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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(More)
While the mass of refrigerant contained in a vapor compression cycle has a significant effect on the cycle's power consumption, conventional cycle architectures cannot optimize energy efficiency by varying the mass as ambient conditions and operational requirements change. This paper proposes a new system architecture that allows the refrigerant mass(More)
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