Scalar Dispersion from Wall-Mounted Cylinders at Large Reynolds Number: Plume Transitions and Regime Classification

TL;DR

This paper experimentally investigates scalar dispersion from wall-mounted cylinders at high Reynolds numbers, identifying plume regime transitions and evaluating dispersion models to improve predictive understanding.

Contribution

It introduces a systematic experimental dataset on plume transitions and assesses existing dispersion models, proposing a new predictive framework for scalar dispersion regimes.

Findings

Transition behavior significantly affects dispersion characteristics.

Gaussian Dispersion Model captures trends but has limitations.

Wall Similarity Model performs better under ground-level source conditions.

Abstract

This study presents a comprehensive experimental investigation of scalar dispersion from the free end of wall-mounted cylindrical obstacles immersed in a large-Reynolds-number turbulent boundary layer. A key focus is the characterization of transition behavior between distinct dispersion regimes: elevated plumes (EP), ground-level plumes (GLP), and ground-level sources (GLS). Experiments systematically vary the primary and secondary aspect ratios ($AR_1, AR_2$) and the velocity ratio ($ r$) to explore their effects on the evolution of scalar plumes. Plume classification is governed by the non-dimensional parameter $\tilde{h}_s / \delta_{cz}$, which quantifies the progressive interaction between the plume and the ground. Here, $\tilde{h}_s$ denotes the effective source height and $\delta_{cz}$, the vertical plume half-width. Detailed concentration measurements demonstrate that the EP--GLP--GLS transitions substantially modify both vertical and lateral dispersion characteristics. The measurements reveal systematic departures from classical dispersion-coefficient scaling. To assess the capability of existing models under these conditions, the experimentally determined dispersion coefficients are used to evaluate the Gaussian Dispersion Model (GDM) and a Wall Similarity Model (WSM). The GDM captures general trends but deviates in specific regimes, whereas the WSM offers improved representation under GLS conditions. The resulting dataset, grounded in systematic laboratory measurements, establishes a critical benchmark for validating numerical simulations and informing the development of next-generation predictive models. Finally, leveraging these results, a concise data-informed predictive framework is introduced that captures the EP--GLP--GLS transitions and provides first-order estimates of ground-level concentration across geometric and momentum-ratio parameter space.