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The optimization of the energy performances of a PMRR by using neural networks.

Number: pap. 132

Author(s) : APREA C., GRECO A., MAIORINO A., et al.

Summary

In recent years, a large number of experimental and numerical studies have highlighted the potential of the permanent magnetic rotary refrigerators (PMRR) than those reciprocating. For a PMMR, it is well known it is possible to obtain the desired performance by contemporary acting on two operational parameters: the mass flow rate and the cycle frequency. Consequently, with the aim to improve the energy performances of an actual PMRR, it is necessary to experience an innumerable amount of operating conditions regarding mass flow rate and cycle frequency. The present work introduces ANNTEO (artificial neural networks technique for
optimization), a technique based on artificial neural networks and able to reduce the number of experiments necessary to define an optimization map for an actual PMRR. The experimental setup and test procedure are here reported to demonstrate the technical soundness of ANNTEO.

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Pages: 7 p.

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Details

  • Original title: The optimization of the energy performances of a PMRR by using neural networks.
  • Record ID : 30019380
  • Languages: English
  • Source: 7th International Conference on Magnetic Refrigeration at Room Temperature (Thermag VII). Proceedings: Turin, Italy, September 11-14, 2016.
  • Publication date: 2016/09/11
  • DOI: http://dx.doi.org/10.18462/iir.thermag.2016.0132

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