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dc.contributor.advisorWang, Xingjian
dc.contributor.authorChen, Wei
dc.date.accessioned2021-04-14T23:00:11Z
dc.date.available2021-04-14T23:00:11Z
dc.date.created2020-12
dc.date.issued2020-12
dc.date.submittedDecember 2020
dc.identifier.urihttp://hdl.handle.net/11141/3250
dc.descriptionThesis (M.S.) - Florida Institute of Technology, 2020.en_US
dc.description.abstractPeng-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 𝛼 function with temperature for PR-EoS to enhance prediction accuracy. In this paper, we propose a Bayesian framework to calibrate a new 𝛼 function, which is a bias-corrected parametrized model form for the PR-EoS. The developed PR-EoS with the calibrated 𝛼function 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.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCC BY NC 4.0en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_US
dc.titleA Modified Peng-Robinson Cubic Equation of State Based on Bayesian Frameworken_US
dc.typeThesisen_US
dc.date.updated2021-01-27T21:53:35Z
thesis.degree.nameMasters of Science in Mechanical Engineeringen_US
thesis.degree.levelMastersen_US
thesis.degree.disciplineMechanical Engineeringen_US
thesis.degree.departmentMechanical and Civil Engineeringen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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Except where otherwise noted, this item's license is described as CC BY NC 4.0