首页|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

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

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  • 国家科技期刊平台
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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,随后建立了基于PSO算法的传统反演模型,最后构建了ANNs来拟合并替代传统反演模型中的正向模型,以达到加快反演速度的目的.结果表明,与传统反演模型相比,混合反演模型的时间成本降低约1 000倍.此外,即使在有测量误差的情况下,混合模型依旧保持了较高的精度.
Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm
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.

semitransparent mediumcoupled conduction-radiation heat transferthermophysical propertiessimultaneous identificationmultilayer artificial neural networks(ANNs)evolutionary algorithmhybrid identification model

刘洋、胡少闯

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中国民航大学中欧航空工程师学院,天津 300300,中国

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

Fundamental Research Funds for the Central UniversitiesMultiinvestment Project of Tianjin Applied Basic Research

312202007223JCQNJC00250

2024

10.16356/j.1005?1120.2024.04.004

南京航空航天大学学报(英文版)
南京航空航天大学

南京航空航天大学学报(英文版)

CSTPCD
影响因子：0.279
ISSN：1005-1120
年,卷(期)：2024.41(4)