Basic Events Research Articles

Risk Analysis and Evaluation of Nuclear Security Radiation Events in Spent Fuel Reprocessing Plants

Spent fuel reprocessing is of great significance to the nuclear fuel cycle and the sustainable development of nuclear energy. At the same time, nuclear security radiation incidents in the spent fuel reprocessing plant are also related to national personal and property safety, which play a pivotal role. In this paper, the spent fuel reprocessing plant is divided into four plant areas: the main process area, the three-waste area, the auxiliary equipment area, and the pre-plant area, which are further subdivided into 12 evaluation units. The expert scoring method is used to score and evaluate the possibility of eight basic nuclear accident types in each area, namely radioactive dispersal device, computer nuclear security, destruction of nuclear facilities, transportation nuclear security, internal threat, potential threat, illegal transfer, and theft. According to the professional titles, length of service, education and other qualifications of experts, different weights are assigned to the experts. The scoring results are applied to the Fault Tree Analysis (FTA) of nuclear security events as the probability of basic events, so as to obtain the risk of each basic event. At the same time, the fuzzy comprehensive evaluation method and probability–mathematical statistics method are used to evaluate each evaluation unit to determine the risk of each evaluation unit and the plant area. There results show that the main process area has the highest risk degree, while the pre-plant area has the lowest risk degree, and there is a 1.5-fold relationship. This research provides theoretical and technical support for the safety management and operation of spent fuel reprocessing plants. The analysis results of this paper can be used as a reference for the proportion of nuclear security protection improvements in each plant area, so as to achieve an efficient safety protection effect. The research method in this paper can be also applicable to other similar places by providing as input the corresponding probability of occurrence to obtain the index of its risk degree, so as to reasonably allocate funds and manpower and reduce risks.

Analysis of Inventory Problems Using the Fault Tree Analysis Method (Case Study: PT Kimpai Dyna Tube)

PT Kimpai Dyna Tube focuses on PBL (Plastic Barier Laminate) Tube products, which currently focuses on providing cosmetic products with plastic-based materials. Based on the results of the field survey on the inventory process, several problems were found, namely overstock material, deadstock material and outstanding material. The analysis process used is the Fault Tree Analysis (FTA) method because of its efficiency. Based on the results of the FTA, it was found that the basic event in the case of overstock material and deadstock material, namely changes in skincare trends in the community that caused very significant changes in the results of purchase orders and forecasts. The solution for the problem of overstock material and material deadstock is the application of a stock level system on inventory. Meanwhile, in the Outstanding material case, 3 basic events were obtained including, long lead time for delivery by overseas suppliers, trouble machines and many rejected quality items. Based on the results of the FTA, solutions were found for these problems, including performing careful calculations for the process of purchasing goods from foreign suppliers, providing machine spare parts and coordinating with suppliers and customers regarding the quality standards of production.

Reliability Estimation of Low-Voltage Power Distribution Systems

Many accidents always happen in the electricity distribution sector, which requires the use of control strategies in this field. Therefore, this study presented a risk assessment method based on a fuzzy Bayesian network (FBN) and William Fine method in low-voltage power distribution systems to reduce the structural uncertainties of the studies and examined the reliability of these systems to achieve proper performance in emergencies. The first step is to develop a fault tree (FT) and event tree (ET) in bow tie (BT) format. In this study, fuzzy theory and expert judgment were used to qualitatively analyze the root events affecting the system failure and determine the relevant probabilities. Then, deductive and inductive reasoning was done after determining the likelihood of basic events (BEs) and establishing the Bayesian network (BN). Then, the final risk was estimated using William Fine’s method and fuzzy logic. The results of a case study in a low-voltage power distribution system showed that there were 36 BEs and 20 intermediate events (IEs) for system failure, and the reliability of the system was obtained as 0.96654476 and 0.96654476 based on the FT and FBN, respectively. According to the results of BE25 (no installation of the earth) and BE18 (contact of metal objects with the live wire), the most critical events were based on FBN. The results showed that FBN simulation and its combination with static methods such as BT and William Fine provide a suitable method for the accurate identification of distribution systems. Therefore, the approach of the present study can help the decision makers and analysts of the electricity industry to prevent possible failures due to system changes.

