TR2016-107

Kernel Regression for the Approximation of Heat Transfer Coefficients


    •  Laughman, C.R., Qiao, H., Nikovski, D.N., "Kernel Regression for the Approximation of Heat Transfer Coefficients", IIR-Gustav Lorentzen Conference on Natural Working Fluids (GL), August 2016.
      BibTeX TR2016-107 PDF
      • @inproceedings{Laughman2016aug,
      • author = {Laughman, Christopher R. and Qiao, Hongtao and Nikovski, Daniel N.},
      • title = {Kernel Regression for the Approximation of Heat Transfer Coefficients},
      • booktitle = {IIR-Gustav Lorentzen Conference on Natural Working Fluids (GL)},
      • year = 2016,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2016-107}
      • }
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  • Research Area:

    Data Analytics

Abstract:

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.