Conference Agenda

The Statistical Fluid Associating Theory (SAFT) approach for modeling thermodynamic properties has seen extensive application in recent years, but not as much in the field of refrigeration. Therefore this study investigates whether one type of SAFT model, the SAFT-VR-Mie model, which has a theoretically sound basis and four adjustable parameters per fluid, provides accurate enough thermodynamic properties for use in system analysis and component design. The addition of one of four different polar contributions is also investigated. The conclusion is that for the most difficult refrigerant to model, that of HFC-32, none of the polar models can compete with the multiparameter EOS in terms of accuracy. For less polar molecules like HFC-245fa or HFC-125, the use of polar SAFT-VR-Mie models appears to be a reasonable option, although clear limitations are found in the critical region.

Vapour-liquid-equilibrium (VLE) measurements are often carried out in a static cell of known volume. Typically known masses of pure components are charged to the measuring cell and pressure data generated over a range of temperatures, then the data are used to regress interaction parameters for a thermodynamic model. At low temperatures and pressures a simple model can be used to solve mass and volume balance equations as it can be assumed that most of the mass is in the liquid with negligible amounts in the vapour. However, once you approach the critical temperature of one or more of the refrigerant components then the vapour pressure and density typically increases significantly. As a consequence, it may be difficult to accurately infer the phase compositions from measured pressure alone, as the calculation becomes sensitive to the modelled liquid and vapour densities. A modified PTx cell will be used to enable sampling of the vapour space by gas chromatography (GC) along with a Coriolis meter to measure liquid and vapour density. These additional parameters will be used to assess potential improvement to regression of EoS parameters around the critical region.

Next-generation vapor compression cycles will rely upon multicomponent refrigerant mixtures to reduce the climate impact of the working fluids, but the computation of thermodynamic property data for these mixtures is numerically challenging and often results in non-physical anomalies that are present in the output of standard calculation tools. In this paper, we explore two alternative techniques for mitigating the effect of these anomalous points in a reference dataset. The first of these approaches is based upon heteroscedastic Gaussian processes, and builds a statistical model of the property to identify outliers in the reference data. The second uses an estimation method based upon constrained optimization to first detect these outliers and then compute optimal perturbations to the reference data so that the resulting target dataset satisfies domain-informed constraints on the reference data. We demonstrate the efficacy of these methods in computing a target dataset for the refrigerant R454C that is free of anomalies, and which can then be used to build computationally efficient models for use in dynamic cycle simulations.

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 10.0. 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. Weighted piecewise polynomial regression is a regression technique that uses piecewise polynomial functions to model the relationship between a dependent variable and one or more independent variables. The method partitions the domain of the independent variable into intervals and fits a separate polynomial function to each interval. As for the datasets of thermophysical properties generated by REFPROP, the ranges of independent variables are divided into certain number of intervals, and the polynomial regression is performed for each interval. For the saturated region, the standard polynomial is used, while for the subcooled and superheated regions, the Chebyshev polynomial is used. To achieve better continuity between these intervals, higher weights are applied to the neighborhood of the break points. Also developed is a golden section-based search 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 thermophysical and thermodynamic properties considered for both refrigerants are pressure, temperature, density, specific enthalpy, thermal conductivity, Prandtl number, dynamic viscosity, and surface tension. The refrigerant properties as functions of temperature or pressure are fitted for the saturated liquid and vapor regions, while the properties as a function of pressure and temperature, or specific enthalpy, are considered for the subcooled and superheated regions. The pressure and temperature range considered for R410A is 170 to 4,269 kPa and 233.15 to 338.15 K, and that for R454B is 100 to 4,000 kPa and 223.15 and 338.15 K, respectively. Cross-validation is carried out by fitting the standard polynomial regression to the same data range and comparing the outcome with that of the weighted piecewise polynomial regression. The results from the proposed method show good fitting accuracy with two or more polynomials of relatively lower orders, across the aforementioned ranges of operation. While achieving the same level of fitting accuracy with the maximum of 0.1% relative error, two to three piecewise polynomials of 2 to 5 order result in an average computation time of 1.5~43% of that with a single full-range polynomial of 10 to 20 order. Such improvement in computational efficiency promises the potential benefit of the proposed method in developing real-time or faster HVAC simulations.

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The chemical stability of a refrigerant and lubricant with the materials used in HVAC&R (heating, ventilation air-conditioning and refrigeration) equipment is critical to ensure reliable equipment operation over the lifetime of the equipment. AHRTI (Air-Conditioning Research Technology Institute), with funding from the US Department of Energy Building Technology Office, and NYSERDA (New York State Energy Research & Development Authority) sponsored the second phase of the AHRTI Project 9016 to continue the study of Low GWP (global warming potential) refrigerants. Phase II of this project expanded upon the chemical stability testing with more system materials of construction and included material compatibility of common non-metallic materials used in refrigerant containing systems.

This paper will summarize the highly accelerated life tests (HALT) results for, conducted according to ASHRAE Standard 97 sealed glass tube methodology, R-1233zd(E) with and without mineral oil, R-514A, R-1234ze(E), and R-1234yf with and without POE lubricant, and R-454B, and R-32 with POE lubricant only; evaluations were conducted with various braze alloys, fluxes, metals oxides, surface coatings, tube expansion lubricants, motor varnish and various corrosion resistant materials. The remaining results are summarized and further discussed in the AHRTI 9016 Phase II final report.