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Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm

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  • Rashidi, M.M.
  • Ali, M.
  • Freidoonimehr, N.
  • Nazari, F.

Abstract

The present study first of all concerns the first and second law analyzes of an electrically conducting fluid past a rotating disk in the presence of a uniform vertical magnetic field, analytically via Homotopy Analysis Method (HAM), and then applies Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) algorithm in order to minimize the entropy generation. In the first part of this study, entropy generation equation is derived as a function of velocity and temperature gradients and non-dimensionalized using geometrical and physical flow field-dependent parameters. A very good agreement can be seen between some of the obtained results of the current study and the results of the previously published data. The effects of physical flow parameters such as magnetic interaction parameter, unsteadiness parameter, disk stretching parameter, Prandtl number, Reynolds number and Brinkman number on all fluid velocity components, temperature distribution and the averaged entropy generation number are checked and analyzed. For minimizing the entropy generation value a procedure based on ANN and PSO is proposed. This procedure comprises three steps. The first step is to find entropy generation for values of some different affecting factors. In the second step, some distinct multi-layer perceptron ANNs based on the data obtained from step one are trained. In step three, PSO is used to minimize the entropy generation in the considered stretchable rotating disk.

Suggested Citation

  • Rashidi, M.M. & Ali, M. & Freidoonimehr, N. & Nazari, F., 2013. "Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm," Energy, Elsevier, vol. 55(C), pages 497-510.
    • Handle: RePEc:eee:energy:v:55:y:2013:i:c:p:497-510
    DOI: 10.1016/j.energy.2013.01.036
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    References listed on IDEAS

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    1. Rashidi, M.M. & Galanis, N. & Nazari, F. & Basiri Parsa, A. & Shamekhi, L., 2011. "Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network," Energy, Elsevier, vol. 36(9), pages 5728-5740.
    2. Basak, Tanmay & Anandalakshmi, R. & Kumar, Pushpendra & Roy, S., 2012. "Entropy generation vs energy flow due to natural convection in a trapezoidal cavity with isothermal and non-isothermal hot bottom wall," Energy, Elsevier, vol. 37(1), pages 514-532.
    3. Arikoglu, Aytac & Ozkol, Ibrahim & Komurgoz, Guven, 2008. "Effect of slip on entropy generation in a single rotating disk in MHD flow," Applied Energy, Elsevier, vol. 85(12), pages 1225-1236, December.
    4. Gandhidasan, P. & Mohandes, M.A., 2011. "Artificial neural network analysis of liquid desiccant dehumidification system," Energy, Elsevier, vol. 36(2), pages 1180-1186.
    5. Ibáñez, Guillermo & López, Aracely & Pantoja, Joel & Moreira, Joel & Reyes, Juan A., 2013. "Optimum slip flow based on the minimization of entropy generation in parallel plate microchannels," Energy, Elsevier, vol. 50(C), pages 143-149.
    6. Ibáñez, Guillermo & Cuevas, Sergio, 2010. "Entropy generation minimization of a MHD (magnetohydrodynamic) flow in a microchannel," Energy, Elsevier, vol. 35(10), pages 4149-4155.
    7. Smrekar, J. & Assadi, M. & Fast, M. & Kuštrin, I. & De, S., 2009. "Development of artificial neural network model for a coal-fired boiler using real plant data," Energy, Elsevier, vol. 34(2), pages 144-152.
    8. Mahian, Omid & Mahmud, Shohel & Heris, Saeed Zeinali, 2012. "Analysis of entropy generation between co-rotating cylinders using nanofluids," Energy, Elsevier, vol. 44(1), pages 438-446.
    9. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
    10. Zhang, Zijun & Zeng, Yaohui & Kusiak, Andrew, 2012. "Minimizing pump energy in a wastewater processing plant," Energy, Elsevier, vol. 47(1), pages 505-514.
    11. Kaluri, Ram Satish & Basak, Tanmay, 2011. "Entropy generation due to natural convection in discretely heated porous square cavities," Energy, Elsevier, vol. 36(8), pages 5065-5080.
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