machine learning algorithm

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.