Dynamic Event Tree Research Articles

A dynamic risk assessment model for evacuation in healthcare facilities during fire scenarios

Evacuation is a critical procedure for safeguarding individuals during a fire incident within a healthcare facility. Assessing the risk associated with the evacuation process is instrumental in enhancing emergency decision-making and developing contingency strategies. Evacuation, a dynamic process, poses challenges in accurate risk evaluation using conventional static quantitative risk assessment (QRA) techniques. This study introduces a dynamic QRA approach to precisely evaluate the evacuation risk to individuals in healthcare settings during a fire. The dynamic event tree methodology is initially utilized to determine the evolving probabilities of various fire scenarios, considering the equipment failure and repair rates. Subsequently, data on fire products and the evacuation of individuals under different fire scenarios are gathered through simulations conducted with Fire Dynamics Simulator (FDS) and Pathfinder software. In addition, this research proposes a dynamic incapacitation model to calculate the consequences of fire, incorporating changes in individuals' locations and the fire products surrounding them during evacuation to accurately estimate the casualty risk. Lastly, the dynamic scenario probability was integrated with the average incapacitation probability for all individuals, introducing the concept of a fire evacuation safety time window to comprehensively assess hospital fire evacuation risk. The findings revealed that the dynamic quantitative risk analysis (DQRA) approach more effectively captures the influence of fire products on the evacuation risk compared to static methods. Specifically, the peak incapacitation probability in a hospital fire reached 0.85, with firefighting equipment, especially the sprinkler system, reducing this risk by over 97% and decreasing evacuation time by 45%. In addition, the risk of fire evacuation in hospitals reaches a critical threshold in the fourth year, enhancing the repair rate of the firefighting system postpones the risk's escalation to a critical threshold until the fourteenth year.

Nuclear safety Enhanced: A Deep dive into current and future RAVEN applications

As the horizon of nuclear energy expands with the advent of small modular reactors, Generation IV reactors, and fusion reactors, there is a growing perspective that the licensing process could benefit from a more comprehensive approach. Moving beyond traditional deterministic and probabilistic risk assessment analyses might pave the way for a novel safety analysis paradigm propelled by the increasing computational power at our disposal.This paper explores different methodologies that can improve the outcomes of nuclear safety analysis. These range from uncertainty quantification techniques, aimed at enhancing the precision of safety margins, to deploying dynamic event trees by driving system code simulations, capturing the potential evolutions of severe accidents. These methodologies offer a better understanding of the management and consequences of nuclear accident scenarios, significantly improving the accuracy and efficiency of safety predictions compared to traditional methods.Specific case studies illustrate the practical application of these advanced techniques, demonstrating substantial improvements in predicting and managing the dynamics of severe accidents. These findings underscore the effectiveness of these methodologies in enhancing risk assessment capabilities and informing decision-making processes for nuclear safety management. The paper also emphasizes the importance of adaptability and continuous evolution, a call for action to address emerging nuclear safety concerns and highlights the utility of advanced tools like RAVEN.

Dynamic probabilistic risk assessment with K-shortest-paths planning for generating discrete dynamic event trees

Conventional risk assessment methods are often not well suited for fast-changing dynamic and complex systems since the results from the analysis may be averages or valid for a short time before the system’s state changes. A response to this problem is dynamic probabilistic risk assessment (DPRA), which considers the ever-changing nature of such systems and how their dynamic behavior affects the likelihood of future accident scenarios. Performing DPRA is difficult for complex systems — i.e., systems with many interconnected subsystems and components, for example, autonomous systems. There is a combinatorial “explosion” when considering how component failures affect one another and the overall system performance, known in the DPRA literature as the “state explosion problem”, causing DPRA methods to have poor computational performance for large-scale systems. Although solutions to state explosion alleviate the average-case performance, most DPRA methods remain computationally expensive, with exponential worst-case time complexities. In this paper, a method for solving DPRA problems with K-Shortest-Paths planning algorithms is proposed. The method, called KPRA, consists in framing a subset of DPRA problems as relaxed versions of the K-Shortest-Paths (KSP) planning problem, allowing these DPRA problems to be solved by a graph search algorithm called K*, which has a theoretical log-linear worst-case complexity. Therefore, in theory, KPRA with K* solves DPRA problems with computational performance better than exponential. KPRA was implemented and applied to a case study of DPRA for an autonomous ship for validation and comparison with conventional DPRA methods. The case study consists of two ships, one of them autonomous, in a crossing encounter with a possible collision risk. The task is to find the most critical situations for the autonomous ship, i.e., the scenarios where its collision risk is the highest. KPRA’s performance in solving the case study is compared with two conventional DPRA methods. The results show that the KPRA implementation in this work can solve the case study, i.e., produce an output equivalent to the other methods, with a polynomial worst-case computational complexity, i.e., more efficiently than the other methods with exponential complexities.

