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Kernel regression for the approximation of heat transfer coefficients.

Number: pap. 1025

Author(s) : LAUGHMAN C. R., QIAO H., NIKOVSKI D. N.

Summary

Experimentally-based correlations and other parametric methods for approximating heat transfer coefficients, while popular, have a number of shortcomings that are manifest when they are used in dynamic simulations of thermofluid systems. This paper studies the application of a nonparametric statistical learning technique, known as kernel regression, to the problem of approximating heat transfer coefficients for single-phase and boiling flows for the use in dynamic simulation. This method is demonstrated to accurately predict heat transfer coefficents for subcooled, two-phase, and superheated flows for a finite volume model of a refrigerant pipe, as compared to results obtained from established correlations drawn from the literature.

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Pages: 8 p.

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Details

  • Original title: Kernel regression for the approximation of heat transfer coefficients.
  • Record ID : 30019053
  • Languages: English
  • Source: 12th IIR Gustav Lorentzen Conference on Natural Refrigerants (GL2016). Proceedings. Édimbourg, United Kingdom, August 21st-24th 2016.
  • Publication date: 2016/08/21
  • DOI: http://dx.doi.org/10.18462/iir.gl.2016.1025

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