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Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm

Jokar, A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1007/s00231-016-1759-8
  3. Publisher: Springer Verlag
  4. Abstract:
  5. In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe’s operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable
  6. Keywords:
  7. Algorithms ; Complex networks ; Evaporators ; Genetic algorithms ; Heat flux ; Heat resistance ; Neural networks ; Optimization ; Exact analysis ; Input variables ; Multi-layer perceptron neural networks ; Nonlinear structure ; Operating constraints ; Operating points ; Pulsating heat pipe ; Simulation and optimization ; Heat pipes
  8. Source: Heat and Mass Transfer/Waerme- und Stoffuebertragung ; Volume 52, Issue 11 , 2016 , Pages 2437-2445 ; 09477411 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s00231-016-1759-8