Direct numerical simulations （DNS） have now become a well established tool to examine complex multiphase flows. Such flows typically exhibit a large range of scales and it is generally necessary to use different des...Direct numerical simulations （DNS） have now become a well established tool to examine complex multiphase flows. Such flows typically exhibit a large range of scales and it is generally necessary to use different descriptions of the flow depending on the scale that we are examining. Here we discuss multiphase flows from a multiscale perspective. Those include both how DNS are providing insight and understanding for modeling of scales much larger than the ＂dominant scale＂ （defined where surface tension, viscous forces or inertia are important）, as well as how DNS are often limited by the need to resolve processes taking place on much smaller scales. Both problems can be cast into a language introduced for general classes of multiscale problems and reveal that while the classification may be new, the issues are not.展开更多
In this paper,we present an efficient computational methodology for diffusion and convection-diffusion problems in highly heterogeneous media as well as convection-dominated diffusion problem.It is well known that the...In this paper,we present an efficient computational methodology for diffusion and convection-diffusion problems in highly heterogeneous media as well as convection-dominated diffusion problem.It is well known that the numerical computation for these problems requires a significant amount of computermemory and time.Nevertheless,the solutions to these problems typically contain a coarse component,which is usually the quantity of interest and can be represented with a small number of degrees of freedom.There are many methods that aim at the computation of the coarse component without resolving the full details of the solution.Our proposed method falls into the framework of interior penalty discontinuous Galerkin method,which is proved to be an effective and accurate class of methods for numerical solutions of partial differential equations.A distinctive feature of our method is that the solution space contains two components,namely a coarse space that gives a polynomial approximation to the coarse component in the traditional way and a multiscale space which contains sub-grid structures of the solution and is essential to the computation of the coarse component.In addition,stability of the method is proved.The numerical results indicate that the method can accurately capture the coarse behavior of the solution for problems in highly heterogeneous media as well as boundary and internal layers for convection-dominated problems.展开更多
Stochastic temperature distribution should be carefully inspected in the thermal-failure design of heterogeneous solids with unexpected random energy excitations.Stochastic multiscale modeling for these problems invol...Stochastic temperature distribution should be carefully inspected in the thermal-failure design of heterogeneous solids with unexpected random energy excitations.Stochastic multiscale modeling for these problems involve multiscale and highdimensional uncertain thermal parameters,which remains limitation of prohibitive computation.In this paper,we propose a multi-modes based constrained energy minimization generalized multiscale finite element method(MCEM-GMsFEM),which can transform the original stochastic multiscale model into a series of recursive multiscale models sharing the same deterministic material parameters by multiscale analysis.Thus,MCEM-GMsFEM reveals an inherent low-dimensional representation in random space,and is designed to effectively reduce the complexity of repeated computation of discretized multiscale systems.In addition,the convergence analysis is established,and the optimal error estimates are derived.Finally,several typical random fluctuations on multiscale thermal conductivity are considered to validate the theoretical results in the numerical examples.The numerical results indicate that the multi-modes multiscale approach is a robust integrated method with the excellent performance.展开更多
In this paper we study some nonoverlapping domain decomposition methods for solving a class of elliptic problems arising from composite materials and flows in porous media which contain many spatial scales. Our precon...In this paper we study some nonoverlapping domain decomposition methods for solving a class of elliptic problems arising from composite materials and flows in porous media which contain many spatial scales. Our preconditioner differs from traditional domain decomposition preconditioners by using a coarse solver which is adaptive to small scale heterogeneous features. While the convergence rate of traditional domain decomposition algorithms using coarse solvers based on linear or polynomial interpolations may deteriorate in the presence of rapid small scale oscillations or high aspect ratios, our preconditioner is applicable to multiple-scale problems without restrictive assumptions and seems to have a convergence rate nearly independent of the aspect ratio within the substructures. A rigorous convergence analysis based on the Schwarz framework is carried out, and we demonstrate the efficiency and robustness of the proposed preconditioner through numerical experiments which include problems with multiple-scale coefficients, as well problems with continuous scales.展开更多
Neural networks are powerful tools for approximating high dimensional data that have been used in many contexts,including solution of partial differential equations(PDEs).We describe a solver for multiscale fully nonl...Neural networks are powerful tools for approximating high dimensional data that have been used in many contexts,including solution of partial differential equations(PDEs).We describe a solver for multiscale fully nonlinear elliptic equations that makes use of domain decomposition,an accelerated Schwarz framework,and two-layer neural networks to approximate the boundary-to-boundarymap for the subdomains,which is the key step in the Schwarz procedure.Conventionally,the boundary-to-boundary map requires solution of boundary-value elliptic problems on each subdomain.By leveraging the compressibility of multiscale problems,our approach trains the neural network offline to serve as a surrogate for the usual implementation of the boundary-to-boundary map.Our method is applied to a multiscale semilinear elliptic equation and a multiscale p-Laplace equation.In both cases we demonstrate significant improvement in efficiency as well as good accuracy and generalization performance.展开更多
