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Document IIF

Modélisation précise du détendeur à spirale au moyen d'une méthode fondée sur des données incorporées au mécanisme.

Accurate modelling of the scroll expander via a mechanism-incorporated data-driven method.

Auteurs : MA X., LV X., LI C., LI K.

Type d'article : Article de la RIF

Résumé

Accurate modelling of the scroll expander is essential for efficiency analysis and optimal control. In this study, we propose a mechanism-incorporated adaptive-network-based fuzzy inference system (MI+ANFIS) to establish the scroll expander model. In this method, to fully utilize the mechanism characteristics and improve the prediction performance, we firstly identify the mechanistic model parameters based on the least squares method. Then, the ANFIS is adopted to construct residual prediction model according to the residual errors from the mechanistic model. The final forecasting outputs of the MI+ANFIS model are obtained by combining the mechanistic model and the ANFIS model. Experiments on forecasting the volume flow rate and torque of the scroll expander are taken separately. To demonstrate the superiorities of the proposed MI+ANFIS, it is compared with several other popular models, including the ANFIS, the extreme learning machine (ELM), the back-propagation neural network (BPNN), and the support vector regression (SVR), while with the MI+ELM, the MI+BPNN and the MI+SVR. Experimental results indicated that the proposed MI+ANFIS exhibits higher accuracy and greater robustness due to the consideration of the mechanistic properties.

Documents disponibles

Format PDF

Pages : 32-46

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 : Accurate modelling of the scroll expander via a mechanism-incorporated data-driven method.
  • Identifiant de la fiche : 30031988
  • Langues : Anglais
  • Sujet : Technologie
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 155
  • Date d'édition : 11/2023
  • DOI : http://dx.doi.org/10.1016/j.ijrefrig.2023.09.005

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