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Prediction of power in solar stirling heat engine by using neural network based on hybrid genetic algorithm and particle swarm optimization

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Abstract

In this paper, the model based on a feed-forward artificial neural network optimized by particle swarm optimization (HGAPSO) to estimate the power of the solar stirling heat engine is proposed. Particle swarm optimization is used to decide the initial weights of the neural network. The HGAPSO-ANN model is applied to predict the power of the solar stirling heat engine which data set reported in literature of china. The performance of the HGAPSO-ANN model is compared with experimental output data. The results demonstrate the effectiveness of the HGAPSO-ANN model.

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Abbreviations

C V :

Specific heat capacity, J mol−1 K−1

h :

Heat transfer coefficient, WK−1 or WK−4 or Wm−2 K−1

n :

The mole number of the working fluid, mol

M :

Regenerative time constant, Ks−1

p :

Power, W

Q :

Heat transfer, J

R :

The gas constant, J mol−1 K−1

t :

Time, s

T :

Temperature, K

W :

Work, J

λ :

Ratio of volume during the regenerative processes

τ :

Cyclic period, s

η :

Thermal efficiency

k 0 :

Heat leak coefficient, WK−1

ɛ R :

Effectiveness of the regenerator

H:

Absorber

HC:

High temperature side convection

HR:

High temperature side radiation

L:

Heat sink

LC:

Low temperature side convection

m:

The system

R:

Regenerator

t:

Stirling engine

0:

Ambient or optics

4-1:

The processes

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, K.N. Toosi University, Pardis Ave, Mollasadra Street, Vanak Sq, Tehran, Iran

    Mohammad Hossien Ahmadi

  2. Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Hormozgan, Iran

    Saman Sorouri Ghare Aghaj & Alireza Nazeri

Authors
  1. Mohammad Hossien Ahmadi
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  2. Saman Sorouri Ghare Aghaj
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  3. Alireza Nazeri
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Correspondence to Mohammad Hossien Ahmadi.

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Ahmadi, M.H., Sorouri Ghare Aghaj, S. & Nazeri, A. Prediction of power in solar stirling heat engine by using neural network based on hybrid genetic algorithm and particle swarm optimization. Neural Comput & Applic 22, 1141–1150 (2013). https://doi.org/10.1007/s00521-012-0880-y

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  • DOI: https://doi.org/10.1007/s00521-012-0880-y

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