Mapping of an Approximate Neutral Density Surface with Ungridded Data

A neutral density surface is a logical study frame for water-mass mixing since water parcels spread along such a surface without doing work against buoyancy restoring force. Mesoscale eddies are believed to stir and subsequently mix predominantly along such surfaces. Because of the nonlinear nature of the equation of state of seawater, the process of accurately mapping a neutral density surface necessarily involves lateral computation from one conductivity, temperature and depth (CTD) cast to the next in a logical sequence. By contrast, the depth of a potential density surface on any CTD cast is found solely from the data on this cast. The lateral calculation procedure causes a significant inconvenience. In a previous paper by present author published in this journal (You, 2006), the mapping of neutral density surfaces with regularly gridded data such as Levitus data has been introduced. In this note, I present a new method to find the depth of a neutral density surface from a cast without having to specify an integration path in space. An appropriate reference point is required that is on the neutral density surface and thereafter the neutral density surface can be de- termined by using the CTD casts in any order. This method is only approximate and the likely errors can be estimated by plotting a scatter diagram of all the pressures and potential temperatures on the neutral density surfaces. The method assumes that the variations of potential temperature and pressure (with respect to the values at the reference point) on the neutral density surface are proportional. It is important to select the most appropriate reference point in order to approximately satisfy this assumption, and in practice this is found by inspecting the θ-p plot of data on the surface. This may require that the algorithm be used twice. When the straight lines on the θ-p plot, drawn from the reference point to other points on the neutral density surface, enclose an area that is external to the clus- ter of θ-p points of the neutral density surface, errors will occur, and these errors can be quantified from this diagram. Examples showing the use of the method are presented for each of the world’s main oceans.

Precision of meshfree methods and application to forward modeling of two-dimensional electromagnetic sources

Meshfree method offers high accuracy and computational capability and constructs the shape function without relying on predefined elements. We comparatively analyze the global weak form meshfree methods, such as element-free Galerkin method （EFGM）, the point interpolation method （PIM）, and the radial point interpolation method （RPIM）. Taking two dimensional Poisson equation as an example, we discuss the support-domain dimensionless size, the field nodes, and background element settings with respect to their effect on calculation accuracy of the meshfree method. RPIM and EFGM are applied to controlled- source two-dimensional electromagnetic modeling with fixed shape parameters. The accuracy of boundary conditions imposed directly and by a penalty function are discussed in the case of forward modeling of two-dimensional magnetotellurics in a homogeneous medium model. The coupling algorithm of EFG-PIM and EFG-RPIM are generated by integrating the PIM or RPIM and EFGM. The results of the numerical modeling suggest the following. First, the proposed meshfree method and corresponding coupled methods are well-suited for electromagnetic numerical modeling. The accuracy of the algorithm is the highest when the support-domain dimensionless size is 1.0 and the distribution of field nodes is consistent with the nodes of background elements. Second, the accuracy of PIM and RPIM are lower than that of EFGM for the Poisson equation but higher than EFGM for the homogeneous medium MT response. Third, RPIM overcomes the matrix inversion problem of PIM and has a wider selection of support-domain dimensionless sizes as compared to RPIM.

A defect-correction method for unsteady conduction convection problems Ⅰ:spatial discretization

In this paper, a semi-discrete defect-correction mixed finite element method (MFEM) for solving the non-stationary conduction-convection problems in two dimension is presented. In this method, we solve the nonlinear equations with an added artificial viscosity term on a finite element grid and correct this solutions on the same grid using a linearized defect-correction technique. The stability and the error analysis are derived. The theory analysis shows that our method is stable and has a good convergence property.

A full multigrid method for nonlinear eigenvalue problems

We introduce a type of full multigrid method for the nonlinear eigenvalue problem. The main idea is to transform the solution of the nonlinear eigenvalue problem into a series of solutions of the corresponding linear boundary value problems on the sequence of finite element spaces and nonlinear eigenvalue problems on the coarsest finite element space. The linearized boundary value problems are solved by some multigrid iterations.Besides the multigrid iteration, all other efficient iteration methods for solving boundary value problems can serve as the linear problem solver. We prove that the computational work of this new scheme is truly optimal,the same as solving the linear corresponding boundary value problem. In this case, this type of iteration scheme certainly improves the overfull efficiency of solving nonlinear eigenvalue problems. Some numerical experiments are presented to validate the efficiency of the new method.

Extrapolation cascadic multigrid method on piecewise uniform grid

The triangular linear finite elements on piecewise uniform grid for an elliptic problem in convex polygonal domain are discussed. Global superconvergence in discrete Hi-norm and global extrapolation in discrete L2-norm are proved. Based on these global estimates the conjugate gradient method （CG） is effective, which is applied to extrapolation cascadic multigrid method （EXCMG）. The numerical experiments show that EXCMG is of the global higher accuracy for both function and gradient.