Contribution to a conference proceedings

FZJ-2017-01995

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Performance of optimization algorithms for deriving material data from bench scale tests

Lauer, P. (Corresponding author) ; Trettin, C. ; Wittbecker, F.-W. ; Arnold, L.FZJ*

2016

Please use a persistent id in citations: http://hdl.handle.net/2128/13912

Abstract: In this work the performance of optimization algorithms forinferring material parameters for fire modeling from bench scale tests iscompared to each other.The well known Shuffled Complex Evolution algorithm (SCE) is compared to Artificial Bee Colony algorithm (ABC) and Fitness ScaledChaotic Artificial Bee Colony algorithm (FSCABC). First, these algorithms are tested with synthetic data, where all the properties are certainin advance. After that, the algorithms are tested with real data gainedfrom bench scale tests, namely thermogravimetric analysis (TGA) andmass loss calorimeter (MLC). Fire Dynamics Simulator (FDS) with itsimplemented pyrolysis model is used to carry out the simulations in anautomated optimization framework on a high performance computingcluster in parallel. The achieved results show which of the compared optimization strategies perform better than SCE related to efficiency and accuracy.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)

Appears in the scientific report 2017

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Database coverage:

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