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数据挖掘框架,深入量化研究从立方形建筑物形状的形态参数对低风速区域的影响
Han Guo1,2,3, Yehao Song4, Yingnan Chu4
1Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources of China, Shenzhen, China.
Heliyon
|April 16, 2024
概括
减少建筑物高度和宽度显著减少低风速区域 (LWVA),提高室外通风. 建筑深度对LWVA的影响有限,为可持续的建筑设计提供了见解.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 计算流体动力学的流体动力学.
背景情况:
- 建筑形态显著影响城市风能环境.
- 优化风力条件对于可持续的建筑设计至关重要.
- 量化建筑形状对风流的影响是必不可少的.
研究的目的:
- 开发一个数据挖掘框架来评估建筑形态对低风速区域 (LWVA) 的影响.
- 量化评估和比较形态参数对立方体建筑物中LWVA的影响.
- 为可持续城市设计提供预测工具.
主要方法:
- 综合参数建模,混合CFD-ML模型,以及用于大数据生成的统计分析.
- 开发了一个数据挖掘框架,将计算流体动力学 (CFD) 和机器学习结合起来.
- 分析了五万多个具有不同参数和风条件的建筑案例.
主要成果:
- 风向侧的建筑高度和宽度显著减少了LWVA,改善了通风.
- 建筑深度对LWVA的影响有限.
- 在LWVA和形态参数之间发现了显著的线性相关性,并提供了预测回归模型.
结论:
- 开发的数据挖掘框架准确有效地评估LWVA的影响.
- 结果为建筑师提供了切实可行的指导,以减少LWVA,并通过形态调整增强户外通风.
- 该研究为可持续建筑设计提供了有价值的数据和预测模型.
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