By Etienne de Rocquigny, Nicolas Devictor, Stefano Tarantola
Dealing with uncertainties in commercial structures is a regular problem to make sure more desirable layout, powerful operation, in charge functionality and responsive chance keep watch over. Authored by way of a number one ecu community of specialists representing a move part of industries, Uncertainty in commercial perform goals to supply a reference for the dissemination of uncertainty remedy in any form of undefined. it really is taken with the quantification of uncertainties within the presence of information, model(s) and information concerning the process, and gives a technical contribution to decision-making tactics when acknowledging business constraints. The strategy awarded will be utilized to various various enterprise contexts, from examine or early layout via to certification or in-service procedures. The authors objective to foster optimum trade-offs among literature-referenced methodologies and the simplified ways frequently inevitable in perform, due to information, time or finances barriers of technical decision-makers.
Uncertainty in commercial perform:
- Features fresh uncertainty case stories conducted within the nuclear, air & area, oil, mechanical and civil engineering industries set in a standard methodological framework.
provides equipment for organizing and treating uncertainties in a commonly used and prioritized point of view.
- Illustrates useful problems and recommendations encountered in accordance with the extent of complexity, details on hand and regulatory and fiscal constraints.
- Discusses most sensible perform in uncertainty modeling, propagation and sensitivity research via quite a few statistical and numerical equipment.
- Reviews contemporary criteria, references and to be had software program, delivering a necessary source for engineers and danger analysts in a wide selection of industries.
This e-book presents a advisor to facing quantitative uncertainty in engineering and modelling and is geared toward practitioners, together with risk-industry regulators and teachers wishing to boost industry-realistic methodologies.
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Additional resources for Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management
1998) (Eds) Handbook of Utility Theory, Volume 2 Extensions, Hingham: Kluwer Academic Publisher. Bedford, T. and Cooke, R. M. (1991) Experts in Uncertainty; Opinion and Subjective Probability in Science, Oxford: Oxford University Press. De Finetti, B. (1974) Theory of Probability, volumes I and II, New York: John Wiley & Sons, Ltd. De Neufville, R. edu). De Neufville, R. pdf). Granger Morgan, M. and Henrion, M. C. (1994) Treatment of Uncertainty in Performance Assessments for Complex Systems, Risk Analysis, 14, pp.
There may be several goals in any given study and they may be combined over the course of a more-or-less elaborate decision-making process. Goals S and C refer to more advanced steps in operational decision-making, while Goals U and A concern more upstream modelling or measurement phases. Importance ranking may serve for model calibration or model simpliﬁcation at an earlier stage, which becomes, after some years of research, the basis for the selection of the best designs and the ﬁnal demonstration of compliance with a decision criterion.
This may involve calibrating sensors, estimating the parameters of the model inputs, simplifying the system model physics or structure, ﬁxing some model inputs, and ﬁnally validating according to a context-dependent level. S (Select): To compare relative performance and optimize the choice of maintenance policy, operation or design of the system. g. nuclear or environmental licensing, aeronautical certiﬁcation, etc). There may be several goals in any given study and they may be combined over the course of a more-or-less elaborate decision-making process.
Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management by Etienne de Rocquigny, Nicolas Devictor, Stefano Tarantola