Dynamic Reliability Modeling of Cooperating Digital-Based Systems

International audienceDynamic reliability explicitly handles the interactions between the stochastic behavior of system components and the deterministic behavior of process variables. However, its industrial level applications are still limited, notably due to the inherent complexity of the theory and the lack of a generic modeling framework. The increased use of digital-based systems has also introduced additional modeling challenges related to the interactions between cooperating digital components. For solving these challenges, the present paper first extends the mathematical framework of dynamic reliability to handle 1) information and data computed and exchanged between digital components; and 2) random parameter deviations. A formalized Petri net approach is then proposed to perform the corresponding reliability analyses, using a finite element method. Finally, the framework's effectiveness is demonstrated on a simplified model of a nuclear reactor case study

Quantitative risk assessment system (QRAS)

A quantitative risk assessment system (QRAS) builds a risk model of a system for which risk of failure is being assessed, then analyzes the risk of the system corresponding to the risk model. The QRAS performs sensitivity analysis of the risk model by altering fundamental components and quantifications built into the risk model, then re-analyzes the risk of the system using the modifications. More particularly, the risk model is built by building a hierarchy, creating a mission timeline, quantifying failure modes, and building/editing event sequence diagrams. Multiplicities, dependencies, and redundancies of the system are included in the risk model. For analysis runs, a fixed baseline is first constructed and stored. This baseline contains the lowest level scenarios, preserved in event tree structure. The analysis runs, at any level of the hierarchy and below, access this baseline for risk quantitative computation as well as ranking of particular risks. A standalone Tool Box capability exists, allowing the user to store application programs within QRAS

Probabilistic reactor dynamics. IV. An example of man/machine interaction

An example is given to illustrate the probabilistic reactor dynamics theory developed earlier. A reservoir with two output valves is considered in which the valves are subject to random failures. The operator must maintain the tank pressure within an upper and a lower bound whatever the initial system state may be. A complete description is given of the physical system, of the instrumentation, and of the human model that was selected to describe the operator's behavior. The results are expressed in terms of state occupation probabilities and mean exit times. A Monte Carlo algorithm is briefly described.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Simulation des séquences industrielles accidentelles prenant en compte le facteur humain. Application au domaine des centrales nucléaires

Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

Probabilistic dynamics as a tool for dynamic PSA

The assumptions, scope and achievements of a probabilistic dynamics theory based on a Chapman-Kolmogorov formulation of mixed probabilistic and deterministic dynamics are reviewed. The formulation of the theory involves both physical (or process) variables and (semi-) Markovian states of the system under study allowing the inclusion of human error modelling. The problem of crossing a safety threshold is used to emphasize the role of timing in concurrent sequences. We show how the adjoint formulation can be used to obtain information on the outcomes of transients as a function of its starting characteristics. These outcomes may, for instance, be damage resulting from safety boundary crossing, or reliability functions. A comparison is made between a Monte-Carlo solution and a DYLAM analysis of a simple multicomponent benchmark problem which shows that for the same accuracy a Monte-Carlo method is much less sensitive to the size of the problem. © 1996 Elsevier Science Limited.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Probabilistic reactor dynamics - I: Theory of continuous event trees

The concept of probabilistic reactor dynamics is formalized in which deterministic reactor dynamics is supplemented by the fact that deterministic trajectories in phase-space switch to other trajectories because of stochastic changes in the structure of the reactor such as a change of state of components as a result of a malfunction, regulation feedback, or human error. A set of partial differential equations is obtained under a Markovian assumption from the Chapman-Kolmogorov equation giving the probability π(x,i,t) that the reactor is in a state x where vector x describes neutronic and thermohydraulic variables, and in a component state i at time t. The integral form is equivalent to an event tree where branching occurs continuously. A backward Kolmogorov equation allows evaluation of the probability and the average time for x(t) to escape from a given safety domain.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Probabilistic reactor dynamics. III A framework for time-dependent interaction between operator and reactor during a transient involving human error

During an accident, components fail or evolve within operating states because of operator actions. Physical variables such as pressure and temperature vary, and alarms appear and disappear. Operators diagnose the situation and effect countermeasures to recover the accidential sequence in due time. A mathematical modeling of the complex interaction process that takes place between the operating crew and the reactor during an accident is proposed. This modeling derives from a generalization of the theory of continuous event trees developed for hardware systems to a mixture of human and hardware systems. Such a generalization requires extension of the evolution equations built under the Markovian assumption to semi-Markovian processes because dead times as well as nonexponential distributions must be modeled. Operator and reactor states have transitions due to their own evolution (dQ00, dQRR) or to their mutual influence (dQ0R, dQR0). The correspondence between the estimates yielded by current human reliability models and the transition rates required as input data by the model is given. This model should be seen as a mold in which most existing human reliability models fit.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Probabilistic reactor dynamics - II: Monte Carlo study of a fast reactor transient

The concept of how probabilistic reactor dynamics applies to a realistic problem, an accidental transient of the primary side of a fast reactor, is demonstrated. A full description of the reactor model, including physical variables, evolution laws, and failure rates with their dependence on physical variables, is given. Failure probabilities and failure and success time distributions are evaluated. Vectorized and nonvectorized versions of a Monte Carlo algorithm as well as biased and nonbiased versions of this algorithm are compared.SCOPUS: ar.jinfo:eu-repo/semantics/publishe