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Application of a Bayesian Filter to Estimate Unknown Heat Fluxes in a Natural Convection Problem

[+] Author Affiliations
Marcelo J. Colaço, Helcio R. B. Orlande, Wellington B. da Silva

Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil

George S. Dulikravich

Florida International University, Miami, FL

Paper No. DETC2011-47652, pp. 425-434; 10 pages
doi:10.1115/DETC2011-47652
From:
  • ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 31st Computers and Information in Engineering Conference, Parts A and B
  • Washington, DC, USA, August 28–31, 2011
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5479-2
  • Copyright © 2011 by ASME

abstract

Sequential Monte Carlo (SMC) or Particle Filter Methods, which have been originally introduced in the beginning of the 50’s, became very popular in the last few years in the statistical and engineering communities. Such methods have been widely used to deal with sequential Bayesian inference problems in fields like economics, signal processing, and robotics, among others. SMC Methods are an approximation of sequences of probability distributions of interest, using a large set of random samples, named particles. These particles are propagated along time with a simple Sampling Importance distribution. Two advantages of this method are: they do not require the restrictive hypotheses of the Kalman filter, and can be applied to nonlinear models with non-Gaussian errors. This papers uses a SMC filter, namely the ASIR (Auxiliary Sampling Importance Resampling Filter) to estimate a heat flux in the wall of a square cavity undergoing a natural convection. Measurements, which contain errors, taken at the boundaries of the cavity are used in the estimation process. The mathematical model, as well as the initial condition, are supposed to have some error, which are taken into account in the probabilistic evolution model used for the filter.

Copyright © 2011 by ASME

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