Probabilistic Analysis Method Research Articles

Failure Behavior and Vulnerability of Containment Structures Subjected to Overpressure Loads Considering Different Failure Criteria

This paper investigates the failure behavior and vulnerability of the containment structure (CS) under internal pressure according to different functional and structural failure criteria. Through nonlinear numerical analysis, the deformation of the structure, degree of concrete cracking, and the distribution of stresses and strains in the prestressing tendons and steel liner under different failure criteria are compared. Combined with probabilistic analysis methods, the vulnerability curves, HCLPF capacities, and total failure probabilities of CS determined by different failure criteria are systematically discussed. Results show that there are some differences in the failure behavior and pressure capacity of CS under different failure criteria for both functional and structural failure modes. Under the same failure mode, the variability of the pressure capacities obtained using different criteria is relatively small. Generally, the total failure probabilities of CS subjected to overpressure loads determined by different failure criteria exhibit significant differences. If it is considered that all the investigated failure criteria have a certain ability to predict containment failure, the probabilistic assessment results derived from the functional failure criterion based on the fracture strain of steel liners and the structural failure criterion based on the global strain are deemed to be more reasonable.

State inference for low-observable distribution system based on graph convolutional network

Distribution network state inference refers to the process of calculating the state variables of each node by using measurement data and network models in the operation of the distribution system. However, the uneven measurement layout and insufficient measurement accuracy in the distribution network have brought great challenges to the state inference of the distribution network. This paper proposes a low-observable distribution network state inference method based on a graph convolution network (GCN), which uses sparse measurement data to infer missing measurement information. Firstly, the observability of the distribution network is analyzed by the numerical probability analysis method. Secondly, the GCN is employed to extract feature information from measurement data and integrate these features. The state inference model of the distribution network based on the GCN is established. Subsequently, power flow constraints of the distribution network are incorporated into the GCN training process to enhance the precision of the generated data. Ultimately, the efficacy of the proposed method is validated using the IEEE 33-node distribution system.

A probabilistic analysis method for evaluating the safety & resilience of urban gas pipeline network

Urban gas pipeline networks (UGPN) provide important support for high-quality urbanization. Therefore, it is imperative to analyze and assess the potential failures of UGPN. A novel probabilistic analysis method is proposed for assessing the safety and resilience of UGPN. Firstly, Bow-Tie analysis is used to identify faults. Then, a four-dimensional resilience assessment network is developed. Bayesian network is utilized to model the relationship between the variables, while dynamic Bayesian network is used to consider the dynamic nature of the system. The results of the study show that the proposed model can accurately estimate faults, consequences, and influence paths. Furthermore, the resilience analysis shows that monitoring the objective conditions is crucial and that the initial failure probability of the UGPN decreases from 0.03767 % to 0.01435 % when connected to a resilience network, indicating that considering resilience can effectively improve the reliability and safety of the UGPN. Two application examples are presented in the paper to validate the functionality of the proposed model. The proposed model can be used to set the state of UGPN to predict the probability of occurrence of a specific event and its consequences and to simulate the improvement trend of UGPN based on the direction of focus of future work.

Quantification of Uncertainty Cost Functions for Controllable Solar Power Modeling

Navigating the secheduling of generation resources of energy in power systems marked by a significant presence of renewable generation involves intricate optimization challenges. The conventional tools for resolving such challenges include programming techniques and heuristic approaches, both contingent upon a precisely articulated target function for optimization. Traditional optimization tools rely on precisely defined target functions, but the evolving landscape of power systems introduces complexity, especially with unpredictable behaviors of renewable sources. The research specifically quantifies penalty costs associated with photovoltaic (PV) generators, employing probabilistic methods for a robust mathematical analysis. The developed analytical model enhances adaptability in economic dispatch problems, considering uncertainty in decision-making. Validation using Monte Carlo simulation emphasizes uncertainty in PV generation and highlights the advantages of the proposed analytic model. The quadratic form of the model aligns coherently with simulation outcomes, contributing significantly to understanding uncertainty quantification in solar power modeling. The research aims to refine controllable solar power models, establish robust uncertainty cost functions, and improve the accuracy of economic dispatch strategies. Ultimately, this work promotes the seamless integration of solar energy into diverse and dynamic energy grids.

