Efficient availability assessment of reconfigurable complex multi-state systems with interdependencies

Complex topology, multi-state behaviour, component interdependencies and interactions with external phenomena are prominent attributes of many realistic systems. Analytical reliability evaluation techniques have limited applicability to such systems and efficient simulation models are therefore required. In this paper, we present a simulation framework to simplify the availability assessment of these systems. It allows tracking of changes in performance levels of components from which system performance is deduced by solving a set of flow equations. This framework is adapted to the availability modelling of an offshore plant with interdependencies, operated in the presence of limited maintenance teams and operational loops. The underlying principles of the approach are based on an extension of the load-flow simulation presented recently by the current authors (George-Williams & Patelli 2016)

An efficient computational strategy for robust maintenance scheduling : application to corroded pipelines

The ability to predict correctly the future remaining life time of components is of paramount importance to improve the safety and reliability of systems and networks via an effective maintenance policy. However, simplifications and assumptions are usually adopted to compensate lack of data, imprecision and vagueness, which cannot be justified completely and may, thus lead to biased results. To overcome these issues, an imprecise probabilities approach is proposed for reliability analysis and risk-based maintenance strategy. A novel efficient computational approach is proposed for identifying robust maintenance strategies. The optimal solution is obtained through only one reliability assessment based on Advanced Line Sampling and reusing the outcome of maintenance activities in a force Monte Carlo approach. The proposed methodology remove the huge computational cost of reliability-base optimization making the analysis of industrial size problem feasible. The applicability of the approach is demonstrated by identifying the optimal maintenance policy of buried pipelines and it is shown how this approach can improve the current industrial practice

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

Influence of trust in institutions on public acceptance of nuclear power from a historical context across nuclear countries

Several studies have tried to determine what is behind peoples’ attitudes to different energy sources and their overall rather negative opinion on nuclear power. The issue of public perception of nuclear power has been going on for decades. Recently it gained even greater interest thanks to the support of the UK governmental nuclear industrial strategy to promote and support nuclear growth. Nuclear power is negatively influenced by events from the past such as nuclear accidents and connection of nuclear power with cold war and the use of nuclear bombs. As one of many other factors, the level of trust in authorities is perceived to influence the opposition or support for nuclear power. This study aims at analyzing data on trust in four main institutions (government, businesses, media and non-governmental organizations) from a historical perspective in several nuclear countries and finding evidence for relationship between trust in authorities and support for nuclear power. Structural Equation Modelling and Multiple Regression Analysis have been used to analyze data Structural Equation modelling requires a large sample set to provide meaningful results, therefore performance can deteriorate when sample size reduces. Hence, Multiple Regression analysis has been carried out. Results from Multiple Regression analysis did not prove that the trust in institutions is significant predictor of support for nuclear power. Although around 55% of variance in support for nuclear was explained by trust institutions in UK as well as in USA case. Large sample size is required to authenticate model and obtain more robust results. It is likely that Multiple Regression analysis will be used for future data analyses when more data will be available

Component importance measures for complex repairable system

In recent years, the system signature has been recognized as an important tool to quantify the reliability of systems consist of independent and identically distributed (iid) or exchangeable components with respect the random failure times. System signature separates the system structure from the component probabilistic failure distribution. However, when it is adopted to solve a complex system with more than one component type, it requires the computation of the probabilities of all possible different ordering statistics of each component failure lifetime distributions, which is often an intractable procedure

A reinforcement learning framework for optimal operation and maintenance of power grids

International audienc

Uncertainty management in multidisciplinary design of critical safety systems

Managing the uncertainty in multidisciplinary design of safety-critical systems requires not only the availability of a single approach or methodology to deal with uncertainty but a set of different strategies and scalable computational tools (that is, by making use of the computational power of a cluster and grid computing). The availability of multiple tools and approaches for dealing with uncertainties allows cross validation of the results and increases the confidence in the performed analysis. This paper presents a unified theory and an integrated and open general-purpose computational framework to deal with scarce data, and aleatory and epistemic uncertainties. It allows solving of the different tasks necessary to manage the uncertainty, such as uncertainty characterization, sensitivity analysis, uncertainty quantification, and robust design. The proposed computational framework is generally applicable to solve different problems in different fields and be numerically efficient and scalable, allowing for a significant reduction of the computational time required for uncertainty management and robust design. The applicability of the proposed approach is demonstrated by solving a multidisciplinary design of a critical system proposed by NASA Langley Research Center in the multidisciplinary uncertainty quantification challenge problem

Model Updating Strategy of the DLR-AIRMOD Test Structure

Considerable progresses have been made in computer-aided engineering for the high fidelity analysis of structures and systems. Traditionally, computer models are calibrated using deterministic procedures. However, different analysts produce different models based on different modelling approximations and assumptions. In addition, identically constructed structures and systems show different characteristic between each other. Hence, model updating needs to take account modelling and test-data variability. Stochastic model updating techniques such as sensitivity approach and Bayesian updating are now recognised as powerful approaches able to deal with unavoidable uncertainty and variability. This paper presents a high fidelity surrogate model that allows to significantly reduce the computational costs associated with the Bayesian model updating technique. A set of Artificial Neural Networks are proposed to replace multi non-linear input-output relationships of finite element (FE) models. An application for updating the model parameters of the FE model of the DRL-AIRMOD structure is presented. © 2017 The Authors. Published by Elsevier Ltd

Investigations on nucleophilic layers made with a novel plasma jet technique

In this work a novel plasma jet technique is used for the deposition of nucleophilic films based on (3-aminopropyl)trimethoxysilane at atmospheric pressure. Film deposition was varied with regard to duty cycles and working distance. Spectral ellipsometry and chemical derivatization with 4-(trifluoromethyl)benzaldehyde using ATR- FTIR spectroscopy measurements were used to characterize the films. It was found that the layer thickness and the film composition are mainly influenced by the duty cycle

Human error analysis: Review of past accidents and implications for improving robustness of system design

Since the establishment of the high-technology industry and industrial systems, developments of new materials and fabrication techniques, associated with cutting-edge structural and engineering assessments, are contributing to more reliable and consistent systems, thus reducing the likelihood of losses. However, recent accidents are acknowledged to be linked to human factors which led to catastrophic consequences. Therefore, the understanding of human behavioural characteristics interlaced with the actual technology aspects and organisational context is of paramount importance for the safety & reliability field. This study first approaches this multidisciplinary problem by classifying and reviewing 200 major accident data from insurance companies and regulatory authorities under the Cognitive Reliability and Error Analysis framework. Then, specific attention is dedicated to discuss the implications for improving robustness of system design and tackling the surrounding factors and tendencies that could lead to the manifestation of human errors