Optimal error estimates for the pseudostress formulation of the Navier–Stokes equations

In this paper, we prove optimal a priori error estimates for the pseudostress-velocity mixed finite element formulation of the incompressible Navier–Stokes equations, thus improve the result of Cai et al. (SINUM 2010). This is achieved by applying Petrov–Galerkin type Brezzi–Rappaz–Raviart theory.     

Generating functions of multi-symplectic RK methods via DW Hamilton–Jacobi equations

The De Donder–Weyl (DW) Hamilton–Jacobi equation is investigated in this paper, and the connection between the DW Hamilton–Jacobi equation and multi-symplectic Hamiltonian system is established. Based on the DW Hamilton–Jacobi theory, generating functions for multi-symplectic Runge–Kutta (RK) methods and partitioned Runge–Kutta (PRK) methods are presented. The work is supported by the Foundation of ICMSEC, LSEC, AMSS and CAS, the NNSFC (No.10501050, 19971089 and 10371128) and the Special Funds for Major State Basic Research Projects of China (2005CB321701).     

Solving a multi periodic stochastic model of the rail–car fleet sizing by two-stage optimization formulation

We propose a formulation and solution procedure for optimizing the fleet size and freight car allocation under uncertainty demands. There are important interactions between decisions on sizing a rail–car fleet and utilizing that fleet. Consequently, the optimum use of empty rail–cars for demands response in the length of the time periods one of advantages the proposed model. The model also provides rail network information such as yard capacity, unmet demands, and number of loaded and empty rail–car at any given time and location. Consequently, the model helping managers or decision makers of any train company for planning and decision making. We propose two-stage solution procedure for solve rail–car fleet sizing problem. Numerical examples are given to illustrate the model and solution methodology.     

Random periodic solutions of SPDEs via integral equations and Wiener–Sobolev compact embedding

In this paper, we study the existence of random periodic solutions for semilinear SPDEs on a bounded domain with a smooth boundary. We identify them as the solutions of coupled forward–backward infinite horizon stochastic integral equations on $L^2(D)$ in general cases. For this we use Mercer?s Theorem and eigenvalues and eigenfunctions of the second order differential operators in the infinite horizon integral equations. We then use the argument of the relative compactness of Wiener–Sobolev spaces in $C^0([0,T],L^2(\Omega \times D))$ and generalized Schauder?s fixed point theorem to prove the existence of a solution of the integral equations. This is the first paper in literature to study random periodic solutions of SPDEs. Our result is also new in finding semi-stable stationary solution for non-dissipative SPDEs, while in literature the classical method is to use the pull-back technique so researchers were only able to find stable stationary solutions for dissipative systems.     

The multi–phase averaging techniques and the modulation theory of the focussing and defocusing nonlinear schrodinger equations

Multi–phase averaging techniques have been applied successfully in the investigations of the modulational and generalized Benjamine–Feir instabilities for the quasi–periodic, N–phase, inverse spectral solutions of KdV [1], sine–Gordon (s–G) [2,3,4], and focussing and defocusing nonlinear Schrodinger equation [5,10], The key is that the multi–phase averagings, as the N–fold integrals, can be transferred to the N–iterated integrals, and therefore, can be evaluated, which is essential in the analysis of PDE perturbations analyzed by the averaging methods. In this paper, the transformations from cerain N–fold integrals to the N–iterated integrals for NLS are developed rigorously, and made to be numerically computable. Those integrals are also closely related to KdV and s–G. As an application, the modulation theory of the modulating N–phuse NLS solutions are Presented, a result given by Forest and Lee in [5,10].     

Numerical solution of the Yukawa-coupled Klein–Gordon–Schrödinger equations via a Chebyshev pseudospectral multidomain method

The Klein–Gordon–Schrödinger equations describe a classical model of the interaction between conservative complex neutron field and neutral meson Yukawa in quantum field theory. In this paper, we study the long-time behavior of solutions for the Klein–Gordon–Schrödinger equations. We propose the Chebyshev pseudospectral collocation method for the approximation in the spatial variable and the explicit Runge–Kutta method in time discretization. In comparison with the single domain, the domain decomposition methods have good spatial localization and generate a sparse space differentiation matrix with high accuracy. In this study, we choose an overlapping multidomain scheme. The obtained numerical results show the Pseudospectral multidomain method has excellent long-time numerical behavior and illustrate the effectiveness of the numerical scheme in controlling two particles. Some comparisons with single domain pseudospectral and finite difference methods will be also investigated to confirm the efficiency of the new procedure.     

The local regularity conditions for the Navier–Stokes equations via one directional derivative of the velocity

Lithuanian Mathematical Journal - We study the local regularity of solutions to the Navier–Stokes equations. We show for a suitable weak solution (u, p) on an open space-time domain D that if...     

A regularity criterion for 3D Navier–Stokes equations via one component of velocity and vorticity

In this paper we show that a Leray–Hopf weak solution u to 3D Navier–Stokes initial value problem is smooth if there is some \(\alpha \in {{{\mathbb {R}}}}, \alpha \ne 0,\) such that \(\alpha u_3+(-\Delta )^{-1/2}\omega _3\) is suitably smooth, where \(\omega =\text {curl}\,u\).     

Modelling and control of flexible joint robot based on Takagi–Sugeno fuzzy approach and its stability analysis via sum of squares

This article proposes a Takagi–Sugeno (T-S) fuzzy model of single-link rotary flexible joint robot. The proposed control method is based on parallel distributed control. The parameters of T-S controller are improved by distributed population genetic algorithm (GA) with chaos GA. Using Hermite–Biehler theorem in distributed population, GA is made to have a fast convergence. Dividing search space into several sub-spaces causes a better response, and chaos disturbance helps the whole algorithm to reach a best answer. The stability of the controller is analysed via the sum of squares programming, and finally, it is implemented on the plant.