Directions of Fiscal Policy Transformation in the Crisis Period of Economic Development of Ukraine

"The purpose of the study is to determine the directions of transformation of the country’s fiscal policy at the expense of the main fiscal sources during the crisis period. The methods of analysis, interpolation, probabilistic analysis, entropy estimation and entropy production were used during the research. The article presents the results of the assessment of the dynamics of the main fiscal indicators of Ukraine in two time frames for the period 2011-2020 and for the period 2011-2022. It was determined that the expansion/ extension of the period of assessment of the dynamics during the crisis of 2021-2022 made it possible to identify qualitative changes in the dynamics of fiscal indicators while maintaining their cyclicality. Such changes are especially destructive in the real sector of economy, which has led to a significant increase in the level of uncertainty of all fiscal indicators. Based on the results of the analysis of entropy production in the system of connections between fiscal indicators, directions for mitigating crisis phenomena in the economy of Ukraine are proposed. In particular, a change from a restrictive fiscal policy to a discretionary (stimulating) one and an increase in public expenditures aimed at stimulating the development of the real sector of the economy and consumption, which will determine positive economic dynamics with a lag of 9 months, are recognized as expedient."

Dependability of local power systems based on autonomous generation units

Aim. The paper aims to create and verify methods for predicting the dependability of autonomous power supply to local low-power consumers. Methods. The work uses simulation tools, i.e., computational experiment for predicting the dependability and safe service life of autonomous cluster distributed energy networks that include various power generation installations, power transmission lines and an automated self-regulation system for the purpose of diversifying the risks of failure. Results. Using methods of probabilistic analysis of technical system safety, the dependability of power supply is predicted in terms of possible energy network failures and threats to the life of consumers. The problem can be solved by simulating and predicting the moment of failure of a power network, i.e., critical violation of design conditions, accident, etc. This scenario allows – for long periods of operation – simulating failure situations due to degradation and destruction of elements, units, power lines, as well as failures under multidimensional uncertainty of prediction of dependability and quality of power supply. The key output of simulation is a set of recommendations to ensure energy security. The paper presents simulated dependability indicators, analysis of the effect of structural design solutions underlying a distributed energy network on the dependability and energy security. Conclusion. The use of computational experiments and results of power supply dependability simulation as part of predicting probabilistic indicators of failures of equipment and elements of cluster energy networks, identifying their consequences for the energy security of sparsely populated and remote locations lays the foundation for quality management of energy networks. Additionally, in the future, additional causes and initial events may be included in the model for the purpose of studying the facts of failures. For instance, such causes as uneven generation using renewable sources, finite fuel supply for conventional generation sources, reliability of the power grid personnel. The proposed approach allows verifying promising new energy network design solutions, for example, including energy storage systems into a distributed energy network that – at various points in time – may be either “generators” or “consumers”. Simulation of local energy networks is of practical interest and is important for improving their consumer quality, resistance to hazardous environmental effects.

Study on seismic risk assessment model of water supply systems in mainland China

Abstract. Using the peak ground acceleration (PGA) under four exceedance probabilities calculated via a probabilistic seismic hazard analysis method for China, the probability density function of PGA was obtained by fitting with the Cornell seismic hazard exceedance probability PGA function model. Combined with the seismic fragility function of the water supply system calculated on the basis of the empirical matrix of actual earthquake damage and the exposure of fixed assets, the expected seismic loss and expected loss rate models of the water supply system were established, and the classification standard with the expected seismic loss rate of the water supply system as the index was proposed. The seismic fragility of the water supply system was classified, and the exposure of the water supply system was analyzed. The total fixed assets in the Statistical Yearbook of Urban Water Supply, henceforth called Water Supply Yearbook, were taken as the exposure to earthquake in the region. The accuracy of the fragility model in this paper was verified through the actual earthquake damage losses in Deyang. Taking the water supply system of 720 cities in mainland China as an example, the distribution maps of expected seismic loss and expected loss rate were calculated and drawn. The expected loss rate model was verified by the key earthquake prevention areas in mainland China. The assessment model based on the expected loss and expected loss rate was taken as the seismic risk assessment model of water supply systems in mainland China.

Analysis of Soil Vulnerability Level Due to Earthquake in Tarutung Region and Its Surroundings Using Probabilistic Seismic Hazard Analysis Method

Analysis of Soil Vulnerability Level Due to Earthquake in Tarutung Region and Its Surroundings Using Probabilistic Seismic Hazard Analysis Method

Probabilistic analysis of bolted joint anti-loosening under cyclic transverse load

