Gast ::
| Hauptseite > Publikationsdatenbank > Application cases of inverse modelling with the PROPTI framework > print |
| Hauptseite > Publikationsdatenbank > Application cases of inverse modelling with the PROPTI framework > print |
| 001 | 863819 | ||
| 005 | 20210130002326.0 | ||
| 024 | 7 | _ | |a 10.1016/j.firesaf.2019.102835 |2 doi |
| 024 | 7 | _ | |a 0379-7112 |2 ISSN |
| 024 | 7 | _ | |a 1873-7226 |2 ISSN |
| 024 | 7 | _ | |a altmetric:63449711 |2 altmetric |
| 024 | 7 | _ | |a WOS:000499949900012 |2 WOS |
| 037 | _ | _ | |a FZJ-2019-03800 |
| 082 | _ | _ | |a 690 |
| 100 | 1 | _ | |a Arnold, Lukas |0 P:(DE-Juel1)132044 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Application cases of inverse modelling with the PROPTI framework |
| 260 | _ | _ | |a New York, NY [u.a.] |c 2019 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1582126362_3364 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a This 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. |
| 536 | _ | _ | |a 511 - Computational Science and Mathematical Methods (POF3-511) |0 G:(DE-HGF)POF3-511 |c POF3-511 |f POF III |x 0 |
| 536 | _ | _ | |a Pyrolysis and Structural Mechanics (jjsc27_20160501) |0 G:(DE-Juel1)jjsc27_20160501 |c jjsc27_20160501 |f Pyrolysis and Structural Mechanics |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Hehnen, Tristan |0 P:(DE-Juel1)174283 |b 1 |
| 700 | 1 | _ | |a Lauer, Patrick |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Trettin, Corinna |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Vinayak, Ashish |0 P:(DE-HGF)0 |b 4 |
| 773 | _ | _ | |a 10.1016/j.firesaf.2019.102835 |g Vol. 108, p. 102835 - |0 PERI:(DE-600)1483569-1 |p 102835 - |t Fire safety journal |v 108 |y 2019 |x 0379-7112 |
| 909 | C | O | |p VDB |o oai:juser.fz-juelich.de:863819 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)132044 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)174283 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-511 |2 G:(DE-HGF)POF3-500 |v Computational Science and Mathematical Methods |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
| 914 | 1 | _ | |y 2019 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b FIRE SAFETY J : 2017 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1160 |2 StatID |b Current Contents - Engineering, Computing and Technology |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-7-20180321 |k IAS-7 |l Zivile Sicherheitsforschung |x 0 |
| 920 | 1 | _ | |0 I:(DE-82)080012_20140620 |k JARA-HPC |l JARA - HPC |x 1 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-7-20180321 |
| 980 | _ | _ | |a I:(DE-82)080012_20140620 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|