Bayesian approach for thermal diffusivity mapping from infrared images with spatially random heat pulse heating

Fudym, Olivier; Orlande, Helcio R. B.; Bamford, Matthieu; Batsale, Jean Christophe

doi:10.1088/1742-6596/135/1/012042

Cited by 30 publications

(17 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Figure (2)(3)(4) shows the sampling distribution for k, h and respectively at standard deviation ±0.3K obtained using simple MCMC algorithm and from these figures it is clear that the sampling distribution for all parameters are very close to prior distribution. This parameter estimation problem is repeated again using the present modified MCMC algorithm and the resulting sampling distributions for all parameters at standard deviation ±0.3K are shown in Fig.…”

Section: Resultsmentioning

confidence: 82%

Updating Prior Parameters Based on Likelihood Function-Bayesian Method for Parameter Estimation at High Measurement Uncertainty

Somasundharam¹,

Reddy²

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

View full text Add to dashboard Cite

Abstract. One approach in statistical analysis that distinguishes between frequentist and Bayesian is the inclusion of available prior information about the data even before measuring/ surveying the data. Many researchers argued that the inclusion of prior information resulted in a better model prediction/parameter estimation. Bayesian inference is repeatedly used in inverse problems to retrieve parameters due to the development of high efficient sampling algorithms such as Markov Chain Monte-Carlo (MCMC). Inverse problems are generally ill-posed in nature. Nevertheless, the inclusion of prior information reduces the ill-posedness of the problem to an extent. Any inverse problem relies on measured data by physical sensors, therefore induced random errors greatly affects the quality of estimation. When the uncertainty of the measured data is high, the inferences made from the resulting sampling distributions are nearly the same as the supplied prior information. The reason is that, probability of samples nearer to prior information is more at higher uncertainties of measurement and has more chance to get repeatedly accepted than the samples that are close to actual value. Therefore, in this work an effort is made to update the prior hyper parameters in each iterations of the MCMC algorithm based on the history of the likelihood function. The applicability of method is demonstrated by retrieving parameters from 1-D fin experiment. Three thermal properties such as thermal conductivity, heat transfer coefficient and emissivity are retrieved simultaneously. The estimation is carried out for both error and errorless temperature measurements and the results show that, the estimated parameters with the proposed method are in excellent agreement with the true parameter value and a maximum of 7% deviation occurs in estimating heat transfer coefficient at a measurement error of ±0.3K 4073

show abstract

Section: Resultsmentioning

confidence: 82%

Updating Prior Parameters Based on Likelihood Function-Bayesian Method for Parameter Estimation at High Measurement Uncertainty

Somasundharam¹,

Reddy²

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

View full text Add to dashboard Cite

show abstract

“…A superfície inferior do canalé simétrica com respeito ao eixo x. O problema definido acima foi resolvido numericamente e sua solução verificada contra resultados benchmark encontrados na literatura [38,39] para a função corrente na parede:…”

Section: Resultsunclassified

Problemas Inversos em Transferência de Calor

2011

Notas Em Matemática Aplicada

View full text Add to dashboard Cite

A Sociedade Brasileira de Matemática Aplicada e Computacional -SBMAC publica, desde as primeiras edições do evento, monografias dos cursos que são ministrados nos CNMAC.Para a comemoração dos 25 anos da SBMAC, que ocorreu durante o XXVI CNMAC, foi criada a série Notas em Matemática Aplicada para publicar as monografias dos minicursos ministrados nos CNMAC, o que permaneceu até o XXXIII CNMAC em 2010.A partir de 2011, a série passa, também, a publicar livros nasáreas de interesse da SBMAC. Os autores que submeterem textosà série Notas em Matemática Aplicada devem estar cientes de que poderão ser convidados a ministrarem minicursos nos eventos patrocinados pela SBMAC, em especial nos CNMAC, sobre assunto a que se refere o texto.O livro deve ser preparado em Latex (compatível com o Miktex versão 2.7), as figuras em eps e deve ter entre 80 e 150 páginas. O texto deve ser redigido de forma clara, acompanhado de uma excelente revisão bibliográfica e de exercícios de verificação de aprendizagem ao final de cada capítulo.Veja outros títulos publicados em formato e-book na página

show abstract

“…A linear predictive error model is chosen herein for the estimation procedure [7,8]. The main point in this approach is that the sensitivity matrix is directly computed with the experimental data obtained from the tracking algorithm, as shown in Eq.…”

Section: Heat Transfer Coefficient Estimation With a Total Least Squamentioning

confidence: 99%

“…This study was then recovered and developed by Bamford et al [7]: The texture analysis of the images was replaced by the application of a spatial periodic thermal marker for the thermal diffusivity and velocity field mapping. Alternatively, a spatial random mask was also proposed [8]. The main advantage in comparison with the PIV techniques is that the marker is nonintrusive.…”

Section: Introductionmentioning

confidence: 99%

Infrared Tracking From Morphological Image Processing Tools—Application to Heat Transfer Characterization in Granular Media

Sepúlveda¹,

Fudym²

2011

Heat Transfer Engineering

View full text Add to dashboard Cite

The main objective of this study is to track some moving objects in an infrared image sequence and retrieve their temperature field. We first implement some tracking techniques commonly used in the computer vision field. Unfortunately, trackers of this kind are not directly well suited for thermal frame monitoring, since heat diffusion yields both light level changes and threedimensional effects. The proposed algorithms are thus adapted and then tested for velocity measurements in real experiments involving the stochastic moving of spheres on an oscillating plate. Circle Hough transforms, digital image correlation, and mathematical morphology tools are considered and their efficiency is evaluated. The labeling of the moving objects is then implemented in order to retrieve and discriminate the thermal field of each target within a lagrangian approach. Heat transfer between the moving object and the surrounding medium can then be characterized: In the case presented herein, the convective heat transfer coefficient is estimated.

show abstract

Bayesian approach for thermal diffusivity mapping from infrared images with spatially random heat pulse heating

Cited by 30 publications

References 6 publications

Updating Prior Parameters Based on Likelihood Function-Bayesian Method for Parameter Estimation at High Measurement Uncertainty

Updating Prior Parameters Based on Likelihood Function-Bayesian Method for Parameter Estimation at High Measurement Uncertainty

Problemas Inversos em Transferência de Calor

Infrared Tracking From Morphological Image Processing Tools—Application to Heat Transfer Characterization in Granular Media

Contact Info

Product

Resources

About