Uncertainty quantification in computational fluid dynamics and aircraft engines
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 require...
| Other Authors: | Montomoli, Francesco,, SpringerLink (Online service) |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer,
[2019]
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| Physical Description: |
1 online resource. |
| Edition: | Second edition. |
| Series: |
SpringerBriefs in applied sciences and technology.
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| Subjects: |
Table of 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.