000844684 001__ 844684
000844684 005__ 20210129233035.0
000844684 020__ $$a978-91-88695-48-2
000844684 0247_ $$2Handle$$a2128/17755
000844684 037__ $$aFZJ-2018-02069
000844684 041__ $$aEnglish
000844684 1001_ $$0P:(DE-HGF)0$$aBerchtold, Florian$$b0
000844684 1112_ $$aEighth International Symposium on Tunnel Safety and Security$$cBorås$$d2018-03-14 - 2018-03-16$$gISTSS 2018$$wSweden
000844684 245__ $$aRisk Analysis for Road Tunnels – A Metamodel to Efficiently Integrate Complex Fire Scenarios
000844684 260__ $$aStockholm$$bRISE Research Institutes of Sweden AB$$c2018
000844684 29510 $$aProceedings from the 8th International Symposium on Tunnel Safety and Security
000844684 300__ $$a349 - 360
000844684 3367_ $$2ORCID$$aCONFERENCE_PAPER
000844684 3367_ $$033$$2EndNote$$aConference Paper
000844684 3367_ $$2BibTeX$$aINPROCEEDINGS
000844684 3367_ $$2DRIVER$$aconferenceObject
000844684 3367_ $$2DataCite$$aOutput Types/Conference Paper
000844684 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1522141509_7129
000844684 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
000844684 520__ $$aFires in road tunnels constitute complex scenarios with interactions between the fire, tunnel users and safety measures. More and more methodologies for risk analysis quantify the consequences of these scenarios with complex models. Examples for complex models are the computational fluid dynamics model Fire Dynamics Simulator (FDS) and the microscopic evacuation model FDS+Evac. However, the high computational effort of complex models often limits the number of scenarios in practice. To balance this drawback, the scenarios are often simplified. Accordingly, there is a challenge to consider complex scenarios in risk analysis.To face this challenge, we improved the metamodel used in the methodology for risk analysis presented on ISTSS 2016. In general, a metamodel quickly interpolates the consequences of few scenarios simulated with the complex models to a large number of arbitrary scenarios used in risk analysis. Now, our metamodel consists of the projection array-based design, the moving least squares method, and the prediction interval to quantify the metamodel uncertainty. Additionally, we adapted the projection array-based design in two ways: the focus of the sequential refinement on regions with high metamodel uncertainties; and the combination of two experimental designs for FDS and FDS+Evac.To scrutinise the metamodel, we analysed the effects of three sequential refinement steps on the metamodel itself and on the results of risk analysis. We observed convergence in both after the second step (ten scenarios in FDS, 192 scenarios in FDS+Evac). In comparison to ISTSS 2016, we then ran 20 scenarios in FDS and 800 scenarios in FDS+Evac. Thus, we reduced the number of scenarios remarkably with the improved metamodel. In conclusion, we can now efficiently integrate complex scenarios in risk analysis. We further emphasise that the metamodel is broadly applicable on various experimental or modelling issues in fire safety engineering.
000844684 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000844684 7001_ $$0P:(DE-HGF)0$$aKnaust, Christian$$b1
000844684 7001_ $$0P:(DE-Juel1)132044$$aArnold, Lukas$$b2$$eCorresponding author$$ufzj
000844684 7001_ $$0P:(DE-HGF)0$$aThöns, Sebastian$$b3
000844684 7001_ $$0P:(DE-HGF)0$$aRogge, Andreas$$b4
000844684 8564_ $$uhttps://juser.fz-juelich.de/record/844684/files/Berchtold_ISTSS2018.pdf$$yOpenAccess
000844684 8564_ $$uhttps://juser.fz-juelich.de/record/844684/files/Berchtold_ISTSS2018.gif?subformat=icon$$xicon$$yOpenAccess
000844684 8564_ $$uhttps://juser.fz-juelich.de/record/844684/files/Berchtold_ISTSS2018.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
000844684 8564_ $$uhttps://juser.fz-juelich.de/record/844684/files/Berchtold_ISTSS2018.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
000844684 8564_ $$uhttps://juser.fz-juelich.de/record/844684/files/Berchtold_ISTSS2018.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
000844684 909CO $$ooai:juser.fz-juelich.de:844684$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000844684 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132044$$aForschungszentrum Jülich$$b2$$kFZJ
000844684 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000844684 9141_ $$y2018
000844684 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000844684 920__ $$lyes
000844684 9201_ $$0I:(DE-Juel1)IAS-7-20180321$$kIAS-7$$lZivile Sicherheitsforschung$$x0
000844684 980__ $$acontrib
000844684 980__ $$aVDB
000844684 980__ $$aUNRESTRICTED
000844684 980__ $$acontb
000844684 980__ $$aI:(DE-Juel1)IAS-7-20180321
000844684 9801_ $$aFullTexts