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Optimisation de l'empilement d'un réfrigérateur thermo-acoustique à onde stationnaire en utilisant des algorithmes génétiques.

Optimization of standing-wave thermoacoustic refrigerator stack using genetic algorithm.

Auteurs : PENG Y., FENG H., MAO X.

Type d'article : Article, Article de la RIF

Résumé

The main focus of this work is the optimization of a thermoacoustic plate stack in a standing-wave thermoacoustic refrigerator using genetic algorithm. A numerical model of the thermoacoustic stack and its iterative solving process are firstly presented. A comparison to DeltaEC modelling shows that the presented method is effective in predicting the acoustic field and the energy flow. Based on the numerical model, the stack is optimized in terms of four and five variables for both single objective and multiple objectives. In the four-variable models, the length and position of the stack, the plate spacing and the stack porosity are investigated. In the five-variable model, the acoustic frequency is considered additionally. In the single-objective optimization, the objective function is either the cooling power or the coefficient of performance of the stack, and the multi-objective model has two objective functions, namely, the coefficient of performance of the stack and the cooling power. For the optimization, genetic algorithm hybridized by pattern search and implemented in Matlab is adopted. The optimal values of the stack length and the stack position, obtained from the single-objective optimization, agree with those in the published work. The extended multi-objective models present the Pareto optimal, which provides more design choices depending on the preference.

Documents disponibles

Format PDF

Pages : 246-255

Disponible

  • Prix public

    20 €

  • Prix membre*

    Gratuit

* meilleur tarif applicable selon le type d'adhésion (voir le détail des avantages des adhésions individuelles et collectives)

Détails

  • Titre original : Optimization of standing-wave thermoacoustic refrigerator stack using genetic algorithm.
  • Identifiant de la fiche : 30024604
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 92
  • Date d'édition : 08/2018
  • DOI : http://dx.doi.org/10.1016/j.ijrefrig.2018.04.023

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