Conference Presentation (After Call)

FZJ-2016-07835

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

Abstract: In this work the performance of optimization algorithms for inferring material parameters for re modeling from bench scale tests is compared to each other. The well known Shu ed Complex Evolution algorithm (SCE) is compared to Arti cial Bee Colony algorithm (ABC) and Fitness Scaled Chaotic Arti cial Bee Colony algorithm (FSCABC). First, these algorithms are tested with synthetic data, where all the properties are certain in advance. After that, the algorithms are tested with real data gained from bench scale tests, namely thermogravimetric analysis (TGA) and mass loss calorimeter (MLC). Fire Dynamics Simulator (FDS) with its implemented pyrolysis model is used to carry out the simulations in an automated optimization framework on a high performance computing cluster in parallel. The achieved results show which of the compared optimization strategies perform better than SCE related to e ciency 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 2016

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