The loosening of bolted joints under cyclic transverse load is a crucial failure mode, and the uncertainty of the relevant parameters of the bolted joint will affect the loosening state. However, there are few bolt-loosening probability analysis and evaluation methods based on parameter uncertainty in previous studies. In this paper, different loosening states are analyzed based on the relationship between pitch torque, thread friction torque, and bearing friction torque. Considering the uncertainty of process parameters, a probabilistic analysis method of the bolted joint anti-loosening under cyclic transverse load is proposed. In the proposed method, the finite element model verified by the fastening experiment is used to verify the accuracy of the bolted joints’ loosening criterion. Then, the loosening criterion is used as the Limit State Function (LSF). According to the LSF, an Adaptive Kriging-Kernel Density Importance Sampling (AK-KDIS) method is proposed for solving the anti-loosening reliability. This method combines adaptive Kriging model with the importance sampling method, effectively reducing the number of sampling samples and the number of LSF calls. The classical and robust Quasi-Monte Carlo (QMC) method is also used to evaluate the reliability of anti-loosening to verify the accuracy of the proposed AK-KDIS method. Finally, the practical application of probabilistic analysis of anti-loosening is presented, and the effectiveness of the proposed method is analyzed in detail. The results indicate that the AK-KDIS method has higher efficiency while ensuring calculation accuracy compared with the QMC method.

Reliability Evaluation of EB-PVD Thermal Barrier Coatings in High-Speed Rotation and Gas Thermal Shock

The uncertain service life of thermal barrier coatings (TBCs) imposes constraints on their secure application. In addressing this uncertainty, this study employs the Monte Carlo simulation method for reliability evaluation, quantifying the risk of TBC peeling. For reliability evaluation, the failure mode needs to be studied to determine failure criteria. The failure mode of high-speed rotating TBCs under gas thermal shock was studied by combining fluid dynamics simulations and experiments. Based on the main failure mode, the corresponding failure criterion was established using the energy release rate, and its limit state equation was derived. After considering the dispersion of parameters, the reliability of TBCs was quantitatively evaluated using failure probability and sensitivity analysis methods. The results show that the main mode is the fracture of the ceramic layer itself, exhibiting a distinctive top-down “step-like” thinning and peeling morphology. The centrifugal force emerges as the main driving force for this failure mode. The failure probability value on the top side of the blade is higher, signifying that coating failure is more likely at this location, aligning with the experimental findings. The key parameters influencing the reliability of TBCs are rotation speed, temperature, and the thermal expansion coefficient. This study offers a valuable strategy for the secure and reliable application of TBCs on aeroengine turbine blades.

Improving computer network security evaluation with grey relational analysis and probabilistic simplified neutrosophic sets

The popularity of computer network has provided great convenience for people’s work and life, but it has also brought corresponding information security problems. It is very important to do a good job in computer network security evaluation. Conventional computer network security evaluation can be realized with the help of firewalls, antivirus software, etc., while in the face of complex computer network applications, it is necessary to adopt a security evaluation method with good operability and wider application range. The computer network security evaluation is viewed as multiple attribute decision-making (MADM) issue. In this paper, an extended probabilistic simplified neutrosophic number grey relational analysis (PSNN-GRA) method is established for computer network security evaluation. The PSNN-GRA method integrated with Criteria Importance Though Intercrieria Correlation (CRITIC) method in probabilistic simplified neutrosophic sets (PSNSs) circumstance is applied to rank the optional alternatives and a numerical example for computer network security evaluation is used to proof the newly proposed method’s practicability along with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute.

Research on decision and risk analysis for portal frame

As lightweight and high-strength structures, steel structures are gradually widely used in various buildings, such as high-rise buildings and industrial plants. After comparing various probability analysis methods, Monte Carlo simulation is chosen to analyze the moment of inertia design value for a non-sway steel frame. This paper mainly introduces the engineering load and reliability design principles of gantry steel frames. By consulting specifications and using relevant calculation formulas and basic parameters, different engineering load effects are discussed and calculated. After completing the load condition calculations, the resistance design value is calculated by using the limit state design principles. Finally, the sectional dimension design is conducted based on Monte Carlo simulation. By means of limit state design and Monte Carlo simulation, the ideal design value for calculating the moment of inertia can be easily calculated. Through these analyses, the superiority of Monte Carlo simulation in analyzing the failure probability of complex buildings is demonstrated. Unlike other methods, it does not need to compute functions of various factors in advance.

