Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions
•Proposed a multi-scale physics-informed neural networks scheme for solving high Reynolds number boundary layer flows.•Applied the matched asymptotic expansions to ensure the continuity of the whole domain solutions after dividing.•Demonstrated the effectiveness of multi-scale physics-informed neura...
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Published in | Theoretical and applied mechanics letters Vol. 14; no. 2; p. 100496 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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01.03.2024
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Abstract | •Proposed a multi-scale physics-informed neural networks scheme for solving high Reynolds number boundary layer flows.•Applied the matched asymptotic expansions to ensure the continuity of the whole domain solutions after dividing.•Demonstrated the effectiveness of multi-scale physics-informed neural networks capturing flow details in different scales.
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Multi-scale system remains a classical scientific problem in fluid dynamics, biology, etc. In the present study, a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data. The flow is divided into several regions with different scales based on Prandtl’s boundary theory. Different regions are solved with governing equations in different scales. The method of matched asymptotic expansions is used to make the flow field continuously. A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale. The results are compared with the reference numerical solutions, which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows. This scheme can be developed for more multi-scale problems in the future. |
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AbstractList | •Proposed a multi-scale physics-informed neural networks scheme for solving high Reynolds number boundary layer flows.•Applied the matched asymptotic expansions to ensure the continuity of the whole domain solutions after dividing.•Demonstrated the effectiveness of multi-scale physics-informed neural networks capturing flow details in different scales.
[Display omitted]
Multi-scale system remains a classical scientific problem in fluid dynamics, biology, etc. In the present study, a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data. The flow is divided into several regions with different scales based on Prandtl’s boundary theory. Different regions are solved with governing equations in different scales. The method of matched asymptotic expansions is used to make the flow field continuously. A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale. The results are compared with the reference numerical solutions, which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows. This scheme can be developed for more multi-scale problems in the future. Multi-scale system remains a classical scientific problem in fluid dynamics, biology, etc. In the present study, a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data. The flow is divided into several regions with different scales based on Prandtl’s boundary theory. Different regions are solved with governing equations in different scales. The method of matched asymptotic expansions is used to make the flow field continuously. A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale. The results are compared with the reference numerical solutions, which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows. This scheme can be developed for more multi-scale problems in the future. |
ArticleNumber | 100496 |
Author | Qiu, Rundi Wang, Jingzhu Huang, Jianlin Wang, Yiwei |
Author_xml | – sequence: 1 givenname: Jianlin surname: Huang fullname: Huang, Jianlin organization: Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China – sequence: 2 givenname: Rundi surname: Qiu fullname: Qiu, Rundi organization: Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China – sequence: 3 givenname: Jingzhu surname: Wang fullname: Wang, Jingzhu organization: Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China – sequence: 4 givenname: Yiwei surname: Wang fullname: Wang, Yiwei email: wangyw@imech.ac.cn organization: Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China |
