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A model for process equipment damage probability assessment due to lightning

TitleA model for process equipment damage probability assessment due to lightning
Publication TypeJournal Article
Year of Publication2013
AuthorsNecci, A., G. Antonioni, V. Cozzani, E. Krausmann, A. Borghetti, and C. A. Nucci
JournalReliability Engineering & System Safety
Volume115
Pagination91 - 99
Date Published7/2013
ISSN09518320
ISBN Number09518320
Abstract

In recent years, severe natural events raised concern about so-called NaTech accident scenarios: technological accidents caused by the impact of a natural event on an industrial facility or infrastructure. Lightning strikes are one of the most important triggers of NaTech scenarios. Moreover, previous studies showed that lightning strikes are among the main causes of loss of containment (LOC) of atmospheric storage vessels containing hazardous materials. Although the lightning hazard is well known, well accepted quantitative procedures to assess the contribution of accidents triggered by lightning to industrial risk are still lacking. In particular, the approaches to the assessment of lightning strike probability and to the damage caused by lightning strike are mainly qualitative or semi-quantitative and are mostly based on expert judgment. In the present study, a quantitative methodology for the assessment of the equipment damage probability due to lightning is presented. The lightning severity was quantified by means of probability distribution functions of two parameters: peak current intensity and lightning charge. Through the application of a Monte Carlo simulation the expected frequency of lightning strikes on the equipment and the equipment damage probability were determined. The results of the equipment damage model were validated by available experimental data on metal perforation in simulated lightning strikes. The results of the validated Monte Carlo simulations were fit to empirical functions obtaining a simplified model suitable for use in a quantitative risk assessment framework.

DOI10.1016/j.ress.2013.02.018
Short TitleReliability Engineering & System Safety
Refereed DesignationRefereed