\`x^2+y_1+z_12^34\`
Advanced Search
Article Contents
Article Contents

Small defects reconstruction in waveguides from multifrequency one-side scattering data

  • * Corresponding author: Angèle Niclas

    * Corresponding author: Angèle Niclas 
Abstract Full Text(HTML) Figure(14) / Table(2) Related Papers Cited by
  • Localization and reconstruction of small defects in acoustic or electromagnetic waveguides is of crucial interest in nondestructive evaluation of structures. The aim of this work is to present a new multi-frequency inversion method to reconstruct small defects in a 2D waveguide. Given one-side multi-frequency wave field measurements of propagating modes, we use a Born approximation to provide a $ \text{L}^2 $-stable reconstruction of three types of defects: a local perturbation inside the waveguide, a bending of the waveguide, and a localized defect in the geometry of the waveguide. This method is based on a mode-by-mode spacial Fourier inversion from the available partial data in the Fourier domain. Indeed, in the available data, some high and low spatial frequency information on the defect are missing. We overcome this issue using both a compact support hypothesis and a minimal smoothness hypothesis on the defects. We also provide a suitable numerical method for efficient reconstruction of such defects and we discuss its applications and limits.

    Mathematics Subject Classification: 35R30, 78A46.

    Citation:

    \begin{equation} \\ \end{equation}
  • 加载中
  • Figure 1.  Representation of the three types of defects: in $ (1) $ a local perturbation $ q $, in $ (2) $ a bending of the waveguide, in $ (3) $ a localized defect in the geometry of $ \Omega $. A controlled source $ s $ generates a wave field $ u^\text{inc}_k $. When it crosses the defect, it generates a scattered wave field $ u^s_k $. Both $ u^\text{inc}_k $ and $ u^s_k $ are measured on the section $ \Sigma $

    Figure 2.  Condition number of $ M_t^TM_t $ for different sizes of support and values of $ \omega_0 $. Here, $ X $ is the discretization of $ [1-r, 1+r] $ with $ 500r+1 $ points. The $ x $-axis represents the evolution of $ r $, and the $ y $-axis $ \text{cond}_2(M_t^TM_t) $. Each curve corresponds to value of $ \omega_0 $ as indicated in the left rectangle

    Figure 3.  Representation of a bend in a waveguide

    Figure 4.  Representation of a shape defect in a waveguide

    Figure 5.  Reconstruction of $ f(x) = (x-0.8)(1.2-x)\textbf{1}_{0.8\leq x\leq 1.2} $ for different values of $ \omega_1 $ using the discrete operator $ \gamma $ and the algorithm (91) with $ \lambda = 0.001 $. Here, $ X $ is the discretization of $ [0.5, 1.5] $ with $ 10\omega_1 $ points, and $ K $ is the discretization of $ [0.01, \omega_1] $ with $ 1000 $ points

    Figure 6.  $ \text{L}^2 $-error between $ f(x) = (x-0.8)(1.2-x)\textbf{1}_{0.8\leq x\leq 1.2} $ and its reconstruction $ f_{app} $ for different values of $ \omega_1 $ using the discrete operator $ \gamma $ and the algorithm (91) with $ \lambda = 0.001 $. Here, $ X $ is the discretization of $ [0.5, 1.5] $ with $ 10\omega_1 $ points, and $ K $ is the discretization of $ [0.01, \omega_1] $ with $ 1000 $ points

    Figure 7.  Reconstruction of $ f(x) = (x-0.8)(1.2-x)\textbf{1}_{0.8\leq x\leq 1.2} $ for different values of $ \omega_0 $ and $ r = 0.5 $ using the discrete operator $ \gamma $ and the algorithm (91) with $ \lambda = 0.001 $. Here, $ X $ is the discretization of $ [0.5, 1.5] $ with $ 251 $ points, and $ K $ is the discretization of $ [\omega_0, 50] $ with $ 1000 $ points

