西安建筑科技大学学报(自然科学版)

居住区室外环境作为人们活动发生较为频繁的主要区域,其室外舒适度研究日益受到重视.随着计算机模拟辅助设计领域的发展,越来越多的研究方法开始选用遗传算法开展自动优化布局.为优化西安地区居住区室外热舒适并探究建筑布局与其相关性,本研究以西安市某住区前期设计为例,结合进化算法与帕累托原理,使用多目标优化平台Octopus制定了基于室外热舒适的建筑布局自生成实验.该实验通过解集空间选优探究文章背景环境下的最终优化布局方案,实验结果表明:该方法可行有效,能够辅助设计者进行方案生成与决策,为城市建筑布局提供优化方法策略.而西安地区的实验数据表明,本研究所提出优化方案中,建筑布局在冬季对室外热舒适的改善作用更为显著.

As residential outdoor area is the main place where people's activities occur frequently, the study of outdoor comfort has been paid more and more attention. With the development of computer simulation aided design, more and more research methods begin to use genetic algorithm to optimize layout automatically. In order to improve outdoor thermal comfort of residential areas in Xi'an and explore correlation between building layout and outdoor thermal comfort, this research took the early design of a residential area in Xi'an as an example and used multi-objective optimization platform Octopus, combined with evolutionary algorithm and Pareto principle, to develop the self-generating experiment of building layout based on outdoor thermal comfort. In experiment, final optimal layout scheme under the research background environment was explored through solution set spatial optimization. Experimental results show that this method is feasible and effective enough to assist designers in scheme design and decision-making, and could provide further optimization methods and strategies for urban architectural layout. Analysis of experimental data shows that building layout has a significant effect on the improvement of outdoor thermal comfort in winter among optimization schemes proposed in this paper.

引言
1 应用于热环境研究的多目标优化研究方法
2 模拟及分析
3 结果及讨论
4 结论

图1 西安月平均气象资料<br/>Fig.1 Monthly average meteorological data in Xi'an

图1 西安月平均气象资料
Fig.1 Monthly average meteorological data in Xi'an

图2 西安市某住区初始方案<br/>Fig.2 Original design plan of a residential area in Xi'an

图2 西安市某住区初始方案
Fig.2 Original design plan of a residential area in Xi'an

表1 OUT_SET、PET、UTCI理论基础与影响因素对比分析[3]<br/>Tab.1 Comparative analysis of theoretical basis and influencing factors

表1 OUT_SET、PET、UTCI理论基础与影响因素对比分析[3]
Tab.1 Comparative analysis of theoretical basis and influencing factors

图3 基于参数化平台的实验框架<br/>Fig.3 Experimental framework based on parametric platform

图3 基于参数化平台的实验框架
Fig.3 Experimental framework based on parametric platform

表2 预设约束条件<br/>Tab.2 Pre-defined constraints

表2 预设约束条件
Tab.2 Pre-defined constraints

表3 Octopus相关参数设置表<br/>Tab.3 Parameters settings

表3 Octopus相关参数设置表
Tab.3 Parameters settings

图4 Octopus运算结果<br/>Fig.4 Operation results

图4 Octopus运算结果
Fig.4 Operation results

表4 优化方案
Tab.4 Optimization scheme

表5 优化方案
Tab.5 Optimization scheme

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备注

引言

1 应用于热环境研究的多目标优化研究方法

2 模拟及分析

3 结果及讨论

4 结论

期刊信息