We will be concerned with the mathematical modeling, numerical simulation, and shape optimization of micro fluidic biochips that are used for various biomedical applications. A particular feature is that the fluid flo...We will be concerned with the mathematical modeling, numerical simulation, and shape optimization of micro fluidic biochips that are used for various biomedical applications. A particular feature is that the fluid flow in the fluidic network on top of the biochips is in- duced by surface acoustic waves generated by interdigital transducers. We are thus faced with a multiphysics problem that will be modeled by coupling the equations of piezoelectricity with the compressible Navier-Stokes equations. Moreover, the fluid flow exhibits a multiscale character that will be taken care of by a homogenization approach. We will discuss and analyze the mathematical models and deal with their numerical solution by space-time discretizations featuring appropriate finite element approximations with respect to hierarchies of simplicial triangulations of the underlying computational domains. Simulation results will be given for the propagation of the surface acoustic waves on top of the piezoelectric substrate and for the induced fluid flow in the microchannels of the fluidic network. The performance of the operational behavior of the biochips can be significantly improved by shape optimization. In particular, for such purposes we present a multilevel interior point method relying on a predictor-corrector strategy with an adaptive choice of the continuation steplength along the barrier path. As a specific example, we will consider the shape optimization of pressure driven capillary barriers between microchannels and reservoirs.展开更多
The numerical approximation of high frequency wave propagation is important in many applications.Examples include the simulation of seismic,acoustic,optical waves and microwaves.When the frequency of the waves is high...The numerical approximation of high frequency wave propagation is important in many applications.Examples include the simulation of seismic,acoustic,optical waves and microwaves.When the frequency of the waves is high,this is a difficult multiscale problem.The wavelength is short compared to the overall size of the computational domain and direct simulation using the standard wave equations is very expensive.Fortunately,there are computationally much less costly models,that are good approximations of many wave equations precisely for very high frequencies.Even for linear wave equations these models are often nonlinear.The goal of this paper is to review such mathematical models for high frequency waves,and to survey numerical methods used in simulations.We focus on the geometrical optics approximation which describes the infinite frequency limit of wave equations.We will also discuss finite frequency corrections and some other models.展开更多
文摘Direct numerical simulations （DNS） have now become a well established tool to examine complex multiphase flows. Such flows typically exhibit a large range of scales and it is generally necessary to use different descriptions of the flow depending on the scale that we are examining. Here we discuss multiphase flows from a multiscale perspective. Those include both how DNS are providing insight and understanding for modeling of scales much larger than the ＂dominant scale＂ （defined where surface tension, viscous forces or inertia are important）, as well as how DNS are often limited by the need to resolve processes taking place on much smaller scales. Both problems can be cast into a language introduced for general classes of multiscale problems and reveal that while the classification may be new, the issues are not.
基金supported by a grant from the Research Grant Council of the Hong Kong SAR(Project No.CUHK401010).
文摘In this paper,we present an efficient computational methodology for diffusion and convection-diffusion problems in highly heterogeneous media as well as convection-dominated diffusion problem.It is well known that the numerical computation for these problems requires a significant amount of computermemory and time.Nevertheless,the solutions to these problems typically contain a coarse component,which is usually the quantity of interest and can be represented with a small number of degrees of freedom.There are many methods that aim at the computation of the coarse component without resolving the full details of the solution.Our proposed method falls into the framework of interior penalty discontinuous Galerkin method,which is proved to be an effective and accurate class of methods for numerical solutions of partial differential equations.A distinctive feature of our method is that the solution space contains two components,namely a coarse space that gives a polynomial approximation to the coarse component in the traditional way and a multiscale space which contains sub-grid structures of the solution and is essential to the computation of the coarse component.In addition,stability of the method is proved.The numerical results indicate that the method can accurately capture the coarse behavior of the solution for problems in highly heterogeneous media as well as boundary and internal layers for convection-dominated problems.
基金the Natural Science Foundation of Shanghai(No.21ZR1465800)the Science Challenge Project(No.TZ2018001)+2 种基金the Interdisciplinary Project in Ocean Research of Tongji University,the Aeronautical Science Foundation of China(No.2020001053002)the National Key R&D Program of China(No.2020YFA0713603)the Fundamental Research Funds for the Central Universities.
