Dynamic Safety Analysis Research Articles

Risk Contextualization for Nuclear Systems

Risk management strives to reach the standards of theoretical systematicity and empirical precision achieved in natural science models. To this end, a set of risk-informed and performance-based standards was developed in the form of statistically validated measures. The set enables the systematic extraction by deterministic and probabilistic analysis of potentially objective risk assessments and well-defined decisions. However, much of the data and models are subjectively influenced by the uncertainty of the context in which they are related and derived. Current risk analysis contains a large amount of risk-related information, but without the context of the models, its results lack sufficient predictive and explanatory power to be a solid basis for decisions. Therefore, to model the entire site of a multi-unit nuclear power plant as an integrated system connecting facility and activity, it is necessary to consider not only the technological conditions, but also the entire site context, including human, organizational, and environmental factors. An interface tool for dynamic deterministic-probabilistic safety analysis should be used to contextualize and complement existing risk indicators, but not to replace them. This article presents the possibilities of risk contextualization for nuclear systems through the symptom-based context quantification procedure of the Performance Evaluation Teamwork method.

Dynamic Safety Analysis of Integrated Servo-Motor for Tracked Military Vehicle

Dynamic Safety Analysis of Integrated Servo-Motor for Tracked Military Vehicle

Complex physical-model based dynamic system safety analysis of Aviation Piston Engine considering hybrid uncertainty of fault

Aviation piston engines constitute an essential segment of general aviation, whilst safety issues are becoming increasingly prominent by high system complexity. However, the conventional methods are gradually becoming insufficient to support the safety analysis in complex systems with multi-physical during the lifetime. Therefore, an innovative analysis that can depict the dynamic safety and potential coupled faults of the entire engine with multi-physical systems considering the effects of hybrid uncertainty faults is proposed in this study. Consequently, safety feedback is provided to the design in a more timely, accurate, and efficient manner based on the safety distribution throughout the lifecycle during the design stage. The analysis involves an integration of the nominal physical model and stochastic fault model via functionalized process variables. Firstly, a deterministic nominal model of the multi-physical domain of the engine is developed to characterize the coupled relationship between different domains by differential equations. Secondly, a stochastic fault model with hybrid uncertainty is quantitatively integrated into the nominal model for safety analysis by affecting process variables. Specifically, hybrid uncertainty encompasses the uncertainty of occurrence and severity. Finally, a simplified dynamic safety variation of the entire-engine is investigated by the approach. Result of Monte Carlo simulations of safety highlighted the coupled failures of different components in the respective safety ranges could be discriminated from the entire-engine perspective. The identification of potential coupled faults demonstrates the effectiveness and advantages of the proposed method in the design stage.

Dynamic response and safety analysis of polyethylene pipeline under rockfall conditions

AbstractThe safety of buried gas pipelines in mountainous cities is at risk due to potential hazards such as landslides and rock falls. To ensure the safety of pipelines, this paper develops a three‐dimensional dynamic response model to reveal the impact of rockfall radius, rockfall impact velocity, and pipe burial depth on the dynamic response of buried polyethylene pipelines (PE pipelines) under rockfall conditions. Subsequently, an orthogonal experimental design and analysis of variance (ANOVA) are performed to identify critical factors. Ultimately, a multi‐factor limit state equation is constructed to assess the safety of PE pipelines accordingly failure prevention actions for buried gas pipelines under the impact of rockfall is specified. Overall, this proposed method contributes to safety analysis of buried PE pipelines in rockfall‐prone areas.

Rigorous dynamic simulation methodology for scenario-based safety analysis of pressure-swing distillation considering independent protections

Process engineers often assess process safety performance early in the design stage to reduce or eliminate potential hazards cost-effectively. Process simulation tools widely used in design can provide fast and precise hazard evaluation using limited design information. However, there is a knowledge gap on how to build rigorous dynamic process models and how model configurations can affect safety analysis results. This study applied commercial dynamic simulation software, Aspen Dynamics, to address these issues through rigorous dynamic simulations and safety analysis of a two-column pressure-swing distillation with top recycling. The study implements and simulates various layers of protection, including basic process controls, alarms, safety instrumented systems, and pressure relief systems, and evaluates their impact on accident prevention. The hazardous scenarios considered are overpressure and flooding in the distillation columns, and the dynamic responses to a set of deviations such as coolant and steam utility failures and undesired throughput and composition disturbances are investigated. A scenario-based safety analysis is performed to assess the dynamic safety performance and the effectiveness of protection layers. The results indicate that the presented scenario-based dynamic safety analysis methodology is essential for accurately determining dynamic column behaviors, thereby better assisting in determining process safety time and designing effective safety systems.

