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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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書目名稱Uncertainty Management for Robust Industrial Design in Aeronautics
副標(biāo)題Findings and Best Pr
編輯Charles Hirsch,Dirk Wunsch,Jordi Pons-Prats
視頻videohttp://file.papertrans.cn/942/941090/941090.mp4
概述Reports on innovative methods for uncertainty quantification and robust design optimization in engineering practice.Includes extensive data on prescribed uncertainties relevant to the industrial pract
叢書名稱Notes on Numerical Fluid Mechanics and Multidisciplinary Design
圖書封面Titlebook: Uncertainty Management for Robust Industrial Design in Aeronautics; Findings and Best Pr Charles Hirsch,Dirk Wunsch,Jordi Pons-Prats Book 2
描述.This book covers cutting-edge findings related to uncertainty quantification and optimization under uncertainties (i.e. robust and reliable optimization), with a special emphasis on aeronautics and turbomachinery, although not limited to these fields. It describes new methods for uncertainty quantification, such as non-intrusive polynomial chaos, collocation methods, perturbation methods, as well as adjoint based and multi-level Monte Carlo methods. It includes methods for characterization of most influential uncertainties, as well as formulations for robust and reliable design optimization. A distinctive element of the book is the unique collection of test cases with prescribed uncertainties, which are representative of the current engineering practice of the industrial consortium partners involved in UMRIDA, a level 1 collaborative project within the European Commission‘s Seventh Framework Programme (FP7). All developed methods are benchmarked against these industrial challenges. Moreover, the book includes a section dedicated to .Best Practice Guidelines. for uncertainty quantification and robust design optimization, summarizing the findings obtained by the consortium members w
出版日期Book 2019
關(guān)鍵詞Uncertainty quantification methods; Applications of uncertainty quantification; Robust design optimiza
版次1
doihttps://doi.org/10.1007/978-3-319-77767-2
isbn_softcover978-3-030-08534-6
isbn_ebook978-3-319-77767-2Series ISSN 1612-2909 Series E-ISSN 1860-0824
issn_series 1612-2909
copyrightSpringer International Publishing AG, part of Springer Nature 2019
The information of publication is updating

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