Learning Centre

Uncertainty management for the probabilistic simulation of the thermal resistance of fire barriers

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Paudel, Deepak
dc.date.accessioned 2020-11-11T10:00:10Z
dc.date.available 2020-11-11T10:00:10Z
dc.date.issued 2020
dc.identifier.isbn 978-952-64-0136-2 (electronic)
dc.identifier.isbn 978-952-64-0135-5 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/47577
dc.description Defence: 26.11.2020 12:00 – 16:00 Online https://aalto.zoom.us/j/63613284138
dc.description.abstract Due to the inadequate availability of engineering tools, the conventional test-based method, currently, regulates the design of fire barriers. This thesis aims to provide a supporting simulation-based approach. The objective is to develop numerical models and a simulation framework for the prediction of fire barrier thermal resistance under uncertain fire load and material conditions. The main challenges are the complexity of the thermal behaviour of fibrous barriers to be simulated, the high computational cost of the stochastic simulations accounting for the input uncertainties, and the propagation of model uncertainty to the output distribution. To predict the thermal behaviour of the stone wool, I present a multiphysics model of a fibrous layer, capable of tracking the heat transfer, chemical decomposition and oxygen transfer. As an alternative, I use heat conduction -based model with reaction kinetics coupled to the stone wool's organic content. The results show that the exothermic oxidation of the stone wool's organic matter is responsible for the observed peaks in the cold-side surface temperatures, but the amount of released energy and the height of these temperature peaks are limited by the unavailability of oxygen in stone wools with high organic content. To reduce the computational burden of the probabilistic fire barrier resistance evaluation, I present the use of the Response Surface Method (RSM) and Gaussian Process (GP) regression. The results show that the simple polynomial-based RSM approximation fails when the heat transfer is affected by exothermic reactions. Fortunately, this is in contrast with GP, where the kernel combination made the approximation possible even for such a case. Alongside, I studied the propagation of modelling uncertainty to the predicted output distributions using various examples: compartment fire experiment, stone wool thermal resistance test, a chain of two models, and meta-model based analysis. I propose a simple method of eliminating the propagated modelling uncertainty from the stochastically simulated output distribution. The results show that the proposed method effectively corrects the outputs if the uncertainty correction metric well represents the model uncertainty of the investigated stochastic analysis scenario. The illustrated examples mostly use normal or uniform input distributions, but the method is not bound to any distribution type. en
dc.format.extent 70 + app. 88
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 186/2020
dc.relation.haspart [Publication 1]: Paudel, D. and Hostikka, S., 2019. Propagation of modelling uncertainty in stochastic heat-transfer simulation using a chain of deterministic models. International Journal for Uncertainty Quantification, 9(1), pp.1-14. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201905062783. DOI: 10.1615/Int.J.UncertaintyQuantification.2018027275
dc.relation.haspart [Publication 2]: Paudel, D. and Hostikka, S., 2019. Propagation of model uncertainty in the stochastic simulations of a compartment fire. Fire Technology, 55(6), pp.2027-2054. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201905062860. DOI: 10.1007/s10694-019-00841-9
dc.relation.haspart [Publication 3]: Paudel, D., Rinta-Paavola, A., Mattila, HP., and Hostikka, S., 2020. Multiphysics modelling of stone wool fire resistance. Fire Technology, pp.1-30. DOI: 10.1007/s10694-020-01050-5
dc.relation.haspart [Publication 4]: Paudel, D., and Hostikka, S., 2020. Meta-model based stochastic simulation of fire barrier cold-side temperature. Fire Safety Journal, pp.1-12. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202008214834. DOI: 10.1016/j.firesaf.2020.103175
dc.subject.other Civil engineering en
dc.title Uncertainty management for the probabilistic simulation of the thermal resistance of fire barriers en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Insinööritieteiden korkeakoulu fi
dc.contributor.school School of Engineering en
dc.contributor.department Rakennustekniikan laitos fi
dc.contributor.department Department of Civil Engineering en
dc.subject.keyword fire barriers en
dc.subject.keyword uncertainty propagation en
dc.subject.keyword heat transfer en
dc.identifier.urn URN:ISBN:978-952-64-0136-2
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.contributor.supervisor Hostikka, Simo, Assoc. Prof., Aalto University, Department of Civil Engineering, Finland
dc.opn Assistant Professor Ruben Van Coile, Ghent University, Belgium
dc.contributor.lab Civil Engineering (ENG030Z) en
dc.rev Assistant Professor Ruben Van Coile, Ghent University, BelgiumDr David Lange, University of Queensland, Australia
dc.date.defence 2020-11-26
local.aalto.acrisexportstatus checked 2020-12-28_1745
local.aalto.formfolder 2020_11_10_klo_12_29
local.aalto.archive yes

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search archive

Advanced Search

article-iconSubmit a publication