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dc.contributor.authorKadoura, Ahmad Salim
dc.contributor.authorSun, Shuyu
dc.contributor.authorSalama, Amgad
dc.date.accessioned2015-08-03T12:05:32Z
dc.date.available2015-08-03T12:05:32Z
dc.date.issued2014-08
dc.identifier.issn00219991
dc.identifier.doi10.1016/j.jcp.2014.03.038
dc.identifier.urihttp://hdl.handle.net/10754/563666
dc.description.abstractAccurate determination of thermodynamic properties of petroleum reservoir fluids is of great interest to many applications, especially in petroleum engineering and chemical engineering. Molecular simulation has many appealing features, especially its requirement of fewer tuned parameters but yet better predicting capability; however it is well known that molecular simulation is very CPU expensive, as compared to equation of state approaches. We have recently introduced an efficient thermodynamically consistent technique to regenerate rapidly Monte Carlo Markov Chains (MCMCs) at different thermodynamic conditions from the existing data points that have been pre-computed with expensive classical simulation. This technique can speed up the simulation more than a million times, making the regenerated molecular simulation almost as fast as equation of state approaches. In this paper, this technique is first briefly reviewed and then numerically investigated in its capability of predicting ensemble averages of primary quantities at different neighboring thermodynamic conditions to the original simulated MCMCs. Moreover, this extrapolation technique is extended to predict second derivative properties (e.g. heat capacity and fluid compressibility). The method works by reweighting and reconstructing generated MCMCs in canonical ensemble for Lennard-Jones particles. In this paper, system's potential energy, pressure, isochoric heat capacity and isothermal compressibility along isochors, isotherms and paths of changing temperature and density from the original simulated points were extrapolated. Finally, an optimized set of Lennard-Jones parameters (ε, σ) for single site models were proposed for methane, nitrogen and carbon monoxide. © 2014 Elsevier Inc.
dc.description.sponsorshipThe work presented in this paper has been supported in part by the project entitled "Simulation of Subsurface Geochemical Transport and Carbon Sequestration" (award number 7000000058), funded by the GRP-AEA Program at KAUST.
dc.publisherElsevier BV
dc.subjectCanonical ensemble
dc.subjectLennard-Jones model
dc.subjectMarkov chain reweighting and reconstruction
dc.subjectMolecular simulation
dc.subjectMonte Carlo
dc.titleAccelerating Monte Carlo molecular simulations by reweighting and reconstructing Markov chains: Extrapolation of canonical ensemble averages and second derivatives to different temperature and density conditions
dc.typeArticle
dc.contributor.departmentComputational Transport Phenomena Lab
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentChemical and Biological Engineering Program
dc.contributor.departmentEarth Science and Engineering Program
dc.identifier.journalJournal of Computational Physics
kaust.personKadoura, Ahmad Salim
kaust.personSun, Shuyu
kaust.personSalama, Amgad


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