A non-intrusive reduced-order model for compressible fluid and fractured solid coupling and its application to blasting

This work presents the first application of a non-intrusive reduced order method to model solid interacting with compressible fluid flows to simulate crack initiation and propagation. In the high fidelity model, the coupling process is achieved by introducing a source term into the momentum equation, which represents the effects of forces of the solid on the fluid. A combined single and smeared crack model with the Mohr-Coulomb failure criterion is used to simulate crack initiation and propagation. The non-intrusive reduced order method is then applied to compressible fluid and fractured solid coupled modelling where the computational cost involved in the full high fidelity simulation is high. The non-intrusive reduced order model (NIROM) developed here is constructed through proper orthogonal decomposition (POD) and a radial basis function (RBF) multi-dimensional interpolation method.The performance of the NIROM for solid interacting with compressible fluid flows, in the presence of fracture models, is illustrated by two complex test cases: an immersed wall in a fluid and a blasting test case. The numerical simulation results show that the NIROM is capable of capturing the details of compressible fluids and fractured solids while the CPU time is reduced by several orders of magnitude. In addition, the issue of whether or not to subtract the mean from the snapshots before applying POD is discussed in this paper. It is shown that solutions of the NIROM, without mean subtracted before constructing the POD basis, captured more details than the NIROM with mean subtracted from snapshots.

[1]  C. Farhat,et al.  Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations , 2011 .

[2]  Juan Du,et al.  Non-linear Petrov-Galerkin methods for reduced order hyperbolic equations and discontinuous finite element methods , 2013, J. Comput. Phys..

[3]  Charbel Farhat,et al.  POD-based Aeroelastic Analysis of a Complete F-16 Configuration: ROM Adaptation and Demonstration , 2005 .

[4]  Danny C. Sorensen,et al.  Nonlinear Model Reduction via Discrete Empirical Interpolation , 2010, SIAM J. Sci. Comput..

[5]  Matthew F. Barone,et al.  A stable Galerkin reduced order model for coupled fluid–structure interaction problems , 2013 .

[6]  Christopher C. Pain,et al.  A POD reduced order model for resolving angular direction in neutron/photon transport problems , 2015, J. Comput. Phys..

[7]  Gianluigi Rozza,et al.  Efficient geometrical parametrisation techniques of interfaces for reduced-order modelling: application to fluid–structure interaction coupling problems , 2014 .

[8]  Adrian Sandu,et al.  Comparison of POD reduced order strategies for the nonlinear 2D shallow water equations , 2014, International Journal for Numerical Methods in Fluids.

[9]  Alain Dervieux,et al.  Reduced-order modeling for unsteady transonic flows around an airfoil , 2007 .

[10]  Ionel M. Navon,et al.  A Dual-Weighted Approach to Order Reduction in 4DVAR Data Assimilation , 2008, Monthly Weather Review.

[11]  Earl H. Dowell,et al.  Reduced order models in unsteady aerodynamics , 1999 .

[12]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[13]  David J. Lucia,et al.  Reduced Order Modeling For High Speed Flows with Moving Shocks , 2001 .

[14]  Charbel Farhat,et al.  Reduced-order fluid/structure modeling of a complete aircraft configuration , 2006 .

[15]  Hao Chen,et al.  On the use and interpretation of proper orthogonal decomposition of in-cylinder engine flows , 2012, Measurement Science and Technology.

[16]  Christopher C. Pain,et al.  Modelling of fluid-structure interaction with multiphase viscous flows using an immersed-body method , 2016, J. Comput. Phys..

[17]  N. Nguyen,et al.  An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations , 2004 .

[18]  J. Hahn,et al.  State-preserving nonlinear model reduction procedure , 2011 .

[19]  Ionel M. Navon,et al.  Non-intrusive reduced order modelling of the Navier-Stokes equations , 2015 .

[20]  M. Diez,et al.  Design-space dimensionality reduction in shape optimization by Karhunen–Loève expansion , 2015 .

[21]  Matthew Fotia,et al.  Reduced-Order Modeling of Two-Dimensional Supersonic Flows with Applications to Scramjet Inlets , 2010 .

[22]  Christopher D. Marley,et al.  Reduced Order Modeling of Compressible Flows with Unsteady Normal Shock Motion , 2015 .

[23]  Rémi Abgrall,et al.  Robust model reduction by $$L^{1}$$L1-norm minimization and approximation via dictionaries: application to nonlinear hyperbolic problems , 2016, Adv. Model. Simul. Eng. Sci..

[24]  L. Franca,et al.  Stabilized Finite Element Methods , 1993 .

[25]  Charbel Farhat,et al.  The GNAT nonlinear model reduction method and its application to uid dynamics problems , 2011 .

