000863819 001__ 863819
000863819 005__ 20210130002326.0
000863819 0247_ $$2doi$$a10.1016/j.firesaf.2019.102835
000863819 0247_ $$2ISSN$$a0379-7112
000863819 0247_ $$2ISSN$$a1873-7226
000863819 0247_ $$2altmetric$$aaltmetric:63449711
000863819 0247_ $$2WOS$$aWOS:000499949900012
000863819 037__ $$aFZJ-2019-03800
000863819 082__ $$a690
000863819 1001_ $$0P:(DE-Juel1)132044$$aArnold, Lukas$$b0$$eCorresponding author
000863819 245__ $$aApplication cases of inverse modelling with the PROPTI framework
000863819 260__ $$aNew York, NY [u.a.]$$bElsevier$$c2019
000863819 3367_ $$2DRIVER$$aarticle
000863819 3367_ $$2DataCite$$aOutput Types/Journal article
000863819 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1582126362_3364
000863819 3367_ $$2BibTeX$$aARTICLE
000863819 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000863819 3367_ $$00$$2EndNote$$aJournal Article
000863819 520__ $$aThis paper introduces a generalised inverse modelling framework, called PROPTI, with application examples in pyrolysis modelling. It is an open source tool, implemented in the programming language Python. With its generalised formulation, it is tailored to enable communication between arbitrary simulation models, different optimisation algorithms, as well as various experimental data series as optimisation targets. The framework aims to facilitate high performance computing resources, via multi-threading and the Message Passing Interface(MPI), in order to speed up the overall process. As simulation models, Fire Dynamics Simulator (FDS) and OpenFOAM are utilised during the presented work. A mock-up of a thermogravimetric analysis (TGA) is used for verification and shows that PROPTI can be employed to determine material parameter sets from experimental data. In this example, based on an artificial data set, the simulation data converges towards the exact solution. A series of mass loss calorimeter (MLC) tests provide real world examples. Here, the mass loss rates (MLR) of poly(methyl methacrylate) (PMMA) samples, subjected to different irradiance levels, were used. They provide target data sets for two different optimisation algorithms: shuffled complex evolution (SCE-UA) and fitness scaled chaotic artificial bee colony (FSCABC). The resulting, i.e. best fitting, material parameters are used for pyrolysis simulation with FDS. The convergence of the results and the performance of different optimisation strategies are discussed, by comparing the resulting simulation data and convergence series. In order to demonstrate the capability to use simulation tools other than FDS, a mock-up example in OpenFOAM for the steady state simulation of an iron beam, placed under lateral stress, is presented. The PROPTI framework is not limited to time series as target data, but any kind of data sets can be used. Combinations of temporal and spatial data can be used and may open new optimisation targets and approaches for the determination of pyrolysis parameters.
000863819 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000863819 536__ $$0G:(DE-Juel1)jjsc27_20160501$$aPyrolysis and Structural Mechanics (jjsc27_20160501)$$cjjsc27_20160501$$fPyrolysis and Structural Mechanics$$x1
000863819 588__ $$aDataset connected to CrossRef
000863819 7001_ $$0P:(DE-Juel1)174283$$aHehnen, Tristan$$b1
000863819 7001_ $$0P:(DE-HGF)0$$aLauer, Patrick$$b2
000863819 7001_ $$0P:(DE-HGF)0$$aTrettin, Corinna$$b3
000863819 7001_ $$0P:(DE-HGF)0$$aVinayak, Ashish$$b4
000863819 773__ $$0PERI:(DE-600)1483569-1$$a10.1016/j.firesaf.2019.102835$$gVol. 108, p. 102835 -$$p102835 -$$tFire safety journal$$v108$$x0379-7112$$y2019
000863819 909CO $$ooai:juser.fz-juelich.de:863819$$pVDB
000863819 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132044$$aForschungszentrum Jülich$$b0$$kFZJ
000863819 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)174283$$aForschungszentrum Jülich$$b1$$kFZJ
000863819 9131_ $$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$$x0
000863819 9141_ $$y2019
000863819 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bFIRE SAFETY J : 2017
000863819 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000863819 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000863819 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000863819 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000863819 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000863819 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000863819 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000863819 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology
000863819 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000863819 920__ $$lyes
000863819 9201_ $$0I:(DE-Juel1)IAS-7-20180321$$kIAS-7$$lZivile Sicherheitsforschung$$x0
000863819 9201_ $$0I:(DE-82)080012_20140620$$kJARA-HPC$$lJARA - HPC$$x1
000863819 980__ $$ajournal
000863819 980__ $$aVDB
000863819 980__ $$aI:(DE-Juel1)IAS-7-20180321
000863819 980__ $$aI:(DE-82)080012_20140620
000863819 980__ $$aUNRESTRICTED