In this study, we propose a new immersed boundary method for compressible fluid-structure interaction simulations. We take a partitioned coupling strategy that comprises three parts: fluid solver, structure solver, and fluid-structure boundary treatment. The fluid solver is implemented by a second-order finite volume scheme, and the structure is solved by an implicit finite element solver. For the

This article presents a general framework for recovering missing dynamical systems using available data and machine learning techniques. The proposed framework reformulates the prediction problem as a supervised learning problem to approximate a map that takes the memories of the resolved and identifiable unresolved variables to the missing components in the resolved dynamics. We demonstrate the effectiveness

Realistic two-phase flow problems of interest often involve high Re flows with high density ratios. Accurate and robust simulation of such problems requires special treatments. In this work, we present a consistent, kinetic energy conserving momentum transport scheme in the context of a second order conservative phase field method. This is achieved by (1) accounting for the mass flux associated with

In this paper, we propose an analysis method for the so-called grid orientation effect (GOE) in the numerical simulation of two-phase flows in porous media. The GOE, which occurs when using coupled finite volume schemes on structured grids, is well known to engineers. Several attempts, most of which are of empirical nature, have been put forward in order to alleviate this undesirable phenomenon. Here

We present a novel numerical method to solve the incompressible Navier-Stokes equations for two-phase flows with phase change. Separate phases are tracked using a geometric Volume-Of-Fluid (VOF) method with piecewise linear interface construction (PLIC). Thermal energy advection is treated in conservative form and the geometric calculation of VOF fluxes at computational cell boundaries is used consistently

A new numerical method to solve three-dimensional wave equations in media with arbitrarily-shaped interfaces on a Cartesian grid is proposed. The present method aims to achieve two objectives to simulate wave propagation through realistic geometries: (1) handling wave interaction at the interface with high ratios of acoustic material properties and (2) treating complex geometries involving both smooth

We present a new physics-informed machine learning approach for the inversion of partial differential equation (PDE) models with heterogeneous parameters. In our approach, the space-dependent partially observed parameters and states are approximated via Karhunen-Loève expansions (KLEs). Each of these KLEs is then conditioned on their corresponding measurements, resulting in low-dimensional models of

In this paper, we propose to improve the stabilized POD-ROM introduced in [48] to deal with the numerical simulation of advection-dominated advection-diffusion-reaction equations. In particular, we propose a three-stage stabilizing strategy that will be very useful when considering very low diffusion coefficients, i.e. in the strongly advection-dominated regime. This approach mainly consists in three

Free boundary problems appear naturally in numerous areas of mathematics, science and engineering. These problems present a great computational challenge because they necessitate numerical methods that can yield an accurate approximation of free boundaries and complex dynamic interfaces. In this work, we propose a multi-network model based on physics-informed neural networks to tackle a general class

We propose a Bayesian physics-informed neural network (B-PINN) to solve both forward and inverse nonlinear problems described by partial differential equations (PDEs) and noisy data. In this Bayesian framework, the Bayesian neural network (BNN) combined with a PINN for PDEs serves as the prior while the Hamiltonian Monte Carlo (HMC) or the variational inference (VI) could serve as an estimator of the

When solving the first-order form of the linear Boltzmann equation, a common misconception is that the matrix-free computational method of “sweeping the mesh”, used in conjunction with the Discrete Ordinates method, is too complex or does not scale well enough to be implemented in modern high performance computing codes. This has led to considerable efforts in the development of matrix-based methods

Cut-cell methods for unsteady flow problems can greatly simplify the grid generation process and allow for high-fidelity simulations on complex geometries. However, cut-cell methods have been limited to low orders of accuracy. This is driven, largely, by the variety of procedures typically introduced to evaluate derivatives in a stable manner near the highly irregular embedded geometry. In the present

In this paper, an effective finite element iterative algorithm is presented for solving a Poisson-Nernst-Planck ion channel (PNPic) model with Neumann boundary value condition and a membrane surface charge density. It is constructed by a solution decomposition scheme to avoid singularity problems caused by atomic charges, an alternating block iterative scheme to sharply reduce computation complexity

In this article, we present a numerical iterative method for the solution of internal viscous and incompressible flows in real porous three-dimensional bodies at their pore scale. We use the penalized formulation of the problem involving velocity and vorticity: an operator splitting allows to split apart the diffusion (inherited from Stokes equation) and the penalization phenomena (which takes into

We develop a novel numerical scheme for the simulation of dissipative quantum dynamics, following from two-body Lindblad master equations. It exactly preserves the trace of the density matrix and shows only mild deviations from hermiticity and positivity, which are the defining properties of the continuum Lindblad dynamics. The central ingredient is a new spatial difference operator, which not only

