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University of Pittsburgh

The conditions where a fluid mixture exists as a pure vapor or pure liquid are important parameters in the design of a thermodynamic cycle. The temperatures, pressures, and compositions are used to determine the best working fluids and optimal operating parameters required to improve cycle performance, and consequently, energy efficiency. Numerical simulations are often used for the calculation of this data to avoid extremely time-consuming experimental measurement. These simulations rely on vapor-liquid equilibrium (VLE) models, which are sets of equations that seek to describe the physical state of a fluid mixture. However, due to the existence of a huge number of vastly different VLE models, there is a gap in the understanding of their predictive ability. As a result, it can be very difficult to choose a model that will give reasonable data in an acceptable amount of time. This research seeks to distinguish the level to which different VLE models either predict or correlate data in order to simplify this decision. To this end, a working fluid mixture of water and ethanol was simulated using many common VLE models and each model was evaluated in terms of both predictive and correlative accuracy. It was observed that as correlative ability increased, predictive accuracy decreased. This knowledge can help guide a researcher in choosing the best method to calculate mixture properties for the optimization of thermodynamic cycles.

The conditions where a fluid mixture exists as a pure vapor or pure liquid are important parameters in the design of a thermodynamic cycle. The temperatures, pressures, and compositions are used to determine the best working fluids and optimal operating parameters required to improve cycle performance, and consequently, energy efficiency. Numerical simulations are often used for the calculation of this data to avoid extremely time-consuming experimental measurement. These simulations rely on vapor-liquid equilibrium (VLE) models, which are sets of equations that seek to describe the physical state of a fluid mixture. However, due to the existence of a huge number of vastly different VLE models, there is a gap in the understanding of their predictive ability. As a result, it can be very difficult to choose a model that will give reasonable data in an acceptable amount of time. This research seeks to distinguish the level to which different VLE models either predict or correlate data in order to simplify this decision. To this end, a working fluid mixture of water and ethanol was simulated using many common VLE models and each model was evaluated in terms of both predictive and correlative accuracy. It was observed that as correlative ability increased, predictive accuracy decreased. This knowledge can help guide a researcher in choosing the best method to calculate mixture properties for the optimization of thermodynamic cycles.

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Presented by IGERT.org.

Funded by the National Science Foundation.

Copyright 2021 TERC.

Presented by IGERT.org.

Funded by the National Science Foundation.

Copyright 2021 TERC.

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