Title Uncertainty quantification in computational fluid dynamics and aircraft engines / Francesco Montomoli, Mauro Carnevale, Antonio D'Ammaro, Michela Massini, Simone Salvadori

Edition Second edition Published Cham : Springer, [2019] ©2019

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Description 1 online resource Series Springer briefs in applied sciences and technology SpringerBriefs in applied sciences and technology. Contents Intro; Contents; Introduction; 1 Manufacturing/In-Service Uncertainty and Impact on Life and Performance of Gas Turbines/Aircraft Engines; Abstract; 1.1 Fan; 1.2 Axial Compressor; 1.2.1 Compressor Leading Edge Shape; 1.2.2 Compressor Rotor Tip; 1.2.3 Compressor Aero-Foils Roughness; 1.2.4 Compressor Real Geometries Effects; 1.3 Combustion Chamber; 1.3.1 Fuel Variability and Aviation; 1.3.2 Boundary Conditions Variations; 1.4 High-Pressure Turbine; 1.4.1 Turbine Entry Temperature; 1.4.2 Real Geometry Effects; 1.4.3 Coolant System; 1.4.4 Surface Roughness; 1.5 Low-Pressure Turbine 1.5.1 LPT Impact of Roughness1.5.2 LPT Trailing Edge Thickness; 1.5.3 LPT Aero-Foils Thickness; 1.6 Bearings; 1.6.1 Fluid Film Journal Bearings; 1.6.2 Ball Bearings; 1.7 Summary; References; 2 Uncertainty Quantification in CFD: The Matrix of Knowledge; Abstract; 2.1 Into the Matrix of Knowledge; 2.1.1 Deterministic Approaches and Turbulence Effects; 2.2 Verification and Validation; 2.3 Mesh Dependence Analysis; 2.4 Uncertainty Quantification and "Black Swans"; 2.5 Limitations in Turbomachinery CFD; 2.5.1 Boundary Conditions; 2.5.2 Reproduction of the Real Geometry 2.5.3 Steady/Unsteady Interaction2.5.4 Component Interaction; 2.5.5 Cooling Devices; 2.6 Summary; References; 3 Mathematical Formulation; Abstract; 3.1 Preliminaries of Probability Theory; 3.1.1 Probability and Cumulative Distribution Functions; 3.1.2 Gaussian Distribution; 3.2 Simulation Under Uncertainty; 3.2.1 Uncertainty Definition; 3.2.2 Uncertainty Propagation; 3.2.3 Uncertainty Certification; 3.3 Overview of Techniques; 3.3.1 Monte Carlo and Sampling-Based Methods; 3.3.2 Perturbation Methods; 3.3.3 Moment Equations; 3.3.4 Operator-Based Methods; 3.3.5 Generalized Polynomial Chaos 3.4 Deterministic Model Versus Stochastic Model3.4.1 Deterministic Model; 3.4.2 Stochastic Model; 3.4.3 Output: Quantities of Interest; 3.4.4 Error Bounds for the Expectation and Variance of Outputs of Interest; 3.4.5 Software Framework for Non-intrusive Uncertainty Propagation with Computable Error Bounds; 3.5 Sampling Techniques; 3.5.1 Monte Carlo Method-MCM; 3.5.2 Improved Sampling Strategies: LHS and LB; 3.6 Quadrature Methods; 3.6.1 Metamodels: Response Surface Models; 3.6.2 Moment Methods; 3.6.3 Gaussian Quadrature; 3.6.4 Node Nested Quadrature; 3.6.5 Dense Product Global Quadrature 3.6.6 Gauss-Kronrod Quadrature3.6.7 Clenshaw-Curtis Quadrature; 3.7 Methods for Numerical Statistics; 3.7.1 Stochastic and Probabilistic Collocation Methods; 3.7.2 Polynomial Chaos Expansion; 3.7.3 Polynomial Chaos Projection; 3.7.4 Polynomial Chaos Projection-Regression; 3.7.5 Practical Aspects of Spectral Expansion of Random Processes; 3.7.6 Legendre Polynomials; 3.7.7 Hermite Polynomials; 3.7.8 Laguerre Polynomials; 3.7.9 Padè-Legendre Polynomials; 3.7.10 1-D Formulation; 3.7.11 N-D Formulation; 3.7.12 Uncertainty Propagation Using Adaptive Piecewise Polynomial Approximation Summary This 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 Bibliography Includes bibliographical references and index Notes Online resource; title from PDF title page (EBSCO, viewed June 26, 2018) Subject Airplanes -- Motors -- Design and construction Computational fluid dynamics. Uncertainty (Information theory) Mechanics of fluids. Fluid mechanics. Engines & power transmission. Reliability engineering. Aerospace & aviation technology. TECHNOLOGY & ENGINEERING -- Engineering (General) Airplanes -- Motors -- Design and construction Computational fluid dynamics Uncertainty (Information theory) Form Electronic book Author Montomoli, Francesco, editor ISBN 9783319929439 3319929437

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