Date of Award

12-2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Mechanical and Civil Engineering

First Advisor

Xingjian Wang

Second Advisor

Daniel Kirk

Third Advisor

Chelakara Subramanian

Fourth Advisor

Ashok Pandit

Abstract

Peng-Robinson cubic equations of state (PR-EoSs) as one of the most popular two-parameter cubic equations of state (2P-EoSs) are widely used to calculate thermodynamic properties of pure substances and their mixtures. However, the prediction accuracy of 2P-EoSs varies significantly among different substances due to its intrinsic limitation. To this end, many modifications have focused on changing the dependence structure of ๐›ผ function with temperature for PR-EoS to enhance prediction accuracy. In this paper, we propose a Bayesian framework to calibrate a new ๐›ผ function, which is a bias-corrected parametrized model form for the PR-EoS. The developed PR-EoS with the calibrated ๐›ผ function is applied to evaluate the thermodynamic properties of representative substances, including oxygen, carbon dioxide, and n-decane. Results show that the new developed PR EoS significantly improves the prediction accuracy of densities for the representative substances when compared to the original PR EoS.

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