Surface Transfer Coefficients Estimation for Heat Conduction Problem Using the Bayesian Framework

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 13 May 2022Embargo end date: 01 Jan 2022 English Publisher:Informa UK LimitedJournal:Heat Transfer Engineering, volume 44, pages 391-410 (issn: 0145-7632, eissn: 1521-0537,

Copyright policy )

Authors: Berger, Julien; Legros, Clemence;

doi: 10.1080/01457632.2022.2068217 , 10.48550/arxiv.2206.08371

arXiv: 2206.08371

Surface Transfer Coefficients Estimation for Heat Conduction Problem Using the Bayesian Framework

- Summary
- Subjects
- Metrics

Abstract

This work deals with an inverse two-dimensional nonlinear heat conduction problem to determine the top and lateral surface transfer coefficients. For this, the \textsc{B}ayesian framework with the \textsc{M}arkov Chain \textsc{M}onte \textsc{C}arlo algorithm is used to determine the posterior distribution of unknown parameters. To handle the computational burden, a lumped one-dimensional model is proposed. The lumped model approximations are considered within the parameter estimation procedure thanks to the Approximation Error Model. The experiments are carried out for several configurations of chamber ventilator speed. Experimental observations are obtained through a complete measurement uncertainty propagation. By solving the inverse problem, accurate probability distributions are determined. Additional investigations are performed to demonstrate the reliability of the lumped model, in terms of accuracy and computational gains.

Related Organizations

Grenoble Alpes University
France
CHA University
Korea (Republic of)
Biomolécules : Conception, Isolement, Synthèse
France
Laboratoire des Sciences de l'Ingénieur pour l'Environnement
France
Institut National de l'Energie Solaire
France

View all View all

Keywords

FOS: Computer and information sciences, [SPI.GCIV.CD]Engineering Sciences [physics]/Civil Engineering/Construction durable, [SPI.FLUID]Engineering Sciences [physics]/Reactive fluid environment, inverse heat conduction problem, lumped model, [SPI.GCIV.CD] Engineering Sciences [physics]/Civil Engineering/Construction durable, model reliability, [SPI.GCIV.EC] Engineering Sciences [physics]/Civil Engineering/Eco-conception, [SPI.FLUID] Engineering Sciences [physics]/Reactive fluid environment, Bayesian estimation, [SPI.MAT] Engineering Sciences [physics]/Materials, [SPI.GCIV.RHEA] Engineering Sciences [physics]/Civil Engineering/Rehabilitation, [SPI.MAT]Engineering Sciences [physics]/Materials, 620, Computational Engineering, Finance, and Science (cs.CE), surface heat transfer coefficient, [SPI.GCIV.RHEA]Engineering Sciences [physics]/Civil Engineering/Rehabilitation, [SPI.GCIV.MAT]Engineering Sciences [physics]/Civil Engineering/Matériaux composites et construction, [SPI.GCIV.MAT] Engineering Sciences [physics]/Civil Engineering/Matériaux composites et construction, Computer Science - Computational Engineering, Finance, and Science, [SPI.GCIV.EC]Engineering Sciences [physics]/Civil Engineering/Eco-conception

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	6
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%