Classification Study of New Power System Stability Considering Stochastic Disturbance Factors

Power system instability causes many local or large-scale power outage accidents. To maintain sustainable development, a new power system construction aimed at maximizing new energy consumption is being put on the agenda. However, with a large increase in stochastic disturbance factors (SDFs), the system gradually shows strong stochasticity, and the stability presents greater complexity. It is necessary to analyze the impact on the system based on different processing methods of SDFs to maintain system stability. This paper delves into the impact of SDFs on system stability by analyzing and summarizing both stochastic variables and processes. Initially, the SDFs in the power system are meticulously analyzed and categorized. When the SDFs are treated as stochastic variables, the probabilistic stability is classified and evaluated based on a probability analysis method, which includes the probabilistic small-disturbance stability, the probabilistic transient stability, and the probabilistic voltage stability. When the SDFs are treated as stochastic processes, the stochastic stability is classified and evaluated by using a stochastic analysis method, including the stochastic small-disturbance stability, the stochastic transient stability, and the stochastic voltage stability. Finally, the research perspectives on SDFs and system stability are discussed and prospected.

Probabilistic analysis of tunnel face seismic stability in layered rock masses using polynomial Chaos Kriging metamodel

Face stability is an essential issue in tunnel design and construction. Layered rock masses are typical and ubiquitous; uncertainties in rock properties always exist. In view of this, a comprehensive method, which combines the Upper bound Limit analysis of Tunnel face stability, the Polynomial Chaos Kriging, the Monte-Carlo Simulation and Analysis of Covariance method (ULT-PCK-MA), is proposed to investigate the seismic stability of tunnel faces. A two-dimensional analytical model of ULT is developed to evaluate the virtual support force based on the upper bound limit analysis. An efficient probabilistic analysis method PCK-MA based on the adaptive Polynomial Chaos Kriging metamodel is then implemented to investigate the parameter uncertainty effects. Ten input parameters, including geological strength indices, uniaxial compressive strengths and constants for three rock formations, and the horizontal seismic coefficients, are treated as random variables. The effects of these parameter uncertainties on the failure probability and sensitivity indices are discussed. In addition, the effects of weak layer position, the middle layer thickness and quality, the tunnel diameter, the parameters correlation, and the seismic loadings are investigated, respectively. The results show that the layer distributions significantly influence the tunnel face probabilistic stability, particularly when the weak rock is present in the bottom layer. The efficiency of the proposed ULT-PCK-MA is validated, which is expected to facilitate the engineering design and construction.

A probabilistic analysis method based on Noisy-OR gate Bayesian network for hydrogen leakage of proton exchange membrane fuel cell

The proton exchange membrane fuel cell (PEMFC) is one of the crucial power units of the hydrogen vehicle. This work proposes a method based on the Noisy-OR gate Bayesian network to evaluate the hydrogen leakage probability of PEMFC. The generation mechanism of hydrogen leakage in fuel cells was analyzed using the fault tree theory. The conditional probability was determined using the Noisy-OR gate model in the Bayesian network, and the probability of hydrogen leakage in the fuel cell was 0.04. Through sensitivity analysis, the key factors that can cause hydrogen leaks in fuel cell were identified. The method proposed in this study can aid in PEMFC accident prevention and control strategies.

Probabilistic assessment and expression of load factor design model for explosive blast loading

Explosive blast loads are subject to variability and uncertainty, but most current studies of building blast resistance are deterministic. To quantitatively assess the safety and reliability of a structure under explosion, the variability of the blast load needs to be quantified. A probabilistic analysis method of blast loads based on the finite element model and Monte Carlo simulation was proposed, and the applicability of this method was verified. The blast load probability model was developed for the scaled distances range from 1 m/kg1/3 to 40 m/kg1/3. The effect of the mass safety factor of 20 % on the probability of exceeding the design blast load was assessed. At last, based on the established probabilistic model for the blast loads, the expression for calculating the load factor of any reliability level at any location was presented.

SS-MASVM: An advanced technique for assessing failure probability of high-dimensional complex systems using the multi-class adaptive support vector machine

The integrity of structures in contemporary engineering practices is heavily influenced by inherent uncertainties, which necessitates the development of reliable structural reliability analysis methods for uncertainty quantification and probabilistic analysis. This paper investigates the integration of surrogate models and subset simulation (SS) to accurately compute the failure probabilities of high-dimensional complex systems. Focus is placed on enhancing the computational efficiency and accuracy through using the multi-class adaptive support vector machine (MASVM). The proposed SS-MASVM method incorporates more efficient learning functions, integrates K-means clustering, and employs SS technology for precise estimation of failure probabilities of high-dimensional complex systems. Numerical and engineering structural examples validate the effectiveness of the proposed method and highlight its potential in practical reliability analysis, and the results disclose that the SS-MASVM is efficient and accurate for assessing the complex system reliability.

