Research on the optimal layout form and optimization of trees in the main square of urban campus based on thermal environment simulation

Faming LiChunQing GeDongxue Wei^*Yiqing Liu

• School of Architecture, Tianjin Chengjian University, Tianjin, China

In the process of rapid urbanization in China, the arbor planting form is often used in the majority of urban squares, due to the large area of hard pavement in this type of square space leads to poor human comfort in the microenvironment of the summer site, so the planting of arbor vegetation in the site is an important factor to enhance the comfort of the microenvironment of this type of site space. This paper takes the main square of the central axis of a university in the north as an example, carries out multi-type simulation on the planting method of square arborvitae by point cloud modeling and Computational Fluid Dynamics (CFD) simulation, and explores the optimal layout form of arbor in this type of square based on the simulation of thermal comfort. The results show that the thermal comfort in different forms can be divided into three levels: 1) when the planting distance is 2.5 times the crown width at an angle of 45°to the wind direction, the thermal comfort effect of the site is optimal(ΔSET*=0.243℃，ΔWBGT=0.141℃ reduction); 2) when the planting distance is 2 times the crown width parallel to the direction of the prevailing winds in summer, the thermal comfort effect of the site is better(ΔSET*=0.198℃，ΔWBGT=0.130℃ reduction); 3) when the planting distance is less than 2 times the crown width in the perpendicular wind direction, the thermal comfort effect of the site is the worst(ΔSET*=0.079℃，ΔWBGT=0.036℃ reduction). This paper intervenes in the point cloud modeling method to provide reference for the subsequent simulation accuracy in this field, and the results of the study have certain reference significance for the design of tree layout when optimizing the thermal comfort environment in this type of main square.In the future, through the integration of real-time meteorological data and multi-scale models, it can dynamically respond to climate change and provide accurate decision support for urban green space system planning, carbon neutral pathway design and extreme weather emergency planning.