Vegetation plays a key role in improving wind environment of residential districts, and is helpful for creating a comfortable and beautiful living environment. The optimal design of vegetation for wind environment improvement in winter was investigated by carrying out field experiments in Heqingyuan residential area in Beijing, and after that, numerical simulation with SPOTE (simulation platform for outdoor thermal environment) experiments for outdoor thermal environment of vegetation was adopted for comparison. The conclusions were summarized as follows: 1) By comparing the experimental data with simulation results, it could be concluded that the wind field simulated was consistent with the actual wind field, and the flow distribution impacted by vegetation could be accurately reflected; 2) The wind velocity with vegetation was lower than that without vegetation, and the wind velocity was reduced by 46%; 3) By adjusting arrangement and types of vegetation in the regions with excessively large wind velocity, the pedestrian-level wind velocity could be obviously improved through the simulation and comparison.
Calculating the velocity and particle concentration indoor is critical for isolation rooms design. Computational fluid dynamics (CFD) is regarded as a powerful tool aiding in the indoor environment design for isolation rooms by enabling us to predict the velocity and particle concentration distribution in detail. However, CFD method is time-consuming and relatively expensive, especially for actual engineering application. So this study proposes a simplified methodology to predict the mean air velocity and particle concentration in the occupied zone of isolation rooms with downward ventilation systems. The methodology is based on a similarity theory analysis, by which the key similarity criteria are deduced. The correlating equation to calculate the mean air velocity and particle concentration in the occupied zone in isolation rooms is established by multiple linear regression (MLR) which is based on the numerical test results obtained by CFD. The equation correlates the mean air velocity and particle concentration with air supply volume rate, indoor particle generating rate, and other parameters. The calculated results agree with those from measurement and CFD simulations for the studied cases, generating a relative error less than 25%. It could offer the engineers a simpler path to calculate the mean air velocity and particle concentration.