The Shifted Boundary Method (SBM) belongs to the class of unfitted (or immersed, or embedded) finite element methods and was recently introduced for the Poisson, linear advection/diffusion, Stokes, Navier-Stokes, acoustics, and shallow-water equations. By reformulating the original boundary value problem over a surrogate (approximate) computational domain, the SBM avoids integration over cut cells

The molecular motion and collision processes are usually decoupled in the traditional molecular simulations, where the simulation process is divided into a series of time steps and the two processes are sequentially executed during each time step. The numerical errors in transport properties and flow-field solutions will become noticeable when the time step is much larger than the mean time interval

In the recent decade, meshless methods have been handled for solving some PDEs due to their easiness. One of the most efficient meshless methods is the element free Galerkin (EFG) method. The test and trial functions of the EFG are based upon the special basis. Recently, some modifications have been developed to improve the EFG method. One of these improvements is the variational multiscale EFG (VMEFG)

In this paper, the moving particle semi-implicit (MPS) method is developed towards the simulation of multiphase incompressible flows with mass transfer due to vaporization. Traditional MPS method assumes particle volume to be constant and encounters difficulty in modeling vapor-liquid phase change due to abrupt volume change in vaporization process. In the proposed model, particle volume is assumed

This paper presents a newly developed parallel implementation of solving the 3–D vorticity equation to fully simulate the incompressible laminar flow in the Eulerian frame. This method is designed to solve 3–D problems with irregular wall boundaries in small and compact computational domains in general shapes efficiently. The curl form of vorticity equation is discretized using the Finite Volume Method

We propose an approach to design an optimized heterogeneous interconnected porous structure over a fixed macro-design mesh for performance optimization, which has seldom been addressed despite its extensive potential industrial applications. We achieve this by introducing the concept of the extended triply periodic minimal surface (ETPMS), defined by an implicit function with additional control parameters

High fidelity numerical simulation of compressible flow requires the numerical method being used to have both stable shock-capturing capability and high spectral resolution. Recently, a family of Targeted Essentially Non-Oscillatory (TENO) schemes is developed to fulfill such requirements. Although TENO has very low dissipation for smooth flow, it introduces a cutoff value CT to maintain the non-oscillatory

A novel high-order three-scale reduced homogenization (HTRH) approach is introduced for analyzing the nonlinear heterogeneous materials with multiple periodic microstructure. The first-order, second-order and high-order local cell solutions at microscale and mescoscale gotten by solving the distinct multiscale cell functions are derived at first. Then, two kinds of homogenized parameters are calculated

We propose novel connections between several neural network architectures and viscosity solutions of some Hamilton–Jacobi (HJ) partial differential equations (PDEs) whose Hamiltonian is convex and only depends on the spatial gradient of the solution. To be specific, we prove that under certain assumptions, the two neural network architectures we proposed represent viscosity solutions to two sets of

In this paper, we develop and analyze two types of new energy-preserving local mesh-refined splitting finite difference time-domain (EP-LMR-S-FDTD) schemes for two-dimensional Maxwell's equations. For the local mesh refinements, it is challenging to define the suitable local interface schemes which can preserve energy and guarantee high accuracy. The important feature of the work is that based on energy

For numerical schemes to the incompressible Navier-Stokes equations with variable density, it is a critical property to preserve the bounds of density. A bound-preserving high order accurate scheme can be constructed by using high order discontinuous Galerkin (DG) methods or finite volume methods with a bound-preserving limiter for the density evolution equation, with any popular numerical method for

In Bayesian optimization, accounting for the importance of the output relative to the input is a crucial yet challenging exercise, as it can considerably improve the final result but often involves inaccurate and cumbersome entropy estimations. We approach the problem from the perspective of importance-sampling theory, and advocate the use of the likelihood ratio to guide the search algorithm towards

In this work, we introduce two novel reformulations of the weakly hyperbolic model for two-phase flow with surface tension, recently forwarded by Schmidmayer et al. In the model, the tracking of phase boundaries is achieved by using a new vector field, rather than a scalar tracer, so that the surface-force stress tensor can be expressed directly as an algebraic function of the state variables, without

We propose an Eulerian approach to compute the expected finite time Lyapunov exponent (FTLE) of uncertain flow fields. The definition extends the usual FTLE for deterministic dynamical systems. Instead of performing Monte Carlo simulations as in typical Lagrangian computations, our approach associates each initial flow particle with a probability density function (PDF) which satisfies an advection-diffusion

Although the transported probability density function (PDF) method has been developed for decades, its application has been mainly focused on the low-Mach number flow problems. This work extends the transported PDF method to compressible flow problems. The Eulerian Monte Carlo fields (EMCF) solution method is employed to solve the transported PDF equation for compressible flow problems. A pseudo stagnation

