L-shaped directional heat transfer based on deep learning

WANG Zelin; JI Ritian; HUI Xinyu; DING Chen; WANG Hui; BAI Junqiang

doi:10.7527/S1000-6893.2020.24242

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2021 , Vol. 42 >Issue 6: 124242 - 124242

DOI: https://doi.org/10.7527/S1000-6893.2020.24242

Fluid Mechanics and Flight Mechanics

L-shaped directional heat transfer based on deep learning

WANG Zelin ,
JI Ritian ,
HUI Xinyu ,
DING Chen ,
WANG Hui ,
BAI Junqiang

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1. School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. China Academy of Launch Vehicle Technology, Beijing 100076, China

Received date: 2020-05-19

Revised date: 2020-06-12

Online published: 2020-07-10

Supported by

National Natural Science Foundation of China (11802245)

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Abstract

Carbon/Carbon (C/C) composite materials are widely used in thermal protection systems of aircraft for their excellent characteristics such as high thermal conductivity, high specific strength, ablation resistance and erosion resistance, among which the effective thermal conductivity is an important physical property for practical applications. Traditional methods of studying effective thermal conductivity of composite materials such as the effective medium theory, the direct solution of heat diffusion equation or the Boltzmann transport equation are usually time-consuming. This paper introduces a deep learning method with the Lattice Boltzmann Method's (LBM's) three-dimensional lattice model as the microstructure sample of the Three-Dimensional Convolutional Neural Network (3D-CNN). This method not only overcomes the difficulty of capturing the three-dimensional microstructure model, but facilitates simultaneous simplification of the numerical calculation model and the CNN model. In this way, the effective thermal conductivity of the three-dimensional three-phase C/C composite structure can be predicted quickly and accurately by the 3D-CNN method. In addition, we quickly predict and study the effective thermal conductivity of the directional heat transfer C/C composite structure with built-in L-shaped carbon fiber with high thermal conductivity. Results show that the CNN model displays a strong learning ability in the LBM heat transfer calculation; however, when the porosity of the testing sample structure surpasses the training set excessively, the prediction error will increase significantly. When the porosity changes from 30%-35% to 55%-60%, the relative error of the CNN model "interpolation" is 0.93%-30.72% lower than that of the model "extrapolation". The built-in L-shaped carbon fiber with high thermal conductivity in the C/C composite structure can direct the heat in high temperature areas to low temperature areas along the fiber.

Key words： L-shaped directional heat transfer; effective thermal conductivity; machine learning; convolutional neural networks; lattice Boltzmann method

Cite this article

WANG Zelin , JI Ritian , HUI Xinyu , DING Chen , WANG Hui , BAI Junqiang . L-shaped directional heat transfer based on deep learning[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(6) : 124242 -124242 . DOI: 10.7527/S1000-6893.2020.24242

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