Research on lean management and innovation capability evaluation of technological small and medium sized enterprises under probabilistic hesitant fuzzy sets

The lean management and innovation capability evaluation of technological small and medium sized enterprises is a classical multi-attributes group decision-making (MAGDM). Recently, the probabilistic hesitant fuzzy sets (PHFSs) have been extended to apply in many fields. However, the existing models don’t evaluate the alternative considering the psychological factors. Thus, in this paper, an extended probabilistic hesitant fuzzy grey relational analysis (PHF-GRA) method is proposed to reduce the restrictions of GRA method by combining with cumulative prospect theory (CPT), considering the psychological preference. In addition, the PHFSs assigns probability values to different degrees of hesitancy, which shows its superiority in complex environment. At the same time, the weight vectors of each attribute are calculated by the entropy values of different foreground decision elements. Then, probabilistic hesitant fuzzy GRA (PHF-GRA) model based on CPT model is constructed for MAGDM under PHFSs. Finally, a practical example study for lean management and innovation capability evaluation of technological small and medium sized enterprises is constructed to validate the proposed GRA (PHF-GRA) model based on model CPT and some comparative studies are constructed to verify the applicability.

Improved computational methods for probabilistic liquefaction hazard analysis

Current procedures for analysis of and design against liquefaction hazards focus primarily on the use of probabilistic ground motions at a single ground-shaking hazard level, with the cyclic loading represented by a peak ground acceleration (PGA) corresponding to a target return period and a single representative moment magnitude Mw. These parameters are typically used in conjunction with deterministic simplified procedures for estimating liquefaction triggering and consequences that largely neglect the considerable uncertainties inherent to liquefaction problems. Motivated by these limitations and the resulting inconsistencies in liquefaction design levels, early methods for probabilistic liquefaction hazard analysis (PLHA) were proposed that incorporate the full ground motion hazard space, integrated with probabilistic liquefaction triggering models. Such methods provide liquefaction factor of safety (FSL) hazard curves for standard penetration test (SPT) data. Recognizing the increased use of higher-resolution cone penetrometer test (CPT) data in engineering analysis and design and the potential computational challenges it presents, an expanded suite of probabilistic triggering models, and wider availability of more detailed seismic hazard data, an improved PLHA computational methodology is presented in this study. The methods described utilize the U.S. Geological Survey National Seismic Hazard Model directly through web services to obtain PGA hazard and disaggregation calculations for any site and average shear wave velocity in the upper 30 m of the site (Vs30) in the conterminous United States, to reconstruct the PGA hazard space for use in PLHA calculations, and to employ array calculations for efficient liquefaction hazard curve estimates for the thousands of CPT measurements in a given profile. The framework presented here is modular in nature, and discussion on the use of alternative models and extension to probabilistic liquefaction consequence evaluation is presented. The importance of appropriate representation of probabilistic liquefaction model uncertainties is also highlighted, along with the impacts of different levels of uncertainty on the PLHA calculation. Finally, a potential roadmap for incorporation of the PLHA framework in seismic provisions is presented, with an illustration of how it can address current limitations and impacts in liquefaction hazard analysis and design.

Quantitative microbiological risk assessments for Salmonella spp. contaminated Taiwanese salty chicken in the Taiwanese population

The aim of the study is to develop Salmonella spp. Quantitative microbial risk assessments (QMRA) and to evaluate the risk of Salmonellosis illness in the Taiwanese population after consumption of Taiwanese salty chicken (TSC). We assume that Salmonella spp. May contaminate the fresh raw chicken used in TSC. After transport to the diner, fresh raw chicken is received, cleaned, and surface-washed by diner staff. The TSC is then cooked and sold to consumers. We set four different cross-contamination scenarios to evaluate the contamination level of Salmonella spp. In TSC. We used a Monte Carlo simulation method, a probabilistic analysis method, and exceedance risk to evaluate the risk of Salmonellosis illness. When the exceedance risk was 5 %, and taking the Taiwanese population above 19 years old as an example, the rate of contracting Salmonellosis from the consumption of TSC will be 2.94 % (2.94 million per 100 million people) if the chef does not clean their hands, knives, or cutting boards. However, if the chef washes their hands, knives, and cutting boards with cold water and soap, the illness rate of Salmonellosis from consuming TSC will be 1.93E-04 % (193 per 100 million people). Sensitivity analysis indicates that the most important risk factor in the QMRAs of TSC is the temperature of the fresh raw chicken during transportation, following which were the Salmonella spp. Residual. If the staff of the diner separates the cooking tools used for raw ingredients and those for cooked food, the illness risk of Salmonellosis will be very low.