Dynamic Event Trees for Aircraft Collision Risk Assessment

Dynamic Event Trees for Aircraft Collision Risk Assessment

Dynamic event tree analysis of a severe accident sequence in a boiling water reactor experiencing a cyberattack scenario

Over the last decade, probabilistic risk assessment (PRA) for nuclear power plants shifted its focus toward simulation-based methods of evaluating safety and risk aspects. Classical reliability modeling based on static Boolean structures does not capture the dynamic nature of plants experiencing accident sequences. Dynamic PRA extends conventional reliability modeling, adding a higher degree of variability to events occuring during accident sequences. This paper demonstrates the capability of a new dynamic event tree (DET) analysis tool applicable to severe accident sequences. A cyberattack was assumed to penetrate the instrumentation system of a boiling water reactor during its hot shutdown phase, altering the thermal–hydraulic variables being processed. The analysis focused on scenarios that lead to the core being uncovered, in which the peak cladding temperature is reached due to the disabling of the cooling systems. A limiting surface is defined, associating the cyberattack timing to the recovery time limit that would compromise the core integrity.

Development of dynamic integrated consequence evaluation (DICE) for dynamic event tree approaches: Numerical validation for a loss of coolant accident

Although conventional probabilistic safety assessment (PSA) makes a sufficient contribution to evaluating the safety of nuclear facilities, it suffers some limitations in performing optimal assessments close to the actual behavior by reflecting time dependencies in accident management. However, integrated deterministic and probabilistic safety assessment (IDPSA) literally combines two methodologies to enable synchronized evaluation based on physical behavior, system availability of facilities, and operator's response. Dynamic integrated consequence evaluation (DICE) is one of the IDPSA tools developed for this purpose, which is the first achievement in South Korea and supported by a regulatory authority. Based on dynamic discrete event tree (DDET) methodology, DICE consists of a scheduler that supports the exchange of information between modules, including a physical module that computes thermal-hydraulic simulations, a diagnosis module that specifies branching points for safety systems, and a reliability module that provides system availability. This paper deals with numerical validation comparing the conventional deterministic and probabilistic results for a sample scenario with those of DICE, and discusses an application of DICE to support and extend the scope of conventional PSA through a case study. It is expected that DICE emphasizes the compliance with conventional PSA methodologies while providing magnified perception in investigating unforeseen scenarios.

Integrated Safety and Security Analysis of Nuclear Power Plants Using Dynamic Event Trees

Nuclear security relies on the method of vital area identification (VAI) to determine which locations within the nuclear power plant (NPP) need to be protected from radiological sabotage. The VAI methodology uses fault trees (FTs) and event trees (ETs) to identify locations in the NPP that contain vital equipment: structures and components that may result in reactor significant core damage if direct or indirect sabotage occurred. However, the traditional FT/ET process cannot fully capture the dynamics of NPP systems and mitigating measures at play. Existing safety systems or possible operator procedures may be able to avert or mitigate core damage despite the loss of one or more vital areas. Dynamic probabilistic risk assessment (DPRA) methodologies are those that, unlike traditional probabilistic risk assessment, explicitly consider time effects when modeling a system. One common DPRA methodology is that of the use of dynamic event trees (DETs) that drive computer models of a system with user-specified branching conditions to account for uncertainties in a scenario. The DPRA process allows analysts to explore the uncertainties and state space of a scenario in a systematic fashion. A scenario was developed that uses the novel leading simulator/trailing simulator methodology to perform a DET analysis of a combined nuclear safety and nuclear security analysis. The scenario under consideration models the successful sabotage of a vital area by adversaries and determines the effects of timing and the extent of sabotage, as well as possible recovery actions, on the state of the plant. The results of this integrated analysis include the timing and extent of core damage as well as the extent of any radiological release that may occur as a result of sabotage.

