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Research Papers: Two-Phase Flow and Heat Transfer

Artificial Neural Network Based Prediction of Heat Transfer From Horizontal Tube Bundles Immersed in Gas–Solid Fluidized Bed of Large Particles

[+] Author and Article Information
L. V. Kamble

SIT,
Symbiosis International University,
Pune 412 115, Maharashtra, India
e-mail: klaxmanv@rediffmail.com

D. R. Pangavhane

Prestige Institute of Engineering and Science,
Indore 452 010, Madhya Pradesh, India
e-mail: drpangavhane@yahoo.co.in

T. P. Singh

SIT,
Symbiosis International University,
Pune 412115, Maharashtra, India
e-mail: director@sitpune.edu.in

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received December 27, 2013; final manuscript received July 18, 2014; published online November 5, 2014. Assoc. Editor: Giulio Lorenzini.

J. Heat Transfer 137(1), 012901 (Jan 01, 2015) (9 pages) Paper No: HT-13-1669; doi: 10.1115/1.4028645 History: Received December 27, 2013; Revised July 18, 2014

Artificial neural network (ANN) modeling of heat transfer from horizontal tube bundles immersed in gas fluidized bed of large particles (mustard, raagi and bajara) was investigated. The effect of fluidizing gas velocity on the heat transfer coefficient in the immersed tube bundles in in-line and staggered arrangement is discussed. The parameters particle diameter, temperature difference between bed and immersed surface were used in the neural network (NN) modeling along with fluidizing velocity. The feed-forward network with back propagation structure implemented using Levenberg–Marquardt's learning rule in the NN approach. The predictions of the ANN were found to be in good agreement with the experiment's values, as well as the results achieved by the developed correlations.

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References

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Figures

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Fig. 1

Schematic diagram of experimental setup

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Fig. 2

In-line and staggered arrangement of tubes

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Fig. 3

ANN structure used in the study

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Fig. 4

(a) Variation of heat transfer coefficient with fluidization velocity (in-line) and (b) variation of heat transfer coefficient with fluidization velocity (staggered)

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Fig. 5

(a) Performance graph (in-line) and (b) performance graph (staggered)

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Fig. 6

(a) Comparison between experimental and ANN values of heat transfer coefficient for training data (in-line) and (b) comparison between experimental and ANN values of heat transfer coefficient for training data (staggered)

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Fig. 7

(a) Comparison between experimental and ANN values of Nusselt number for training data (in-line) and (b) comparison between experimental and ANN values of Nusselt number for training data (staggered)

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Fig. 8

(a) Comparison between experimental and ANN values of heat transfer coefficient for testing data (in-line) and (b) comparison between experimental and ANN values of heat transfer coefficient for testing data (staggered)

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Fig. 9

(a) Comparison between experimental and ANN values of Nusselt number for testing data (in-line) and (b) comparison between experimental and ANN values of Nusselt number for testing data (staggered)

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Fig. 10

(a) Comparison of heat transfer coefficient by experiment, correlation, and ANN (in-line) and (b) comparison of heat transfer coefficient by experiment, correlation, and ANN (staggered)

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