- Title
- Airblast variability and fatality risks from a VBIED in a complex urban environment
- Creator
- Marks, Nicholas A.; Stewart, Mark G.; Netherton, Michael D.; Stirling, Chris G.
- Relation
- ARC.DP160100855 http://purl.org/au-research/grants/arc/DP160100855
- Relation
- Reliability Engineering and System Safety Vol. 209, Issue May 2021, no. 107459
- Publisher Link
- http://dx.doi.org/10.1016/j.ress.2021.107459
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2021
- Description
- Explosive blasts and prediction of fatality risks in urban environments is a complicated task due to the variability in blast wave reflection and propagation. The terrorist threats considered in this paper are vehicle-borne improvised explosive devices (VBIED) containing 225 kg or 450 kg of TNT or ammonium nitrate fuel oil (ANFO) detonated in an open street. This paper uses Viper::Blast CFD software to estimate the variability of explosive blast loads using Monte-Carlo sampling. To probabilistically model the blast wave, the paper takes into consideration the variability of explosive charge mass, detonation location, height of detonation, net equivalent quantity, atmospheric pressure and temperature, and model errors. The fatality risk assessment combines lung-rupture, whole-body displacement and skull fracture dependant on the pressure and impulse. It was found that the mean fatality risk for a 450 kg home-made ANFO explosive device detonated at a road T-intersection is 16% for people exposed in the street. If bollards were placed 10 m from the main street then fatality risk for people in the main street is reduced by over 90%. It was found that a deterministic analysis yielded fatality risks 10–60% higher than a probabilistic analysis, leading to an overly conservative assessment of safety risks.
- Subject
- airblast; uncertainty; fatality; risk; terrorism; prohabilistic modelling; SDG 11; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1461897
- Identifier
- uon:46330
- Identifier
- ISSN:0951-8320
- Language
- eng
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