Automated Design Exploration and Dynamic Safety Analysis for Optimization of Mechatronic Systems in Safety-Critical Automotive Applications

Automated Design Exploration and Dynamic Safety Analysis for Optimization of Mechatronic Systems in Safety-Critical Automotive Applications

Dynamic Process Safety Assessment Using Adaptive Bayesian Network with Loss Function

Fault detection and diagnosis (FDD) is crucial for dynamic process safety analysis. Integrated with failure prediction models, it enables us to realize how a deviation in process variable(s) can affect system safety (measured as risk). This work aims to overcome the challenges of nonlinear, non-Gaussian, and multimodal behavior of the processing systems to detect abnormal process operations, predict dynamic operational risk, and diagnose root cause of the abnormal situation. A methodology is proposed here by integrating different techniques. The artificial neural network (ANN) is used to identify process modes, while the Bayesian network (BN) is used for fault detection. How a fault will lead to a process failure is modeled using the event tree (ET), whereas time-dependent losses associated with the failure scenarios are assessed using the inverted normal loss function (INLF). A probability adaption mechanism is used to estimate the conditional probabilities in each time slice. The complexity of estimating conditional probabilities is handled using the copula theory. The proposed framework is validated using numerical, simulated, and industrial datasets. The results suggest that the developed framework can provide greater flexibility and wider applications.

Simultaneous design for a complex heterogeneous wastewater separation and recovery process: process improvement, plantwide control and safety analysis

Recently, a new industrial relevant wastewater (water/Cyclohexane/sec-butyl alcohol) separation process with vapor recompression and thermal coupling was proposed as the conventional homogeneous extractive distillation. This highly integrated and interactive multivariable process presents great challenges to the dynamic controllability and process safety analysis. In this paper, based on the concept of “simultaneous design”, the newly proposed process is systematically investigated from the perspective of process improvement, plantwide control and safety analysis. Firstly, for energy conservation and CO2 emission reduction, the new complex process is further improved by applying the features of heterogeneous distillation. And a modified heterogeneous distillation process with heat pump is proposed, which can save reboiler duty about 16.8 % and save compressor electricity up to 67.7 %, while the reduction of CO2 emissions is 23.5 %. Secondly, for the efficient heterogeneous distillation process, an effective and robust plantwide control strategy is developed and tested under large throughput and feed composition disturbances. Last, with the dynamic simulation assisted HAZOP, a quantitative safety analysis is conducted, which can help experts to consider the nature, scale and response time of hazards and identify the potential propagation of deviation between different units, and then propose more specific or accurate safety recommendations correspondingly.

Dynamic performance and safety analysis of car-following models considering collision sensitivity

Car-following behavior is strongly related to the dynamic performance and safety of vehicles, and these behaviors must be studied more comprehensively. This study first quantitatively analyzes the relationship between time to collision and car-following behavior, and then proposes the concept of collision sensitivity coefficient. Based on the full velocity difference model, we introduce the collision sensitivity coefficient into the car-following model to explore its effect on vehicle dynamic performance and safety from a different perspective. The stability of traffic flow is investigated using linear stability theory and obtain the neutral stability condition of the proposed model. Numerical simulation results show that the proposed model can improve the stability of traffic flow and successfully suppress traffic jams. The computation results using various safety evaluation indicators reveal that the proposed model not only has better dynamic performance than the full velocity difference model but also can effectively improve the safety of vehicles. We conclude that collision sensitivity substantially affects car-following behavior.

Novel Approach for Dynamic Safety Analysis of Natural Gas Leakage in Utility Tunnel

AbstractThis paper proposes a systemic framework for dynamic safety risk analysis for natural gas pipeline leakage in a utility tunnel, which merges the bow-tie model (BT), Bayesian network (BN), a...

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.

A novel Fuzzy Bayesian Network approach for safety analysis of process systems; An application of HFACS and SHIPP methodology

Abstract Chemical process industries (CPI) are inherently hazardous complex systems where large inventory of extremely flammable and explosive chemicals are processed and stored in a highly congested process area. A reliable safety analysis method plays a significant role to measure risks and to develop preventive strategies in process industries. This paper proposed a novel Fuzzy Bayesian Network for dynamic safety analysis of process systems by incorporating Bayesian network (BN) with Fuzzy Best Worst Method (Fuzzy-BWM). In the proposed approach a comprehensive and in-depth analysis of human and organizational factors (HOFs) involving in the accident scenario occurrence was also provided by integrating Human Factor Analysis and Classification System (HFACS) and System Hazard Identification, Prediction and Prevention (SHIPP) methodology into the model. An ethylene storage tank was selected to verify the applicability of the proposed approach and its application potential. The study also explained a comparison between the results of the proposed Fuzzy-BWM approach with the conventional BN approach and a quantitative risk assessment (QRA) conventional technique such as bow-tie (BT). The findings revealed the capability of the proposed Fuzzy-BWM approach to provide high reliable results and to detect risks that using the BT and BN approaches were not identified.

