Mathematical Methods in Elasticity Imaging

This book is the first to comprehensively explore elasticity imaging and examines recent, important developments in asymptotic imaging, modeling, and analysis of deterministic and stochastic elastic wave propagation phenomena. It derives the best possible functional images for small inclusions and cracks within the context of stability and resolution, and introduces a topological derivative–based imaging framework for detecting elastic inclusions in the time-harmonic regime. For imaging extended elastic inclusions, accurate optimal control methodologies are designed and the effects of uncertainties of the geometric or physical parameters on stability and resolution properties are evaluated. In particular, the book shows how localized damage to a mechanical structure affects its dynamic characteristics, and how measured eigenparameters are linked to elastic inclusion or crack location, orientation, and size. Demonstrating a novel method for identifying, locating, and estimating inclusions and cracks in elastic structures, the book opens possibilities for a mathematical and numerical framework for elasticity imaging of nanoparticles and cellular structures.

"1120351065"
Mathematical Methods in Elasticity Imaging

This book is the first to comprehensively explore elasticity imaging and examines recent, important developments in asymptotic imaging, modeling, and analysis of deterministic and stochastic elastic wave propagation phenomena. It derives the best possible functional images for small inclusions and cracks within the context of stability and resolution, and introduces a topological derivative–based imaging framework for detecting elastic inclusions in the time-harmonic regime. For imaging extended elastic inclusions, accurate optimal control methodologies are designed and the effects of uncertainties of the geometric or physical parameters on stability and resolution properties are evaluated. In particular, the book shows how localized damage to a mechanical structure affects its dynamic characteristics, and how measured eigenparameters are linked to elastic inclusion or crack location, orientation, and size. Demonstrating a novel method for identifying, locating, and estimating inclusions and cracks in elastic structures, the book opens possibilities for a mathematical and numerical framework for elasticity imaging of nanoparticles and cellular structures.

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Mathematical Methods in Elasticity Imaging

Mathematical Methods in Elasticity Imaging

Mathematical Methods in Elasticity Imaging

Mathematical Methods in Elasticity Imaging

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Overview

This book is the first to comprehensively explore elasticity imaging and examines recent, important developments in asymptotic imaging, modeling, and analysis of deterministic and stochastic elastic wave propagation phenomena. It derives the best possible functional images for small inclusions and cracks within the context of stability and resolution, and introduces a topological derivative–based imaging framework for detecting elastic inclusions in the time-harmonic regime. For imaging extended elastic inclusions, accurate optimal control methodologies are designed and the effects of uncertainties of the geometric or physical parameters on stability and resolution properties are evaluated. In particular, the book shows how localized damage to a mechanical structure affects its dynamic characteristics, and how measured eigenparameters are linked to elastic inclusion or crack location, orientation, and size. Demonstrating a novel method for identifying, locating, and estimating inclusions and cracks in elastic structures, the book opens possibilities for a mathematical and numerical framework for elasticity imaging of nanoparticles and cellular structures.


Product Details

ISBN-13: 9781400866625
Publisher: Princeton University Press
Publication date: 04/05/2015
Series: Princeton Series in Applied Mathematics , #52
Sold by: Barnes & Noble
Format: eBook
Pages: 240
File size: 42 MB
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About the Author

Habib Ammari is director of research at the French National Center for Scientific Research and professor of mathematics at the École Normale Superiéure. Elie Bretin is a postdoctoral researcher in mathematics at the École Polytechnique. Josselin Garnier is professor of mathematics at Université Paris VII. Hyeonbae Kang is the Jungseok Chair Professor of Mathematics at Inha University in South Korea. Hyundae Lee is assistant professor of mathematics at Inha University. Abdul Wahab is a postdoctoral researcher in mathematics at Université Paris VII.

