The generation of a fully turbulent boundary layer profile is investigated using analytical and numerical methods over the Reynolds number range 300 ≤ Re θ ≤ 31000. The predictions are validated against reference wind tunnel measurements under zero streamwise pressure gradient. The analytical method is then tested for a low and moderate adverse pressure gradient. Comparison against experimental and DNS data show a good predictive ability under a zero pressure gradient and a moderate adverse pressure gardient, with the numerical method providing a complete velocity profile through the laminar sub-layer down to the wall. The application of the method is useful to computational fluid dynamic practitioners for generating an equilibrium thick turbulent boundary layer at the computational domain inflow.
A successful model of high Reynolds number cavity flows involves reproducing the flow physics with adequate accuracy, given the available computational resources. The process of planning high Reynolds number cavity flow simulations is systematically reviewed to extract the dependence of different programmer's choices on the CFD mesh size and on the cost of the computation.This process has been broken down into five phases: i) description of the problem in the continuous domain, ii) problem order reduction by turbulence modelling, iii) discretization in space and time, iv) integration of the governing equations, v) costing the numerical operations of the flow solver. This paper examines the influence of each phase on the spectral width and the grid density, which are the key CFD indicators that determine the cost of the computation.A dimensional analysis was conducted to separate the effects of the geometry of the enclosure, the boundary layer resolution, the turbulence model, and the numerical scheme order of accuracy. Regression analysis on the non-dimensional groups of published cavity CFD simulations determined the range of practical values used by current state-of-the-art computations. This analysis is a useful tool for obtaining design trade-offs by a multivariate optimization in cavity flow CFD and for estimating the order of magnitude of the computational resources required by the simulations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.