A combined strategy for dynamic probabilistic risk assessment of fission battery designs using EMRALD and DEPM

The notion of nuclear reactors with battery-like capabilities, called fission batteries, puts forth system requirements and design constraints that have so far been unseen in the nuclear power production industry. Such restrictions require fission batteries to be modular, integrated, autonomous, tamper-proof (i.e., resilient, fault-tolerant, all-weather, and safe), and affordable. With design requirements specifying no human intervention for operation, and minimal connectivity to remote monitoring networks, fission batteries are unique among existing nuclear power plants and emerging advanced reactor designs. Given these attributes, traditional probabilistic risk assessment (PRA) of fission batteries is expected to require dynamic methods to model advanced aspects, such as self-diagnosis, self-adjustment, and duration-prediction capabilities, as they are key ingredients for unattended operations. In addition, availability models need to integrate autonomous control, associated error-detection algorithms, and adversarial human actions. Currently, no existing framework demonstrably assesses these advanced attributes. This paper introduces and demonstrates an integrated framework for the dynamic modeling of fission battery designs. The proposed framework comprises a combined modeling strategy that uses the dual-graph error propagation methodology (DEPM) based on the continuous-time Markov chain (CTMC) models implemented in OpenPRA Error Propagation (OpenErrorPro) and the dynamic PRA tool, Event Modeling Risk Assessment using Linked Diagrams (EMRALD), based on discrete dynamic event trees (DDET). This combination overcomes some of the limitations of the tools when used independently. It enables detailed dynamic analysis to produce time explicit results to support the development of fission battery traditional PRA models. To evaluate the utility of this novel approach, a demonstration case is shown that models the hypothesized response of a fission battery design to an external fire event. DEPM CTMCs and alternative failure approaches are coupled with EMRALD to characterize and quantify the likelihood of the event sequences. The results show that the combined framework effectively captures the dynamic aspects of fission battery design in terms of the timing and realism of modeled events. Given the complexity of the failure scenarios, we believe that EMRALD and DEPM are necessary and complementary when the need for high-resolution analysis offsets the challenges of detailed modeling.

Novel method of dynamic event tree keeping the number of simulations in risk analysis small

Novel method of dynamic event tree keeping the number of simulations in risk analysis small

Safety Benefits Assessment for Accident Tolerant Fuels in Consideration of Steam Generator Tube Degradation Using Dynamic Event Tree Analysis

Accident tolerant fuel (ATF) is expected to delay or prevent core damage by providing additional coping time under accidents involving loss of core cooling. The effect of extended coping time may vary depending on the plant response to accidents. Age-related component degradation that deteriorates plant performance over time could have an impact on the actual advantages of ATF. The potential safety benefits of two near-term ATF candidates, including Cr-coated Zr cladding and FeCrAl cladding, are assessed for a 2-in. loss-of-coolant accident with failed high-pressure safety injection using the dynamic event tree (DET) approach considering possible stress corrosion cracking of steam generator (SG) tubing under aging. The DET approach allows likelihood quantification of accident sequences leading to core damage, including stochastic variation of system response and human actions during accident mitigation. The safety benefits of the selected ATF claddings in terms of additional coping time and the core damage frequency reduction rate under specified accident situations were quantitatively estimated. The results show that the deployment of the two selected ATF claddings is expected to lead to longer coping times and lower core damage frequency due to the wider safety margin to peak cladding temperature they provide. The safety advantages would be greater as SG tube degradation proceeds. Thus, the two ATF candidates would lead to less severe consequences in terms of likelihood of core damage and susceptibility to the SG tube degradation than UO2-Zr fuel.

Dynamic Event Tree Analysis as a Tool for Risk Assessment in Nuclear Fusion Plants Using RAVEN and MELCOR

