Title: A Model for Process Equipment Damage Probability Assessment due to Lightning
Authors: NECCI AANTONIONI GiacomoCOZZANI ValerioKRAUSMANN ElisabethBORGHETTI AlbertoNUCCI Carlo Alberto
Citation: RELIABILITY ENGINEERING & SYSTEM SAFETY vol. 115 p. 91-99
Publisher: ELSEVIER SCI LTD
Publication Year: 2013
JRC N°: JRC79448
ISSN: 0951-8320
URI: http://www.sciencedirect.com/science/article/pii/S0951832013000537
http://publications.jrc.ec.europa.eu/repository/handle/JRC79448
DOI: 10.1016/j.ress.2013.02.018
Type: Articles in periodicals and books
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.
JRC Directorate:Space, Security and Migration

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