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Application of Hybrid Monte Carlo Algorithm in Heat Transfer
S. Reetik Kumar, B. Konda Reddy,
Published in American Society of Mechanical Engineers (ASME)
2017
Volume: 139
   
Issue: 8
Abstract
This article presents a new method of estimation of thermophysical parameters using the hybrid Monte Carlo (HMC) algorithm that synergistically combines the advantages of a Markov chain Monte Carlo (MCMC) method and molecular dynamics. The advantages of this technique over the conventional MCMC are elucidated by considering the multiparameter estimation in heat transfer. Four situations were analyzed. The first two involve a two- and a three-parameters estimation in a lumped capacitance model, third involves estimation in a distributed system, and the fourth involves estimation in a fin system. The goal is to establish the potency and usefulness of the HMC method for a wide class of engineering problems. Copyright © 2017 by ASME.
About the journal
JournalJournal of Heat Transfer
PublisherAmerican Society of Mechanical Engineers (ASME)
ISSN00221481
Open AccessNo
Concepts (17)
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    Bayesian networks
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    Capacitance
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    Heat transfer
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    Inference engines
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    Inverse problems
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    Markov processes
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    Molecular dynamics
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    Monte carlo methods
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    Bayesian inference
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    CAPACITANCE MODEL
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    Distributed systems
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    Engineering problems
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    HYBRID MONTE CARLO
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    HYBRID MONTE CARLO ALGORITHM
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    Markov chain monte carlo method
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    THERMO-PHYSICAL PARAMETERS
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    Parameter estimation