Improved blast capacity of pre-engineered metal buildings using coupled CFD and FEA modeling

Current industry accepted methods for predicting structural blast damage to pre-engineered metal buildings (PEMBs) subject to vapor cloud explosions are quick and cost-effective to perform. However, they also tend to be overly conservative; while costs may be saved on analysis, the results may promp...

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Bibliographic Details
Published inJournal of loss prevention in the process industries Vol. 56; pp. 486 - 497
Main Authors Milner, David, Wesevich, James, Nikodym, Lisa, Nasri, Vincent, Lawver, Darell, Mould, John
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.11.2018
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Summary:Current industry accepted methods for predicting structural blast damage to pre-engineered metal buildings (PEMBs) subject to vapor cloud explosions are quick and cost-effective to perform. However, they also tend to be overly conservative; while costs may be saved on analysis, the results may prompt unnecessary mitigation. This paper investigates the effectiveness of using more advanced computational techniques with increasing levels of refinement and sophistication to produce more realistic estimates of structural damage and corresponding human injury. The goal is to reduce the amount of over-conservatism in results and avoid the degree of costly mitigation measures that may be unnecessary. In this study, a typical PEMB is selected and assessed for selected loads using a variety of approaches of increasing analytical sophistication. The extent of building damage is determined by each method and the results are compared to demonstrate the benefit of each analytical method. The use of refined analytical methods is shown to estimate significantly less damage in the PEMB for the loads considered. •Finite element analysis predicted dramatically better performance than simplified methods.•Consideration of internal air pressure can help improve building performance.•Consideration of more representative blast waveforms can help improve building performance.
ISSN:0950-4230
DOI:10.1016/j.jlp.2018.10.008