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Cites_doi | 10.1016/j.jcp.2022.111768 10.1063/1.168744 10.1017/jfm.2016.615 10.1103/PhysRevFluids.4.054603 10.1017/jfm.2022.463 10.1038/s42254-021-00314-5 10.1016/j.euromechflu.2014.10.004 10.1016/j.taml.2019.01.005 10.2307/2371625 10.1016/j.jcp.2018.10.045 10.1016/j.earscirev.2006.05.001 10.1016/j.cma.2019.112789 10.1126/science.aaw4741 10.1002/cnm.3148 10.1146/annurev.fl.09.010177.000511 10.1016/j.taml.2020.01.039 10.1063/5.0048909 10.1016/j.ces.2004.01.024 10.1016/j.taml.2021.100238 10.1016/j.jcp.2020.109951 10.1002/sapm195332183 10.4208/cicp.OA-2020-0179 10.1063/1.869789 10.1016/j.taml.2015.11.005 |
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Keywords | Physics-informed neural networks (PINNs) Multi-scale Fluid dynamics Boundary layer |
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References | Raissi, Yazdani, Karniadakis (bib0009) 2020; 367 Ling, Kurzawski, Templeton (bib0004) 2016; 807 He, Jin (bib0003) 2021; 11 Friedrichs, Stoker (bib0022) 1941; 63 Kuo (bib0023) 1953; 32 Schoppa, Hussain (bib0015) 1998; 10 Steinrueck (bib0025) 2010; 532 Arzani, Cassel, D’Souza (bib0013) 2023; 473 Mofateh, Ghafouri, Kosarineia, Changizian (bib0020) 2023 Karniadakis, Kevrekidis, Lu, Perdikaris, Wang, Liu (bib0008) 2019; 3 Dixit, Gupta, Choudhary, Prabhakaran (bib0019) 2022; 943 Liu, Cai, Xu (bib0024) 2020; 28 Hart, Martinez (bib0001) 2006; 78 Raissi, Perdikaris, Karniadakis (bib0007) 2019; 378 Wang, He, Xia, Ke, Bai (bib0002) 2004; 59 Weller, Tabor, Jasak, Fureby (bib0026) 1998; 12 Jin, Cai, Li, Karniadakis (bib0012) 2021; 426 Citro, Luchini (bib0016) 2015; 50 Hansen, Arzani, Shadden (bib0021) 2019; 35 Tani (bib0014) 1977; 9 Mao, Jagtap, Karniadakis (bib0010) 2020; 360 Srinivasan, Guastoni, Azizpour, Schlatter, Vinuesa (bib0005) 2019; 4 Chen, Zheng, Shen, Chen (bib0017) 2015; 5 Rao, Sun, Liu (bib0011) 2020; 10 Jiang, Vinuesa, Chen, Mi, Laima, Li (bib0006) 2021; 33 Joe, Yu, Seiichiro, Yu (bib0018) 2019; 9 Weller (10.1016/j.taml.2024.100496_bib0026) 1998; 12 Mofateh (10.1016/j.taml.2024.100496_bib0020) 2023 Hansen (10.1016/j.taml.2024.100496_bib0021) 2019; 35 Tani (10.1016/j.taml.2024.100496_bib0014) 1977; 9 Hart (10.1016/j.taml.2024.100496_bib0001) 2006; 78 Ling (10.1016/j.taml.2024.100496_bib0004) 2016; 807 Liu (10.1016/j.taml.2024.100496_bib0024) 2020; 28 Jiang (10.1016/j.taml.2024.100496_bib0006) 2021; 33 Dixit (10.1016/j.taml.2024.100496_bib0019) 2022; 943 Joe (10.1016/j.taml.2024.100496_bib0018) 2019; 9 Raissi (10.1016/j.taml.2024.100496_bib0007) 2019; 378 Karniadakis (10.1016/j.taml.2024.100496_bib0008) 2019; 3 Jin (10.1016/j.taml.2024.100496_bib0012) 2021; 426 Arzani (10.1016/j.taml.2024.100496_bib0013) 2023; 473 Rao (10.1016/j.taml.2024.100496_bib0011) 2020; 10 Citro (10.1016/j.taml.2024.100496_bib0016) 2015; 50 Steinrueck (10.1016/j.taml.2024.100496_bib0025) 2010; 532 Schoppa (10.1016/j.taml.2024.100496_bib0015) 1998; 10 Chen (10.1016/j.taml.2024.100496_bib0017) 2015; 5 Kuo (10.1016/j.taml.2024.100496_bib0023) 1953; 32 Friedrichs (10.1016/j.taml.2024.100496_bib0022) 1941; 63 Raissi (10.1016/j.taml.2024.100496_sbref0009) 2020; 367 Mao (10.1016/j.taml.2024.100496_bib0010) 2020; 360 Wang (10.1016/j.taml.2024.100496_bib0002) 2004; 59 He (10.1016/j.taml.2024.100496_bib0003) 2021; 11 Srinivasan (10.1016/j.taml.2024.100496_bib0005) 2019; 4 |