Dynamic risk assessment for underground gas storage facilities based on Bayesian network

Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage process safety.

Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning network

Abstract Among all kinds of coal production disasters, the consequences of gas disaster are the most serious. As the existing coal mine gas explosion disaster pre-control management theory and method system is not satisfactory, the neural Turing machine (NTM) deep learning network algorithm is used to calculate and analyse the risk source early warning identification of coal mine gas explosion accidents. Institute with data sets of gas gas accident knowledge base matter each event to cause an (basic or intermediate events) as an example, through the study of the depth of NTM network algorithm calculation analysis shows that self-rescuer failure, personnel peccancy operation, such as downhole safety management does not reach the designated position is easy to cause important hazard of gas explosion accident, the probability to cause an 0.567. Based on the constructed NTM deep learning network algorithm, the risk factors and their weights in the early warning identification of gas explosion accidents are calculated and analysed. Through calculation analysis, it can be seen that the highest weight of risk factors is gas concentration, with a weight of 96. In the early warning identification of hazard sources, the hazard factor next to gas concentration is mine combustibles, with a weight of 75.

How dependent basic events with interval‐valued probabilities affect fault tree analysis

AbstractThis paper studies the effect of the dependence state between basic events (BEs) on fault tree analysis (FTA) when the probabilities of events are characterized by interval values. The well‐known Frèchent bounds are extended for modeling six different types of dependence states between BEs. Three indices, called average dependence effect (ADE), location effect (LE) and size effect (SE), are defined for evaluating the effect of the dependence states between BEs on the probability of top event (TE) and identifying influential and non‐influential dependence states. Then, the proposed method is applied to fault tree (FT) examples, thereby explaining the dependence problem in FTA. To further verify the practicability of the method, FTA of the unilateral asymmetric movement failure of an aircraft flap mechanism is performed. The results show that: (i) the opposite and negative dependence contribute to the reliability of a parallel system while the perfect and positive dependence reduce it, (ii) the perfect and positive dependence contribute to the reliability of a series system while the opposite and negative dependence reduce it, and (iii) parallel systems are more reliable than series systems regardless of the dependence between BEs.

Simulating Complex for Debugging Algorithms of Autonomous Underwater Robots

The program simulating complex for debugging of developing systems and control algorithms of autonomous underwater robots (AUR) is considered. The complex allow to simulation wide spectrum of modeling data, tune up developing algorithms and operate as simulator directly in the AUR control system environment. Into its structure enter model of movement, copy of AUR control system environment with wide spectrum of modeling data, the editors of basic complex elements processes and events.

A new approach to chemicals warehouse risk analysis using computational fluid dynamics simulation and fuzzy Bayesian network

This study aims to assess the risk of chemicals warehouse using a Bayesian networks (BNs) and computational fluid dynamics (CFD). A methodology combining Bow-Tie (BT), fuzzy set theory (FST), and Bayesian network was employed, in which the BT was drawn for chemical spill scenarios. FST was utilized for the estimation of the basic events (BEs) occurrence probability, and the probability of interaction among a set of variables was obtained using BNs. Pool fire scenario radiation heat flux was evaluated using CFD code, fire dynamic simulator (FDS), and the solid flame model (SFM). Fail in forklift brake system (BE1), was the most significant cause for a chemical spill. Based on the CFD model, the heat flux is 31 kW/m2 at a distance of 3.5 m from the fire, decreasing to 6.5 m gradually. The maximum safety distance of 4 m is predicted by the CFD for heat flux that exceeds 12.5 kW/m2; however, SFM predicts approximately 4.5 m. According to the results, the amount of posterior risk is higher than the prior value. The framework presented in the chemicals warehouse for consequence analysis and dynamic risk assessment (DRA) of pool fire could be used for preventing the accidents and domino effects in the chemicals warehouse.