[26]  K. Morgan,et al.  Reduced order modelling for unsteady fluid flow using proper orthogonal decomposition and radial basis functions , 2013 .

[27]  D. Birchall,et al.  Computational Fluid Dynamics , 2020, Radial Flow Turbocompressors.

[28]  L. Franca,et al.  Stabilized finite element methods. II: The incompressible Navier-Stokes equations , 1992 .

[29]  P. Nair,et al.  Nonintrusive reduced‐order modeling of parametrized time‐dependent partial differential equations , 2013 .

[30]  Charbel Farhat,et al.  Projection‐based model reduction for contact problems , 2015, 1503.01000.

[31]  C. Pain,et al.  Non‐intrusive reduced‐order modelling of the Navier–Stokes equations based on RBF interpolation , 2015 .

[32]  Mehdi Ghommem,et al.  Global-Local Nonlinear Model Reduction for Flows in Heterogeneous Porous Media Dedicated to Mary Wheeler on the occasion of her 75-th birthday anniversary , 2014, 1407.0782.

[33]  Tina R. White A clustering algorithm for reduced order modeling of shock waves , 2015 .

[34]  Christopher C. Pain,et al.  Non-intrusive reduced order modelling of fluid–structure interactions , 2016 .

[35]  Erwan Liberge,et al.  Reduced-order modelling by POD-multiphase approach for fluid-structure interaction , 2010 .

[36]  Gianluigi Rozza,et al.  POD–Galerkin monolithic reduced order models for parametrized fluid‐structure interaction problems , 2016 .

[37]  Han Chen,et al.  Blackbox Stencil Interpolation Method for Model Reduction , 2012 .

[38]  Christopher C. Pain,et al.  Non‐intrusive reduced‐order modeling for multiphase porous media flows using Smolyak sparse grids , 2017 .

[39]  C. C. Pain,et al.  Reduced‐order modelling of an adaptive mesh ocean model , 2009 .

[40]  Jiansheng Xiang,et al.  Numerical simulation of breakages of concrete armour units using a three-dimensional fracture model in the context of the combined finite-discrete element method , 2015 .

[41]  A. Munjiza The Combined Finite-Discrete Element Method , 2004 .

[42]  Razvan Stefanescu,et al.  POD/DEIM nonlinear model order reduction of an ADI implicit shallow water equations model , 2012, J. Comput. Phys..

[43]  Dimitrios Pavlidis,et al.  The immersed-body gas-solid interaction model for blast analysis in fractured solid media , 2017 .

[44]  A Viré,et al.  An immersed-shell method for modelling fluid–structure interactions , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[45]  B. R. Noack,et al.  On the need for a nonlinear subscale turbulence term in POD models as exemplified for a high-Reynolds-number flow over an Ahmed body , 2013, Journal of Fluid Mechanics.

[46]  Matthew Robert Brake,et al.  Reduced order modeling of fluid/structure interaction. , 2009 .

[47]  Juan Du,et al.  Non-linear model reduction for the Navier-Stokes equations using residual DEIM method , 2014, J. Comput. Phys..

[48]  Christopher C. Pain,et al.  Reduced order modelling of an unstructured mesh air pollution model and application in 2D/3D urban street canyons , 2014 .

[49]  A. Megretski,et al.  Model Reduction for Large-Scale Linear Applications , 2003 .

[50]  David A. Ham,et al.  POD reduced-order unstructured mesh modeling applied to 2D and 3D fluid flow , 2013, Comput. Math. Appl..

[51]  C.R.E. de Oliveira,et al.  Three-dimensional unstructured mesh ocean modelling , 2005 .

[52]  B. R. Noack,et al.  On long-term boundedness of Galerkin models , 2013, Journal of Fluid Mechanics.

[53]  Charbel Farhat,et al.  The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows , 2012, J. Comput. Phys..

[54]  Wing Kam Liu,et al.  The immersed/fictitious element method for fluid–structure interaction: Volumetric consistency, compressibility and thin members , 2008 .

[55]  Ionel M. Navon,et al.  Non-linear Petrov-Galerkin methods for reduced order modelling of the Navier-Stokes equations using a mixed finite element pair , 2013 .

[56]  Antonio Munjiza,et al.  Combined single and smeared crack model in combined finite-discrete element analysis , 1999 .

[57]  L. Heltai,et al.  Reduced Basis Isogeometric Methods (RB-IGA) for the real-time simulation of potential flows about parametrized NACA airfoils , 2015 .

[58]  P. Nair,et al.  Reduced‐order modeling of parameterized PDEs using time–space‐parameter principal component analysis , 2009 .

[59]  Feriedoun Sabetghadam,et al.  α Regularization of the POD-Galerkin dynamical systems of the Kuramoto-Sivashinsky equation , 2012, Appl. Math. Comput..