Read an Excerpt

Mathematical Methods in Elasticity Imaging


By Habib Ammari, Elie Bretin, Josselin Garnier, Hyeonbae Kang, Hyundae Lee, Abdul Wahab

PRINCETON UNIVERSITY PRESS

Copyright © 2015 Princeton University Press
All rights reserved.
ISBN: 978-1-4008-6662-5



CHAPTER 1

Layer Potential Techniques


The asymptotic theory for elasticity imaging described in this book relies on layer potential techniques. In this chapter we prepare the way by reviewing a number of basic facts and preliminary results regarding the layer potentials associated with both the static and time-harmonic elasticity systems. The most important results in this chapter are on one hand the decomposition formulas for the solutions to transmission problems in elasticity and characterization of eigenvalues of the elasticity system as characteristic values of layer potentials and on the other hand, the Helmholtz-Kirchhoff identities. As will be shown later, the Helmholtz-Kirchhoff identities play a key role in the analysis of resolution in elastic wave imaging. We also note that when dealing with exterior problems for harmonic elasticity, one should introduce a radiation condition, known as the Sommerfeld radiation condition, in order to select the physical solution to the problem.

This chapter is organized as follows. In Section 1.1 we first review commonly used function spaces. Then we introduce in Section 1.2 equations of linear elasticity and use the Helmholtz decomposition theorem to decompose the displacement field into the sum of an irrotational (curl-free) and a solenoidal (divergence-free) field. Section 1.3 is devoted to the radiation condition for the time-harmonic elastic waves, which is used to select the physical solution to exterior problems. In Section 1.4 we introduce the layer potentials associated with the operators of static and time-harmonic elasticity, study their mapping properties, and prove decomposition formulas for the displacement fields. In Section 1.5 we derive the Helmholtz-Kirchhoff identities, which play a key role in the resolution analysis in Chapters 7 and 8. In Section 1.6 we characterize the eigenvalues of the elasticity operator on a bounded domain with Neumann or Dirichlet boundary conditions as the characteristic values of certain layer potentials which are meromorphic operator-valued functions. We also introduce Neumann and Dirichlet functions and write their spectral decompositions. These results will be used in Chapter 11. Finally, in Section 1.7 we state a generalization of Meyer's theorem concerning the regularity of solutions to the equations of linear elasticity, which will be needed in Chapter 11 in order to establish an asymptotic theory of eigenvalue elastic problems.


1.1 SOBOLEV SPACES

Throughout the book, symbols of scalar quantities are printed in italic type, symbols of vectors are printed in bold italic type, symbols of matrices or 2-tensors are printed in bold type, and symbols of 4-tensors are printed in blackboard bold type.

The following Sobolev spaces are needed for the study of mapping properties of layer potentials for elasticity equations.

Let [partial derivative]i denote [partial derivative]/[partial derivative]xi. We use [nabla] = ([partial derivative]i)di=1 and [partial derivative]2 = ([partial derivative]2ij))di,j=1 to denote the gradient and the Hessian, respectively.

Let Ω be a smooth domain in Rd, with d = 2 or 3. We define the Hilbert space H1(Ω) by

H1(Ω) = {u [member of] L2(Ω) : [nabla]u [member of] L2(Ω)},


where [nabla]u is interpreted as a distribution and L2(Ω) is defined in the usual way, with

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]


The space H1(Ω) is equipped with the norm

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].


If Ω is bounded, another Banach space H10(Ω) arises by taking the closure of C∞0(Ω), the set of infinitely differentiable functions with compact support in Ω, in H1(Ω). We will also need the space H1loc(Rd\[bar.Ω]) of functions u [member of] L2loc(Rd\[bar.Ω]), the set of locally square summable functions in Rd\[bar.Ω], such that

h u [member of] H1 (Rd\[bar.Ω]) [for all] h [member of] C∞0(Rd\[bar.Ω].


Furthermore, we define H2(Ω) as the space of functions u [member of] H1(Ω) such that [partial derivative]2ij u [member of] L2(Ω), for i, j = 1, ..., d, and the space H3/2(Ω) as the interpolation space [H1(Ω), H2(Ω)]1/2 (see, for example, the book by Bergh and Löfström [49]).