In the broad framework of the nuclear power plants (NPPs) industry, the dynamic probabilistic risk assessment could answer the time dependence deficiency of the event tree and fault tree analysis. The basic event tree approach relies on experts’ pre-constructed accident sequences without exploring the time-dependent nature of an accident scenario, which could strongly affect the accident sequence. Conversely, the effects of events timing can be studied by adopting a dynamic event tree (DET) approach. Developing a DET methodology requires integrating a system code capable of replicating an accident scenario and a logic-driver code able to generate the event tree sequence, trigger plant safety systems, and manage other relevant events throughout the simulation. For this purpose, Methods for Estimation of Leakages and Consequences of Release (MELCOR) and reactor analysis and virtual control environment (RAVEN) have been coupled through a Python script developed by the Sapienza University of Rome, Rome, Italy, to perform DET studies during accident transients in fusion and fission reactors. RAVEN is a software tool developed at the Idaho National Laboratory (INL), Idaho Falls, ID, USA, to act as a control logic driver and post-processing tool for different applications. MELCOR for fusion is a fully integrated design basis and severe accident code that simulates thermal-hydraulic behavior and self-consistently accounting for aerosol transport in nuclear facilities and reactor cooling systems for the evaluation of the source term in fusion reactors. The coupling between these codes will provide a wide range of NPP risk assessment analyses, establishing new best practices. In this work, a preliminary DET study has been performed, selecting as initiating event an ex-vessel loss of coolant accident (LOCA) in the water-cooled lithium lead test blanket system (WCLL TBS) to be tested in International Thermonuclear Experimental Reactor (ITER). Time-dependent parameters such as the intervention of the plasma shutdown system and the closure of the main system isolation valves have been sampled to study evolving system scenarios.

A hybrid computing framework for risk‐oriented reliability analysis in dynamic PSA context: A case study

AbstractNowadays, the emerging digital technologies and digitalization trend in safety‐critical industrial process systems are bringing great opportunities for system performance improvements. However, new big challenges are also encountered in the reliability and safety evaluation of large complex industrial process systems due to its multi‐state, multi‐phase dynamic interactions, resilience on software and inter‐dependencies among digital components. The objectives of this study are i) to present an introductory overview of a hybrid computing framework as a supplementary to conventional fault tree analysis toolkit for risk‐oriented reliability analysis in dynamic probabilistic safety assessment context; ii) to illustrate how to combine the three methods of DDET, Markov/CCMT, and GO‐FLOW for integrated risk solutions by a case study of small‐break LOCA in nuclear power plants. Within the hybrid computing framework, the DDET model is implemented based on graph‐based search and sequence diagram refactoring by linking with Markov/CCMT and GO‐FLOW solver for branch probability estimation. The dynamic event tree model is adopted to represent the accident sequence of small‐break LOCA, where the heading events of system failure of digital RPS and phased‐mission ECCS in realization of safety‐critical functions of reactivity control and emergency core cooling are respectively modeled and analyzed by Markov chain and GO‐FLOW method. The demonstration results show that the failure analysis of complex dynamic process interactions together with time‐dependent mission reliability analysis of safety systems involved in accident prevention and mitigation can be easily implemented with accurate modeling and fast evaluation within the hybrid integration platform. The core algorithms and principles implemented for dynamic risk scenarios development by DDET, modeling, and analysis of dynamic process interactions by Markov/CCMT, time‐dependent and multi‐phase mission reliability analysis by GO‐FLOW as well as their integration to provide comprehensive solutions for the application of dynamic reliability in risk assessment are also discussed as open problems for future research.

A dynamic event tree for a blowout accident in an oil deep-water well equipped with a managed pressure drilling condition monitoring and operation system

Blowout is one of the most dreaded accidents for Oil and Gas companies. It is of particular concern during the drilling phase of deep-water oil & gas wells. This is due to the largely uncertain and extremely harsh environmental conditions that affect the design, drilling and operation activities of these wells. Seeking new technological solutions to prevent blowout has led, in the last few decades, to develop Managed Pressure Drilling (MPD) techniques.MPD offers many advantages compared to conventional drilling techniques, such as the capability (1) to detect the gas influx that initiates the kick that might lead to blowout and (2) to optimally control and circulate out this influx, to avoid the blowout. Nevertheless, effects of uncertainties on the MPD functionality are not fully understood and satisfactorily modelled within conventional safety assessment that relies on Event Trees (ETs).In this work, we propose a Dynamic Event Tree (DET) modelling framework of the scenario of kick escalation into blowout to allow accounting for the uncertainties that affect not only the kick variables, but also for the fundamental role played by the time and the delay of the kick detection task. The uncertainties affecting the kick variables are evaluated from kick events records taken from 2000 oil wells drilled in the Niger Delta, whereas the uncertainties affecting the time and delay of kick detection are evaluated by simulating the performance of the tool embedded into the MPD for kick detection (which applies the CUSUM statistical test to differential flow measurements), and assuming a possibilistic distribution for the confirmation time needed by the operator to take counteracting measures with respect to the evolving accidental scenario.A Hybrid Monte Carlo and Possibilistic method is utilized to represent and propagate uncertainties associated to the events occurring throughout the DET. Results are compared with those of a Purely Probabilistic method in support of the blowout probability quantification.

