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Research Papers: Evaporation, Boiling, and Condensation

Modeling of Finned-Tube Evaporator Using Neural Network and Response Surface Methodology

[+] Author and Article Information
Ze-Yu Li, Liang-Liang Shao

School of Mechanical Engineering,
Tongji University,
Shanghai 201804, China

Chun-Lu Zhang

School of Mechanical Engineering,
Tongji University,
Shanghai 201804, China
e-mail: chunlu.zhang@gmail.com

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received February 5, 2015; final manuscript received November 27, 2015; published online January 27, 2016. Assoc. Editor: Amitabh Narain.

J. Heat Transfer 138(5), 051502 (Jan 27, 2016) (9 pages) Paper No: HT-15-1090; doi: 10.1115/1.4032358 History: Received February 05, 2015; Revised November 27, 2015

A new response surface methodology (RSM) based neural network (NN) modeling method is proposed for finned-tube evaporator performance evaluation under dry and wet conditions. Two RSM designs, Box–Behnken design (BBD) and central composite design (CCD), are applied to collect a small but well-designed dataset for NN training, respectively. Compared with additional 7000 sets of test data, for all the evaporator performance including total cooling capacity, sensible heat ratio and pressure drops on both refrigerant and air sides, the standard deviation (SD) and coefficient of determination of trained NNs are less than 2% and higher than 0.998, respectively, under dry conditions while those are less than 4% and greater than 0.974, respectively, under wet conditions. Classic quadratic polynomial response surface models were also included for reference. By comparison, the proposed model achieves higher accuracy. Finally, parametric study based on the trained NNs is conducted. This new method can remarkably downsize the training dataset and mitigate the over-fitting risk of NN.

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References

Figures

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Fig. 1

Schematic of the finned-tube evaporator

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Fig. 2

Relationship between independent and dependent variables of finned-tube evaporator

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Fig. 3

Comparison of trained NNs and experimental data

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Fig. 4

Cooling capacity change with mass flow rate under dry conditions

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Fig. 5

Cooling capacity change with mass flow rate under wet conditions

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Fig. 6

Refrigerant pressure drop change with mass flow rate under dry conditions

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Fig. 7

Refrigerant pressure drop change with mass flow rate under wet conditions

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Fig. 8

Air pressure drop change with air flow rate under dry conditions (a) and wet conditions (b)

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Fig. 9

Sensible heat ratio change with mass flow rate

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