2016
DOI: 10.1177/1744259116649405
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Estimation of temperature-dependent thermal conductivity using proper generalised decomposition for building energy management

Abstract: A proper generalised decomposition for solving inverse heat conduction problems is proposed in this article as an innovative method offering important numerical savings. It is based on the solution of a parametric problem, considering the unknown parameter as a coordinate of the problem. Then, considering this solution, all sets of cost function can be computed as a function of the unknown parameter of the defined domain, identifying the argument that minimises the cost function. In order to illustrate the app… Show more

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Cited by 9 publications
(6 citation statements)
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“…A normal distribution with a standard deviation of σ = 0.01 was assumed. The inverse problem is solved using Levenberg-Marquardt method [6,21,33]. After the solution of the N e × 30 inverse problems, the empirical mean square error is computed: where P • is the estimated parameter by the resolution of the inverse problem.…”
Section: Verification Of the Odementioning
confidence: 99%
“…A normal distribution with a standard deviation of σ = 0.01 was assumed. The inverse problem is solved using Levenberg-Marquardt method [6,21,33]. After the solution of the N e × 30 inverse problems, the empirical mean square error is computed: where P • is the estimated parameter by the resolution of the inverse problem.…”
Section: Verification Of the Odementioning
confidence: 99%
“…. , 2 10 , 2 11 . Two dense layers are appended after the TCN block, and the encoded latent dimension is n l = 100. l 2 regularization with a penalty factor λ = 1e−8 is used in the loss function.…”
Section: New Parameter Predictionmentioning
confidence: 99%
“…The governing equations of the full order model are then projected on to the lower dimensional subspace by choosing an appropriate test basis. Other linear basis construction methods include balanced truncation [4,5], reduced basis methods [6,7,8], rational interpolation [9], and proper generalized decomposition [10,11]. Linear basis ROMs have achieved considerable success in complex problems such as turbulent flows [12,13,14] and combustion instabilities [15,16].…”
Section: Introductionmentioning
confidence: 99%
“…23,24 The construction of the PGD solution is made in an offline phase and leads to a cost-effective evaluation of the numerical model depending on parameters in the online inversion phase. 25,26 The issue of the model evaluation cost is emphasized in Bayesian inference where the posterior density has to be explored to derive useful information on parameter pdfs such as mean, standard deviation, maximum, and marginals. Those quantities require the computation of multidimensional integrals over the parametric space.…”
Section: Introductionmentioning
confidence: 99%