The local discontinuous Galerkin method for linearized incompressible fluid flow: a review

In this paper, we review the development of the so-called local discontinuous Galerkin method for linearized incompressible fluid flow. This is a stable, high-order accurate and locally conservative finite element method whose approximate solution is discontinuous across inter-element boundaries; this property renders the method ideally suited for hp-adaptivity. In the context of the Oseen problem, we present the method and discuss its stability and convergence properties. We also display numerical experiments that show that the method behaves well for a wide range of Reynolds numbers.     

Development of a second order approximation for the Navier-Stokes equations

The purpose of this paper is the development of a 2nd order finite difference approximation to the steady state Navier-Stokes equations governing flow of an incompressible fluid in a closed cavity. The approximation leads to a system of equations that has proved to be very stable. In fact, numerical convergence was obtained for Reynolds numbers up to 20,000. However, it is shown that extremely small mesh sizes are needed for excellent accuracy with a Reynolds number of this magnitude. The method uses a nine point finite difference approximation to the convection term of the vorticity equation. At the same time it is capable of avoiding values at corner nodes where discontinuities in the boundary conditions occur. Figures include level curves of the stream and vorticity functions for an assortment of grid sizes and Reynolds numbers.     

Numerical analysis of a self-adaptive formulation for the viscous/inviscid coupling

In this paper we consider the problem of the viscous/inviscid coupling, which often occurs in the study of fluids at high Reynolds number. We deal with a new formulation, introduced by Brezzi, Canuto and Russo [BCR], of a model convection-diffusion problem. In particular we perform the numerical analysis of this problem, proving the existence and convergence of some approximations of the exact solution obtained by applying the finite element method. Work performed using a CNR scholarship for final year students.     

Numerical experiments with the lid driven cavity flow problem

The centerline velocity profiles obtained from the solution of the two- and three-dimensional representations of the lid driven cavity flow problem are compared for different Reynolds numbers. Two configurations were used in this study: a unit cavity and a cavity with an aspect ratio of 2. The Reynolds numbers ranged from 100 to 5000 for all of the configurations studied. A new method of extending the Jacobi collocation technique called spectral difference is developed in this paper together with a unique computational grid. In addition, an iterative method for solving the pressure problem is also developed. This new numerical method allowed the calculation of three-dimensional Navier-Stokes equations to be performed in computers with very modest computational capabilities such as workstations.     

Convergence of an online gradient method with inner-product penalty and adaptive momentum

In this paper, we study the convergence of an online gradient method with inner-product penalty and adaptive momentum for feedforward neural networks, assuming that the training samples are permuted stochastically in each cycle of iteration. Both two-layer and three-layer neural network models are considered, and two convergence theorems are established. Sufficient conditions are proposed to prove weak and strong convergence results. The algorithm is applied to the classical two-spiral problem and identification of Gabor function problem to support these theoretical findings.     

On the use of reduced models obtained through identification for feedback optimal control problems in a heat convection–diffusion problem

This paper deals with the use of reduced models for solving some optimal control problems. More precisely, the reduced model is obtained through the modal identification method. The test case which the algorithms is tested on is based on the flow over a backward-facing step. Though the reduction for the velocity fields for different Reynolds numbers is treated elsewhere [1], only the convection–diffusion equation for the energy problem is treated here. The model reduction is obtained through the solution of a gradient-type optimization problem where the objective function gradient is computed through the adjoint-state method. The obtained reduced models are validated before being coupled to optimal control algorithms. In this paper the feedback optimal control problem is considered. A Riccati equation is solved along with the Kalman gain equation. Additionally, a Kalman filter is performed to reconstruct the reduced state through previous and actual measurements. The numerical test case shows the ability of the proposed approach to control systems through the use of reduced models obtained by the modal identification method.     

A globally and locally superlinearly convergent inexact Newton-GMRES method for large-scale variational inequality problem

In this paper, we propose an inexact Newton-generalized minimal residual method for solving the variational inequality problem. Based on a new smoothing function, the variational inequality problem is reformulated as a system of parameterized smooth equations. In each iteration, the corresponding linear system is solved only approximately. Under mild assumptions, it is proved that the proposed algorithm has global convergence and local superlinear convergence properties. Preliminary numerical results indicate that the method is effective for a large-scale variational inequality problem.     

