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OACSTPCD
Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm
为了提高对半透明材料导热系数和等效吸收系数的同时反演效率,本文提出了一种基于多层人工神经网络(Artificial neural networks,ANNs)和粒子群优化(Particle swarm optimization,PSO)算法的混合反演模型.对于正向模型,在激光闪光法的背景下,采用球谐法和有限体积法求解了吸收、发射、非散射的二维轴对称灰介质中的导热-辐射耦合传热问题.对于反演部分,首先选取不同位置的温度场和入射辐射场作为观测量,随后…查看全部>>
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…查看全部>>
刘洋;胡少闯
中国民航大学中欧航空工程师学院,天津 300300,中国中国民航大学中欧航空工程师学院,天津 300300,中国
能源与动力
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
semitransparent mediumcoupled conduction-radiation heat transferthermophysical propertiessimultaneous identificationmultilayer artificial neural networks(ANNs)evolutionary algorithmhybrid identification model
《南京航空航天大学学报（英文版）》 2024 (4)
458-475,18
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).
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