Thermo-Mechanical Modeling of Plasterboard Lined Partition Submitted to Fire Load

Sakji, Saoussen; Soize, Christian; Heck, J.-V.

doi:10.1061/40878(202)40

Cited by 1 publication

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References 5 publications

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“…where [G K ] is a random matrix with values in M + m (R) which the probability density function is defined by Equation (19). Obviously it is clear that m = n for the first case and m = n −1 in the second one.…”

Section: Construction Of the Random Matrix [K(t)]mentioning

confidence: 99%

“…An ideal modeling of an LLP subjected to ISO thermal load should be a thermo-hygro-chemico-mechanical modeling. The complexity of such an approach and the important number of parameters which has to be identified led us to choose a thermomechanical approach [19]. In this approach, the hydrous phase is implicitly taken into account in the model as well as in the experimental protocol.…”

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Computational stochastic heat transfer with model uncertainties in a plasterboard submitted to fire load and experimental validation

2008

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SUMMARYThe paper deals with probabilistic modeling of heat transfer throughout plasterboard plates when exposed to an equivalent ISO thermal load. The proposed model takes into account data and model uncertainties. This research addresses a general need to perform robust modeling of plasterboard-lined partition submitted to fire load. The first step of this work concerns the development of an experimental thermo physical identification data base for plasterboard. These experimental tests are carried out by the use of a bench test specially designed within the framework of this research. A computational heat transfer model is constructed using data from the literature and also the identified plasterboard thermophysical properties. The developed mean model constitutes the basis of the computational stochastic heat transfer model that has been constructed employing the nonparametric probabilistic approach. Numerical results are compared to the experimental ones.

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Section: Construction Of the Random Matrix [K(t)]mentioning

confidence: 99%

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confidence: 99%