南京航空航天大学学报（英文版）2024，Vol.41Issue(4)：458-475,18.DOI:10.16356/j.1005⁃1120.2024.04.004
基于人工神经网络与演化算法混合模型的半透明介质热物性同时反演
Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm
摘要
Abstract
A hybrid identification model based on multilayer artificial neural networks(ANNs)and particle swarm optimization(PSO)algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.关键词
半透明介质/导热-辐射耦合传热/热物性/同时反演/多层人工神经网络/演化算法/混合反演模型Key words
semitransparent medium/coupled conduction-radiation heat transfer/thermophysical properties/simultaneous identification/multilayer artificial neural networks(ANNs)/evolutionary algorithm/hybrid identification model分类
能源科技引用本文复制引用
刘洋,胡少闯..基于人工神经网络与演化算法混合模型的半透明介质热物性同时反演[J].南京航空航天大学学报（英文版）,2024,41(4):458-475,18.基金项目
This work was supported by the Fun-damental Research Funds for the Central Universities(No.3122020072)and the Multi-investment Project of Tianjin Ap-plied Basic Research(No.23JCQNJC00250). （No.3122020072）