Bayesian Stochastic Petri Nets (BSPN) - A new modelling tool for dynamic safety and reliability analysis

An efficient formalism for safety analysis should be: (i) able to consider the failure behaviour of complex engineering systems, and (ii) dynamic in nature to capture changing conditions and have wider applicability. The current formalisms used for safety analysis are lacking in one of the above-listed criteria. Bayesian network (BN) allows the modelling of failure of systems where the inter-nodal dependencies are represented exclusively by conditional probabilities. Stochastic Petri nets (SPN) enable the study of the dynamic behaviour of complex systems; however, they lack the ability to adapt to changes in the data and operating conditions. This paper proposes a hybrid formalism that strengthens SPN with BN capabilities. The proposed formalism is graphical and uses advance feature such as predicates to perform the data updating functions. This ability enables the analysis of continuous input data without the necessity of time-slice discretization process. The proposed formalism is termed “Bayesian Stochastic Petri Nets” (BSPN). It provides a dynamic assessment of safety by capturing additional sets of data rends. In BSPN, the conditional probability is captured as a time-dependent function to allow consideration of the cumulative effect of the failure scenario. The BSPN implementation is demonstrated with an example illustrating the modelling capabilities.

Safety analysis of offshore decommissioning operation through Bayesian network

ABSTRACTDecommissioning of offshore platforms is becoming increasingly popular. The removal of these heavy steel structures is characterised by high risks that may compromise personnel safety and loss of assets. The removal operation relies on dedicated barges and heavy lift vessels that may descent or capsize because of mechanical or structural failure. The knowledge of associated hazards is driven by experience and failure data are often obtained empirically through analogous operations, which further introduces uncertainty to the risk analysis. This paper proposes an integrated safety analysis approach for conducting a decommissioning risk analysis of offshore installations. The approach incorporates hierarchical Bayesian analysis (HBA) with Bayesian network (BN) to assess the accident causations leading to futile decommissioning operation. First, the overall system failure of a lifting vessel was reviewed with an emphasis on where safety issues arise. In addition, the failure data obtained from expert judgements were aggregated through statistical distribution based on HBA. The aggregated failure data are then used to conduct dynamic safety analysis using BN, to assess and evaluate the risks of offshore jacket removal operations. The accident model is illustrated with a case study from Brent Alpha decommissioning technical document to demonstrate the capability of incorporating HBA with BN to conduct a risk analysis.

Dynamic availability assessment of safety critical systems using a dynamic Bayesian network

Availability analysis of safety critical systems is an integral part of ensuring safety both in onshore and offshore process operations. However, the availability assessment of these systems is complex due to their multistate failure scenarios (dormant failure and failure on demand) and multistate functionality (operational failure). In the present study, a dynamic Bayesian network (DBN)-based dynamic availability assessment technique is proposed. This approach offers much flexibility in representing different failure scenarios and the interdependence of failure causes. Sensitivity and importance analyses have also been performed to identify the most influential failure causes. This helps to design better management strategies and provides more realistic reliance on safety critical systems. Applications of the proposed methodology are demonstrated on two safety critical systems: a fire alarm system and a hazard scenario in a steam generation system. This methodology will offer a pivotal step forward in dynamic safety analysis.

Dynamic system safety analysis in HiP-HOPS with Petri Nets and Bayesian Networks

Dynamic systems exhibit time-dependent behaviours and complex functional dependencies amongst their components. Therefore, to capture the full system failure behaviour, it is not enough to simply determine the consequences of different combinations of failure events: it is also necessary to understand the order in which they fail. Pandora temporal fault trees (TFTs) increase the expressive power of fault trees and allow modelling of sequence-dependent failure behaviour of systems. However, like classical fault tree analysis, TFT analysis requires a lot of manual effort, which makes it time consuming and expensive. This in turn makes it less viable for use in modern, iterated system design processes, which requires a quicker turnaround and consistency across evolutions. In this paper, we propose for a model-based analysis of temporal fault trees via HiP-HOPS, which is a state-of-the-art model-based dependability analysis method supported by tools that largely automate analysis and optimisation of systems. The proposal extends HiP-HOPS with Pandora, Petri Nets and Bayesian Networks and results to dynamic dependability analysis that is more readily integrated into modern design processes. The effectiveness is demonstrated via application to an aircraft fuel distribution system.

