Modelling under risk and uncertainty de rocquigny etienne. Modelling Under Risk and Uncertainty av Etienne de Rocquigny (Innbundet) 2019-03-10

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Etienne de Rocquigny

modelling under risk and uncertainty de rocquigny etienne

The implementation in a general purpose Finite Element Code is then addressed and illustrated in the context of Risk assessment of geological storage of carbon dioxide. The estimation quality, as suggested through a ratio of epistemic uncertainty to intrinsic variability, proves to be closely linked to classical sensitivity indices. Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated: How uncertain is my model? Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated: How uncertain is my model? How robust is the analysis or the computational methods involved? How rare are the events to be considered? It is concerned with the quantification of uncertainties in the presence of data, model s and knowledge about the problem, and aims to make a technical contribution to decision-making processes. Double-level probabilistic uncertainty models that separate aleatory and epistemic components enjoy significant interest in risk assessment. It goes beyond the black-box view that some analysts, modelers, risk experts or statisticians develop on the underlying phenomenology of the environmental or industrial processes, without valuing enough their physical properties and inner modelling potential nor challenging the practical plausibility of mathematical hypotheses; conversely it is also to attract environmental or engineering modellers to better handle model confidence issues through finer statistical and risk analysis material taking advantage of advanced scientific computing, to face new regulations departing from deterministic design or support robust decision-making.

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1 The Scope of Risk and Uncertainty Considered

modelling under risk and uncertainty de rocquigny etienne

Provides an original review of the advanced inverse probabilistic approaches for model identification, calibration or data assimilation, key to digest fast-growing multi-physical data acquisition. The solution of the inverse problem for the calibration of the roughness coefficient proves useful for several reasons, including the quantification of model error. What kind of decision can be truly supported and how can I handle residual uncertainty? Numerical experiments on simulated and real data sets in nuclear thermal hydraulics highlight the good performances of these algorithms, provided an adequate parametrization with respect to identifiability. In the uncertainty treatment framework considered, the intrinsic variability of the inputs of a physical simulation model is modelled by a multivariate probability distribution. Written by a leader at the crossroads of industry, academia and engineering, and based on decades of multi-disciplinary field experience, Modelling Under Risk and Uncertainty gives a self-consistent introduction to the methods involved by any type of modeling development acknowledging the inevitable uncertainty and associated risks.

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Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods

modelling under risk and uncertainty de rocquigny etienne

Before the acquisition of a turbogenerator, energy power operators perform independent design assessment in order to assure safe operating conditions of the new machine in its environment. Does it make sense to handle uncertainty, risk, lack of knowledge, variability or errors altogether? Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated: How uncertain is my model? Should it be reduced now or later? Research perspectives are given to extend the efficiency of the methods in higher dimensions and address the relaxing of full monotony hypotheses. It is compared with iterated linear approximation on the basis of numerical experiments on simulated data sets coming from a simplified but realistic modelling of a dyke overflow. Although based on completely different assumptions, both methods provide remarkably close results, i. How much refined should the mathematical description be, given the true data limitations? How rare are the events to be considered? This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis. We do feel that, whatever the hard changes that will probably have to be made to various software aspects, we should not lose sight of the fact that continuity paths also have to be found in order to make those big changes acceptable and profitable to many.

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Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods

modelling under risk and uncertainty de rocquigny etienne

No caso de serem apresentados dois preços, o preço mais elevado, normalmente cortado, corresponde ao preço fixado pelo editor ou importador, sendo o outro o preço de venda na wook. In the framework of design verification, epistemic uncertainties are preponderant. How reasonable is the choice of probabilistic modeling for rare events? Is it truly valuable to support decision-making? Are there connex domains that could provide models or inspiration for my problem? The E-mail message field is required. To begin with, we focus on the root-finding algorithm required for the directional approach: we present a stop criterion for the dichotomic method and a strategy to reduce the required number of calls to the costly physical model under monotonic hypothesis. We feel that exaflops software should not only be thought of as a way of tackling daunting research problems but should also take into account the sometimes equally daunting requirements that stem from an industrial usage perspective. Modelling Under Risk and Uncertainty: Addresses a concern of growing interest for large industries, environmentalists or analysts: robust modeling for decision-making in complex systems. It goes beyond the 'black-box' viewthat some analysts, modelers, risk experts or statisticians developon the underlying phenomenology of the environmental or industrialprocesses, without valuing enough their physical properties andinner modelling potential nor challenging the practicalplausibility of mathematical hypotheses; conversely it is also toattract environmental or engineering modellers to better handlemodel confidence issues through finer statistical and risk analysismaterial taking advantage of advanced scientific computing, to facenew regulations departing from deterministic design or supportrobust decision-making.