References_xml | – volume: 11 start-page: 1 year: 2021 ident: bib0003 article-title: Multiscale mechanics publication-title: Theor. Appl. Mech. Lett. contributor: fullname: Jin – volume: 807 start-page: 155 year: 2016 end-page: 166 ident: bib0004 article-title: Reynolds averaged turbulence modelling using deep neural networks with embedded invariance publication-title: J. Fluid Mech. contributor: fullname: Templeton – volume: 367 year: 2020 ident: bib0009 article-title: Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations publication-title: Science contributor: fullname: Karniadakis – volume: 78 start-page: 117 year: 2006 end-page: 191 ident: bib0001 article-title: Environmental sensor networks: a revolution in the earth system science? publication-title: Earth-Science Rev. contributor: fullname: Martinez – volume: 4 start-page: 054603 year: 2019 ident: bib0005 article-title: Predictions of turbulent shear flows using deep neural networks publication-title: Phys. Rev. Fluids contributor: fullname: Vinuesa – volume: 50 start-page: 1 year: 2015 end-page: 8 ident: bib0016 article-title: Multiple-scale approximation of instabilities in unsteady boundary layers publication-title: Eur. J. Mech. B-Fluids contributor: fullname: Luchini – volume: 9 start-page: 32 year: 2019 end-page: 35 ident: bib0018 article-title: Key structure in laminar-turbulent transition of boundary layer with streaky structures publication-title: Theor. Appl. Mech. Lett. contributor: fullname: Yu – volume: 28 start-page: 1970 year: 2020 end-page: 2001 ident: bib0024 article-title: Multi-scale deep neural network (MscaleDNN) for solving Poisson-Boltzmann equation in complex domains publication-title: Commun. Comput. Phys. contributor: fullname: Xu – volume: 5 start-page: 262 year: 2015 end-page: 266 ident: bib0017 article-title: Time-space dependent fractional boundary layer flow of maxwell fluid over an unsteady stretching surface publication-title: Theor. Appl. Mech. Lett. contributor: fullname: Chen – volume: 59 start-page: 1677 year: 2004 end-page: 1686 ident: bib0002 article-title: Multiscale coupling in complex mechanical systems publication-title: Chem. Eng. Sci. contributor: fullname: Bai – volume: 63 start-page: 839 year: 1941 end-page: 888 ident: bib0022 article-title: The non linear boundary value problem of the buckled plate publication-title: Am. J. Math. contributor: fullname: Stoker – volume: 33 start-page: 055133 year: 2021 ident: bib0006 article-title: An interpretable framework of data-driven turbulence modeling using deep neural networks publication-title: Phys. Fluids contributor: fullname: Li – volume: 10 start-page: 207 year: 2020 end-page: 212 ident: bib0011 article-title: Physics-informed deep learning for incompressible laminar flows publication-title: Theor. Appl. Mech. Lett. contributor: fullname: Liu – volume: 532 start-page: 1 year: 2010 end-page: 21 ident: bib0025 article-title: Introduction to matched asymptotic expansions publication-title: Symposium on Asymptotic Methods in Fluid Mechanics–Survey and Recent Advances contributor: fullname: Steinrueck – volume: 360 start-page: 112789 year: 2020 ident: bib0010 article-title: Physics-informed neural networks for high-speed flows publication-title: Comput. Methods Appl. Mech.Eng. contributor: fullname: Karniadakis – volume: 943 start-page: A43 year: 2022 ident: bib0019 article-title: Universal scaling of mean skin friction in turbulent boundary layers and fully developed pipe and channel flows publication-title: J. Fluid Mech. contributor: fullname: Prabhakaran – volume: 9 start-page: 87 year: 1977 end-page: 111 ident: bib0014 article-title: History of boundary-layer theory publication-title: Annu. Rev. Fluid Mech. contributor: fullname: Tani – start-page: 2450027 year: 2023 ident: bib0020 article-title: Numerical investigation of rotational speed effects on flow separation and boundary layer dynamics in ducted wind turbines publication-title: Int. J. Mod. Phys. C contributor: fullname: Changizian – volume: 473 start-page: 111768 year: 2023 ident: bib0013 article-title: Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation publication-title: J. Comput. Phys. contributor: fullname: D’Souza – volume: 378 start-page: 686 year: 2019 end-page: 707 ident: bib0007 article-title: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations publication-title: J. Comput. Phys. contributor: fullname: Karniadakis – volume: 10 start-page: 1049 year: 1998 end-page: 1051 ident: bib0015 article-title: A large-scale control strategy for drag reduction in turbulent boundary layers publication-title: Phys. Fluids contributor: fullname: Hussain – volume: 12 start-page: 620 year: 1998 end-page: 631 ident: bib0026 article-title: A