M
Marcelo J. Colaço
Researcher at Federal University of Rio de Janeiro
Publications - 107
Citations - 1563
Marcelo J. Colaço is an academic researcher from Federal University of Rio de Janeiro. The author has contributed to research in topics: Inverse problem & Heat flux. The author has an hindex of 19, co-authored 96 publications receiving 1358 citations. Previous affiliations of Marcelo J. Colaço include Instituto Militar de Engenharia.
Papers
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Journal ArticleDOI
Inverse and optimization problems in heat transfer
TL;DR: In this article, the basic concepts of inverse and optimization problems are presented, and 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.
Journal ArticleDOI
Hydrous ethanol–gasoline blends – Combustion and emission investigations on a Flex-Fuel engine
Tadeu Cavalcante Cordeiro de Melo,Guilherme Bastos Machado,Carlos Rodrigues Pereira Belchior,Marcelo J. Colaço,José Eduardo Mautone Barros,Edimilson Jesus De Oliveira,Daniel G. de Oliveira +6 more
TL;DR: In this paper, a programmable engine control unit was used to manage engine operation and spark timing calibration for maximum break torque at different speeds and torques, calculated based on in-cylinder pressure curve data, measured by a special data acquisition system.
Journal ArticleDOI
Comparison of different versions of the conjugate gradient method of function estimation
TL;DR: In this article, the inverse problem of estimating the spatial and transient variations of the heat transfer coefficient at the surface of a plate, with no information regarding its functional form, is solved by applying the conjugate gradient method with adjoint problem.
Journal ArticleDOI
Approximation of the likelihood function in the Bayesian technique for the solution of inverse problems
TL;DR: In this article, the use of radial basis functions for the interpolation of the likelihood function in parameter estimation problems was proposed, which reduced the computational cost associated with the implementation of such Markov Chain Monte Carlo (MCMC) methods without loss of accuracy in the estimated parameters.
Book ChapterDOI
A Survey of Basic Deterministic, Heuristic, and Hybrid Methods for Single-Objective Optimization and Response Surface Generation
TL;DR: In this article, the authors propose a method to solve the problem of "10.10.2010.0" and "11.11.2010" in the following order: