Reverse engineering of fluid selection for thermodynamic cycles with cubic equations of state, using a compression heat pump as example. (1st March 2015) - Roskosch, Dennis; Atakan, Burak
Abstract: Fluid selection for thermodynamic cycles like refrigeration cycles, heat pumps or organic Rankine cycles remains an actual topic. Generally the search for a working fluid is based on experimental approaches or on a not very systematic trial and error approach, far from being elegant. An alternative method may be a theory based reverse engineering approach, proposed and investigated here: The design process should start with an optimal process and with (abstract) properties of the fluid needed to fit into this optimal process, best described by some general equation of state and the corresponding fluid-describing parameters. These should be analyzed and optimized with respect to the defined model process, which also has to be optimized simultaneously. From this information real fluids can be selected or even synthesized which have fluid defining properties in the optimum regime like critical temperature or ideal gas capacities of heat, allowing to find new working fluids, not considered so far. The number and kind of the fluid-defining parameters is mainly based on the choice of the used EOS (equation of state). The property model used in the present work is based on the cubic Peng–Robinson equation, chosen due to its moderate numerical expense, sufficient accuracy as well as a general availability of the fluid-defining parameters for many compounds. The considered model-process works between the temperature levels of 273.15 and 333.15 K and can be used as heat pump for supplying buildings with heat, typically. The objective functions are the COP (coefficient of performance) and the VHC (volumetric heating capacity) as a function of critical pressure, critical temperature, acentric factor and two coefficients for the temperature-dependent isobaric ideal gas heat capacity. Also, the steam quality at the compressor entrance has to be regarded as a problem variable. The results give clear hints regarding optimal fluid parameters of the analyzed process and deepen the thermodynamic understanding of the process. Finally, for the COP optimization a strategy for screening large databases is explained. Several fluids from different substance groups like hydrogen iodide (COP = 3.68), formaldehyde (3.61) or cyclopropane (3.42) were found to have higher COPs than the often used R134a (3.12). These fluids will also have to fulfill further criteria, prior to their usage, but the method appears to be a good base for fluid selection. Highlights: A new reverse engineering approach for fluid selection. Able to find less common fluids for cycles. Increased understanding of important fluid parameters. Demonstrated for a heat pump cycle.