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Simulation-based predicting the position of road tank explosions. Part I: data and models

    Egidijus R. Vaidogas Affiliation
    ; Lina Linkutė Affiliation
    ; Dainius Stulgys Affiliation

Abstract

Road tankers used for the transportation of flammable liquids and liquefied gases can be involved in accidents which escalate into fires and the so-called boiling liquid expanding vapour explosions. The damaging effects of these phenomena on roadside property depend on the position and orientation of exploding tanks in relation to vulnerable roadside objects. This study presents a simulation-based approach to the prediction of the position of road tank explosions. The position is expressed by longitudinal and transverse rest position of an exploding tank as well as departure angle of the tank. As a part of this study, data on transverse rest position and departure angle was collected and used to fit probability distributions which express uncertainties in these circumstantial characteristics of road tank accidents. It was found that data on the longitudinal rest position is difficult to obtain and modelling this accident characteristic will have to rely on a subjective specification of probability distributions. Such distributions can be chosen by applying approaches used in the field of quantitative risk assessment. Probability distributions, partly subjective and partly based on hard data, are applied to simulate values of potential explosion coordinates. The simulation results have the premise to be applied to forecasting mechanical and thermal effects of explosions on road and assessing damage from them. A case study used to evaluate the performance of the models proposed in this study is presented in the second part of the paper.


First Published Online: 30 Mar 2012

Keyword : explosion, BLEVE, fire, simulation, road accident, road tank, risk, hazardous material, Bayesian approach

How to Cite
Vaidogas, E. R., Linkutė, L., & Stulgys, D. (2012). Simulation-based predicting the position of road tank explosions. Part I: data and models. Transport, 27(1), 14-24. https://doi.org/10.3846/16484142.2012.663732
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Mar 31, 2012
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This work is licensed under a Creative Commons Attribution 4.0 International License.