Dynamic PRA Prospects for the Nuclear Industry

This review paper highlights approaches and tools available to the nuclear industry for dynamic probabilistic risk assessment (DPRA) using dynamic event trees. DPRA is an emerging methodology that has advantages as compared to traditional, static PRA predominantly owing to the addition of time dependent modeling. Traditional PRAs predefine events and outcomes into Event Trees (ET) and Fault Trees (FT), that are coupled with various combinations of Initiating Events (IE), Top Events (TE), branches, end states and sequences. A more complete depiction of the system and accident progression behavior can be quantified using DPRA to account for dynamic events such as those involving human actions. This paper discusses the strengths and needs of existing DPRA tools to align with the risk informed methodology currently used in the nuclear industry. DPRA is evolving during an exciting time in the nuclear industry with emerging advanced reactor designs also coming on the scene. Advanced nuclear (Gen IV) designs often incorporate passively safe systems that have less readily available data for traditional PRA due to their limited operating history. DPRA is a promising methodology that can address this challenge and demonstrate to the regulatory bodies and public that advanced designs operate within safety margins. In this light, the paper considers the historical role of PRA in the nuclear industry and motivation for considering dynamic PRA models. An introduction to the differences inherent in DPRA and how it complements and enhances existing PRA approaches is discussed. Additionally, a review of research from U.S national laboratories and universities features recent DPRA tool advancements that could be applied in the nuclear industry. These DPRA approaches and tools are summarized and examined to thoughtfully provide a path forward to best leverage existing research and integrate DPRA into advanced reactor design and analysis.

Development of a dynamic event tree (DET) to analyze SBO accident in VVER-1000/V446 nuclear reactor

Probabilistic safety assessment (PSA) is the main approach currently applied in safety assessment of nuclear power plants to calculate the core damage frequency (CDF). The conventional event tree/fault tree methodology, adopted in current PSA studies, has weaknesses such as nonrealistic modeling of operator and recovery actions, inability for functional reliability modelling of passive safety systems, using predetermined timing and order of accident sequences without any interaction of system responses, insensitivity of the system failure to uncertainties in the process physics and consequently its output is not comprehensive for safety considerations, risk management and decision making tasks. In this study to overcome these bottlenecks, a dynamic approach based on dynamic event tree (DET) is applied to calculate the CDF for the station blackout (SBO) accident in VVER-1000/V446 nuclear reactor. To implement the DET, deterministic and probabilistic models are developed using the plant data and the characteristics of the SBO accident. These models are then run simultaneously to investigate the time of operator actions, AC power supply recovery, and the availability of safety systems on the accident progression and consequences. The results show that the value of CDF calculated with DET is equal to 1.97e-6 (yr−1). The results also illustrates that the DET can provide useful insights concerning potential risk contributors and the effectiveness of operator and recovery actions.

Risk analysis virtual ENvironment for dynamic event tree-based analyses

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also known as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used DPRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). Since 2012, the Idaho National Laboratory (INL) is developing its own tool to perform Dynamic PRA: RAVEN (Risk Analysis and Virtual ENvironment). RAVEN has been designed in a high modular and pluggable way to enable easy integration of different programming languages (i.e., Python, C++) and coupling with other application including, among the others, several thermal–hydraulic and severe accident codes (e.g., RELAP5-3D, MELCOR, MAAP5, TRACE, etc.). RAVEN is aimed to provide a framework/container of capabilities for engineers and scientists to analyze the response of systems, physics and multi-physics, employing advanced numerical techniques and algorithms. Moreover, RAVEN models stochastic events, such as components failures, and performs uncertainty quantification (UQ). Such stochastic modeling is employed by using sampling strategies among which both MC and DET algorithms, which are going to be employed in this paper. In addition, RAVEN processes the large amount of data generated by sampling the physical models using data-mining based algorithms and risk assessment techniques. This paper provides an overview of the DET methodologies that have been deployed within the RAVEN framework, showing the potential of such techniques for the analysis of complex systems. A brief background of classical methodologies and their limitation is also reported and represent the motivation for the deployment of such dynamic technique. In addition, results from a pressurized water reactor loss of coolant accident scenario, using RELAP5-3D as physical model, are reported.

