A SURVEY ON AVAILABILITY CALCULATION AND DEFINITION FOR INFORMATION TECHNOLOGY SERVICES

Nowadays companies outsource a lot of their IT resources and capabilities by contracting them with external IT providers. The agreements between providers and customers concerning different quality aspects of the contracted services such as availability, maintainability, security and continuity are formalized through Server Level Agreements (SLAs). One of the most important quality aspects and, at the same time, most difficult to agree is the availability level to be reached. Indeed, the process, methods and types of inputs used by providers and customers to calculate this level are still very informal and in many cases the resulting availability target is not suited to the customer requirements and the provider capabilities. In this boarder, this work presents a survey aimed at identifying and analysing the research literature to analyse what are the most used inputs and methods for availability calculation and prediction as wells as to analyse their applicability in the industry

Development of Approaches to Common Cause Dependencies with Applications to Multi-Unit Nuclear Power Plant

The term “common cause dependencies” encompasses the possible mechanisms that directly compromise components performances and ultimately cause degradation or failure of multiple components, referred to as common cause failure (CCF) events. The CCF events have been a major contributor to the risk posed by the nuclear power plants and considerable research efforts have been devoted to model the impacts of CCF based on historical observations and engineering judgment, referred to as CCF models. However, most current probabilistic risk assessment (PRA) studies are restricted to single reactor units and could not appropriately consider the common cause dependencies across reactor units. Recently, the common cause dependencies across reactor units have attracted a lot of attention, especially following the 2011 Fukushima accident in Japan that involved multiple reactor unit damages and radioactive source term releases. To gain an accurate view of a site's risk profile, a site-based risk metric representing the entire site rather than single reactor unit should be considered and evaluated through a multi-unit PRA (MUPRA). However, the multi-unit risk is neither formally nor adequately addressed in either the regulatory or the commercial nuclear environments and there are still gaps in the PRA methods to model such multi-unit events. In particular, external events, especially seismic events, are expected to be very important in the assessment of risks related to multi-unit nuclear plant sites. The objective of this dissertation is to develop three inter-related approaches to address important issues in both external events and internal events in the MUPRA. 1) Develop a general MUPRA framework to identify and characterize the multi-unit events, and ultimately to assess the risk profile of multi-unit sites. 2) Develop an improved approach to seismic MUPRA through identifying and addressing the issues in the current methods for seismic dependency modeling. The proposed approach can also be extended to address other external events involved in the MUPRA. 3) Develop a novel CCF model for components undergoing age-related degradation by superimposing the maintenance impacts on the component degradation evolutions inferred from condition monitoring data. This approach advances the state-of-the-art CCF analysis in general and assists in the studies of internal events of the MUPRA

Availability and Reliability Analysis of Computer Software Systems Considering Maintenance and Security Issues