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Modelling Under Risk and Uncertainty

modelling under risk and uncertainty de rocquigny etienne

The approach,is illustrated on the problem of updating the prediction of long-term creep strains in concrete. How robust is the analysis or the computational methods involved? Preface xv Acknowledgements xvii Introduction and reading guide xix Notation xxxiii Acronyms and abbreviations xxxvii 1 Applications and practices of modelling, risk and uncertainty 1 1. While uncertainty treatment has been initially largely developed in risk or environmental assessment, it is gaining large-spread interest in many industrial fields generating knowledge and practices going beyond the classical risk versus uncertainty or epistemic versus aleatory debates. The idea of a variational coupling between a probabilistic local model and a deterministic or probabilistic model is developed here in the context of a coupling along a surface and not over a volume. Then, the inverse problem is re-interpreted as a statistical estimation problem with missing data structure. Risk and uncertainty analysis prove to be so connected in application that the precise delimitation between the practical meanings of the two terms will not appear to be central with regard to modelling: it depends essentially on the definition of the underlying system or on the terminological habits of the field under consideration.

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Etienne de Rocquigny

modelling under risk and uncertainty de rocquigny etienne

This lack of knowledge is due to inexistent or imprecise information about the design as well as to interaction of the rotating machinery with supporting and sub-structures. Does it make sense to handle uncertainty, risk, lack of knowledge, variability or errors altogether? A more com-plex case considering large distances and including micrometeorological effects has also been fulfilled with promising results which are presented in this paper. It goes beyond the black-box view that some analysts, modelers, risk experts or statisticians develop on the underlying phenomenology of the environmental or industrial processes, without valuing enough their physical properties and inner modelling potential nor challenging the practical plausibility of mathematical hypotheses; conversely it is also to attract environmental or engineering modellers to better handle model confidence issues through finer statistical and risk analysis material taking advantage of advanced scientific computing, to face new regulations departing from deterministic design or support robust decision-making. With Safari, you learn the way you learn best. Written by a leader at the crossroads of industry, academia and engineering, and based on decades of multi-disciplinary field experience, Modelling Under Risk and Uncertainty gives a self-consistent introduction to the methods involved by any type of modeling development acknowledging the inevitable uncertainty and associated risks. Finally, the non-linear stochastic dynamical system submited to the uncertain stochastic loads is used to identify the probability model of its uncertainties. These arguments will be illustrated by two exemplary case studies.

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Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods

modelling under risk and uncertainty de rocquigny etienne

Preface xv Acknowledgements xvii Introduction and reading guide xix Notation xxxiii Acronyms and abbreviations xxxvii 1 Applications and practices of modelling, risk and uncertainty 1 1. Axes of further research proving critical for the environmental or industrial issues are outlined: the information challenges posed by uncertainty modeling in the context of data scarcity, and the corresponding calibration and inverse probabilistic techniques, bound to be developed to best value industrial or environmental monitoring and data acquisition systems under uncertainty; the numerical challenges entailing considerable development of high-performance computing in the field; the acceptability challenges in the context of the precautionary principle. Should it be reducednow or later? In the context of structural reliability, a small probability to be assessed, a high computational time model and a relatively large input dimension are typical constraints which brought together lead to an interesting challenge. Written by a leader at the crossroads of industry, academia and engineering, and based on decades of multi-disciplinary field experience, Modelling Under Risk and Uncertainty gives a self-consistent introduction to the methods involved by any type of modeling development acknowledging the inevitable uncertainty and associated risks. Gives new insights into the peculiar mathematical and computational challenges generated by recent industrial safety or environmental control analysis for rare events. How much refined should the mathematical description be, given the true data limitations? How reasonable is the choice of probabilistic modeling for rare events? Written by a leader at the crossroads of industry, academia and engineering, and based on decades of multi-disciplinary field experience, Modelling Under Risk and Uncertainty gives a self-consistent introduction to the methods involved by any type of modeling development acknowledging the inevitable uncertainty and associated risks. In a first part, the theory is presented.

