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Video of spiral being propagated by level sets (curvature flow) in 2D.Left image shows zero-level solution. Right image shows the level-set scalar field. The Level-set method (LSM) is a conceptual framework for using level sets as a tool for numerical analysis of surfaces and shapes.
PISO algorithm (Pressure-Implicit with Splitting of Operators) was proposed by Issa in 1986 without iterations and with large time steps and a lesser computing effort.It is an extension of the SIMPLE algorithm used in computational fluid dynamics to solve the Navier-Stokes equations.
Ansys, Inc. is an American multinational company with its headquarters based in Canonsburg, Pennsylvania. It develops and markets CAE / multiphysics engineering simulation software for product design, testing and operation and offers its products and services to customers worldwide.
The first idea behind the Proper Orthogonal Decomposition (POD), as it was originally formulated in the domain of fluid dynamics to analyze turbulences, is to decompose a random vector field u(x, t) into a set of deterministic spatial functions Φ k (x) modulated by random time coefficients a k (t) so that:
Schematic of D2Q9 lattice vectors for 2D Lattice Boltzmann. Unlike CFD methods that solve the conservation equations of macroscopic properties (i.e., mass, momentum, and energy) numerically, LBM models the fluid consisting of fictive particles, and such particles perform consecutive propagation and collision processes over a discrete lattice.
In computational fluid dynamics QUICK, which stands for Quadratic Upstream Interpolation for Convective Kinematics, is a higher-order differencing scheme that considers a three-point upstream weighted by quadratic interpolation for the cell face values.
In computational physics, the term advection scheme refers to a class of numerical discretization methods for solving hyperbolic partial differential equations.In the so-called upwind schemes typically, the so-called upstream variables are used to calculate the derivatives in a flow field.
A comparison of the convergence of gradient descent with optimal step size (in green) and conjugate vector (in red) for minimizing a quadratic function associated with a given linear system. Conjugate gradient, assuming exact arithmetic, converges in at most n steps, where n is the size of the matrix of the system (here n = 2).