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Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks

机译：基于计算流体动力学和神经网络选择窗户尺寸以优化职业舒适度和卫生状况

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摘要

The present paper presents a novel computational method to optimize window sizes for thermal comfort and indoor air quality in naturally ventilated buildings. The methodology is demonstrated by means of a prototype case, which corresponds to a single-sided naturally ventilated apartment. Initially, the airflow in and around the building is simulated using a Computational Fluid Dynamics model. Local prevailing weather conditions are imposed in the CFD model as inlet boundary conditions. The produced airflow patterns are utilized to predict thermal comfort indices, i.e. the PMV and its modifications for non-air-conditioned buildings, as well as indoor air quality indices, such as ventilation effectiveness based on carbon dioxide and volatile organic compounds removal. Mean values of these indices (output/ objective variables) within the occupied zone are calculated for different window sizes (input/design variables), to generate a database of input-output data pairs. The database is then used to train and validate Radial Basis Function Artificial Neural Network input-output "meta-models". The produced meta-models are used to formulate an optimization problem, which takes into account special constraints recommended by design guidelines. It is concluded that the proposed methodology determines appropriate windows architectural designs for pleasant and healthy indoor environments.
机译：本文提出了一种新颖的计算方法，可优化自然通风建筑中的热舒适度和室内空气质量的窗户尺寸。该方法通过一个原型案例进行了演示，该案例对应于一个单侧自然通风的公寓。最初，使用计算流体动力学模型来模拟建筑物内部和周围的气流。 CFD模型将当地的主要天气条件作为入口边界条件。产生的气流模式用于预测热舒适指数，即PMV及其对非空调建筑物的修改，以及室内空气质量指数，例如基于二氧化碳和挥发性有机化合物去除的通风效果。针对不同的窗口大小（输入/设计变量）计算占用区域内这些索引（输出/目标变量）的平均值，以生成输入-输出数据对的数据库。然后，该数据库用于训练和验证径向基函数人工神经网络输入输出“元模型”。生成的元模型用于制定优化问题，其中考虑了设计准则建议的特殊约束。结论是，所提出的方法为宜人和健康的室内环境确定了合适的窗户建筑设计。

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  • 来源
    《Building and Environment》 |2011年第2期|p.298-314|共17页
  • 作者

    CM. Stavrakakis; D.P. Karadimou; P.L Zervas; H. Sarimveis; N.C. Markatos;

  • 作者单位

    Computational Fluid Dynamics Unit, School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, CR-15780 Athens, Greece;

    Computational Fluid Dynamics Unit, School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, CR-15780 Athens, Greece;

    Computational Fluid Dynamics Unit, School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, CR-15780 Athens, Greece;

    Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, CR-15780 Athens, Greece;

    Computational Fluid Dynamics Unit, School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, CR-15780 Athens, Greece;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    thermal comfort; indoor air quality; computational fluid dynamics; artificial neural network; window sizes optimization;

    机译：热舒适度;室内空气质量计算流体动力学;人工神经网络;窗口大小优化;

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