It is known that the trace operator [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is a bounded linear surjective operator from H1(Ω) into H1/2([partial derivative]Ω), where H1/2([partial derivative]Ω) is the collection of functions f [member of] L2([partial derivative]Ω) such that

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]


We set H-1/2([partial derivative]Ω) = (H1/2([partial derivative]Ω))* and let < , >1/2, -1/2 denote the duality pair between these dual spaces.

We introduce a weighted norm, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], in two dimensions. Let

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.1)


This weighted norm is introduced because, as will be shown later, the solutions of the static elasticity equation behave like O(|x|-1) in two dimensions as |x| -> ∞. For convenience, we set

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.2)


In three dimensions, W(Rd\[bar.Ω]) is the usual Sobolev space.

We also define the Banach space W1,∞(Ω) by

W1,∞(Ω) = {u [member of] L∞(Ω) : [nabla]u [member of] L∞(Ω) (1.3)


where [nabla]u is interpreted as a distribution and L∞(Ω) is defined in the usual way, with

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].


We will need the following Hilbert spaces for deriving the Helmholtz decomposition theorem

Hcurl(Ω):= {u [member of] L2(Ω)d, [nabla] × u [member of] L2 (Ω)d},


equipped with the norm

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],


and

Hdiv(Ω) := {u [member of] L2(Ω)d, [nabla] · u [member of] L2(Ω)},


equipped with the norm

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]


Finally, let T1, ..., Td-1 be an orthonormal basis for the tangent plane to [partial derivative]Ω at x and let

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.4)


denote the tangential derivative on [partial derivative]Ω. We say that f [member of] H1([partial derivative]Ω) if f [member of] L2([partial derivative]Ω) and [partial derivative]f/[partial derivatve]T [member of] L2([partial derivative]Ω)d-1. Furthermore, we define H-1([partial derivative]Ω) as the dual of H1([partial derivative]Ω) and the space Hs([partial derivative]Ω), for 0 ≥ s ≥ 1, as the interpolation space [L2([partial derivative]Ω), H1([partial derivative]Ω)]s or, equivalently, as the set of functions f [member of] L2([partial derivative]Ω) such that

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]


see again [49].


1.2 ELASTICITY EQUATIONS

Let Ω be a domain in Rd, d = 2,3. Let λ and μ be the Lamé constants for Ω satisfying the strong convexity condition

μ > 0 and dλ + 2μ > 0. (1.5)


The constants [lamda] and μ are respectively referred to as the compression modulus and the shear modulus. The compression modulus measures the resistance of the material to compression and the shear modulus measures the resistance to shearing. We also introduce the bulk modulus β := λ + 2μ/d. We refer the reader to [122, p.11] for an explanation of the physical significance of (1.5).

In a homogeneous isotropic elastic medium, the elastostatic operator corresponding to the Lamé constants λ, μ, is given by

Lλ,μ u := μΔu + (λ + μ)[nabla][nabla] · u, u : Ω -> Rd. (1.6)


If Ω is bounded with a connected Lipschitz boundary, then we define the conormal derivative [partial derivative]u/[partial derivative]v by

[partial derivative]u/[partial derivative]v = λ([nabla] · u)n + μ([nabla]u + [nabla]ut)n, (1.7)


where [nabla]u is the matrix ([partial derivative]jui)di,j=1 with ui being the i-th component of u, the superscript t denotes the transpose, and n is the outward unit normal to the boundary [partial derivative]Ω.

Note that the conormal derivative has a direct physical meaning:

[partial derivative]u/[partial derivative]v = traction on [partial derivative]Ω.


The vector u is the displacement field of the elastic medium having the Lamé coefficients λ and μ, and the symmetric gradient

[nabla]su := ([nabla]u + [nabla]ut)/2


is the strain tensor.