The Impact of Marine Engine Component Failures upon an Explosion in the Starting Air Manifold

Based on available sources, the frequency of explosions in the marine engine’s starting air manifolds is determined under real conditions. A cause-and-effect analysis of these explosions and their root causes are identified. A probabilistic model of an explosion in the starting air manifold of a marine engine is built using a fault tree analysis (FTA). Using a stochastic simulation (Monte Carlo) and an exact reliability availability calculation (ERAC) algorithm applied to the developed FTA model, selected reliability measures are calculated to describe an incident of the top event, which involves an explosion in the starting air manifold. For such an event, several factors are calculated, including the availability, the unavailability, the failure frequency, and the mean time to failure. Based on the simulations, the relative frequency of the top event is determined in relation to the number of events that can simultaneously occur and lead to an explosion. The significance of each basic event is assessed to determine their individual impact on the explosion incident. The following measures are used: the Vesely–Fussell measure of importance, the criticality measure of importance, the Birnbaum measure of reliability importance, and the Birnbaum measure of structural importance. The results of the analysis show that defective starting air valves are most responsible for the explosion incident in the starting air manifold. During the first year of the ship’s operation, the reliability does not fall below the value of 0.65, while the mean time to failure and the top event frequency are statistically at the level of one explosion per approximately 2.28 years of continuous engine operation.

Data-driven extraction and analysis of repairable fault trees from time series data

Fault tree analysis is a probability-based technique for estimating the risk of an undesired top event, typically a system failure. Traditionally, building a fault tree requires involvement of knowledgeable experts from different fields, relevant for the system under study. Nowadays’ systems, however, integrate numerous Internet of Things (IoT) devices and are able to generate large amounts of data that can be utilized to extract fault trees that reflect the true fault-related behavior of the corresponding systems. This is especially relevant as systems typically change their behaviors during their lifetimes, rendering initial fault trees obsolete. For this reason, we are interested in extracting fault trees from data that is generated from systems during their lifetimes. We present DDFTAnb algorithm for learning fault trees of systems using time series data from observed faults, enhanced with Naïve Bayes classifiers for estimating the future fault-related behavior of the system for unobserved combinations of basic events, where the state of the top event is unknown. Our proposed algorithm extracts repairable fault trees from multinomial time series data, classifies the top event for the unseen combinations of basic events, and then uses proxel-based simulation to estimate the system’s reliability. We, furthermore, assess the sensitivity of our algorithm to different percentages of data availabilities. Results indicate DDFTAnb’s high performance for low levels of data availability, however, when there are sufficient or high amounts of data, there is no need for classifying the top event.

The effects of combined spinal-epidural analgesia and epidural anesthesia on maternal intrapartum temperature: a randomized controlled trial

BackgroundLabor epidural analgesia has been suggested to be associated with intrapartum fever. We designed this study to investigate the effects of epidural analgesia and combined spinal-epidural analgesia on maternal intrapartum temperature.MethodsFour hundred healthy nullipara patients were randomly assigned to receive either epidural analgesia (EA group) or combined spinal-epidural analgesia (CSEA group). Maternal temperature was measured hourly after analgesia administration. The primary outcome was the incidence of maternal fever, and the secondary outcomes were the duration of analgesia, analgesia to full cervical dilation and analgesia to delivery. Neonatal outcomes and other basic labor events were also recorded.ResultsMaternal temperature gradually increased with time in both analgesia groups during labor. However, the CSEA group had a lower incidence of maternal fever, and a lower mean maternal temperature at 5 h, 6 h, and 9 h after analgesia. In addtion, the CSEA group also had a shorter time of analgesia duration, analgesia to full cervical dilation, analgesia to delivery, and less dose of epidural local anesthetic than the EA group.ConclusionOur findings suggest that combined spinal-epidural analgesia is associated with a lower risk of intrapartum fever than epidural analgesia.Trial registrationChiCTR1900026606. Registered on 16/10/2019.

Diffusion: Isotope Effect and Manning Factor in Liquids

In this paper, the diffusion isotope effect and the Manning factor are investigated by means of Molecular Dynamics simulations in liquid Cu-Ag alloys. The values for the diffusion isotope effect parameter allow for the estimate of the number of atoms that are moving cooperatively in a basic diffusion event as ‘seen’ by a given (tracer) atom. On average, in the considered alloys and considered temperatures, this is limited to between 5 and 15. This is consistent with results of Molecular Dynamics simulations on the average coordination number calculations. This would suggest that, together with a given atom, a majority of the neighbouring atoms are involved in a basic diffusion event. Results for the Manning factor (MD simulation) for Cu-Ag liquid alloys are seemingly in agreement with the direct exchange mechanism where only two atoms are involved in the elementary diffusion event. This is not in apparent agreement with the isotope effect results. It was shown, however, that any ring mechanism, or, more complex, cage mechanism are, in fact, a combination of several simultaneously happening direct exchanges. Any other possible mechanisms for diffusion in liquids is most likely a combination of direct exchanges as well. It can be seen then that the collective nature of all considered mechanisms is very similar and follows the direct exchange signature properties.

