Titlebook: Uncertainty Management for Robust Industrial Design in Aeronautics; Findings and Best Pr Charles Hirsch,Dirk Wunsch,Jordi Pons-Prats Book 2

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M. Bauerheim,A. Ndiaye,F. Nicoudproves no new "meaningful" statements, even the intuitionist will fondly embrace the infinite. To this euphoric revelation, a shy young man cautions~ "According to the formalist conception one adjoins to the meaningful statements of mathematics transfinite (pseudo-‘)statements which in themselves ha

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Robin Schmidt,Matthias Voigt,Michele Pisaroni,Fabio Nobile,Penelope Leyland,Jordi Pons-Prats,Gabrielfrom the United States. The book is divided into three parts: (A) Foundations of SRL in TELEs, (B) Empirical studies on SRL in TELEs and (C) SRL in TELEs: perspectives on future developments. The book presents a rich resource of information for researchers and educators at all levels who are interes

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Robin Schmidt,Matthias Voigt,Ronald Mailachally defined states of behavioral attention. The occurrence of the event-related potential (ERSP) in the FC seems to be the harbinger of both EEG desynchronization and sensory channel selection. Both of these latter event-related activities are explainable by FC effects on R cells. We have found tha

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Vision, Objectives and Research Activitiesysis and simulation tools are currently applied with a unique set of input data and model variables, although realistic operating conditions are a superposition of numerous uncertainties under which the industrial products operate (uncertainties on operational conditions, on geometries resulting fro

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Uncertainties in Compressor and Aircraft Designsign process of compressors and aircraft is a key element to ensure safe and economic operation. This chapter focuses on revealing the actual sources of these uncertainties and proposes how they can be quantified and modelled such that they can be integrated into an uncertainty quantification or a r

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Estimation of Model Error Using Bayesian Model-Scenario Averaging with ,-Estimatesng error without additional validation data. Bayesian Model-Scenario Averaging (BMSA), which exploits the variability on model closure coefficients across several flow scenarios and multiple models, gives a stochastic, a posteriori estimate of a quantity of interest. The full BMSA requires the propa

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Numerical Uncertainties Estimation and Mitigation by Mesh Adaptatione is based on the control of the interpolation error of a solution field. The second one relies on the control of the approximation error of a scalar-output functional. Both of them have been widely used in aeronautics and derived in an anisotropic context by using a metric-based approach. If Hessia