Yanqing solar field: Dynamic optical model and operational safety analysis

A dynamic optical model was established for the Yanqing solar field at the parabolic trough solar thermal power plant and a simulation was conducted on four separate days of clear weather (March 3rd, June 2nd, September 25th, December 17th). The solar collector assembly (SCA) was comprised of a North-South and East-West layout. The model consisted of the following modules: DNI, SCA operational, and SCA optical. The tracking angle characteristics were analyzed and the results showed that the East-West layout of the tracking system was the most viable. The average energy flux was simulated for a given time period and different SCA layouts, yielding an average flux of 6kW/m2, which was then used as the design and operational standards of the Yanqing parabolic trough plant. The mass flow of North-South layout was relatively stable. The influences of the defocus angles on both the average energy flux and the circumferential flux distribution were also studied. The results provided a theoretical basis for the following components: solar field design, mass flow control of the heat transfer fluid, design and operation of the tracking system, operational safety of SCAs, and powerproduction prediction in the Yanqing 1MW parabolic trough plant.

Dynamic safety analysis of process systems using nonlinear and non-sequential accident model

Analysis of the safety and reliability of complex engineering systems is becoming challenging and highly demanding. In complex engineering systems, accident causation is a function of nonlinear interactions of several accident contributory factors. Traditional accident models normally use a fault and event trees sequential approach to predict cause–consequence relationships, which unable to capture real interaction thus have limited predictability of accident.This paper presents a new non-sequential barrier-based process accident model. The conditional dependencies among accident contributory factors within prevention barriers are modelled using the Bayesian network with various relaxation strategies, and non-sequential failure of prevention (safety) barriers. The modelling of non-linear interactions in the model led to significant improvement of the predicted probability of an accident when compared with that of sequential technique. This renders valuable information for process safety management. The proposed accident model is tested on a real life case study from the U.S. Chemical Safety Board.

Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network

The growing demand for natural gas has pushed oil and gas exploration to more isolated and previously untapped regions around the world where construction of LNG processing plants is not always a viable option. The development of FLNG will allow floating plants to be positioned in remote offshore areas and subsequently produce, liquefy, store and offload LNG in the one position. The offloading process from an FLNG platform to a gas tanker can be a high risk operation. It consists of LNG being transferred, in hostile environments, through loading arms or flexible cryogenic hoses into a carrier which then transports the LNG to onshore facilities. During the carrier's offloading process at onshore terminals, it again involves risk that may result in an accident such as collision, leakage and/or grounding. It is therefore critical to assess and monitor all risks associated with the offloading operation. This study is aimed at developing a novel methodology using Bayesian Network (BN) to conduct the dynamic safety analysis for the offloading process of an LNG carrier. It investigates different risk factors associated with LNG offloading procedures in order to predict the probability of undesirable accidents. Dynamic failure assessment using Bayesian theory can estimate the likelihood of the occurrence of an event. It can also estimate the failure probability of the safety system and thereby develop a dynamic failure assessment tool for the offloading process at a particular FLNG plant. The main objectives of this paper are: to understand the LNG offloading process, to identify hazardous events during offloading operation, and to perform failure analysis (modelling) of critical accidents and/or events. Most importantly, it is to evaluate and compare risks. A sensitivity analysis has been performed to validate the risk models and to study the behaviour of the most influential factors. The results have indicated that collision is the most probable accident to occur during the offloading process of an LNG carrier at berth, which may have catastrophic consequences.

Risk analysis of deepwater drilling operations using Bayesian network

Deepwater drilling is one of the high-risk operations in the oil and gas sector due to large uncertainties and extreme operating conditions. In the last few decades Managed Pressure Drilling Operations (MPD) and Underbalanced Drilling (UBD) have become increasingly used as alternatives to conventional drilling operations such as Overbalanced Drilling (OVD) technology. These newer techniques provide several advantages however the blowout risk during these operations is still not fully understood. Blowout is regarded as one of the most catastrophic events in offshore drilling operations; therefore implementation and maintenance of safety measures is essential to maintain risk below the acceptance criteria. This study is aimed at applying the Bayesian Network (BN) to conduct a dynamic safety analysis of deepwater MPD and UBD operations. It investigates different risk factors associated with MPD and UBD technologies, which could lead to a blowout accident. Blowout accident scenarios are investigated and the BNs are developed for MPD and UBD technologies in order to predict the probability of blowout occurrence. The main objective of this paper is to understand MPD and UBD technologies, to identify hazardous events during MPD and UBD operations, to perform failure analysis (modelling) of blowout events and to evaluate plus compare risk. Importance factor analysis in drilling operations is performed to assess contribution of each root cause to the potential accident; the results show that UBD has a higher occurrence probability of kick and blowout compared to MPD technology. The Rotating Control Devices (RCD) failure in MPD technology and increase in flow-through annulus in UBD technology are the most critical situations for kick and blowout.