<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE root>
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Acoustical Physics</journal-id><journal-title-group><journal-title xml:lang="en">Acoustical Physics</journal-title><trans-title-group xml:lang="ru"><trans-title>Акустический журнал</trans-title></trans-title-group></journal-title-group><issn publication-format="print">0320-7919</issn><issn publication-format="electronic">3034-5006</issn><publisher><publisher-name xml:lang="en">The Russian Academy of Sciences</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">690841</article-id><article-id pub-id-type="doi">10.31857/S0320791925040095</article-id><article-id pub-id-type="edn">vppygh</article-id><article-categories><subj-group subj-group-type="toc-heading"><subject>ОБРАБОТКА АКУСТИЧЕСКИХ СИГНАЛОВ. КОМПЬЮТЕРНОЕ МОДЕЛИРОВАНИЕ</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Numerical modeling of elastic wave phenomena by grid-characteristic method on chimera computational meshes</article-title><trans-title-group xml:lang="ru"><trans-title>Численное моделирование упругих волновых явлений сеточно-характеристическим методом на химерных расчетных сетках</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Favorskaya</surname><given-names>A. V.</given-names></name><name xml:lang="ru"><surname>Фаворская</surname><given-names>А. В.</given-names></name></name-alternatives><email>aleanera@yandex.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Khokhlov</surname><given-names>N. I.</given-names></name><name xml:lang="ru"><surname>Хохлов</surname><given-names>Н. И.</given-names></name></name-alternatives><email>k_h@inbox.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Kozhemyachenko</surname><given-names>A. A.</given-names></name><name xml:lang="ru"><surname>Кожемяченко</surname><given-names>А. А.</given-names></name></name-alternatives><email>anton-kozhemyachenko@yandex.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Petrov</surname><given-names>I. B.</given-names></name><name xml:lang="ru"><surname>Петров</surname><given-names>И. Б.</given-names></name></name-alternatives><email>petrov@mipt.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Moscow Institute of Physics and Technology</institution></aff><aff><institution xml:lang="ru">Московский физико-технический институт</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Scientific Research Institute for System Analysis of the National Research Centre “Kurchatov Institute”</institution></aff><aff><institution xml:lang="ru">Научно-исследовательский институт системных исследований Национального исследовательского центра “Курчатовский институт”</institution></aff></aff-alternatives><aff-alternatives id="aff3"><aff><institution xml:lang="en">Innopolis University</institution></aff><aff><institution xml:lang="ru">Университет Иннополис</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2025-08-15" publication-format="electronic"><day>15</day><month>08</month><year>2025</year></pub-date><volume>71</volume><issue>4</issue><issue-title xml:lang="en">VOL 71, NO4 (2025)</issue-title><issue-title xml:lang="ru">ТОМ 71, №4 (2025)</issue-title><fpage>598</fpage><lpage>608</lpage><history><date date-type="received" iso-8601-date="2025-09-21"><day>21</day><month>09</month><year>2025</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2025, Russian Academy of Sciences</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2025, Российская академия наук</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="en">Russian Academy of Sciences</copyright-holder><copyright-holder xml:lang="ru">Российская академия наук</copyright-holder></permissions><self-uri xlink:href="https://modernonco.orscience.ru/0320-7919/article/view/690841">https://modernonco.orscience.ru/0320-7919/article/view/690841</self-uri><abstract xml:lang="en"><p>Nowadays, the solution of applied problems of seismic exploration and ultrasonic non-destructive testing is accompanied by the use of computer simulation. This poses a challenge for scientists to develop new modifications of numerical methods that allow increasing the accuracy of calculations while minimizing the cost of computing resources. Unlike numerical methods on unstructured computational grids, the use of Chimera computational grids (or superimposed ones, or adaptive ones) also allows describing boundaries and contact boundaries of arbitrary shape, but at the same time spending less RAM and time on calculations. This is especially important in connection with the active use of neural networks for solving inverse problems, since when generating training samples, both the accuracy of modeling and the speed of calculations and the amount of RAM spent are important. The paper considers and compares various modifications of the grid-characteristic method on Chimera computational grids. Examples of test calculations are given.