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E-raamat: Polynomial Chaos Methods for Hyperbolic Partial Differential Equations: Numerical Techniques for Fluid Dynamics Problems in the Presence of Uncertainties

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  • Sari: Mathematical Engineering
  • Ilmumisaeg: 10-Mar-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319107141
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  • Formaat: PDF+DRM
  • Sari: Mathematical Engineering
  • Ilmumisaeg: 10-Mar-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319107141

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This monograph presents computational techniques and numerical analysis to study conservation laws under uncertainty using the stochastic Galerkin formulation. With the continual growth of computer power, these methods are becoming increasingly popular as an alternative to more classical sampling-based techniques. The text takes advantage of stochastic Galerkin projections applied to the original conservation laws to produce a large system of modified partial differential equations, the solutions to which directly provide a full statistical characterization of the effect of uncertainties.Polynomial Chaos Methods of Hyperbolic Partial Differential Equations focuses on the analysis of stochastic Galerkin systems obtained for linear and non-linear convection-diffusion equations and for a systems of conservation laws; a detailed well-posedness and accuracy analysis is presented to enable the design of robust and stable numerical methods. The exposition is restricted to one spatial d

imension and one uncertain parameter as its extension is conceptually straightforward. The numerical methods designed guarantee that the solutions to the uncertainty quantification systems will converge as the mesh size goes to zero.Examples from computational fluid dynamics are presented together with numerical methods suitable for the problem at hand: stable high-order finite-difference methods based on summation-by-parts operators for smooth problems, and robust shock-capturing methods for highly nonlinear problems.Academics and graduate students interested in computational fluid dynamics and uncertainty quantification will find this book of interest. Readers are expected to be familiar with the fundamentals of numerical analysis. Some background in stochastic methods is useful but notnecessary.

Random Field Representation.- Polynomial Chaos Methods.- Numerical Solution of Hyperbolic Problems.- Linear Transport.- Nonlinear Transport.- Boundary Conditions and Data.- Euler Equations.- A Hybrid Scheme for Two-Phase Flow.- Appendices.

Arvustused

The authors explain in the preface that the book was written for readers with knowledge of uncertainty quantification, probability theory, statistics and numerical analysis, and this knowledge is definitely required to make the best use of the book. For such readers, the book is readable and interesting, in particular because of the extensive range of numerical examples presented in later chapters. (Philipp Dörsek, Mathematical Reviews, May, 2016)

This monograph presents computational techniques and numerical analysis to study conservation laws under uncertainty using the stochastic Galerkin formulation. Academics and graduate students interested in computational fluid dynamics and uncertainty quantification will find this book of interest. (Titus Petrila, zbMATH, Vol. 1325.76004, 2016)