Integrated Deterministic and Probabilistic Safety Analysis of Integrated Energy Systems with bi-directional conversion

The rapid development of the technology of energy conversion is changing the global energy landscape. In this study, a reliability analysis framework for Integrated Energy Systems (IESs) is developed, based on the concept of Integrated Deterministic and Probabilistic Safety Analysis (IDPSA). A bi-directional energy conversion system model is developed to simulate the deterministic evolution of the IES. Then, the dynamic event tree (DET) analysis technique is used to describe the stochastic evolution of IES, based on the physics of IES. Given the scenarios from the DET, the probabilistic safety margin method is used to evaluate the reliability of IES. The contract pressures at delivery points are adopted as critical safety parameters for the evaluation of the safety margins. To reduce the computational burden, Order Statistics, combining the Bracketing and Coverage approaches, are used to obtain the percentiles of the probabilistic distribution of the safety parameters. An application of the developed framework is performed on an IES. The results indicate that the framework can evaluate the reliability of IES from the perspectives of dynamic failure probability and operation performance. Furthermore, the impact of P2G (power to gas) on the energy supply and operation security of the IES is analysed in detail.

Extension of a Level 2 PSA Event Tree Based on Results of a Probabilistic Dynamic Safety Analysis of Induced Steam Generator Tube Rupture

This paper presents the approach of extending a classical generic event tree (ET) of a Level 2 Probabilistic Safety Analysis to the results of a probabilistic dynamic safety analysis. The example of creep-induced steam generator tube rupture has been chosen. The results of an Analysis of Thermal Hydraulics of Leaks and Transients with Core Degradation (ATHLET-CD)/Monte Carlo Dynamic Event Tree (MCDET) simulation analyzing the failure of reactor coolant system components by creeping in a scenario of a high-pressure core melt accident in a generic pressurized water reactor (PWR) have been implemented in the ET. From the results of these simulations, a set of parameters has been extracted and integrated into the ET along with their probability distributions. The effect of these parameters on both the progression of the severe accident sequence under consideration and the release categories of a generic German PWR plant, respectively, is discussed in this paper.

An online operator support tool for severe accident management in nuclear power plants using dynamic event trees and deep learning

Operating staffs of a nuclear power plant (NPP) are responsible for returning the NPP to a stable state and alert authorities if there is the potential for offsite radiological consequences following an accident. An operator support tool (OST) using deep learning techniques and trained by data from dynamic probabilistic safety/risk assessment (DPSA/DPRA) is proposed to assist the NPP personnel in decision-making. The DPSA/DPRA methodology employs time-dependent branching conditions based on the evolving state of the NPP and accounts for complex hardware/process/software/human interactions to predict possible outcomes of the initiating event. A large number of scenarios generated from the DPSA/DPRA performed for a pressurized water reactor station blackout as a function of time were used to train the OST to predict possible offsite dose outcomes at 2-mile and 10-mile site boundaries for emergency response planning. The results show that the OST can predict offsite dose levels with greater than 90% accuracy.

Dynamic vulnerability assessment of process plants with respect to vapor cloud explosions

Vapor cloud explosion (VCE) accidents in recent years such as the Buncefield accident in 2005 indicate that VCEs in process plants may lead to unpredicted overpressures, resulting in catastrophic disasters. Although a lot of attempts have been done to assess VCEs in process plants, little attention has been paid to the spatial-temporal evolution of VCEs. This study, therefore, aims to develop a dynamic methodology based on discrete dynamic event tree to assess the likelihood of VCEs and the vulnerability of installations. The developed methodology consists of six steps: (i) identification of hazardous installations and potential loss of containment (LOC), (ii) analysis of vapor cloud dispersion, (iii) identification and characterization of ignition sources, (iv) explosion frequency and delayed time assessment using the dynamic event tree, (v) overpressure calculation by the Multi-Energy method and (vi) damage assessment based on probit models. This methodology considers the time dependencies in vapor cloud dispersion and in the uncertainty of delayed ignitions. Application of the methodology to a case study shows that the methodology can reflect the characteristics of large VCEs and avoid underestimating the consequences. Besides, this study indicates that ignition control may be regarded as a delay measure, effective emergency actions are needed for preventing VCEs.