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Reconstruction of buried thermal sources from surface temperature fields by Bayesian inference

dc.rights.licenseopenen_US
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorBOUE, Hugo
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorGROZ, Marie-Marthe
IDREF: 228324599
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorMEZIANE, Anissa
IDREF: 121291421
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorABISSET, Emmanuelle
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorGIREMUS, Audrey
IDREF: 163238766
dc.date.accessioned2023-12-20T09:21:13Z
dc.date.available2023-12-20T09:21:13Z
dc.date.issued2023-01-01
dc.identifier.issn1940-2503en_US
dc.identifier.urioai:crossref.org:10.1615/computthermalscien.2023049325
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/186748
dc.description.abstractEnEarly detection of defects (e.g., cracks, delamination in composites, defects in adhesive bonds) is critical to prevent potential accidents in industrial environments. In this context, nondestructive evaluation or testing (NDT/E) is essential to avoid such disastrous failure. Among nondestructive testing techniques, sonothermography (also called vibrothermography) comprises coupling powerful ultrasounds and thermal imaging to induce material defects and evaluate the extent of the damage. In the presence of a defect, the mechanical energy induced by the ultrasonic waves is converted into thermal energy, resulting in the buried defect being converted into a thermal source. By studying the resulting thermal and mechanical fields, inverse methods have been developed to reconstruct these heat sources, enabling the evaluation of physical properties for materials or defects. Here, we focus on the reconstruction of volumic sources in material regardless of their origin from thermal imaging registered at the surface of the material using a Bayesian approach denoted "GB". This problem is notably ill-posed and requires the use of specific tools to ensure this reconstruction from noisy experimental data. We consider prior modelling in the form of a mixture of distributions, which promote spatial sparsity and thereby regularize the inference problem. This method enables the reconstruction of sources with high fidelity, including the estimation of this intensity, thus overcoming the limitations mentioned in previous works. Its performance is discussed in relation to noise, and it is compared to other methods, showing favourable potential for NDT/E applications.
dc.language.isoENen_US
dc.sourcecrossref
dc.title.enReconstruction of buried thermal sources from surface temperature fields by Bayesian inference
dc.typeArticle de revueen_US
dc.identifier.doi10.1615/computthermalscien.2023049325en_US
dc.subject.halSciences de l'ingénieur [physics]en_US
bordeaux.journalComputational Thermal Sciences: An International Journalen_US
bordeaux.hal.laboratoriesIMS : Laboratoire de l'Intégration du Matériau au Système - UMR 5218en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionCNRSen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcedissemin
hal.identifierhal-04355250
hal.version1
hal.date.transferred2023-12-20T09:21:15Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
workflow.import.sourcedissemin
dc.rights.ccPas de Licence CCen_US
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