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dc.contributor.advisorKeyes, David E.
dc.contributor.authorAmir, Sahar
dc.date.accessioned2013-05-29T08:57:28Z
dc.date.available2014-05-21T00:00:00Z
dc.date.issued2013-05
dc.identifier.doi10.25781/KAUST-10L65
dc.identifier.urihttp://hdl.handle.net/10754/292974
dc.description.abstractWe introduce an efficient thermodynamically consistent technique to extrapolate and interpolate normalized Canonical NVT ensemble averages like pressure and energy for Lennard-Jones (L-J) fluids. Preliminary results show promising applicability in oil and gas modeling, where accurate determination of thermodynamic properties in reservoirs is challenging. The thermodynamic interpolation and thermodynamic extrapolation schemes predict ensemble averages at different thermodynamic conditions from expensively simulated data points. The methods reweight and reconstruct previously generated database values of Markov chains at neighboring temperature and density conditions. To investigate the efficiency of these methods, two databases corresponding to different combinations of normalized density and temperature are generated. One contains 175 Markov chains with 10,000,000 MC cycles each and the other contains 3000 Markov chains with 61,000,000 MC cycles each. For such massive database creation, two algorithms to parallelize the computations have been investigated. The accuracy of the thermodynamic extrapolation scheme is investigated with respect to classical interpolation and extrapolation. Finally, thermodynamic interpolation benefiting from four neighboring Markov chains points is implemented and compared with previous schemes. The thermodynamic interpolation scheme using knowledge from the four neighboring points proves to be more accurate than the thermodynamic extrapolation from the closest point only, while both thermodynamic extrapolation and thermodynamic interpolation are more accurate than the classical interpolation and extrapolation. The investigated extrapolation scheme has great potential in oil and gas reservoir modeling.That is, such a scheme has the potential to speed up the MCMC thermodynamic computation to be comparable with conventional Equation of State approaches in efficiency. In particular, this makes it applicable to large-scale optimization of L-J model parameters for hydrocarbons and other important reservoir species. The efficiency of the thermodynamic dependent techniques is expected to make the Markov chains simulation an attractive alternative in compositional multiphase flow simulation.
dc.language.isoen
dc.subjectmolecular Simulation
dc.subjectMonte Carlo
dc.subjectLenward-Jones Fluid
dc.subjectInterpolation
dc.subjectextrapolation
dc.subjectcanonical ensemble
dc.titleAccelerating Monte Carlo Molecular Simulations Using Novel Extrapolation Schemes Combined with Fast Database Generation on Massively Parallel Machines
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.rights.embargodate2014-05-21
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberMoshkov, Mikhail
dc.contributor.committeememberSun, Shuyu
thesis.degree.disciplineComputer Science
thesis.degree.nameMaster of Science
dc.rights.accessrightsAt the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2014-05-21.
refterms.dateFOA2014-05-21T00:00:00Z


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