Data-driven prediction of the equivalent sand-grain roughness.

ABSTRACT: Surface roughness affects the near-wall fluid velocity profile and surface drag, and is commonly quantified by the equivalent sand-grain roughness [Formula: see text]. It is essential to estimate [Formula: see text] for accurate fluid dynamic problem modeling. While numerous roughness correlation formulas have been proposed to predict [Formula: see text] in the fully rough regime, most of them are restricted to certain roughness types, with various geometric parameters considered in each case, leading to ongoing disagreements regarding its parameterization and lack of universality. In this study, a Particle Swarm Optimized Backpropagation (PSO-BP) method is proposed to predict [Formula: see text] based on the selected surface parameters from previous DNS, LES, and experimental results for

SUBMITTER: Ma H 

PROVIDER: S-EPMC10625589 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature