Improved pool fire-initiated domino effect assessment in atmospheric tank farms using structural response

Domino effects are severe accident scenarios affecting storage tanks and are often initiated by pool fires. Flame engulfment and heat radiation are the two major sources triggering domino effect. Threshold-based and probit-based methods are widely used to assess the possibility and probability of a secondary accident. These methods are also a part of advanced methods devoted to examining synergic or coupling effects. The current work ex-amines (i) how effective the threshold-based methods are and (ii) how accurate the current time to failure (TTF) estimation models are, which are the basis of probit-based methods. The results suggest that threshold-based methods are not pertinent for the quantitative assessment of domino effect and that significant improvement can be made in the existing TTF prediction models using site-specific structural response data. A new set of equations for TTF estimation using data analytics is proposed. Application to 4,080 pool fire scenarios demon-strates that the newly developed model can improve the TTF prediction performance compared to the existing models (around 22% in terms of R2). In addition, a method has been proposed and validated to correlate time with the failure probability for time-dependent domino effect assessment, which is a limitation of probit-based methods.

Automated summary

This article examines the effectiveness and accuracy of threshold-based and probit-based methods in assessing domino effects. The results indicate that threshold-based methods are not suitable for quantitative assessment, and there are limitations in probit-based methods for time-dependent domino effect assessment. By utilizing site-specific structural response data and data analytics, a new improved time to failure prediction model is proposed, which demonstrates better performance compared to existing models.