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Computationally efficient property calculation for mixed refrigerants using weighted piecewise polynomial regression.

Number: 2482

Author(s) : OLAOKE A., MU B., LI Y.

Summary

Fast, accurate, and stable thermophysical property calculations of refrigerants are crucial to steady-state and transient simulations of refrigeration and air conditioning systems. Property calculation accounts for most of the simulation time, so faster calculations lead to faster simulations. Typically, property calculation resorts to regression models with the data obtained from a standard database such as REFPROP. While polynomial regression has been well received, when dealing with mixed refrigerants, it tends to end up with high-order models to obtain satisfactory accuracy, thus being computationally expensive. In this paper, we propose a weighted piecewise polynomial regression framework to improve the computational efficiency for property calculations of mixed refrigerants. As for the REFPROP-generated datasets, the ranges of independent variables are divided into certain number of intervals, and the polynomial regression is performed for each interval. To achieve better continuity between these intervals, higher weights are applied to the neighborhood of the break points. Also developed is an algorithm to optimize the choice of junction points, which balances the orders of piecewise polynomials and number of intervals. To evaluate the effectiveness of the proposed work, two mixed refrigerants, R410A and R454B, are used. The results show good fitting accuracy with relatively low-order polynomials across a wide operation range. The piecewise polynomials of 2-5 order result in the average computation time of 0.4~53% of that with a full-range polynomial of 10 -20 order, under the same level of fitting accuracy. Such improvement in computational efficiency promises the potential benefit in developing real-time or faster HVAC simulations.

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  • Original title: Computationally efficient property calculation for mixed refrigerants using weighted piecewise polynomial regression.
  • Record ID : 30033052
  • Languages: English
  • Subject: Technology
  • Source: 2024 Purdue Conferences. 20th International Refrigeration and Air-Conditioning Conference at Purdue.
  • Publication date: 2024/07/17

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