A comparison of dynamic event tree methods – Case study on a chemical batch reactor. (January 2018)

• Record Type: Journal Article
• Authors: Karanki, D.R.; Dang, V.N.; MacMillan, M.T.; Podofillini, L.
• Is Part Of: Reliability engineering & system safety. Volume 169(2018)
• Page Start: 542
• Page End: 553
• Publication Date: 2018-01
• Subjects: Probabilistic safety assessment -- Dynamic event tree analysis -- Epistemic and aleatory uncertainties -- Monte Carlo simulation; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2017.10.003
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Comparison is performed to assess strengths and weaknesses of Dynamic Event Trees. DET methods match the reference results with less computations. Computational efficiency of discrete DET depends on number of continuous variables. Sampling efficiency should be evaluated to avoid underestimating risk. Abstract: Dynamic event tree (DET) analysis, one of the main dynamic Probabilistic Safety Assessment methods, provides a framework to capture the effect of dynamics on the risk estimate. Depending on how continuous stochastic variables (CSVs) are treated, DETs can be classified into discretization- or sampling-based methods. The accuracy of the estimate and required computational resources depend on the method chosen as well as the nature of the problem. CSVs also include variable initial conditions, some of which significantly impact accident evolution. This work compares alternative DET methods in terms of numerical accuracy and computational resources for a case study of a chemical batch reactor problem, a system sensitive to both accident dynamics as well as variable initial conditions. The reference solution is a computationally intensive analog Monte Carlo simulation. The results show that the DET methods fairly match reference results with significantly less computation required. Further, in light of epistemic uncertainties of model parameters, this paper presents a comparison of DETs that includes detailed analyses of contributors of risk and its uncertainty, which unfolds the strengths and weaknesses of discretization and sampling based DETs.