Ph.DDOCTOR OF PHILOSOPH

Multi-State Reliability Analysis of Nuclear Power Plant Systems

The probabilistic safety assessment of engineering systems involving high-consequence low-probability events is stochastic in nature due to uncertainties inherent in time to an event. The event could be a failure, repair, maintenance or degradation associated with system ageing. Accurate reliability prediction accounting for these uncertainties is a precursor to considerably good risk assessment model. Stochastic Markov reliability models have been constructed to quantify basic events in a static fault tree analysis as part of the safety assessment process. The models assume that a system transits through various states and that the time spent in a state is statistically random. The system failure probability estimates of these models assuming constant transition rate are extensively utilized in the industry to obtain failure frequency of catastrophic events. An example is core damage frequency in a nuclear power plant where the initiating event is loss of cooling system. However, the assumption of constant state transition rates for analysis of safety critical systems is debatable due to the fact that these rates do not properly account for variability in the time to an event. An ill-consequence of such an assumption is conservative reliability prediction leading to addition of unnecessary redundancies in modified versions of prototype designs, excess spare inventory and an expensive maintenance policy with shorter maintenance intervals. The reason for this discrepancy is that a constant transition rate is always associated with an exponential distribution for the time spent in a state. The subject matter of this thesis is to develop sophisticated mathematical models to improve predictive capabilities that accurately represent reliability of an engineering system. The generalization of the Markov process called the semi-Markov process is a well known stochastic process, yet it is not well explored in the reliability analysis of nuclear power plant systems. The continuous-time, discrete-state semi-Markov process model is a stochastic process model that describes the state transitions through a system of integral equations which can be solved using the trapezoidal rule. The primary objective is to determine the probability of being in each state. This process model ensures that time spent in the states can be represented by a suitable non-exponential distribution thus capturing the variability in the time to event. When exponential distribution is assumed for all the state transitions, the model reduces to the standard Markov model. This thesis illustrates the proposed concepts using basic examples and then develops advanced case studies for nuclear cooling systems, piping systems, digital instrumentation and control (I&C) systems, fire modelling and system maintenance. The first case study on nuclear component cooling water system (NCCW) shows that the proposed technique can be used to solve a fault tree involving redundant repairable components to yield initiating event probability quantifying the loss of cooling system. The time-to-failure of the pump train is assumed to be a Weibull distribution and the resulting system failure probability is validated using a Monte Carlo simulation of the corresponding reliability block diagram. Nuclear piping systems develop flaws, leaks and ruptures due to various underlying damage mechanisms. This thesis presents a general model for evaluating rupture frequencies of such repairable piping systems. The proposed model is able to incorporate the effect of aging related degradation of piping systems. Time dependent rupture frequencies are computed and the influence of inspection intervals on the piping rupture probability is investigated. There is an increasing interest worldwide in the installation of digital instrumentation and control systems in nuclear power plants. The main feedwater valve (MFV) controller system is used for regulating the water level in a steam generator. An existing Markov model in the literature is extended to a semi-Markov model to accurately predict the controller system reliability. The proposed model considers variability in the time to output from the computer to the controller with intrinsic software and mechanical failures. State-of-the-art time-to-flashover fire models used in the nuclear industry are either based on conservative analytical equations or computationally intensive simulation models. The proposed semi-Markov based case study describes an innovative fire growth model that allows prediction of fire development and containment including time to flashover. The model considers variability in time when transiting from one stage of the fire to the other. The proposed model is a reusable framework that can be of importance to product design engineers and fire safety regulators. Operational unavailability is at risk of being over-estimated because of assuming a constant degradation rate in a slowly ageing system. In the last case study, it is justified that variability in time to degradation has a remarkable effect on the choice of an effective maintenance policy. The proposed model is able to accurately predict the optimal maintenance interval assuming a non-exponential time to degradation. Further, the model reduces to a binary state Markov model equivalent to a classic probabilistic risk assessment model if the degradation and maintenance states are eliminated. In summary, variability in time to an event is not properly captured in existing Markov type reliability models though they are stochastic and account for uncertainties. The proposed semi-Markov process models are easy to implement, faster than intensive simulations and accurately model the reliability of engineering systems

Aging and Health

Aging is a major risk factor for chronic diseases, which in turn can provide information about the aging of a biological system. This publication serves as an introduction to systems biology and its application to biological aging. Key pathways and processes that impinge on aging are reviewed, and how they contribute to health and disease during aging is discussed. The evolution of this situation is analyzed, and the consequences for the study of genetic effects on aging are presented. Epigenetic programming of aging, as a continuation of development, creates an interface between the genome and the environment. New research into the gut microbiome describes how this interface may operate in practice with marked consequences for a variety of disorders. This analysis is bolstered by a view of the aging organism as a whole, with conclusions about the mechanisms underlying resilience of the organism to change, and is expanded with a discussion of circadian rhythms in aging

Aeronautical Engineering: A continuing bibliography (supplement 138)

This bibliography lists 366 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1981