Efficient Uncertainty Quantification in Computational Fluid Dynamics
Author: Gerhardus Joseph Alex Loeven
Publisher:
Published: 2010
Total Pages:
ISBN-13: 9789088911712
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Uncertainty Quantification in Computational Fluid Dynamics
Author: Hester Bijl
Publisher: Springer Science & Business Media
Published: 2013-09-20
Total Pages: 347
ISBN-13: 3319008854
DOWNLOAD EBOOKFluid flows are characterized by uncertain inputs such as random initial data, material and flux coefficients, and boundary conditions. The current volume addresses the pertinent issue of efficiently computing the flow uncertainty, given this initial randomness. It collects seven original review articles that cover improved versions of the Monte Carlo method (the so-called multi-level Monte Carlo method (MLMC)), moment-based stochastic Galerkin methods and modified versions of the stochastic collocation methods that use adaptive stencil selection of the ENO-WENO type in both physical and stochastic space. The methods are also complemented by concrete applications such as flows around aerofoils and rockets, problems of aeroelasticity (fluid-structure interactions), and shallow water flows for propagating water waves. The wealth of numerical examples provide evidence on the suitability of each proposed method as well as comparisons of different approaches.
Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines
Author: Francesco Montomoli
Publisher: Springer
Published: 2018-06-21
Total Pages: 204
ISBN-13: 3319929437
DOWNLOAD EBOOKThis book introduces design techniques developed to increase the safety of aircraft engines, and demonstrates how the application of stochastic methods can overcome problems in the accurate prediction of engine lift caused by manufacturing error. This in turn addresses the issue of achieving required safety margins when hampered by limits in current design and manufacturing methods. The authors show that avoiding the potential catastrophe generated by the failure of an aircraft engine relies on the prediction of the correct behaviour of microscopic imperfections. This book shows how to quantify the possibility of such failure, and that it is possible to design components that are inherently less risky and more reliable. This new, updated and significantly expanded edition gives an introduction to engine reliability and safety to contextualise this important issue, evaluates newly-proposed methods for uncertainty quantification as applied to jet engines. Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines will be of use to gas turbine manufacturers and designers as well as CFD practitioners, specialists and researchers. Graduate and final year undergraduate students in aerospace or mathematical engineering may also find it of interest.
Efficient and Robust Uncertainty Quantification for Computational Fluid Dynamics and Fluid-structure Interaction
Author: Jeroen Adrianus Simon Witteveen
Publisher:
Published: 2009
Total Pages: 295
ISBN-13: 9789090241678
DOWNLOAD EBOOKUncertainty Quantification In Computational Science: Theory And Application In Fluids And Structural Mechanics
Author: Sunetra Sarkar
Publisher: World Scientific
Published: 2016-08-18
Total Pages: 197
ISBN-13: 9814730599
DOWNLOAD EBOOKDuring the last decade, research in Uncertainty Quantification (UC) has received a tremendous boost, in fluid engineering and coupled structural-fluids systems. New algorithms and adaptive variants have also emerged.This timely compendium overviews in detail the current state of the art of the field, including advances in structural engineering, along with the recent focus on fluids and coupled systems. Such a strong compilation of these vibrant research areas will certainly be an inspirational reference material for the scientific community.
Uncertainty Quantification in Multiscale Materials Modeling
Author: Yan Wang
Publisher: Woodhead Publishing Limited
Published: 2020-03-12
Total Pages: 604
ISBN-13: 0081029411
DOWNLOAD EBOOKUncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales.
Quantification of Uncertainty: Improving Efficiency and Technology
Author: Marta D'Elia
Publisher: Springer Nature
Published: 2020-07-30
Total Pages: 290
ISBN-13: 3030487210
DOWNLOAD EBOOKThis book explores four guiding themes – reduced order modelling, high dimensional problems, efficient algorithms, and applications – by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book’s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.
Handbook of Uncertainty Quantification
Author: Roger Ghanem
Publisher: Springer
Published: 2016-05-08
Total Pages: 0
ISBN-13: 9783319123844
DOWNLOAD EBOOKThe topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (by engineers, scientists and investors) that are consistent with risk aversion. The objective of this Handbook is to facilitate the dissemination of the forefront of UQ ideas to their audiences. We recognize that these audiences are varied, with interests ranging from theory to application, and from research to development and even execution.
Optimization and Computational Fluid Dynamics
Author: Dominique Thévenin
Publisher: Springer Science & Business Media
Published: 2008-01-08
Total Pages: 301
ISBN-13: 3540721533
DOWNLOAD EBOOKThe numerical optimization of practical applications has been an issue of major importance for the last 10 years. It allows us to explore reliable non-trivial configurations, differing widely from all known solutions. The purpose of this book is to introduce the state-of-the-art concerning this issue and many complementary applications are presented.
Uncertainty Management for Robust Industrial Design in Aeronautics
Author: Charles Hirsch
Publisher: Springer
Published: 2018-07-21
Total Pages: 799
ISBN-13: 331977767X
DOWNLOAD EBOOKThis 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 within the UMRIDA project. All in all, the book offers a authoritative guide to cutting-edge methodologies for uncertainty management in engineering design, covers a wide range of applications and discusses new ideas for future research and interdisciplinary collaborations.
