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The interaction between the natural ambient winds found in the atmospheric boundary layer (ABL) and buoyant flames is crucial in broad applications in the scientific and engineering fields. Unlike the buoyancy-driven pool fires in still air that have been studied extensively, the complexity of physics changes significantly in wind presence. This study aims at analysing the characteristics of boundary-layer turbulence in the presence of large fires. The eddy dissipation concept, finite volume discrete ordinate method, and one k-equation model are used for combustion, thermal radiation, and sub-grid scale closure using the Large-Eddy Simulation (LES), respectively. A numerical model on simple cases is validated first to assess its capability to reproduce available experimental observations for a purely buoyant fire in still air. A forced-flow boundary layer combustion in a small chamber with a smooth inflow is further considered. In general, good agreement between the simulation results and available experimental data was achieved for temperature and velocity profiles. The unsteady inflow condition used to consider incoming atmospheric turbulence is generated through a precursor simulation. The wind interaction with the line fire changes the atmospheric boundary layer profile affecting the heat transfer ahead of the flame, thereby creating counter-rotating structures downstream. It is shown that the buoyancy-dominated flow due to the flame reaction induced local pressure variation and perturbed shear flow near the ground, thereby altering the wind speed through which the plume rises. Richardson number was also used as a dominant non-dimensional group to analyse the variation of enhanced flow vertical velocity with distance from the fire source. Thus, it is understood that the pronounced longitudinal shear spreading at the surface affects the behaviour of short term or puff releases, suggesting the shedding of small eddies during the combustion process.

In conventional modeling of air pollution dispersion, pollutants are treated as passive scalars or inert species even though most of them are chemically reactive [1]. Chemical reactions contribute to pollutant dispersion via the generation and depletion of pollutants, in addition to other two mechanisms: advection and turbulent diffusion. This study investigated how chemical reactions affect near-field pollution dispersion by integrating the simple NOx-O3 chemistry into RANS-based computational fluid dynamics (CFD) simulation. CFD simulation was used to model a mixed emission of NO and NO2 from a short stack attached to a building into ambient O3, prompting chemical reactions between the NO, NO2, and O3. Various degrees of chemical reactivity were modeled by varying the Damkholer number (Da) between 0.073 and 4.363. The results showed significant chemical reactivity for cases where Da [NO] > 1, while cases with Da [NO]

Slender structures placed close to each other could greatly magnify the dynamic response due to interference effects. This paper presents the results of experimental studies of the proximity interference induced vibration of two identical square prisms in a turbulent boundary layer to clarify its excitation mechanism. The upstream interfering prism was static, while the principal prism was dynamic and free to vibrate only in the crosswind direction. The divergent vibration behavior at the critical interfered location was examined in detail. Based on the time-resolved particle image velocimetry (TR-PIV) technique and unsteady pressure measurement, the vortex shedding process and corresponding pressure distribution of the dynamic principal prism were described by examining their correlation with the motion time histories. The biased gap flow between the two prisms was found to control the vibration of the dynamic principal prism. The interaction between the vortex shedding in the wake of the dynamic principal prism and the gap flow is dramatically intensified with increasing wind velocity. Such effects create a large fluctuating lift force on the dynamic principal prism at high reduced wind velocity, compared with measurement taken with two static prisms, which drives the prism to undergo a divergent crosswind response.

This paper presents an approach to predict crosswind force spectra and associated response of tall buildings with rectangular cross-section based on machine learning (ML) technique and random vibration-based response analysis. An efficient ML algorithm, light gradient boosting machine (LGBM), was trained to predict crosswind force spectra of the tall buildings by using the database from the Wind Engineering Research Center at the Tamkang University embedded in the aerodynamic database of NatHaz Modelling Laboratory. Furthermore, an unsupervised ML algorithm, K-means clustering, was employed to advance the understanding of the crosswind force spectrum characteristics of the tall buildings. The effects of three factors, i.e., ground roughness, aspect ratio and side ratio, on the force spectra were discussed based on clustering. To predict the crosswind response of tall buildings, case studies were carried out to validate the predictive accuracy of the LGBM model combined with random vibration-based response analysis. The results demonstrate that the proposed method combined with the multiple database-enabled design module for high-rise buildings developed by the NatHaz Modelling Laboratory at the University of Notre Dame is effective and computationally efficient to provide fast and accurate predictions of the crosswind force spectrum and associated crosswind responses of rectangular tall buildings.