Multi-zone simulation results on ASE and sDA daylight metrics for parametric high-rise model with quad grid and diagrid facade in a highly dense hypothetical urban district using dry summer climate weather data

The research focuses on optimizing different zones of high-rise buildings in dense urban districts to improve the overall performance. For this reason, a hypothetical dense urban district is generated in Grasshopper 3d Algorithmic Modeling Environment in Rhino 3D. The models include various design variables depending on the facades type (quad grid and diagrid) and zones. Annual Sunlight Exposure (ASE) and Spatial Daylight Autonomy (sDA) are used as performance metrics. Simulation results are collected with the Latin Hypercube Sampling method from Diva4Rhino, a plug-in for environmental analysis for buildings. 10 zones, having 6 floors each, are considered from ground level to the top level of the high-rise building model for each facade types. In each zone, 2 floors are used for the simulation. Results of each floor, as well as their average results, are given for both metrics. Detailed explanations regarding dependent and independent variables of each zone are given in different “ReadMe” files. The resulting data can be used in future metropolitan studies, for sensitivity analysis, surrogate modeling, and statistical analysis for high-rise buildings in highly dense urban plots located in dry summer climate regions.本研究聚焦于高密度城区内高层建筑的不同分区优化，以提升整体性能表现。为此，依托Rhino 3D中的Grasshopper三维算法建模环境，构建了一处假想的高密度城区。该模型包含两类外立面形式——正交网格外立面（quad grid）与斜交网格外立面（diagrid）——以及对应不同分区的多种设计变量。本研究采用年均日照暴露量（Annual Sunlight Exposure, ASE）与空间日光自治率（Spatial Daylight Autonomy, sDA）作为性能评价指标。仿真结果通过拉丁超立方抽样法（Latin Hypercube Sampling）采集，所用工具为建筑环境分析插件Diva4Rhino。针对两种外立面形式，本研究均从高层建筑模型的首层至顶层选取10个分区，每个分区包含6层楼层；每个分区内选取2层开展仿真分析。两类性能指标均会给出各楼层的仿真结果，以及对应分区的平均结果。各分区的因变量与自变量的详细说明均收录于不同的"ReadMe"文档中。本数据集可应用于后续针对夏干气候区高密度地块内高层建筑的大都市研究、敏感性分析、代理建模及统计分析等相关场景。