Finite‐Time Average Consensus Based Approach for Distributed Convex Optimization

In this paper, we consider a distributed convex optimization problem where the objective function is an average combination of individual objective function in multi‐agent systems. We propose a novel Newton Consensus method as a distributed algorithm to address the problem. This method utilises the efficient finite‐time average consensus method as an information fusion tool to construct the exact Newtonian global gradient direction. Under suitable assumptions, this strategy can be regarded as a distributed implementation of the classical standard Newton method and eventually has a quadratic convergence rate. The numerical simulation and comparison experiment show the superiority of the algorithm in convergence speed and performance.     

Topology optimization of channel flow problems

This paper describes a topology design method for simple two-dimensional flow problems. We consider steady, incompressible laminar viscous flows at low-to-moderate Reynolds numbers. This makes the flow problem nonlinear and hence a nontrivial extension of the work of Borrvall and Petersson (2003).Further, the inclusion of inertia effects significantly alters the physics, enabling solutions of new classes of optimization problems, such as velocity-driven switches, that are not addressed by the earlier method. Specifically, we determine optimal layouts of channel flows that extremize a cost function which measures either some local aspect of the velocity field or a global quantity, such as the rate of energy dissipation. We use the finite element method to model the flow, and we solve the optimization problem with a gradient-based math-programming algorithm that is driven by analytical sensitivities. Our target application is optimal layout design of channels in fluid network systems. Using concepts borrowed from topology optimization of compliant mechanisms in solid mechanics, we introduce a method for the synthesis of fluidic components, such as switches, diodes, etc.     

Topology optimization of internal partitions in a flow-reversing chamber muffler for noise reduction

A topology-optimization-based design method for a flow-reversing chamber muffler is suggested to maximize the transmission loss value at a target frequency considering flow power dissipation. Rigid partitions for high noise reduction should be carefully placed inside the muffler to avoid extreme flow power dissipation due to a 180° change in flow direction from an inlet to an outlet. The optimal flow path for minimum flow power dissipation is well known to change depending on the Reynolds number, which is a function of the inlet flow velocity. To optimize the partition layout with an optimal flow path in an expansion chamber at a given Reynolds number, a flow-reversing chamber muffler design problem is formulated by topology optimization. The formulated topology optimization problem is implemented using the finite element method with a gradient-based optimization algorithm and is solved for various design conditions such as the target frequencies, rigid partition volumes, Reynolds numbers, non-design domain settings, and allowed amounts of flow power dissipation. The effectiveness of our suggested approach is verified by comparing the optimized partition layouts obtained by the suggested method and previous methods.     

Weighted least-squares finite element methods for the linearized Navier–Stokes equations

We implemented weighted least-squares finite element methods for the linearized Navier-Stokes equations based on the velocity–pressure–stress and the velocity–vorticity–pressure formulations. The least-squares functionals involve the L²-norms of the residuals of each equation multiplied by the appropriate weighting functions. The weights included a mass conservation constant, a mesh-dependent weight, a nonlinear weighting function, and Reynolds numbers. A feature of this approach is that the linearized system creates a symmetric and positive-definite linear algebra problem at each Newton iteration. We can prove that least-squares approximations converge with the linearized version solutions of the Navier–Stokes equations at the optimal convergence rate. Model problems considered in this study were the flow past a planar channel and 4-to-1 contraction problems. We presented approximate solutions of the Navier–Stokes problems by solving a sequence of the linearized Navier–Stokes problems arising from Newton iterations, revealing the convergence rates of the proposed schemes, and investigated Reynolds number effects.     

Incompressible moving boundary flows with the finite volume particle method

Mesh-free methods offer the potential for greatly simplified modeling of flow with moving walls and phase interfaces. The finite volume particle method (FVPM) is a mesh-free technique based on interparticle fluxes which are exactly analogous to intercell fluxes in the mesh-based finite volume method. Consequently, the method inherits many of the desirable properties of the classical finite volume method, including implicit conservation and a natural introduction of boundary conditions via appropriate flux terms. In this paper, we describe the extension of FVPM to incompressible viscous flow with moving boundaries. An arbitrary Lagrangian–Eulerian approach is used, in conjunction with the mesh-free discretisation, to facilitate a straightforward treatment of moving bodies. Non-uniform particle distribution is used to concentrate computational effort in regions of high gradients. The underlying method for viscous incompressible flow is validated for a lid-driven cavity problem at Reynolds numbers of 100 and 1000. To validate the simulation of moving boundaries, flow around a translating cylinder at Reynolds numbers of 20, 40 and 100 is modeled. Results for pressure distribution, surface forces and vortex shedding frequency are in good agreement with reference data from the literature and with FVPM results for an equivalent flow around a stationary cylinder. These results establish the capability of FVPM to simulate large wall motions accurately in an entirely mesh-free framework.     

