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Titlebook: Aerospace System Analysis and Optimization in Uncertainty; Loïc Brevault,Mathieu Balesdent,Jérôme Morio Book 2020 Springer Nature Switzerl

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期刊全称Aerospace System Analysis and Optimization in Uncertainty
影响因子2023Loïc Brevault,Mathieu Balesdent,Jérôme Morio
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发行地址Examines the aerospace system design process and provides case studies.Spotlights Multidisciplinary Design Optimization (MDO) within the complex process of aerospace design.Features new techniques for
学科分类Springer Optimization and Its Applications
图书封面Titlebook: Aerospace System Analysis and Optimization in Uncertainty;  Loïc Brevault,Mathieu Balesdent,Jérôme Morio Book 2020 Springer Nature Switzerl
影响因子.Spotlighting the field of Multidisciplinary Design Optimization (MDO), this book illustrates and implements state-of-the-art methodologies within the complex process of aerospace system design under uncertainties. The book provides approaches to integrating a multitude of components and constraints with the ultimate goal of reducing design cycles. Insights on a vast assortment of problems are provided, including discipline modeling, sensitivity analysis, uncertainty propagation, reliability analysis, and global multidisciplinary optimization. The extensive range of topics covered include areas of current open research. This Work is destined to become a fundamental reference for aerospace systems engineers, researchers, as well as for practitioners and engineers working in areas of optimization and uncertainty...Part I is largely comprised of fundamentals. Part II presents methodologies for single discipline problems with a review of existing uncertainty propagation, reliability analysis, and optimization techniques. Part III is dedicated to the uncertainty-based MDO and related issues. Part IV deals with three MDO related issues: the multifidelity, the multi-objective optimization
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Uncertainty Propagation and Sensitivity AnalysisO context, the simulation code represents, for instance, a set of coupled disciplines and the uncertainty propagation consists in characterizing the multidisciplinary system outputs considering a given number of uncertain input variables modeled with the mathematical formalisms presented in Chapter
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Overview of Problem Formulations and Optimization Algorithms in the Presence of Uncertainty phenomena, lack of knowledge, modeling simplifications, etc. Different reviews of optimization techniques in the presence of uncertainty can be found in the literature. The choice of the algorithm is often problem-dependent. The designer has to choose firstly the optimization problem formulation wi
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Uncertainty Propagation for Multidisciplinary Problemsures of multidisciplinary design problems, these methodologies have to be adapted accordingly. This chapter reviews various alternatives described in the literature to efficiently propagate uncertainty in the case of coupled multidisciplinary systems (Figure 6.1).
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Blended Wing Body Designaft fuel consumption and reduce the global air transport environmental footprint. Reaching these two objectives leads to the reduction of the fuel consumption, the reduction of the pollutant emissions (specifically CO. and NO. emissions), and the reduction of noise emissions.
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