In Rd, d = 2, 3, let

I := δij ei [cross product] ej,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]


with (e1, ..., ed) being the canonical basis of Rd and [cross product] denoting the tensor product between vectors in Rd. Here, I is the d × d identity matrix or 2-tensor while I is the identity 4-tensor.

Define the elasticity tensor C = (Cijkl)di,j,k,l=1 for Rd by

Cijkl = λδij δkl + μ([delt]ikδjl + δilδjk), (1.8)


which can be written as

C := λI [cross product] I + 2μI.


With this notation, we have

Lλ,μu = [nabla] · C[nabla]su,


and

[partial derivative]u/[partial derivative]v = (C[nabla]su)n = σ(u)n,


where σ(u) is the stress tensor given by

σ(u) = C[nabla]s u.


Now, we consider the elastic wave equation

ρ[partial derivative]2tU - Lλ,μU = 0,


where the positive constant ρ is the density of the medium. Then, we obtain a time-harmonic solution U(x, t) = Re]e-iωtu(x)] if the space-dependent part u satisfies the time-harmonic elasticity equation for the displacement field,

(Lλ,μ + ω2ρ)u = 0, (1.9)


with ω being the angular frequency.

The time-harmonic elasticity equation (1.9) has a special family of solutions called p- and s-plane waves:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.10)


for θ [member of] Sd-1 := {θ [member of] Rd : |θ| = 1} the direction of the wavevector and θ[perpendicular to] is such that |θ[perpendicular to]| = 1 and θ[perpendicular to] • θ = 0. Note that Up is irrotational while Us is solenoidal.

Taking the limit ω -> 0 in (1.9) yields the static elasticity equation

Lλ,μu = 0. (1.11)


In a bounded domain Ω, the equations (1.9) and (1.11) need to be supplemented with boundary conditions at [partial derivative]Ω. If [partial derivative]Ω is a stress-free surface, the traction acting on [partial derivative]Ω vanishes:

[partial derivative]u/[partial derivative]v = 0.


This boundary condition is appropriate when the surface [partial derivative]Ω forms the outer boundary on the elastic body that is surrounded by empty space.

In a homogeneous, isotropic medium, using the Helmholtz decomposition theorem, the displacement field can be decomposed into the sum of an irrotational and a solenoidal field. Assume that Ω is simply connected and its boundary [partial derivative]Ω is connected. The Helmholtz decomposition states that for w [member of] L2(Ω)d there exist φw [member of] H1(Ω) and ψw [member of] Hcurl(Ω) [intersection] Hdiv(Ω) such that

w = [nabla]φw + [nabla] × φw. (1.12)


The Helmholtz decomposition (1.12) can be found by solving the following weak Neumann problem in Ω [38, 78]:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.3)


The function φw [member of] H1(Ω) is uniquely defined up to an additive constant. In order to uniquely define the function ψw, we impose that it satisfies the following properties [53]:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.14)


The boundary condition ([nabla] × ψw) · n = 0 on [partial derivative]Ω shows that the gradient and curl parts in (1.12) are orthogonal.


(Continues...)

Excerpted from Mathematical Methods in Elasticity Imaging by Habib Ammari, Elie Bretin, Josselin Garnier, Hyeonbae Kang, Hyundae Lee, Abdul Wahab. Copyright © 2015 Princeton University Press. Excerpted by permission of PRINCETON UNIVERSITY PRESS.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