A new technique for nonconvex primal-dual decomposition of a large-scale separable optimization problem

The primal-dual approach is quite effective in decomposing a convex separable optimization problem into several subproblems of smaller sizes. In this paper, we present a new technique which extends the primal-dual approach to nonconvex problems. Since a straightforward application of the multiplier method destroys separability, a new Lagrangian function is proposed which preserves separability. Based on this new function we develop a new iterative method for finding an optimal solution to the problem and show that the method is locally convergent to an optimal solution. Furthermore, the effect of certain parameters on the ratio of convergence is investigated and simple examples are given to illustrate the proposed approach.     

A finite-difference scheme for the solution of the steady Navier-Stokes equations

A new finite-difference scheme of second-order accuracy, free of artificial diffusion, for solving the steady-state incompressible Navier-Stokes equations is presented. The scheme uses a false transient approach with a combination of variable time step size and over-relaxation for the convection and diffusion terms. The driven flow in a square cavity is used as the model problem. The convergence is much better than the conventional Gauss-Seidel type iterative process. Results for Reynolds numbers up to 5000 are presented.     

Optimizing lattice Boltzmann simulations for unsteady flows

We present detailed analysis of a lattice Boltzmann approach to model time-dependent Newtonian flows. The aim of this study is to find optimized simulation parameters for a desired accuracy with minimal computational time. Simulation parameters for fixed Reynolds and Womersley numbers are studied. We investigate influences from the Mach number and different boundary conditions on the accuracy and performance of the method and suggest ways to enhance the convergence behavior.     

输运方程的谱流线扩散耦合方法及其在中子测井中的应用

§１．引言 近年来,中子输运理论得到了很大的发展,对输运问题的数值研究已经取得了很大成绩．这些研究方法的主流是 Ｍｏｎｔｅ Ｃａｒｌｏ方法［１］,确定型方法的研究相对要晚一些．八十年代, Ｌａｒｓｏｎ［２］等人开始用离散坐标Ｓｎ方法数值求解测井问题中的输运方程．离散坐标Ｓｎ方法采用的是空间变量和方向变量的全离散求解方法,要得到一定的求解精度,空间的网格节点数目不能太少,再加上方向变量的离散,所需求解的未知数数目将非常巨大,所以这种方法一般只用于一维和二维空间中的问题．对于三维问题,直接使用这种方法时…     

Nonmonotone trust region methods with curvilinear path in unconstrained optimization

A general nonmonotone trust region method with curvilinear path for unconstrained optimization problem is presented. Although this method allows the sequence of the objective function values to be nonmonotone, convergence properties similar to those for the usual trust region methods with curvilinear path are proved under certain conditions. Some numerical results are reported which show the superiority of the nonmonotone trust region method with respect to the numbers of gradient evaluations and function evaluations.     

A proximal Peaceman–Rachford splitting method for solving the multi-block separable convex minimization problems

ABSTRACT

The Peaceman–Rachford splitting method (PRSM) is well studied for solving the two-block separable convex minimization problems with linear constraints recently. In this paper, we consider the separable convex minimization problem where its objective function is the sum of more than two functions without coupled variables, when applying the PRSM to this case directly, it is not necessarily convergent. To remedy this difficulty, we propose a proximal Peaceman–Rachford splitting method for solving this multi-block separable convex minimization problems, which updates the Lagrangian multiplier two times at each iteration and solves some subproblems parallelly. Under some mild conditions, we prove global convergence of the new method and analyse the worst-case convergence rate in both ergodic and nonergodic senses. In addition, we apply the new method to solve the robust principal component analysis problem and report some preliminary numerical results to indicate the feasibility and effectiveness of the proposed method.