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Research Papers: Melting and Solidification

Conjugate Heat Transfer in Latent Heat Thermal Storage System With Cross Plate Fins

[+] Author and Article Information
Rajesh Alayil

Heat Transfer and Thermal Power Laboratory,
Department of Mechanical Engineering,
Indian Institute of Technology Madras,
Chennai 600036, India
e-mail: rajeshalayiliitm@gmail.com

C. Balaji

Professor
Heat Transfer and Thermal Power Laboratory,
Department of Mechanical Engineering,
Indian Institute of Technology Madras,
Chennai 600036, India
e-mail: balaji@iitm.ac.in

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received May 25, 2014; final manuscript received April 24, 2015; published online June 2, 2015. Assoc. Editor: Jim A. Liburdy.

J. Heat Transfer 137(10), 102302 (Oct 01, 2015) (9 pages) Paper No: HT-14-1347; doi: 10.1115/1.4030496 History: Received May 25, 2014; Revised April 24, 2015; Online June 02, 2015

Latent heat thermal storage systems (LHTS) utilize their latent heat capacity to dissipate high heat fluxes while maintaining quasi-isothermal conditions. Phase change materials (PCMs) absorb a large amount of energy during their phase transformation from solid to liquid, maintaining quasi-isothermal conditions. However, they are often beset with low thermal conductivities which necessitate the use of a thermal conductivity enhancer (TCE) as it is impossible to design a device that can completely avoid sensible heat in the premelting or postmelting phase. In this study, the heat transfer performance of LHTS with cross plate fins as a TCE is numerically investigated for different values of fin thicknesses and fin numbers along the length and breadth. A hybrid artificial neural network coupled genetic algorithm (ANN–GA) is then used to obtain the optimized dimensions for the composite heat sink with cross plate fins as TCE for a fixed volume and a specific heat flux input. The numerically optimized configuration is finally validated with in-house experiments.

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Figures

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Fig. 1

A typical LHTS geometry with thermocouple locations: (a) without TCE and (b) with cross plate fins as TCE

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Fig. 4

Variation of base temperature with time for an optimum LHTS without TCE from numerical results without and with overall heat transfer coefficient (U) and experiments

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Fig. 3

Line diagram of the experimental setup

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Fig. 2

Photograph of the LHTS

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Fig. 6

Neuron architecture employed in the present study

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Fig. 7

Results of the neuron independence study of an ANN for LHTS with cross plate fins as TCE

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Fig. 8

Flow chart for the hybrid ANN–GA algorithm

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Fig. 9

Convergence history of the hybrid ANN–GA algorithm for optimizing composite heat sink with cross plate fins as TCE

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Fig. 10

Optimized geometry of LHTS with cross plate fins as TCE

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Fig. 5

Comparison of base temperatures for different cross plate fin configurations

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Fig. 11

Comparison of numerical and experimental temperature–time histories of the base temperature for an optimum LHTS without TCE and with TCE

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