    Figure 8.  Reconstruction of $ f(x) = (x-0.8)(1.2-x)\textbf{1}_{0.8\leq x\leq 1.2} $ for different sizes of support $ r $ and $ \omega_0 = 3\pi $ using the discrete operator $ \gamma $ and the algorithm (91) with $ \lambda = 0.001 $. Here, $ X $ is the discretization of $ [1-r, 1+r] $ with $ 500r+1 $ points, and $ K $ is the discretization of $ [3\pi, 50] $ with $ 1000 $ points

    Figure 9.  Reconstruction of two different bends. The black lines represent the initial shape of $ \Omega $, and the red the reconstruction of $ \Omega $. In both cases, $ K $ is the discretization of $ [0.01, 40] $ with $ 100 $ points, and the reconstruction is obtain by (94). On the left, the initial parameters of the bend are $ (x_c, r, \theta) = (4, 10, \pi/12) $ and on the right, $ (x_c, r, \theta) = (2, 5, \pi/6) $

    Figure 10.  Reconstruction of a waveguide with two successive bends. The black lines represent the initial shape of $ \Omega $, and the red the reconstruction of $ \Omega $, slightly shifted for comparison purposes. In both cases, $ K $ is the discretization of $ [0.01, 40] $ with $ 100 $ points. The parameters of the two bends are $ (x_c^{(1)}, r^{(1)}, \theta^{(1)}) = (2, 10, \pi/30)) $ and $ (x_c^{(2)}, r^{(2)}, \theta^{(2)}) = (3.8, 8, -\pi/20)) $

    Figure 11.  Reconstruction of two shape defects. In black, the initial shape of $ \Omega $, and in red the reconstruction, slightly shifted for comparison purposes. In both cases, $ K $ is the discretization of $ [0.01, 70]\setminus \{[n\pi-0.2, n\pi+0.2], n\in \mathbb{N}\} $ with $ 300 $ points, $ X $ is the discretization of $ [3, 4.5] $ with $ 151 $ points and we use the algorithm (91) with $ \lambda = 0.08 $ to reconstruct $ s_0 $ and $ s_1 $. On the left, $ h(x) = \frac{5}{16}\textbf{1}_{3.2\leq x\leq 4.2}(x-3.2)^2(4.2-x)^2 $ and $ g(x) = -\frac{35}{16}\textbf{1}_{3.4\leq x\leq 4}(x-3.4)^2(4-x)^2 $. On the right, $ h(x) = \frac{125}{16}\textbf{1}_{3.7\leq x\leq 4.2}(x-3.7)^2(4.2-x)^2 $ and $ g(x) = \frac{125}{16}\textbf{1}_{3.4\leq x\leq 4}(x-3.4)^2(4-x)^2 $

    Figure 12.  Recontruction of $ h_n $ for $ 0\leq n\leq 9 $, where $ h(x) = 0.05\textbf{1}_{\left|\left(\frac{x-4}{0.05}, \frac{y-0.6}{0.15}\right)\right|\leq 1}\left|\left(\frac{x-4}{0.05}, \frac{y-0.6}{0.15}\right)\right|^2 $. In blue, we represent $ h_n $ and in red the reconstruction of $ h_{n_{\text{app}}} $. In every reconstruction, $ K $ is the discretization of $ [0.01, 150] $ with $ 200 $ points, $ X $ is the discretization of $ [3.8, 4.2] $ with $ 101 $ points and we use the algorithm (91) with $ \lambda = 0.002 $ to reconstruct every $ h_n $

    Figure 13.  Recontruction of an inhomogeneity $ h $, where $ h(x) = 0.05\textbf{1}_{\left|\left(\frac{x-4}{0.05}, \frac{y-0.6}{0.15}\right)\right|\leq 1}\left|\left(\frac{x-4}{0.05}, \frac{y-0.6}{0.15}\right)\right|^2 $. On the left, we represent the initial shape of $ h $, and on the right the reconstruction $ h_{\text{app}} $. Here, $ K $ is the discretization of $ [0.01, 150] $ with $ 200 $ points, $ X $ is the discretization of $ [3.8, 4.2] $ with $ 101 $ points and we use the algorithm (91) with $ \lambda = 0.002 $ to reconstruct every $ h_n $. We used $ N = 20 $ modes to reconstruct $ h $