We propose an efficient numerical strategy for solving non-linear parabolic problems defined in a heterogeneous porous medium. This scheme is based on the classical homogenization theory and uses a locally mass-conservative formulation at different scales. In addition, we discuss some properties of the proposed non-linear solvers and use an error indicator to perform a local mesh refinement. The main

In this work, we consider numerical methods for the Poisson–Nernst–Planck–Cahn–Hilliard (PNPCH) equations with steric interactions, which correspond to a non-constant mobility H−1 gradient flow of a free-energy functional that consists of electrostatic free energies, steric interaction energies of short range, entropic contribution of ions, and concentration gradient energies. We propose a novel energy

We extend the recently introduced divergence-conforming immersed boundary (DCIB) method [1] to fluid-structure interaction (FSI) problems involving closed co-dimension one solids. We focus on capsules and vesicles, whose discretization is particularly challenging due to the higher-order derivatives that appear in their formulations. In two-dimensional settings, we employ cubic B-splines with periodic

This article presents a high order conservative flux optimization (CFO) finite element method that preserve the important mass conservation property locally for the convection-diffusion equations. The numerical scheme is an extension of the first order CFO scheme proposed in [26], it is based on the classical Galerkin finite element method enhanced by a flux approximation on the boundary of a prescribed

Compositional simulation is challenging, because of highly nonlinear couplings between multi-component flow in porous media with thermodynamic phase behavior. The coupled nonlinear system is commonly solved by the fully-implicit scheme. Various compositional formulations have been proposed. However, severe convergence issues of Newton solvers can arise under the conventional formulations. Crossing

Unresolved gradients produce numerical oscillations and inaccurate results. The most straightforward solution to such a problem is to increase the resolution of the computational grid. However, this is often prohibitively expensive and may lead to ecessive execution times. By training a neural network to predict the shape of the solution, we show that it is possible to reduce numerical oscillations

Unconditionally stable implicit time-marching methods are powerful in solving stiff differential equations efficiently. In this work, a novel framework to handle stiff physical terms implicitly is proposed. Both physical and numerical stiffness originating from convection, diffusion and source terms (typically related to reaction) can be handled by a set of predefined Time-Accurate and highly-Stable

For the simulation of multiscale gas flows, the numerical scheme should be valid and efficient in both rarefied and continuum regimes. For example, the Direct Simulation Monte Carlo (DSMC) method is not appropriate because of its huge computational cost in the continuum regime. Various kinds of hybrid methods with DSMC have been proposed to deal with this difficulty. One of them is the particle-particle

A new method for mimetic interpolation on polygonal meshes is described. This new method is based on harmonic function interpolation. Explicit formulas for harmonic functions on general polygons do not exist, so truncated harmonic polynomial expansions are used for computational efficiency. We show that the naive harmonic polynomial expansion, is not stable for arbitrary polygons. However, higher level

The so-called von Neumann analysis is a well-established approach used for stability analysis of numerical methods. The crux of this analysis is to bound the amplification factor by unity to ensure stability. However, an implicit but commonly unverified assumption in this approach is that the amplification factor does not vary with time, which as we show here is not always true for multi-level schemes

Partitioned solutions to fluid-structure interaction problems often employ a Dirichlet-Neumann decomposition, where the fluid equations are solved subject to Dirichlet boundary conditions on velocity from the structure, and the structure equations are solved subject to forces from the fluid. In some scenarios, such as an elastic balloon filling with air, an incompressible fluid domain may have pure

We aim at analysing a novel structure preserving difference scheme for the high dimensional nonlinear space-fractional Schrödinger equation. The temporal direction is discretized by the modified Crank-Nicolson scheme, while the spacial variable is approximated by formulas from the shifted convolution quadrature. The energy and mass preserving properties are proved rigorously for the scheme based on

Summation-by-parts (SBP) finite difference methods have several desirable properties for second-order wave equations. They combine the computational efficiency of narrow-stencil finite difference operators with provable stability on curvilinear multiblock grids. While several techniques for boundary and interface conditions exist, weak imposition via simultaneous approximation terms (SATs) is perhaps

Elliptical PDEs are at the core of many computational problems. Sometimes it is necessary to solve them on fine meshes, which entail huge memory footprints and low computational speeds. We provide a method to solve elliptical PDEs on fine grids with lowered memory consumption and improved convergence. This paper considers one typical elliptical PDE – the Poisson equation on various polygonal domains

In this article, we propose a novel high-order multi-moment finite volume method (MMFVM) for solving linear and nonlinear hyperbolic systems on unstructured grids. Different from the previous versions of volume-average/point-value multi-moment (VPM) scheme, the present scheme is developed by using a new reconstruction procedure based on the constrained least-square method which can be naturally extended

