RT Journal Article
T1 Estimating a pressure dependent thermal conductivity coefficient with applications in food technology
A1 Capistrán, Marcos A.
A1 Infante Del Río, Juan Antonio
AB In this paper we introduce a method to estimate a pressure dependent thermal conductivity coefficient arising in a heat diffusion model with applications in food technology. The strong smoothing effect of the corresponding direct problem renders the inverse problem under consideration severely ill-posed. Thus specially tailored methods are necessary in order to obtain a stable solution. Consequently, we model the uncertainty of the conductivity coefficient as a hierarchical Gaussian Markov random field (GMRF) restricted to uniqueness conditions for the solution of the inverse problem established in Fraguela et al. [1]. Furthermore, we propose a Single Variable Exchange Metropolis-Hastings algorithm (SVEMH) to sample the corresponding conditional probability distribution of the conductivity coefficient given observations of the temperature. Sensitivity analysis of the direct problem suggests that large integration times are necessary to identify the thermal conductivity coefficient. Numerical evidence indicates that a signal to noise ratio of roughly 10 cubed suffices to reliably retrieve the thermal conductivity coefficient.
PB Taylor and Francis
SN 1741-5977
YR 2019
FD 2019-06-26
LK https://hdl.handle.net/20.500.14352/12429
UL https://hdl.handle.net/20.500.14352/12429
LA eng
NO “This is an Accepted Manuscript of an article published by Taylor & Francis in Inverse Problems in Science and Engineering on 26-jun-2019, available online: https://www.tandfonline.com/doi/full/10.1080/17415977.2019.1632841.”
NO Ministerio de Economía y Competitividad (MINECO)
NO Universidad Complutense de Madrid
DS Docta Complutense
RD 21 abr 2025