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@ARTICLE{Arnold:863819,
      author       = {Arnold, Lukas and Hehnen, Tristan and Lauer, Patrick and
                      Trettin, Corinna and Vinayak, Ashish},
      title        = {{A}pplication cases of inverse modelling with the {PROPTI}
                      framework},
      journal      = {Fire safety journal},
      volume       = {108},
      issn         = {0379-7112},
      address      = {New York, NY [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2019-03800},
      pages        = {102835 -},
      year         = {2019},
      abstract     = {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.},
      cin          = {IAS-7 / JARA-HPC},
      ddc          = {690},
      cid          = {I:(DE-Juel1)IAS-7-20180321 / $I:(DE-82)080012_20140620$},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / Pyrolysis and Structural Mechanics
                      $(jjsc27_20160501)$},
      pid          = {G:(DE-HGF)POF3-511 / $G:(DE-Juel1)jjsc27_20160501$},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000499949900012},
      doi          = {10.1016/j.firesaf.2019.102835},
      url          = {https://juser.fz-juelich.de/record/863819},
}