Combining the bow-tie method and fuzzy logic using Mamdani inference model

As a hybrid tool, Bowtie might be effective as a qualitative tool. However, when the quantitative contribution is considered, it relies on databases, and as such, the analysis is affected by the subjective judgment of the specialist, data variability, or lack of data which may lead to vagueness and imprecise information. Fuzzy logic can help to shed light on this matter as it is capable of resembling human reasoning. The approach offers various mechanisms to overcome these uncertainties. This paper proposes a combination of Bowtie and fuzzy logic, using a case study concerning the rupture of isobutane storage tank. The Mamdani inference method is investigated. Additionally, the Fussel-Vasely equation was used to calculate the critical importance for each basic event, which brings a complementary understanding of the system. Six tests were performed to analyse the frequencies and fussel-vasely numbers of the five basic events: “PI” (Pressure Indicator), “LIAH” (Level and Indicator Alarm High), “HE” (Human Error), “SV” (SAFE VALVE) and “DS” (Disposal System). The analysis showed that the basic event “SV” as the most critical event, followed by “LIAH” and “Human Error”.

Fire Risk Assessment of a Ship’s Power System under the Conditions of an Engine Room Fire

This paper presents a risk assessment method for a ship’s power system under the conditions of an engine room fire based on the expert comprehensive evaluation (ECE) method combined with the fuzzy fault tree analysis (FFTA) method. The composition of the main engine system in the engine room and the failure logic of each subsystem were analyzed, and the fuzzy fault tree of a ship engine room fire was constructed. The probability of system failure and the importance of basic events were calculated. The fire safety risk assessment model was established using the safety risk matrix. The risk assessment of a ship engine room fire was implemented. The results demonstrated that the fire frequency of the ship engine room was 5.232 × 10−6 h−1. The fire risk of the main engine fuel system was the highest. Fuel leakages from diesel supply tanks and heavy fuel oil tanks are the main cause of accidents. The proposed method eliminated the influence of incomplete statistics in the risk assessment process and improved the accuracy and credibility of the reassessment results.

Research on Common Mode Analysis Method in System Safety Analysis

In system safety analysis, the existence of Common Mode Failure (CMF) greatly undermines the independence of components and poses great threat to safety of the system. In Common Mode Analysis (CMA), whether CMF needs to be considered for basic events under different types of logic gates in Fault Tree, it lacks strict reasoning and comprehensive consideration. This paper takes CMF of the same and different levels under "AND gate" and "OR gate" in fault tree as an example, and uses reasoning analysis and mathematical calculation methods to compare the influence of the existence of CMF on failure rate of the system. The results show that whether CMF need to be considered for the component depends on the type of logic gate correspond to the higher-level component. The research results have certain reference value for the analysis of CMF in CMA.

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.

Characterization of intron 7 to exon 8 of heat shock protein 90-aa1 (hsp90aa1) gene in dominant brown layer chicken using some bioinformatic tools


 HSP90AA1, an isoform of HSP90 has been characterized to indicate it plays important roles in basic cellular events. It is activated in chicken in response to heat stress. This study was aimed at the computational analysis of the biochemical cum structural features and an evolutionary relationship study on the HSP90AA1 gene in Dominant brown layers (DBL) and some selected avian species using bioinformatics tools. ProtParam for physicochemical properties. Scanprosite for post-translational modification sites, Netphos-3.1 for phosphorylation sites, BDM-PUB program for Ubiquitination sites, PDBSUM for Secondary structure and homology modelling with SWISS-model. The findings revealed that intron 7 and exon 8 of HSP90AA1 protein in DBL had a molecular weight of 24681.19Da and an instability index of 27.60, contains N-myristoylation, Protein-kinase-C-phosphorylation and Tyrosine-kinase-phosphorylation site-2 post-translational modification sites, 4-serine and 2-threonine phosphorylation sites and 12-ubiquitination sites. The evolutionary relationship study found Japanese quail to be in a sister branch close to DBL and chicken. Motifs detected in the avian species revealed the gene to be highly conserved. The secondary structure consisted of 16-helices, 3-sheets and 14-strands. The homology modelling was 87.25% sequence identity with human MC-HSP90-alpha. The study elucidates the components and characteristics of HSP90AA1 in DBL in response to heat stress.