Part I Introductory Concepts and Background
1 Introduction
3(8)
1.1 Theory for Initial Boundary Value Problems
4(3)
1.1.1 The Continuous Problem
5(1)
1.1.2 The Semidiscrete Problem
6(1)
1.2 Outline
7(1)
References
8(3)
2 Random Field Representation
11(12)
2.1 Karhunen-Loeve Expansion
12(1)
2.2 Generalized Chaos Expansions
13(7)
2.2.1 Generalized Polynomial Chaos Expansion
13(3)
2.2.2 Haar Wavelet Expansion
16(1)
2.2.3 Multiwavelet Expansion
17(3)
2.2.4 Choice of Basis Functions for Generalized Chaos
20(1)
2.3 Exercises
20(1)
References
20(3)
3 Polynomial Chaos Methods
23(8)
3.1 Intrusive Methods
23(2)
3.1.1 Stochastic Galerkin Methods
23(1)
3.1.2 Semi-intrusive Methods
24(1)
3.2 Non-intrusive Methods
25(3)
3.2.1 Interpolation and Integration Approaches
25(1)
3.2.2 Spectral Projection
26(1)
3.2.3 Stochastic Multi-elements
27(1)
3.3 Exercises
28(1)
References
28(3)
4 Numerical Solution of Hyperbolic Problems
31(16)
4.1 Summation-by-Parts Operators
32(1)
4.1.1 Recipe for Constructing a Scheme
32(1)
4.1.2 The Continuous Problem
33(1)
4.2 Analysis
33(5)
4.2.1 Well-Posedness
33(1)
4.2.2 Stability
34(2)
4.2.3 Convergence for Finite Time
36(1)
4.2.4 An Error Bound in Time
37(1)
4.2.5 Artificial Dissipation Operators
38(1)
4.3 Shock-Capturing Methods
38(2)
4.3.1 MUSCL Scheme
38(2)
4.3.2 HLL Riemann Solver
40(1)
4.4 Time Integration
40(2)
4.5 Exercises
42(1)
References
43(4)
Part II Scalar Transport Problems
5 Linear Transport Under Uncertainty
47(34)
5.1 Problem Definition
48(5)
5.1.1 Uncertainty and Solution Procedure
49(1)
5.1.2 Stochastic Galerkin Projection
49(3)
5.1.3 Diagonalization of the Stochastic Galerkin System
52(1)
5.2 The Eigenvalues of the Diffusion Matrix B
53(2)
5.2.1 General Bounds on the Eigenvalues of B
53(1)
5.2.2 Legendre Polynomial Representation
54(1)
5.2.3 Hermite Polynomial Representation
54(1)
5.3 Boundary Conditions for Well-Posedness
55(1)
5.4 Monotonicity of the Solution
56(4)
5.4.1 Second-Order Operators
56(3)
5.4.2 Fourth-Order Operators
59(1)
5.5 Stability of the Semidiscretized Problem
60(11)
5.5.1 The Initial Value Problem: von Neumann Analysis
60(2)
5.5.2 The Initial Boundary Value Problem
62(4)
5.5.3 Eigenvalues of the Total System Matrix
66(3)
5.5.4 Convergence to Steady-State
69(2)
5.6 Numerical Results
71(6)
5.6.1 The Inviscid Limit
72(4)
5.6.2 Steady-State Calculations
76(1)
5.7 Summary and Conclusions
77(1)
5.8 Supplementary Codes
78(1)
5.9 Exercises
78(1)
References
79(2)
6 Nonlinear Transport Under Uncertainty
81(30)
6.1 Polynomial Chaos Expansion of Burgers' Equation
82(3)
6.1.1 Entropy and Energy Estimates for the M = 2 Case
83(1)
6.1.2 Diagonalization of the System Matrix A(uM)
84(1)
6.2 A Reference Solution
85(3)
6.2.1 Regularity Determined by the gPC Expansion Order
87(1)
6.3 Well-Posedness
88(2)
6.3.1 The Importance of Boundary Conditions
90(1)
6.4 Energy Estimates for Stability
90(3)
6.4.1 Artificial Dissipation for Enhanced Stability
92(1)
6.5 Time Integration
93(1)
6.6 Eigenvalue Approximation
93(2)
6.7 Efficiency of the Polynomial Chaos Method
95(3)
6.7.1 Numerical Convergence
96(2)
6.8 Theoretical Results and Interpretation
98(8)
6.8.1 Analysis of Characteristics: Disturbed Cosine Wave
98(8)
6.9 Summary and Conclusions
106(1)
6.10 Supplementary Codes
107(1)
6.11 Exercises
107(1)
References
108(3)
7 Boundary Conditions and Data
111(14)
7.1 Dependence on Available Data
111(9)
7.1.1 Complete Set of Data
112(1)
7.1.2 Incomplete Set of Boundary Data
112(6)
7.1.3 Discussion of the Results with Incomplete Set of Data
118(2)
7.2 Summary and Conclusions
120(1)
7.3 Exercises
120(1)
Reference
121(4)
Part III Euler Equations and Two-Phase Flow
8 gPC for the Euler Equations
125(24)
8.1 Euler Equations with Input Uncertainty
126(4)
8.1.1 Formulation in Roe Variables
127(1)
8.1.2 Stochastic Galerkin Formulation of the Euler Equations
128(2)
8.2 Numerical Method
130(5)
8.2.1 Expansion of Conservative Variables
130(1)
8.2.2 Expansion of Roe's Variables
131(1)
8.2.3 Stochastic Galerkin Roe Average Matrix for Roe Variables
132(3)
8.3 Numerical Results
135(11)
8.3.1 Spatial Convergence
136(1)
8.3.2 Initial Conditions and Discontinuous Solutions
137(2)
8.3.3 Spatial and Stochastic Resolution Requirements
139(2)
8.3.4 Convergence of Multiwavelet Expansions
141(1)
8.3.5 Robustness
142(2)
8.3.6 Computational Cost
144(2)
8.4 Summary and Conclusions
146(2)
References
148(1)
9 A Hybrid Scheme for Two-Phase Flow
149(26)
9.1 Two-Phase Flow Problem
150(3)
9.2 Smoothness Properties of the Solution
153(3)
9.2.1 Analytical Solution
153(2)
9.2.2 Stochastic Modes
155(1)
9.2.3 The Stochastic Galerkin Solution Modes
155(1)
9.3 Numerical Method
156(9)
9.3.1 Summation-by-Parts Operators
156(1)
9.3.2 HLL Riemann Solver
157(1)
9.3.3 Hybrid Scheme
158(7)
9.4 Numerical Results
165(4)
9.4.1 Convergence of Smooth Solutions
166(2)
9.4.2 Non-smooth Riemann Problem
168(1)
9.5 Summary and Conclusions
169(2)
References
171(2)
A Generation of Multiwavelets 173(2)
B Proof of Constant Eigenvectors of Low-Order MW Triple Product Matrices 175(4)
B.1 Proof of Constant Eigenvectors of A
175(2)
B.2 Eigenvalue Decompositions of A
177(2)
B.2.1 Piecewise Constant Multiwavelets (Haar Wavelets)
177(1)
B.2.2 Piecewise Linear Multiwavelets
177(2)
C Matlab Codes 179(34)
C.1 Linear Transport
179(16)
C.1.1 Main Code
179(4)
C.1.2 Discretization Operators
183(8)
C.1.3 Boundary Treatment
191(2)
C.1.4 Reference Solution
193(2)
C.2 Non-linear Transport
195(18)
C.2.1 Main Code
195(3)
C.2.2 Discretization Operators
198(9)
C.2.3 Boundary Treatment
207(3)
C.2.4 Reference Solution
210(3)
Index 213