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Abstract

Porous media are extensively used in the engineering field. The effective thermal conductivity and porosity are very important properties of porous medium materials. It is of great significance to obtain a porous medium material that meets the needs of effective thermal conductivity and porosity. In this paper, a four-parameter random generation method is used to produce a training data set, a conditional generation adversarial network (CGAN) is built, and a predetermined effective thermal conductivity and porosity are used as inputs to generate a porous medium structure that meets the input conditions. In particular, since the pore structure distribution of porous medium has a great influence on the effective thermal conductivity of the material, a local structure loss function is proposed to participate in the network training, so that the network can better learn the relationship between the pore distribution and the thermal conductivity. By using the lattice Boltzmann method to verify the effective thermal conductivity of the porous medium structure generated by the neural network, the results show that the method can quickly and accurately generate the porous medium structure with predetermined parameters.

Keywords:

porous media/
structure generation/
conditional generation adversarial network/
effective thermal conductivity

Authors and contacts

Corresponding author:Li Ding-Gen,lidinggen@hust.edu.cn

Funds:Project supported by the Joint Funds of the National Natural Science Foundation of China (Grant No. U1713203)

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Cited By

DownLoad: Full-Size Img PowerPoint

Along the foam growing direction			Across the foam growing direction
Prediction/(W·mK^–1)	Experiment/(W·mK^–1)	Deviation/%	Prediction/ (W·mK^–1)	Experiment/(W·mK^–1)	Deviation/%
0.0253	0.0220	15.0	0.0265	0.0245	8.16

DownLoad: CSV

Porosity	0.45	0.45	0.45	0.55	0.65	Mean error
K_effratio	1∶1000	1∶800	1∶600	1∶1000	1∶1000	Mean error
K_efferror	0.006	0.006	0.008	0.007	0.017	0.009
Porosity error	0.049	0.031	0.039	0.029	0.038	0.037

DownLoad: CSV

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Vol. 70, Issue 5 - 2021-03-05

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