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Simulation and optimization of pulsating heat pipe flat-plate solar collectors using neural networks and genetic algorithm: a semi-experimental investigation

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Abstract

This research study presents an investigation on the behavior of a Pulsating Heat Pipe Flat-Plate Solar Collector (PHPFPSC) by artificial neural network method and an optimization of the parameters of the collector by genetic algorithm. In this study, several experiments were performed to study the effects of various evaporator lengths, filling ratios, inclination angles, solar radiation, and input chilled water temperature between 9:00 A.M. to 5:00 P.M., and the output temperature of the water tank, which was the output of the system, was also measured. According to the input and output information, multilayer perceptron neural network was trained and used to predict the behavior of the system. A two-layer neural network with a unipolar sigmoid activation function and a 6-20-1 structure was obtained as the network with the highest performance. The trained network was validated with experimental data and used to evaluate the objective function to maximize the thermal efficiency of the system. Also, a continuous genetic algorithm was developed to optimize the system efficiency. An initial population size of 700 and a mutation rate of 4 % were obtained as the best values. Furthermore, at the evaporator length of 1.08, filling ratio of 56.94 %, and inclination angle of 25.01, the maximum thermal efficiency of the system was 61.4 %. The effect of the input water temperature of the water tank on the optimal values of optimization variables was examined. The results indicated that an increase in the temperature of the input water of the water tank leads to a decrease in the thermal efficiency of the system. A comparison of the results of this study with previous research indicates that the use of heat pipes in solar collectors can increase the efficiency of solar collectors up to 4 %. According to the results, the use of neural networks, as an input–output model, is a proper way to predict the complicated behavior of these systems. Also, genetic algorithm is an efficient method for solving non-linear optimization problems.

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Abbreviations

A :

Area (m2), collector legend

B :

Collector legend

BPAF:

Bipolar activation function

C :

Collector legend, cost

CR:

Filling ratio

E :

Neural network error

G :

Gene

I :

Total solar radiation (W/m2K)

L :

Length (m)

M :

Parent number

N :

Neuron number/number

NH:

Number of neurons in hidden layer

offs:

Offspring

P :

Probability

Par:

Parent

PHP:

Pulsating heat pipe

Q :

Heat (kW)

RMSD:

Root mean square deviation

T :

Temperature (K)

U :

NN Weights between inputs and hidden layer

UPAF:

Unipolar activation function

V :

NN Weights between hidden layer and output layer

W :

Water

h :

Hidden layer in neural network

i :

Epoch counter

j :

Time interval counter

k :

Number of neurons in hidden layer of NN

l :

Number of inputs of NN

m :

Counter

n :

Counter

t :

Time interval

\(\alpha\) :

Activation function parameter

\(\beta\) :

Random number between 0 and 1

\(\gamma\) :

Random number between 1, 2, and 3

\(\theta\) :

Inclination angle of the solar collector

\(\omega\) :

Random number between 0 and 1

\(\lambda\) :

Training rate

\(\tau\) :

Control parameter in simulated annealing

\(\psi\) :

Angle between the direct radiation and normal vector of the collector

NN:

Neural network

PHPFPSC:

Pulsating heat pipe flat-plate solar collector

c :

Solar collector

Co:

Cumulative

par:

Parent

Tr:

Training epoch for the neural network

ev:

Evaporator

good:

Good population

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Authors and Affiliations

  1. The Graduate Faculty of Environment, University of Tehran, P.O. Box 14155 6135, Tehran, Iran

    M. Jalilian

  2. Energy and Sustainable Development Research Center, Semnan Branch, Islamic Azad University, Semnan, Iran

    H. Kargarsharifabad

  3. Department of Energy Engineering, Sharif University of Technology, P.O. Box 11365-9567, Tehran, Iran

    A. Abbasi Godarzi

  4. Department of Mechanical Engineering, Sharif University of Technology, P.O. Box 11365-9567, Tehran, Iran

    A. Ghofrani & M. B. Shafii

Authors
  1. M. Jalilian
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  2. H. Kargarsharifabad
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  3. A. Abbasi Godarzi
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  4. A. Ghofrani
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  5. M. B. Shafii
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Correspondence to H. Kargarsharifabad.

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Jalilian, M., Kargarsharifabad, H., Abbasi Godarzi, A. et al. Simulation and optimization of pulsating heat pipe flat-plate solar collectors using neural networks and genetic algorithm: a semi-experimental investigation. Clean Techn Environ Policy 18, 2251–2264 (2016). https://doi.org/10.1007/s10098-016-1143-x

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  • DOI: https://doi.org/10.1007/s10098-016-1143-x

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