An efficient inverse approach for reconstructing time- and space-dependent heat flux of participating medium

doi:10.1088/1674-1056/aba608

中国物理B ›› 2020, Vol. 29 ›› Issue (11): 110202-.doi: 10.1088/1674-1056/aba608

Shuang-Cheng Sun(孙双成)¹^,²^,†(), Guang-Jun Wang(王广军)¹^,², Hong Chen(陈红)¹^,²

收稿日期:2020-05-04 修回日期:2020-05-26 接受日期:2020-07-15 出版日期:2020-11-05 发布日期:2020-11-03

An efficient inverse approach for reconstructing time- and space-dependent heat flux of participating medium

Shuang-Cheng Sun(孙双成)^{1,2, †}, Guang-Jun Wang(王广军)^1,2, and Hong Chen(陈红)^1,2$

1 School of Energy and Power Engineering, Chongqing University, Chongqing 400044, China
2 Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China

Received:2020-05-04 Revised:2020-05-26 Accepted:2020-07-15 Online:2020-11-05 Published:2020-11-03
Contact: ^†Corresponding author. E-mail: scsun@cqu.edu.cn
Supported by:
the Natural Science Foundation of Chongqing (CSTC, Grant No. 2019JCYJ-MSXMX0441).

摘要/Abstract

Abstract:

The decentralized fuzzy inference method (DFIM) is employed as an optimization technique to reconstruct time- and space-dependent heat flux of two-dimensional (2D) participating medium. The forward coupled radiative and conductive heat transfer problem is solved by a combination of finite volume method and discrete ordinate method. The reconstruction task is formulated as an inverse problem, and the DFIM is used to reconstruct the unknown heat flux. No prior information on the heat flux distribution is required for the inverse analysis. All retrieval results illustrate that the time- and space-dependent heat flux of participating medium can be exactly recovered by the DFIM. The present method is proved to be more efficient and accurate than other optimization techniques. The effects of heat flux form, initial guess, medium property, and measurement error on reconstruction results are investigated. Simulated results indicate that the DFIM is robust to reconstruct different kinds of heat fluxes even with noisy data.

Key words: decentralized fuzzy inference, surface heat flux reconstruction, inverse heat transfer problem, participating medium

Shuang-Cheng Sun(孙双成), Guang-Jun Wang(王广军), Hong Chen(陈红). [J]. 中国物理B, 2020, 29(11): 110202-.

Shuang-Cheng Sun(孙双成), Guang-Jun Wang(王广军), and Hong Chen(陈红)$. An efficient inverse approach for reconstructing time- and space-dependent heat flux of participating medium[J]. Chin. Phys. B, 2020, 29(11): 110202-.

图/表 19

参考文献 42

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Optimization technique	Average relative error/%	Maximum relative error/%	Iteration number	Computational time/h
DFIM	0.0002	0.0092	46	0.3
SQP	0.0003	0.0229	306	86.2
CGM	0.0010	0.1341	698	153.3
L-M	0.0015	0.2056	717	172.9
PSO	44.8900	287.7838	10000	23.8
GA	59.2025	392.2087	10000	36.1

Heat flux	Average relative error/%	Maximum relative error/%	Iteration number	Computational time/s
Q1	0.0002	0.0092	46	921.1
Q2	0.0002	0.0099	46	922.7
Q3	0.0001	0.0028	45	919.8
Q4	0.0001	0.0030	46	922.3
Q5	0.0001	0.0075	46	923.9

Initial guess/W⋅m⁻²	Average relative error/10⁻⁶					Computational time/s
Initial guess/W⋅m⁻²	Q1	Q2	Q3	Q4	Q5	Q1	Q2	Q3	Q4	Q5
0	1.40	1.97	1.07	0.92	0.79	923.2	923.5	921.0	923.0	921.5
5000	1.38	1.96	1.06	0.93	0.83	921.5	926.4	923.4	921.6	919.4
10000	1.50	1.92	1.05	0.92	0.77	921.1	922.7	919.8	922.3	923.9
30000	1.53	1.90	1.03	0.91	0.83	923.0	921.3	920.1	925.6	921.6
50000	1.99	1.93	1.02	0.91	0.81	922.4	925.4	920.4	922.9	924.1
100000	2.21	2.00	1.16	1.05	0.88	921.6	924.3	922.6	921.7	923.8

	ρ/kg⋅m⁻³	c_p/J⋅kg⁻¹⋅ K⁻¹	λ/W⋅m⁻¹⋅ K⁻¹	κ_a/m⁻¹	κ_s/m⁻¹	n
Ceramic	5600	456	1.4	300	12000	1.8
Glass	2219	840	1.025	1244	0	1.4

Measurement error	Average relative error/%	Maximum relative error/%	Computational time/s
0.01	0.0203	0.1560	923.1
0.05	0.1062	0.6897	921.9
0.1	0.2115	1.3735	922.1
0.5	1.0208	6.9042	919.6
1.0	2.0916	12.2036	921.5

An efficient inverse approach for reconstructing time- and space-dependent heat flux of participating medium

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