Urban-Scale Computational Fluid Dynamics Simulations with Boundary Conditions from Similarity Theory and a Mesoscale Model

Abstract

Mesoscale numerical weather prediction models usually provide information regarding environmental parameters near urban areas at a spatial resolution of the order of thousands or hundreds of meters, at best. If detailed information is required at the building scale, an urban-scale model is necessary. Proper definition of the boundary conditions for the urban-scale simulation is very demanding in terms of its compatibility with environmental conditions and numerical modeling. Here, steady-state computational fluid dynamics (CFD) microscale simulations of the wind and thermal environment are performed over an urban area of Kozani, Greece, using both the k-ε and k-ω SST turbulence models. For the boundary conditions, instead of interpolating vertical profiles from the mesoscale solution, which is obtained with the atmospheric pollution model (TAPM), a novel approach is proposed, relying on previously developed analytic expressions, based on the Monin Obuhkov similarity theory, and one-way coupling with minimal information from mesoscale indices (Vy = 10 m, Ty = 100 m, L*). The extra computational cost is negligible compared to direct interpolation from mesoscale data, and the methodology provides design phase flexibility, allowing for the representation of discrete urban-scale atmospheric conditions, as defined by the mesoscale indices. The results compared favorably with the common interpolation practice and with the following measurements obtained for the current study: SODAR for vertical profiles of wind speed and a meteorological temperature profiler for temperature. The significance of including the effects of diverse atmospheric conditions is manifested in the microscale simulations, through significant variations (~30%) in the critical building-related design parameters, such as the surface pressure distributions and local wind patterns.

Large eddy simulation of sneeze plumes and particles in a poorly ventilated outdoor air condition: A case study of the University of Houston main campus

“…2.1 . First, We designed and validated a CFD model, which is capable of solving scalar-transport turbulence equations supported by the URANS schemes, of performing a series of wind simulations (i.e., airflows over, around, and through surface features) under three micro-climate settings employed for this study which include a city-scale ( Bouris et al, 2021 ; Skote et al, 2005 ), neighborhood-scale ( Wen et al, 2017 ; Juan et al, 2017 ), and street-scale ( Kim et al, 2010 ; Moonen et al, 2011 ). Second, we performed scenario-based (i.e., seasonal) numerical assessments of the outdoor ventilation efficiency across the selected study area, the University of Houston main campus, Texas, U.S., which was represented by the estimated time demanded for the existing contaminated air in the study area to be replaced entirely with the incoming fresh air.…”

Calculation of the Pressure Field for Turbulent Flow around a Surface-Mounted Cube Using the SIMPLE Algorithm and PIV Data

“…In the standard implementation of the code, several eddy viscosity-type turbulence models are available [42,43], while previous applications included LES [44]. Here, a novel approach is adopted whereby the Reynolds stress terms, after the implementation of finite volume method and linear interpolation on the faces of each cell (i.e., n, s, w and e), are included in the source terms of Equation (3) in discretised form (for the x and y direction, respectively):…”

Simulation of Wind Field in a Building Complex for Evaluation of the Wind Effect Along UAS Flight Path

“…The simulation setup can be improved by using updated geometry models of the buildings and terrain in Biopolis with more details and higher resolution, so that it would reflect more closely to the real environment. The setup can also be improved to obtain a more accurate wind field, for example by employing Large Eddy Simulation (LES) and time-averaging its results [22], applying more realistic terrain and building boundary conditions to acquire detailed atmospheric flow [23], and coupling the meso-scale and micro-scale weather conditions to obtain more accurate inlet boundary condition for the wind field simulation [24][25][26]. In addition, instead of only inferring the wind effect from the flow results, the simulated wind data can be utilized for UAS flight simulation using software-in-the-loop (SITL) [27,28] to study UAS stability and flight behavior when encountering the flow patterns in the urban wind field.…”