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Modelling Under Risk and Uncertainty av Etienne de Rocquigny (Innbundet)

modelling under risk and uncertainty de rocquigny etienne

Part I introduces the common methodological framework which forms the backbone of the work: this is the essential generic structure unifying the industrial case studies which are sketched briefly in Part I Chapter 2 , notwithstanding the peculiarities of each due to sector, physical or decision-criteria specificities. In relation with the applicable regula-tion and standards and a relevant quantity of interest for decision-making, this approach involves in particular the proper identification of key steps such as : the quantification or modeling of the sources of uncertainty, possibly involving an inverse approach ; their propagation through a pre-existing physical-industrial model; the ranking of importance or sensitivity analysis and sometimes a subsequent optimisation step. Elle est comparée à une méthode utilisant une approximation linéaire itérative sur la base de jeux de données simulées provenant d'un modèle de crues simplifié mais réaliste. Is it truly valuable to support decision-making? L'estimation d'une probabilité de défaillance, ou autrement dit l'estimation d'une intégrale multidimensionnelle, est une problématique classique en fiabilité des structures et de nombreuses méthodes de calculs existent déjà dans la littérature. This paper addresses the definition of importance measures for helping the modeller to detect the factors on which to focus modelling activity and data collection in seismic fragility analysis. Could the uncertainty be reduced through more data, increased modeling investment or computational budget? Dans le cadre du traitement des incertitudes étudié ici, la variabilité intrinsèque des entrées d'un modèle physique est modélisée par une loi de probabilité multivariée.

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Etienne de Rocquigny: Modelling Under Risk and Uncertainty (ePUB)

modelling under risk and uncertainty de rocquigny etienne

Ces études couplant un modèle physique pour la sollicitation et la résistance du composant et un modèle probabiliste des incertitudes sont destinées à estimer la fiabilité de la structure ; elles se heurtent au problème de l'extrême faiblesse des probabilités, et au défi classique de l'optimisation du rapport entre le temps de calcul et la robustesse du résultat obtenu. The latter focuses on the detrimental or beneficial consequences of uncertain events in terms of some given stakes, assets or vulnerability of interest for the decision-maker. In many mechanical or physical systems, the failure function, albeit complex and computationally intensive, happens to be monotonous with respect to its uncertain model inputs. While being noticeable in the cases studied, sensitivity to the safety injection temperature variability proves to be less than the choice of the toughness model. This new method turns out to be competitive compared with the existing techniques.

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Etienne de Rocquigny: Modelling Under Risk and Uncertainty (ePUB)

modelling under risk and uncertainty de rocquigny etienne

In this paper, we present an application of sensitivity analysis for design verification of nuclear turbosets. Preface xv Acknowledgements xvii Introduction and reading guide xix Notation xxxiii Acronyms and abbreviations xxxvii 1 Applications and practices of modelling, risk and uncertainty 1 1. L'objectif est d'identifier cette loi de probabilité à partir d'observations des sorties du modèle. We also apply the methods to a flood model and a nuclear reactor pressurized vessel model, to practically demonstrate their interest on real industrial examples. Uncertainty is a fascinating subject, spanning a wide range of scientific and practicalknowledge domains. Could the uncertainty be reduced through more data, increased modeling investment or computational budget? Their common advantages are that the prediction accuracy of the failure probability is guaranteed with certainty; additional model runs always ameliorate the accuracy; a change of the uncertainty model is possible without additional runs. Could the uncertainty be reduced through more data, increasedmodeling investment or computational budget? It gives new insight on the peculiar mathematical challenges generated by recent industrial safety or environmental control analysis, focusing on implementing decision theory choices related to risk and uncertainty analysis through statistical estimation and computation, in the presence of physical modeling and risk analysis.

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