tensorial approach to computational continuum mechanics using object-oriented techniques publication-title: Comput. Phys. contributor: fullname: Fureby – volume: 3 start-page: 422 year: 2019 end-page: 440 ident: bib0008 article-title: Physics-informed machine learning publication-title: Nat. Rev. Phys. contributor: fullname: Liu – volume: 426 start-page: 109951 year: 2021 ident: bib0012 article-title: NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations publication-title: J. Comput. Phys. contributor: fullname: Karniadakis – volume: 32 start-page: 83 year: 1953 end-page: 101 ident: bib0023 article-title: On the flow of an incompressible viscous fluid past a flat plate at moderate Reynolds number publication-title: J. Math. Phys. contributor: fullname: Kuo – volume: 35 start-page: E3148 year: 2019 ident: bib0021 article-title: Finite element modeling of near-wall mass transport in cardiovascular flows publication-title: Int. J. Numer. MethodsBiomed. Eng. contributor: fullname: Shadden – start-page: 2450027 year: 2023 ident: 10.1016/j.taml.2024.100496_bib0020 article-title: Numerical investigation of rotational speed effects on flow separation and boundary layer dynamics in ducted wind turbines publication-title: Int. J. Mod. Phys. C contributor: fullname: Mofateh – volume: 473 start-page: 111768 year: 2023 ident: 10.1016/j.taml.2024.100496_bib0013 article-title: Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2022.111768 contributor: fullname: Arzani – volume: 12 start-page: 620 year: 1998 ident: 10.1016/j.taml.2024.100496_bib0026 article-title: A tensorial approach to computational continuum mechanics using object-oriented techniques publication-title: Comput. Phys. doi: 10.1063/1.168744 contributor: fullname: Weller – volume: 807 start-page: 155 year: 2016 ident: 10.1016/j.taml.2024.100496_bib0004 article-title: Reynolds averaged turbulence modelling using deep neural networks with embedded invariance publication-title: J. Fluid Mech. doi: 10.1017/jfm.2016.615 contributor: fullname: Ling – volume: 4 start-page: 054603 year: 2019 ident: 10.1016/j.taml.2024.100496_bib0005 article-title: Predictions of turbulent shear flows using deep neural networks publication-title: Phys. Rev. Fluids doi: 10.1103/PhysRevFluids.4.054603 contributor: fullname: Srinivasan – volume: 943 start-page: A43 year: 2022 ident: 10.1016/j.taml.2024.100496_bib0019 article-title: Universal scaling of mean skin friction in turbulent boundary layers and fully developed pipe and channel flows publication-title: J. Fluid Mech. doi: 10.1017/jfm.2022.463 contributor: fullname: Dixit – volume: 3 start-page: 422 year: 2019 ident: 10.1016/j.taml.2024.100496_bib0008 article-title: Physics-informed machine learning publication-title: Nat. Rev. Phys. doi: 10.1038/s42254-021-00314-5 contributor: fullname: Karniadakis – volume: 50 start-page: 1 year: 2015 ident: 10.1016/j.taml.2024.100496_bib0016 article-title: Multiple-scale approximation of instabilities in unsteady boundary layers publication-title: Eur. J. Mech. B-Fluids doi: 10.1016/j.euromechflu.2014.10.004 contributor: fullname: Citro – volume: 9 start-page: 32 year: 2019 ident: 10.1016/j.taml.2024.100496_bib0018 article-title: Key structure in laminar-turbulent transition of boundary layer with streaky structures publication-title: Theor. Appl. Mech. Lett. doi: 10.1016/j.taml.2019.01.005 contributor: fullname: Joe – volume: 63 start-page: 839 year: 1941 ident: 10.1016/j.taml.2024.100496_bib0022 article-title: The non linear boundary value problem of the buckled plate publication-title: Am. J. Math. doi: 10.2307/2371625 contributor: fullname: Friedrichs – volume: 378 start-page: 686 year: 2019 ident: 10.1016/j.taml.2024.100496_bib0007 article-title: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2018.10.045 contributor: fullname: Raissi – volume: 78 start-page: 117 year: 2006 ident: 10.1016/j.taml.2024.100496_bib0001 article-title: Environmental sensor networks: a revolution in the earth system science? publication-title: Earth-Science Rev. doi: 10.1016/j.earscirev.2006.05.001 contributor: fullname: Hart – volume: 360 start-page: 112789 