A hybrid load flow and event driven simulation approach to multi-state system reliability evaluation

Structural complexity of systems, coupled with their multi-state characteristics, renders their reliability and availability evaluation difficult. Notwithstanding the emergence of various techniques dedicated to complex multi-state system analysis, simulation remains the only approach applicable to realistic systems. However, most simulation algorithms are either system specific or limited to simple systems since they require enumerating all possible system states, defining the cut-sets associated with each state and monitoring their occurrence. In addition to being extremely tedious for large complex systems, state enumeration and cut-set definition require a detailed understanding of the system׳s failure mechanism. In this paper, a simple and generally applicable simulation approach, enhanced for multi-state systems of any topology is presented. Here, each component is defined as a Semi-Markov stochastic process and via discrete-event simulation, the operation of the system is mimicked. The principles of flow conservation are invoked to determine flow across the system for every performance level change of its components using the interior-point algorithm. This eliminates the need for cut-set definition and overcomes the limitations of existing techniques. The methodology can also be exploited to account for effects of transmission efficiency and loading restrictions of components on system reliability and performance. The principles and algorithms developed are applied to two numerical examples to demonstrate their applicability

A Framework for Power Recovery Probability Quantification in Nuclear Power Plant Station Blackout Sequences

The safety of Generations II and III nuclear power plants relies on the availability of AC power, which is required for decay heat removal. This AC power (designated offsite power) is provided by sources outside the power plant via a grid that is susceptible to both random and induced failures. When offsite power is lost, alternative emergency sources on-site are started to drive the plant's safety systems. If, however, a situation arises where these sources are also unavailable or unable to provide the required power for the entire period the offsite sources are unavailable, a complete loss of power to the safety buses ensues. This phenomenon is known as Station Blackout (SBO), and its severity depends on its duration as well as, the plant's initial status. Consequently, the time-dependent non-recovery probability of AC power is a key parameter in the risk assessment and management of nuclear power plants. In this work, an easy-to-use and generally applicable reliability framework is proposed to model power recovery in station blackout sequences. It employs a load flow technique integrated into an efficient event-driven Monte-Carlo simulation algorithm. The resulting framework quantifies the probability of power recovery as a function of both time and power level, including other relevant indices. It, therefore, serves the purpose of a rational decision support tool in the mitigation of station blackout accidents. The proposed framework is used to analyse station blackouts emanating from grid and switchyard failures at the Maanshan nuclear power plant in Taiwan

OpenCossan 2.0: an efficient computational toolbox for risk, reliability and resilience analysis

Many complex phenomena and the analysis of large and complex system and network can only be studied\u3cbr/\u3eadopting advanced computational methods. In addition, in many engineering fields virtual prototypes are used to support\u3cbr/\u3eand drive the design of new components, structures and systems. Uncertainty quantification is a key requirement and\u3cbr/\u3echallenge for a realistic and reliable numerical modelling and prediction that spans across various disciplines and industry\u3cbr/\u3eas well.\u3cbr/\u3eThe treatment of uncertainty required the availability of efficient algorithms and computational techniques able to\u3cbr/\u3ereduce the computational cost required by the non-deterministic analysis and to interface with opensource and commercial\u3cbr/\u3emodel (e.g. FE/CFD) and libraries. In order to satisfy these requirements and allowing the inclusion of non-deterministic\u3cbr/\u3eanalyses as a practice standard routing in scientific computing, a general purpose software for uncertainty quantification\u3cbr/\u3eand risk assessment, named COSSAN, is under continuous development.\u3cbr/\u3eThis paper presents an overview of the main capabilities of the recent release of the Matlab open source toolboxes\u3cbr/\u3eOPENCOSSAN. The new release includes interfaces with 3rd party libraries allowing to couple OPENCOSSAN with the\u3cbr/\u3estate-of-the-art tools in Machine Learning and Meta-modelling. In addition, new toolboxes for reliability and resilient\u3cbr/\u3eanalysis of system and network are also presented. OPENCOSSAN is released under the Lesser GNU licence. It is\u3cbr/\u3etherefore freely available. It is also be package as a Python or Java library for distribution to end users who do not need\u3cbr/\u3eMATLAB

Uncertainty in Engineering

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners