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Research Papers: Heat and Mass Transfer

Fuzzy Adaptive Predictive Inverse for Nonlinear Transient Heat Transfer Process

[+] Author and Article Information
Guangjun Wang, Yanhao Li, Shibin Wan, Cai Lv

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

Hong Chen

School of Power Engineering,
Chongqing University,
Chongqing 400044, China;
Key Laboratory of Low-Grade Energy
Utilization Technologies and Systems,
Ministry of Education,
Chongqing University,
Chongqing 400044, China
e-mail: chenh@cqu.edu.cn

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received December 27, 2016; final manuscript received March 13, 2017; published online May 23, 2017. Assoc. Editor: Antonio Barletta.

J. Heat Transfer 139(10), 102002 (May 23, 2017) (9 pages) Paper No: HT-16-1827; doi: 10.1115/1.4036573 History: Received December 27, 2016; Revised March 13, 2017

For nonlinear transient heat transfer system, a fuzzy adaptive predictive inverse method (FAPIM) is proposed to inverse transient boundary heat flux. The influence relationship matrix is utilized to establish time-varying linear prediction model of the temperatures at measurement point. Then, the predictive and measurement temperatures are used to inverse the heat flux at current moment by rolling optimization. A decentralized fuzzy inference (DFI) mechanism is established. The deviation vector of the predictive temperature is adopted to conduct decentralized inference by a set of fuzzy inference units, and then, the influence relationship matrix is updated online to guarantee the adaptive ability of the prediction model by weighting fuzzy inference components. FAPIM is utilized to inverse the unknown heat flux of a heat transfer system with temperature-dependent thermal properties, which has shown that the inverse method has better adaptive ability for the inverse problems of nonlinear heat transfer system.

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Figures

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Fig. 1

Nonlinear transient heat conduction system

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Fig. 2

The structure of fuzzy adaptive predictive inverse system

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Fig. 3

The structure of decentralized fuzzy inference

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Fig. 4

Fuzzy inference unit FIUm

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Fig. 5

Membership functions of em for fuzzy sets Al

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Fig. 6

Membership functions of Δuk+m−1 for fuzzy sets Bl

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Fig. 7

Inverse results of FAPIM under different Δt

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Fig. 8

Inverse results of FAPIM under different r

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Fig. 9

Inverse results of heat flux of two methods with σ = 0

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Fig. 10

The range of calculated temperature

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Fig. 11

Inverse results of MPIM under different nonlinear degrees

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Fig. 12

Inverse results of FAPIM under different nonlinear degrees

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Fig. 13

Inverse results with σ = 0.50

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Fig. 14

Inverse results with σ = 1.0

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Fig. 15

Inverse results with σ = 1.50

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Fig. 16

Inverse results with xm = 0.005 m

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Fig. 17

Inverse results with xm = 0.01 m

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Fig. 18

Inverse results with xm = 0.015 m

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