year: 2020 ident: 10.1016/j.taml.2024.100496_bib0010 article-title: Physics-informed neural networks for high-speed flows publication-title: Comput. Methods Appl. Mech.Eng. doi: 10.1016/j.cma.2019.112789 contributor: fullname: Mao – volume: 367 year: 2020 ident: 10.1016/j.taml.2024.100496_sbref0009 article-title: Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations publication-title: Science doi: 10.1126/science.aaw4741 contributor: fullname: Raissi – volume: 35 start-page: E3148 year: 2019 ident: 10.1016/j.taml.2024.100496_bib0021 article-title: Finite element modeling of near-wall mass transport in cardiovascular flows publication-title: Int. J. Numer. MethodsBiomed. Eng. doi: 10.1002/cnm.3148 contributor: fullname: Hansen – volume: 9 start-page: 87 year: 1977 ident: 10.1016/j.taml.2024.100496_bib0014 article-title: History of boundary-layer theory publication-title: Annu. Rev. Fluid Mech. doi: 10.1146/annurev.fl.09.010177.000511 contributor: fullname: Tani – volume: 532 start-page: 1 year: 2010 ident: 10.1016/j.taml.2024.100496_bib0025 article-title: Introduction to matched asymptotic expansions contributor: fullname: Steinrueck – volume: 10 start-page: 207 year: 2020 ident: 10.1016/j.taml.2024.100496_bib0011 article-title: Physics-informed deep learning for incompressible laminar flows publication-title: Theor. Appl. Mech. Lett. doi: 10.1016/j.taml.2020.01.039 contributor: fullname: Rao – volume: 33 start-page: 055133 year: 2021 ident: 10.1016/j.taml.2024.100496_bib0006 article-title: An interpretable framework of data-driven turbulence modeling using deep neural networks publication-title: Phys. Fluids doi: 10.1063/5.0048909 contributor: fullname: Jiang – volume: 59 start-page: 1677 year: 2004 ident: 10.1016/j.taml.2024.100496_bib0002 article-title: Multiscale coupling in complex mechanical systems publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2004.01.024 contributor: fullname: Wang – volume: 11 start-page: 1 year: 2021 ident: 10.1016/j.taml.2024.100496_bib0003 article-title: Multiscale mechanics publication-title: Theor. Appl. Mech. Lett. doi: 10.1016/j.taml.2021.100238 contributor: fullname: He – volume: 426 start-page: 109951 year: 2021 ident: 10.1016/j.taml.2024.100496_bib0012 article-title: NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2020.109951 contributor: fullname: Jin – volume: 32 start-page: 83 year: 1953 ident: 10.1016/j.taml.2024.100496_bib0023 article-title: On the flow of an incompressible viscous fluid past a flat plate at moderate Reynolds number publication-title: J. Math. Phys. doi: 10.1002/sapm195332183 contributor: fullname: Kuo – volume: 28 start-page: 1970 year: 2020 ident: 10.1016/j.taml.2024.100496_bib0024 article-title: Multi-scale deep neural network (MscaleDNN) for solving Poisson-Boltzmann equation in complex domains publication-title: Commun. Comput. Phys. doi: 10.4208/cicp.OA-2020-0179 contributor: fullname: Liu – volume: 10 start-page: 1049 year: 1998 ident: 10.1016/j.taml.2024.100496_bib0015 article-title: A large-scale control strategy for drag reduction in turbulent boundary layers publication-title: Phys. Fluids doi: 10.1063/1.869789 contributor: fullname: Schoppa – volume: 5 start-page: 262 year: 2015 ident: 10.1016/j.taml.2024.100496_bib0017 article-title: Time-space dependent fractional boundary layer flow of maxwell fluid over an unsteady stretching surface publication-title: Theor. Appl. Mech. Lett. doi: 10.1016/j.taml.2015.11.005 contributor: fullname: Chen |
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Snippet | •Proposed a multi-scale physics-informed neural networks scheme for solving high Reynolds number boundary layer flows.•Applied the matched asymptotic... Multi-scale system remains a classical scientific problem in fluid dynamics, biology, etc. In the present study, a scheme of multi-scale Physics-informed... |
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SubjectTerms | Boundary layer Fluid dynamics Multi-scale Physics-informed neural networks (PINNs) |
Title | Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions |
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