</p></abstract><trans-abstract xml:lang="ru"><p>Решение прикладных задач сейсмической разведки и ультразвуковой дефектоскопии сопровождается применением компьютерного моделирования. Это ставит перед учеными задачу по разработке новых модификаций численных методов, позволяющих увеличить точность расчетов, минимизируя при этом затраты вычислительных ресурсов. В отличие от численных методов на неструктурированных расчетных сетках, использование Химерных (или наложенных, или адаптивных) расчетных сеток позволяет также описывать границы и контактные границы произвольной формы, но при этом затрачивать меньше оперативной памяти и времени на проведение вычислений. Это особенно важно в связи с активным использованием нейронных сетей для решения обратных задач, так как при генерации обучающих выборок важна как точность моделирования, так и скорость вычислений и количество затрачиваемой оперативной памяти. В работе рассматриваются и сравниваются между собой различные модификации сеточно-характеристического метода на Химерных расчетных сетках. Приведены примеры тестовых расчетов.</p></trans-abstract><kwd-group xml:lang="en"><kwd>computer simulation</kwd><kwd>elastic wave equation</kwd><kwd>grid-characteristic method</kwd><kwd>seismic prospecting</kwd><kwd>ultrasonic non-destructive testing</kwd><kwd>railway</kwd><kwd>superimposed meshes</kwd><kwd>adaptive meshes</kwd><kwd>Chimera meshes</kwd><kwd>patch meshes</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>компьютерное моделирование</kwd><kwd>упругое волновое уравнение</kwd><kwd>сеточно-характеристический метод</kwd><kwd>сейсмическая разведка</kwd><kwd>ультразвуковая дефектоскопия</kwd><kwd>железнодорожные рельсы</kwd><kwd>наложенные сетки</kwd><kwd>адаптивные сетки</kwd><kwd>Химерные сетки</kwd><kwd>patch-сетки</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Micucci M., Iula A. Recent advances in machine learning applied to ultrasound imaging // Electronics. 2022. V. 11. № 11. Art. № 1800.</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Wu X., Ma J., Si X., Bi Z., Yang J., Gao H., Xie D., Guo Z., Zhang J. Sensing prior constraints in deep neural networks for solving exploration geophysical problems // Proceedings of the National Academy of Sciences. 2023. V. 120. № 23. Art. № e2219573120.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Golubev V., Anisimov M. Application of convolutional networks for localization and prediction of scalar parameters of fractured geological inclusion // International Journal of Applied Mechanics. 2024. V. 16. № 5. Art. № 2450064.</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Tsukanov A.A., Gorbatikov A.V. Influence of the contribution of body waves to the result of the microseismic sounding method // Acoustical Physics. 2020. V. 66. P. 191–197.</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Favorskaya A., Petrov I. A novel method for investigation of acoustic and elastic wave phenomena using numerical experiments // Theoretical and Applied Mechanics Letters. 2020. V. 10. № 5. P. 307–314.</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Eremin A.A., Glushkov E.V., Glushkova N.V., Lammering R. Localization of inhomogeneities in an elastic plate using the time reversal method // Acoustical Physics. 2017. V. 63. P. 562–569.</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Presnov D.A., Sobisevich A.L., Shurup A.S. Determination of ice cover parameters using seismoacoustic noise // Acoustical Physics. 2023. V. 69. № 5. P. 725–737.</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Zou Q., Huang J.P., Yong P., Li Z.C. 3D elastic waveform modeling with an optimized equivalent staggered-grid finite-difference method // Petroleum Science. 2020. V. 17. P. 967–989.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Bosma S., Hajibeygi H., Tene M., Tchelepi H.A. Multiscale finite volume method for discrete fracture modeling on unstructured grids (MS-DFM) // Journal of Computational Physics. 2017. V. 351. P. 145–164.</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Gulizzi V., Saye R. Modeling wave propagation in elastic solids via high-order accurate implicit-mesh discontinuous Galerkin methods // Computer Methods in Applied Mechanics and Engineering. 2022. V. 395. Art. № 114971.</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Nanda N. Wave propagation analysis of laminated composite shell panels using a frequency domain spectral finite element model // Applied Mathematical Modelling. 2021. V. 89. P. 1025–1040.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Favorskaya A.V., Petrov I.B. Grid-characteristic method // Innovations in Wave Processes Modelling and Decision Making: Grid-Characteristic Method and Applications. Smart Innovation, Systems and Technologies. 2018. V. 90. P. 117–160.</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Shevchenko A.V., Golubev V.I. Boundary and contact conditions of higher order of accuracy for grid-characteristic schemes in acoustic problems // Computational Mathematics and Mathematical Physics. 