Is the drill pipe safe during drilling process?—A new method for evaluating drill pipe failure risk based on the Noisy-OR gate and bayesian network

To evaluate and prevent the failure of drill pipes, this paper proposed a new method for evaluating drill pipe failure risk based on the Noisy-OR gate model and Bayesian network (BN). Different from conventional drill pipe failure analysis, this paper considers the environment-material-human factors as a whole rather than separate. Firstly, the fault tree and the BN model were constructed considering “environment-material-human factors”. Secondly, the influence degree evaluation and drill pipe environmental sensitivity test were investigated. Finally, a case study and basic events sensitivity analysis was carried out with a real well-X as an example. Our results indicated that the top 5 sensitive basic events of drill pipe failure were corrosive medium (0.281), overload (0.178), improper processing technology (0.137), mismanagement of drill pipe (0.102), and structural design defect (0.091) respectively. The mitigation measures were put forward and the risk of drill pipe failure was reduced from 65% to 24%. The real case study showed the applicability and feasibility of the proposed model in this paper.

Role of the CSTO in Promotion of Collective Security

This article presented to the attention of readers is dedicated to the analysis of history, present condition and development prospects of inter-state relations on national and regional security between former Soviet republics, now new sovereign Eurasian states, as well as to the examination of organization forms of such relations. Having formulated basic principles of cooperation on these issues and underlying the need of collective and coordinated activities against threats to their security and stability in new geopolitical realities stipulated by the collapse of the USSR nine post-Soviet states concluded the Collective Security Treaty which came into force in 1994. Five years later, in April 1999, presidents of six these countries, namely Armenia, Belarus, Kazakhstan, Kyrgyzstan, Russia and Tajikistan, signed the protocol on prolongation of the Collective Security Treaty for the next five years while Azerbaijan, Georgia and Uzbekistan refused to extend their membership in the Treaty and left it. It happened since due to a number of various domestic and external reasons the formation of national statehood of new sovereign states began to differ substantially from each other as to their strategic goals, aspirations and orientations which subsequently caused serious changes in their approaches to building relations with their foreign partners in bilateral and multilateral forms. In this connection in May 2002 those countries that retained their membership in the Collective Security Treaty decided to reorganize it into a new alliance with a higher degree of integration of its members in order to increase its role in promoting national and regional security – the Collective Security Treaty Organization. The present article examines basic events connected with the Collective Security Treaty Organization formation over the next 20 years, evolution of its goals and tasks, as well as requirements of making concrete directions of its activities more precise because of fundamental changes in the international environment. In the course of the undertaken analysis most important achievements of the Collective Security Treaty Organization during these years were singled out as well as concrete shortcomings in its activities were noted. Also, alternative variants of further development of this international security organization including those that may exceed the post-Soviet space were mentioned.

Optimizing resource allocations to improve system reliability via the propagation of statistical moments through fault trees

Fault tree analysis remains a significant alternative for modeling and analyzing reliability and failure modes. Traditionally, the probabilities of the basic events are assumed as point estimates from which the fault tree is used to estimate the probability of the top failure event. This paper instead proposes using this framework for a different purpose: framing the model parameters describing the basic events as random variables, which collectively contribute to the statistics of the estimate of the top failure event. An approach for uncertainty propagation via statistical moments is developed for both static and dynamic gates with specific consideration of the effects of the dependency conditions for different distribution types. Assuming there is a cost to acquire model parameters with reduced uncertainty, this framing can thus be used to identify the best set of model parameters possible within a constrained budget to minimize uncertainty in the resulting estimate of the top failure event. With these models in place, the statistical moments of the top event can be connected to all lower levels, and the statistics in the estimate of the top event can be optimized with a given cost constraint (e.g. allocating resources to reducing uncertainty in the knowledge of basic events to minimize variance in the estimate of the top failure event). This optimization of the uncertainty in the top event is demonstrated with a case study.