    Figure 14.  Recontruction of an inhomogeneity $ h $. From top to bottom, the initial representation of $ h $, the reconstruction $ h_{\text{app}} $ and the reconstruction $ h_{\text{app}} $ with the knowledge of the positivity of $ h $. Here, $ K $ is the discretization of $ [0.01, 150] $ with $ 200 $ points, $ X $ is the discretization of $ [3, 6] $ with $ 3001 $ points and we use the algorithm (91) with $ \lambda = 0.01 $ to reconstruct every $ h_n $. We choose used $ N = 20 $ modes to reconstruct $ h $

    Table 1.  Relative errors on the reconstruction of $ (x_c, r, \theta) $ for different bends. In each case, $ K $ is the discretization of $ [0.01, 40] $ with $ 100 $ points, and the reconstruction is obtain by (94)

    $ (x_c, r, \theta) $ $ (2.5, 40, \pi/80) $ $ (4, 10, \pi/12) $ $ (2, 5, \pi/6) $
    relative error on $ x_c $ $ 1.8\% $ $ 0\% $ $ 7.6\% $
    relative error on $ r $ $ 3.0\% $ $ 7.5\% $ $ 23.8\% $
    relative error on $ \theta $ $ 1.6\% $ $ 10.7\% $ $ 16.9\% $
     | Show Table
    DownLoad: CSV

    Table 2.  Relative errors on the reconstruction of $ h $ for different amplitudes $ A $. We choose $ h(x) = A\textbf{1}_{3\leq x\leq 5}(x-3)^2(5-x)^2 $ and $ g(x) = 0 $. In every reconstruction, $ K $ is the discretization of $ [0.01, 40]\setminus \{[n\pi-0.2, n\pi+0.2], n\in \mathbb{N}\} $ with $ 100 $ points, $ X $ is the discretization of $ [1, 7] $ with $ 601 $ points and we use the algorithm (91) with $ \lambda = 0.08 $ to reconstruct $ h' $