Table of Contents

Introduction 1
1 Layer Potential Techniques 4
1.1 Sobolev Spaces 4
1.2 Elasticity Equations 6
1.3 Radiation Condition 10
1.4 Integral Representation of Solutions to the Lamé System 11
1.5 Helmholtz-Kirchhoff Identities 21
1.6 Eigenvalue Characterizations and Neumann and Dirichlet Functions 27
1.7 A Regularity Result 32
2 Elasticity Equations with High Contrast Parameters 33
2.1 Problem Setting 34
2.2 Incompressible Limit 34
2.3 Limiting Cases of Holes and Hard Inclusions 36
2.4 Energy Estimates 38
2.5 Convergence of Potentials and Solutions 42
2.6 Boundary Value Problems 45
3 Small-Volume Expansions of the Displacement Fields 48
3.1 Elastic Moment Tensor 48
3.2 Small-Volume Expansions 55
4 Boundary Perturbations due to the Presence of Small Cracks 66
4.1 A Representation Formula 66
4.2 Derivation of an Explicit Integral Equation 69
4.3 Asymptotic Expansion 71
4.4 Topological Derivative of the Potential Energy 75
4.5 Derivation of the Representation Formula 76
4.6 Time-Harmonic Regime 79
5 Backpropagation and Multiple Signal Classification Imaging of Small Inclusions 80
5.1 A Newton-Type Search Method 80
5.2 A MUSIC-Type Method in the Static Regime 82
5.3 A MUSIC-Type Method in the Time-Harmonic Regime 82
5.4 Reverse-TimeMigration and Kirchhoff Imaging in the Time-Harmonic Regime 84
5.5 Numerical Illustrations 86
6 Topological Derivative Based Imaging of Small Inclusions in the Time-Harmonic Regime 91
6.1 Topological Derivative Based Imaging 91
6.2 Modified Imaging Framework 102
7 Stability of Topological Derivative Based Imaging Functionals 112
7.1 Statistical Stability with Measurement Noise 112
7.2 Statistical Stability with Medium Noise 118
8 Time-Reversal Imaging of Extended Source Terms 125
8.1 Analysis of the Time-Reversal Imaging Functionals 127
8.2 Time-Reversal Algorithm for Viscoelastic Media 129
8.3 Numerical Illustrations 137
9 Optimal Control Imaging of Extended Inclusions 148
9.1 Imaging of Shape Perturbations 149
9.2 Imaging of an Extended Inclusion 152
10 Imaging from Internal Data 160
10.1 Inclusion Model Problem 160
10.2 Binary Level Set Algorithm 162
10.3 Imaging Shear Modulus Distributions 164
10.4 Numerical Illustrations 165
11 Vibration Testing 168
11.1 Small-Volume Expansions of the Perturbations in the Eigenvalues 169
11.2 Eigenvalue Perturbations due to Shape Deformations 181
11.3 Splitting of Multiple Eigenvalues 192
11.4 Reconstruction of Inclusions 193
11.5 Numerical Illustrations 195
A Introduction to Random Processes 201
A.1 Random Variables 201
A.2 Random Vectors 202
A.3 Gaussian Random Vectors 203
A.4 Conditioning 204
A.5 Random Processes 205
A.6 Gaussian Processes 206
A.7 Stationary Gaussian Random Processes 208
A.8 Multi-valued Gaussian Processes 208
B Asymptotics of the Attenuation Operator 210
B.1 Stationary Phase Theorem 210
B.2 Derivation of the Asymptotics 211
C The Generalized Argument Principle and Rouché’s Theorem 213
C.1 Notation and Definitions 213
C.2 Generalized Argument Principle 214
C.3 Generalization of Rouché’s Theorem 214
References 217
Index 229

What People are Saying About This

From the Publisher

"This book covers recent mathematical, numerical, and statistical approaches for elasticity imaging of inclusions and cracks. A precise and timely book, it is easy to follow and will interest readers."—Yanyan Li, Rutgers University

"This book is the first to provide readers with a comprehensive overview of mathematical and computational studies of elasticity imaging. It is a useful resource for scientists and engineers in the relevant fields and a solid reference for any newcomers to elasticity imaging."—Gang Bao, Zhejiang University

"This well-organized book explains recent mathematical, numerical, and statistical approaches for multistatic imaging of targets with waves at single or multiple frequencies. It makes an important contribution to the understanding of trade-offs between data size, computational complexity, signal-to-noise ratio, and resolution in multistatic imaging."—Jin Keun Seo, Yonsei University

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