Papers by Hélcio Rangel Barreto Orlande
Numerical Heat Transfer, Part A: Applications, 2015
International Journal for Uncertainty Quantification, 2012
Numerical Heat Transfer, Part A: Applications, 1999
ABSTRACT
Inverse Problems in Science and Engineering, 2007
Inverse Problems in Science and Engineering, 2008
This work considers the detection of the spatial source term distribution in a multidimensional l... more This work considers the detection of the spatial source term distribution in a multidimensional linear diffusion problem with constant (and known) thermal conductivity. This work can be physically associated with the detection of non-homogeneities in a ...
Inverse Problems in Science and Engineering, 2009
Inverse Problems in Science and Engineering, 2014
Thermal contact resistance/conductance is very important in many heat transfer applications, such... more Thermal contact resistance/conductance is very important in many heat transfer applications, such as electronic packaging, nuclear reactors, aerospace and biomedicine, among others. The determination of the thermal contact resistance/ conductance is often a very difficult task. Previously, the authors presented a computationally fast inverse problem methodology to estimate thermal contact conductance without intrusive measurements, in steady-state heat conduction problems. This paper is an extension of such methodology, applied to transient problems. The methodology presented is formulated in terms of a reciprocity functional approach, together with the method of fundamental solutions. The solution is composed of two steps. In the first step, two steady-state auxiliary problems, which do not depend on the thermal conductance, are solved. With the results of the first step, different thermal conductances can then be recovered through an integration process of the boundary data. Thus, the methodology is fast and can be used to detect contact flaws in different materials with small computational effort. Test cases with simulated measurements containing measurement errors are presented in order to illustrate the robustness of the proposed technique.
Inverse Problems in Science and Engineering
Numerical Heat Transfer, Part A: Applications, 2015
ABSTRACT This paper aims to estimate a location- and time-dependent high-magnitude heat flux in a... more ABSTRACT This paper aims to estimate a location- and time-dependent high-magnitude heat flux in a heat conduction problem. The heat flux is applied on a small region of a surface of a flat plate, while transient temperature measurements are taken on the opposite surface. This inverse problem is solved using the Kalman filter and a reduced forward model, obtained by simplifications of a three-dimensional and nonlinear heat conduction problem. To deal with the modeling errors of this reduced model, the Approximation Error Model is used. The results show that excellent estimates can be obtained at feasible computational times.
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2006
This paper presents basic concepts of inverse and optimization problems. Deterministic and stocha... more This paper presents basic concepts of inverse and optimization problems. Deterministic and stochastic minimization techniques in finite and infinite dimensional spaces are revised; advantages and disadvantages of each of them are discussed and a hybrid technique is introduced. Applications of the techniques discussed for inverse and optimization problems in heat transfer are presented.
Volume 2: 31st Computers and Information in Engineering Conference, Parts A and B, 2011
ABSTRACT Sequential Monte Carlo (SMC) or Particle Filter Methods, which have been originally intr... more 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.
Heat Transfer, Volume 1, 2003
Page 1. 1 Copyright © 2003 by ASME Proceedings of IMECE'03 2003 ASME Internation... more Page 1. 1 Copyright © 2003 by ASME Proceedings of IMECE'03 2003 ASME International Mechanical Engineering Congress & Exposition Washington, DC, USA, November 16-21, 2003 IMECE2003-42058 A COMPARISON ...
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Papers by Hélcio Rangel Barreto Orlande