SUMMARY
An implicit meshless scheme is developed for solving the Euler equations, as well as the laminar and Reynolds-averaged Navier–Stokes equations. Spatial derivatives are approximated using a least squares method on clouds of points. The system of equations is linearised, and solved implicitly using approximate, analytical Jacobian matrices and a preconditioned Krylov subspace iterative method. The details of the spatial discretisation, linear solver and construction of the Jacobian matrix are discussed; and results that demonstrate the performance of the scheme are presented for steady and unsteady two dimensional fluid flows. Copyright © 2012 John Wiley & Sons, Ltd.

An implicit meshless method for application in computational fluid dynamics

A multidimensional characteristic-based method for making incompressible flow calculations on unstructured grids

Adaptive Cartesian grid with mesh-less zones for compressible flow calculations

Efficient and non-reflecting far-field boundary conditions for incompressible flow calculations

This paper presents a novel generalized finite difference method that can achieve arbitrary order of accuracy on a compact stencil nodal set. Accurate reconstruction and flux evaluation are two key steps to achieve high order spatial accuracy. A newly developed variational reconstruction approach is utilized to obtain the piecewise higher order polynomial distribution of flow variables. The implementation of boundary conditions is of critical importance and a flexible variational extrapolation technique is proposed for the high order boundary treatment. The numerical flux derivatives are evaluated using a simple and efficient hybrid approach, in which the linear and high order terms of the flux function are treated differently. Several test cases are solved to verify the accuracy, efficiency, and shock capturing capability of the proposed numerical schemes for inviscid compressible flows.

Y. Hashemi

Papers

Implicit fully mesh-less method for compressible viscous flow calculations