2023. V. 63. № 10. P. 1760–1772.</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Golubev V.I., Nikitin I.S., Mi X. Numerical schemes of higher approximation orders for dynamic problems of elastoviscoplastic media // Journal of Siberian Federal University. Mathematics and Physics. 2024. V. 17. № 1. P. 8–17.</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Golubev V.I., Nikitin I.S. Refined schemes for computing the dynamics of elastoviscoplastic media // Computational Mathematics and Mathematical Physics. 2023. V. 63. № 10. P. 1874–1885.</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Golubev V., Nikitin I., Beklemysheva K. Model of fractured medium and nondestructive control of composite materials // Chinese Journal of Aeronautics. 2024. V. 37. № 2. P. 93–99.</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Steger J.L., Benek J.A. On the use of composite grid schemes in computational aerodynamics // Computer Methods in Applied Mechanics and Engineering. 1987. V. 64. № 1–3, 301–320.</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Khokhlov N., Favorskaya A., Stetsyuk V., Mitskovets I. Grid-characteristic method using Chimera meshes for simulation of elastic waves scattering on geological fractured zones // Journal of Computational Physics. 2021. V. 446. Art. № 110637.</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Zang N., Zhang W., Chen X. An overset-grid finite-difference algorithm for simulating elastic wave propagation in media with complex free-surface topography // Geophysics. 2021. V. 86. № 4. P. T277–T292.</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Muratov M.V., Petrov I.B., Sannikov A.V., Favorskaya A.V. Grid-characteristic method on unstructured tetrahedral meshes // Computational Mathematics and Mathematical Physics. 2014. V. 54. P. 837–847.</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>Favorskaya A.V., Petrov I.B. A study of high-order grid-characteristic methods on unstructured grids // Numerical Analysis and Applications. 2016. V. 9. P. 171–178.</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Duan P., Gu B., Li Z., Li Q. An overset mesh-free finite-difference method for seismic modeling including surface topography // Geophysics. 2023. V. 88. № 5. P. T271–T288.</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Qiu H., Sun Y.C., Fang C., Zhang W., Chen X. An overset-grid finite-difference algorithm for seismic wavefield propagations modelling in the polar coordinate system with a complex free-surface topography // Geophysical Journal International. 2024. V. 241. № 3. P. 1881–1895.</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Kozhemyachenko A.A., Favorskaya A.V. Grid convergence analysis of grid-characteristic method on Chimera meshes in ultrasonic nondestructive testing of railroad rail // Computational Mathematics and Mathematical Physics. 2023. V. 63. № 10. P. 1886–1903.</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Pesnya E., Favorskaya A.V., Petrov I.B., Khokhlov N.I. Parallelization strategies for ultrasonic wave propagation in composite materials considering microstructural details // Supercomputing Frontiers and Innovations. 2024. V. 11 № 4. P. 66–77.</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>Favorskaya A.V., Khokhlov N.I., Golubev V.I., Shevchenko A.V. Boundary conforming Chimera meshes to account for surface topography and curved interfaces in geological media // Lobachevskii Journal of Mathematics. 2024. V. 45. № 1. P. 191–212.</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Favorskaya A.V., Khokhlov N.I., Petrov I.B. Grid-characteristic method on joint structured regular and curved grids for modeling coupled elastic and acoustic wave phenomena in objects of complex shape // Lobachevskii Journal of Mathematics. 2020. V. 41. P. 512–525.</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>Kholodov A.S., Kholodov Y.A. Monotonicity criteria for difference schemes designed for hyperbolic equations // Computational Mathematics and Mathematical Physics. 2006. V. 46. P. 1560–1588.</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>Khokhlov N.I., Favorskaya A., Furgailo V. Grid-characteristic method on overlapping curvilinear meshes for modeling elastic waves scattering on geological fractures // Minerals. 2022. V. 12. № 12. Art. № 1597.</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>Peng L., Nianhua W., Chang X., Zhang L., Yadong W.U. An automatic isotropic/anisotropic hybrid grid generation technique for viscous flow simulations based on an artificial neural network // Chinese Journal of Aeronautics. 2022. V. 35. № 4. P. 102–117.</mixed-citation></ref><ref id="B31"><label>31.</label><mixed-citation>Sang K.H., Yin X.Y., Zhang F.C. Machine learning seismic reservoir prediction method based on virtual sample generation // Petroleum Science. 2021. V. 18. № 6. P. 1662–1674.</mixed-citation></ref></ref-list></back></article>
