Accelerating Monte Carlo Molecular Simulations Using Novel Extrapolation Schemes Combined with Fast Database Generation on Massively Parallel Machines

Handle URI:
http://hdl.handle.net/10754/292974
Title:
Accelerating Monte Carlo Molecular Simulations Using Novel Extrapolation Schemes Combined with Fast Database Generation on Massively Parallel Machines
Authors:
Amir, Sahar Z.
Abstract:
We 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.
Advisors:
Keyes, David E. ( 0000-0002-4052-7224 )
Committee Member:
Moshkov, Mikhail ( 0000-0003-0085-9483 ) ; Sun, Shuyu ( 0000-0002-3078-864X )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Computer Science
Issue Date:
May-2013
Type:
Thesis
Appears in Collections:
Theses; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorKeyes, David E.en
dc.contributor.authorAmir, Sahar Z.en
dc.date.accessioned2013-05-29T08:57:28Z-
dc.date.available2013-05-29T08:57:28Z-
dc.date.issued2013-05en
dc.identifier.urihttp://hdl.handle.net/10754/292974en
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.en
dc.language.isoenen
dc.subjectmolecular Simulationen
dc.subjectMonte Carloen
dc.subjectLenward-Jones Fluiden
dc.subjectInterpolationen
dc.subjectextrapolationen
dc.subjectcanonical ensembleen
dc.titleAccelerating Monte Carlo Molecular Simulations Using Novel Extrapolation Schemes Combined with Fast Database Generation on Massively Parallel Machinesen
dc.typeThesisen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberMoshkov, Mikhailen
dc.contributor.committeememberSun, Shuyuen
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameMaster of Scienceen
dc.person.id118404en
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