Spectral methods solve elliptic partial differential equations (PDEs) numerically with errors bounded by an exponentially decaying function of the number of modes when the solution is analytic. For time dependent problems, almost all focus has been on low-order finite difference schemes for the time derivative and spectral schemes for spatial derivatives. Spectral methods that converge spectrally in

In this work we study the finite difference method for the fractional diffusion equation with high-dimensional hyper-singular integral fractional Laplacian. We first propose a simple and easy-to-implement discrete approximation, i.e., fractional centered difference scheme with γth-order (γ≤2) convergence for the fractional operator. Based on the established approximation, we then construct a finite

A high-order accurate finite-difference scheme modeling the fluid-structure interaction inside a pneumatic seismic source is presented. The model consists of two deforming fluid compartments separated by a moving shuttle. The fluid is governed by the 1D Euler equations. Well-posedness of the continuous problem is analyzed and proven in the frozen coefficient case. A stable discretization is derived

In this paper we present a mathematical and numerical model for human cardiac perfusion which accounts for the different length scales of the vessels in the coronary tree. Epicardial vessels are represented with fully three-dimensional (3D) fluid-dynamics, whereas intramural vessels are modeled as a multi-compartment porous medium. The coupling of these models takes place through interface conditions

A new approach to model and compute the metric tensor corresponding to a-priori prescribed surface and volume boundary layers is presented. This is achieved by the use of sources that model the combined anisotropic metric. Algorithms are presented to both evaluate the resulting metric value at any point in space and the efficient approximation of the minimum and maximum metric over an axis-aligned

A stable spherical harmonics (PN) method for solving the problem of a multi-region sphere with internal sources subject to an externally incident angular flux is developed. The method consists in solving the problem locally (region by region) and using an iterative sweep technique to connect and update the solutions for the different regions until global convergence is attained. A post-processing step

Gene Expression Programming (GEP), a branch of machine learning, is based on the idea to iteratively improve a population of candidate solutions using an evolutionary process built on the survival-of-the-fittest concept. The GEP approach was initially applied with encouraging results to the modeling of the unclosed tensors in the context of RANS (Reynolds Averaged Navier–Stokes) turbulence modeling

Flexible filaments and fibres are essential components of important complex fluids that appear in many biological and industrial settings. Direct simulations of these systems that capture the motion and deformation of many immersed filaments in suspension remain a formidable computational challenge due to the complex, coupled fluid–structure interactions of all filaments, the numerical stiffness associated

With the increasing use of high-order methods and high-order meshes, scientific visualization software need to adapt themselves to reliably render the associated meshes and numerical solutions. In this paper, a novel approach, based on OpenGL 4 framework, enables a GPU-based rendering of high-order meshes as well as an almost pixel-exact rendering of high-order solutions. Several aspects of the OpenGL

Deep Learning research is advancing at a fantastic rate, and there is much to gain from transferring this knowledge to older fields like Computational Fluid Dynamics in practical engineering contexts. This work compares state-of-the-art methods that address uncertainty quantification in Deep Neural Networks, pushing forward the reduced-order modeling approach of Proper Orthogonal Decomposition-Neural

The immersed boundary method (IBM) with direct forcing is very popular in the simulation of rigid particulate flows. In the IBM, an interaction force is introduced at the interface between fluid and particle in order to approximate the no-slip boundary condition. The interaction force is calculated through dividing the velocity difference (or error) between fluid and particle at the interface by the

This work reports on the performances of a fully-discrete hp-adaptive entropy stable discontinuous collocated Galerkin method for the compressible Naiver–Stokes equations. The resulting code framework is denoted by SSDC, the first S for entropy, the second for stable, and DC for discontinuous collocated. The method is endowed with the summation-by-parts property, allows for arbitrary spatial and temporal

We consider the numerical integration of moving boundary problems with the curve-shortening property, such as the mean curvature flow and Hele-Shaw flow. We propose a fully discrete curve-shortening polygonal evolution law. The proposed evolution law is fully implicit, and the key to the derivation is to devise the definitions of tangent and normal vectors and tangential and normal velocities at each

A reduced order modeling algorithm for the estimation of space varying parameter patterns in numerical models is proposed. In this approach domain decomposition is applied to construct separate approximations to the numerical model in every subdomain. We introduce a new local parameterization that decouples the computational cost of the algorithm from the number of global principal components and therefore

We propose a method suitable for the computation of quasiperiodic interface, and apply it to simulate the interface between ordered phases in Lifschitz–Petrich model. The function space, initial and boundary conditions are carefully chosen so that it fixes the relative orientation and displacement, and we follow a gradient flow to let the interface find its optimal structure. The gradient flow is discretized