The numerical computation of turbulent flows
Computer Methods in Applied Mechanics and Engineering - Special edition on the 20th Anniversary
Particle method for turbulent flows: integration of stochastic model equations
Journal of Computational Physics
Robust, vectorized search algorithms for interpolation on unstructured grids
Journal of Computational Physics
A consistent hybrid finite-volume/particle method for the PDF equations of turbulent reactive flows
Journal of Computational Physics
A Hybrid Algorithm for the Joint PDF Equation of Turbulent Reactive Flows
Journal of Computational Physics
Journal of Computational Physics
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
A general mass consistency algorithm for hybrid particle/finite-volume PDF methods
Journal of Computational Physics
Journal of Computational Physics
Filtered density function simulator on unstructured meshes
Journal of Computational Physics
Hi-index | 31.45 |
In probability density function (PDF) methods of turbulent flows, the joint PDF of several flow variables is computed by numerically integrating a system of stochastic differential equations for Lagrangian particles. A set of parallel algorithms is proposed to provide an efficient solution of the PDF transport equation modeling the joint PDF of turbulent velocity, frequency and concentration of a passive scalar in geometrically complex configurations. In the vicinity of walls the flow is resolved by an elliptic relaxation technique down to the viscous sublayer, explicitly modeling the high anisotropy and inhomogeneity of the low-Reynolds-number wall region without damping or wall-functions. An unstructured Eulerian grid is employed to extract Eulerian statistics, to solve for quantities represented at fixed locations of the domain (i.e., the mean pressure and the elliptic relaxation tensor) and to track particles. All three aspects regarding the grid make use of the finite element method employing the simplest linear shapefunctions. To model the small-scale mixing of the transported scalar, the interaction by exchange with the conditional mean (IECM) model is adopted. An adaptive algorithm to compute the velocity-conditioned scalar mean is proposed that homogenizes the statistical error over the sample space with no assumption on the shape of the underlying velocity PDF. Compared to other hybrid particle-in-cell approaches for the PDF equations, the current methodology is consistent without the need for consistency conditions. The algorithm is tested by computing the dispersion of passive scalars released from concentrated sources in two different turbulent flows: the fully developed turbulent channel flow and a street canyon (or cavity) flow. Algorithmic details on estimating conditional and unconditional statistics, particle tracking and particle-number control are presented in detail. Relevant aspects of performance and parallelism on cache-based shared memory machines are discussed.