    $ A $ $ 0.1 $ $ 0.2 $ $ 0.3 $ $ 0.5 $
    $ \Vert h-h_{\text{app}}\Vert_{\text{L}^2( \mathbb{R})}/\Vert h\Vert_{\text{L}^2( \mathbb{R})} $ $ 8.82\% $ $ 10.41\% $ $ 15.12\% $ $ 54.99\% $
     | Show Table
    DownLoad: CSV
  • [1] L. Abrahamsson, Orthogonal grid generation for two-dimensional ducts, J. Comput. Appl. Math., 34 (1991), 305-314.  doi: 10.1016/0377-0427(91)90091-W.
    [2] L. Abrahamsson and H. O. Kreiss, Numerical solution of the coupled mode equations in duct acoustics, J. Comput. Phy., 111 (1994), 1-14.  doi: 10.1006/jcph.1994.1038.
    [3] S. Acosta, S. Chow, J. Taylor and V. Villamizar, On the multi-frequency inverse source problem in heterogeneous media, Inverse Problems, 28 (2012), 075013. doi: 10.1088/0266-5611/28/7/075013.
    [4] H. AmmariE. Iakovleva and H. Kang, Reconstruction of a small inclusion in a two-dimensional open waveguide, SIAM J. Appl. Math., 65 (2005), 2107-2127.  doi: 10.1137/040615389.
    [5] G. Bao and P. Li, Inverse medium scattering problems for electromagnetic waves, SIAM J. Appl. Math., 65 (2005), 2049-2066.  doi: 10.1137/040607435.
    [6] G. Bao and F. Triki, Reconstruction of a defect in an open waveguide, Sci. China Math., 56 (2013), 2539-2548.  doi: 10.1007/s11425-013-4696-8.
    [7] G. Bao and F. Triki, Stability for the multifrequency inverse medium problem, J. Differential Equations, 269 (2020), 7106-7128.  doi: 10.1016/j.jde.2020.05.021.
    [8] J. P. Berenger, A perfectly matched layer for the absorption of electromagnetic waves, J. Comput. Phys., 114 (1994), 185-200.  doi: 10.1006/jcph.1994.1159.
    [9] L. Bourgeois and S. Fliss, On the identification of defects in a periodic waveguide from far field data, Inverse Problems, 30 (2014), 095004. doi: 10.1088/0266-5611/30/9/095004.
    [10] L. Bourgeois and E. Lunéville, The linear sampling method in a waveguide: A modal formulation, Inverse Problems, 24 (2008), 015018. doi: 10.1088/0266-5611/24/1/015018.
    [11] D. Colton and A. Kirsch, A simple method for solving inverse scattering problems in the resonance region, Inverse Problems, 12 (1996), 383-393.  doi: 10.1088/0266-5611/12/4/003.
    [12] D. Colton and R. Kress, Inverse Acoustic and Electromagnetic Scattering Theory, Applied Mathematical Sciences, Springer-Verlag, Berlin, 1992. doi: 10.1007/978-3-662-02835-3.
    [13] S. Dediu and J. R. McLaughlin, Recovering inhomogeneities in a waveguide using eigensystem decomposition, Inverse Problems, 22 (2006), 1227-1246.  doi: 10.1088/0266-5611/22/4/007.
    [14] A. S. B.-B. DhiaL. Chesnel and S. A. Nazarov, Perfect transmission invisibility for waveguides with sound hard walls, J. Math. Pures Appl., 111 (2018), 79-105.  doi: 10.1016/j.matpur.2017.07.020.
    [15] H. Dym and H. P. McKean, Fourier Series and Integrals, Academic Press New York, 1972.
    [16] P. Grisvard, Elliptic Problems in Nonsmooth Domains, Society for Industrial and Applied Mathematics, 2011. doi: 10.1137/1.9781611972030.ch1.
    [17] M. Isaev and R. G. Novikov, Hölder-logarithmic stability in Fourier synthesis, Inverse Problems, 36 (2020), 125003. doi: 10.1088/1361-6420/abb5df.
    [18] V. Isakov and S. Lu, Increasing stability in the inverse source problem with attenuation and many frequencies, SIAM J. Appl. Math., 78 (2018), 1-18.  doi: 10.1137/17M1112704.
    [19] M. KharratO. BareilleW. Zhou and M. Ichchou, Nondestructive assessment of plastic elbows using torsional waves: Numerical and experimental investigations, Journal of Nondestructive Evaluation, 35 (2016), 1-14.  doi: 10.1007/s10921-015-0324-6.
    [20] M. Kharrat, M. N. Ichchou, O. Bareille and W. Zhou, Pipeline inspection using a torsional guided-waves inspection system. part 1: Defect identification, International Journal of Applied Mechanics, 6 (2014). doi: 10.1142/S1758825114500343.
    [21] Y. Y. Lu, Exact one-way methods for acoustic waveguides, Math. Comput. Simulation, 50 (1999), 377-391.  doi: 10.1016/S0378-4754(99)00111-1.
    [22] W. McLeanStrongly Elliptic Systems and Boundary Integral Equations, Cambridge University Press, 2000. 
    [23] M. Sini and N. T. Thanh, Inverse acoustic obstacle scattering problems using multifrequency measurements, Inverse Probl. Imaging, 6 (2012), 749-773.  doi: 10.3934/ipi.2012.6.749.
    [24] J. Todd, The condition of the finite segment of the Hilbert matrix, National Bureau of Standarts, Applied Mathematics Series, (1954), 109–119.
  • 加载中

Figures(14)

Tables(2)

SHARE

Article Metrics

HTML views(409) PDF downloads(319) Cited by(0)

Access History

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return