文摘Stochastic temperature distribution should be carefully inspected in the thermal-failure design of heterogeneous solids with unexpected random energy excitations.Stochastic multiscale modeling for these problems involve multiscale and highdimensional uncertain thermal parameters,which remains limitation of prohibitive computation.In this paper,we propose a multi-modes based constrained energy minimization generalized multiscale finite element method(MCEM-GMsFEM),which can transform the original stochastic multiscale model into a series of recursive multiscale models sharing the same deterministic material parameters by multiscale analysis.Thus,MCEM-GMsFEM reveals an inherent low-dimensional representation in random space,and is designed to effectively reduce the complexity of repeated computation of discretized multiscale systems.In addition,the convergence analysis is established,and the optimal error estimates are derived.Finally,several typical random fluctuations on multiscale thermal conductivity are considered to validate the theoretical results in the numerical examples.The numerical results indicate that the multi-modes multiscale approach is a robust integrated method with the excellent performance.
基金Supported by STATOIL under the VISTA programSupported in part by a grant from National Science Foundation under the contract DMS-0073916by a grant from Army Research Office under the contract DAAD19-99-1-0141.
文摘In this paper we study some nonoverlapping domain decomposition methods for solving a class of elliptic problems arising from composite materials and flows in porous media which contain many spatial scales. Our preconditioner differs from traditional domain decomposition preconditioners by using a coarse solver which is adaptive to small scale heterogeneous features. While the convergence rate of traditional domain decomposition algorithms using coarse solvers based on linear or polynomial interpolations may deteriorate in the presence of rapid small scale oscillations or high aspect ratios, our preconditioner is applicable to multiple-scale problems without restrictive assumptions and seems to have a convergence rate nearly independent of the aspect ratio within the substructures. A rigorous convergence analysis based on the Schwarz framework is carried out, and we demonstrate the efficiency and robustness of the proposed preconditioner through numerical experiments which include problems with multiple-scale coefficients, as well problems with continuous scales.
基金supported in part by National Science Foundation via grant 1934612a DOE Subcontract 8F-30039 from Argonne National Laboratory+1 种基金an AFOSR subcontract UTA20-001224 from UT-Austin.The work of SCsupported in part by NSF-DMS-1750488 and ONR-N00014-21-1-2140.
文摘Neural networks are powerful tools for approximating high dimensional data that have been used in many contexts,including solution of partial differential equations(PDEs).We describe a solver for multiscale fully nonlinear elliptic equations that makes use of domain decomposition,an accelerated Schwarz framework,and two-layer neural networks to approximate the boundary-to-boundarymap for the subdomains,which is the key step in the Schwarz procedure.Conventionally,the boundary-to-boundary map requires solution of boundary-value elliptic problems on each subdomain.By leveraging the compressibility of multiscale problems,our approach trains the neural network offline to serve as a surrogate for the usual implementation of the boundary-to-boundary map.Our method is applied to a multiscale semilinear elliptic equation and a multiscale p-Laplace equation.In both cases we demonstrate significant improvement in efficiency as well as good accuracy and generalization performance.
基金support by the NSF under Grants No. DMS-0511611, DMS-0707602, DMS-0810156, DMS-0811153by the German National Science Foundation DFG within the Priority Program SPP 1253
文摘We will be concerned with the mathematical modeling, numerical simulation, and shape optimization of micro fluidic biochips that are used for various biomedical applications. A particular feature is that the fluid flow in the fluidic network on top of the biochips is in- duced by surface acoustic waves generated by interdigital transducers. We are thus faced with a multiphysics problem that will be modeled by coupling the equations of piezoelectricity with the compressible Navier-Stokes equations. Moreover, the fluid flow exhibits a multiscale character that will be taken care of by a homogenization approach. We will discuss and analyze the mathematical models and deal with their numerical solution by space-time discretizations featuring appropriate finite element approximations with respect to hierarchies of simplicial triangulations of the underlying computational domains. Simulation results will be given for the propagation of the surface acoustic waves on top of the piezoelectric substrate and for the induced fluid flow in the microchannels of the fluidic network. The performance of the operational behavior of the biochips can be significantly improved by shape optimization. In particular, for such purposes we present a multilevel interior point method relying on a predictor-corrector strategy with an adaptive choice of the continuation steplength along the barrier path. As a specific example, we will consider the shape optimization of pressure driven capillary barriers between microchannels and reservoirs.
文摘The numerical approximation of high frequency wave propagation is important in many applications.Examples include the simulation of seismic,acoustic,optical waves and microwaves.When the frequency of the waves is high,this is a difficult multiscale problem.The wavelength is short compared to the overall size of the computational domain and direct simulation using the standard wave equations is very expensive.Fortunately,there are computationally much less costly models,that are good approximations of many wave equations precisely for very high frequencies.Even for linear wave equations these models are often nonlinear.The goal of this paper is to review such mathematical models for high frequency waves,and to survey numerical methods used in simulations.We focus on the geometrical optics approximation which describes the infinite frequency limit of wave equations.We will also discuss finite frequency corrections and some other models.
