Data-driven prediction of indoor airflow distribution in naturally ventilated residential buildings using combined CFD simulation and machine learning (ML) approach

IF 1.8 4区 工程技术 Q3 CONSTRUCTION & BUILDING TECHNOLOGY
Tran Van Quang, Dat Tien Doan, Nguyen Lu Phuong, Geun Young Yun
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引用次数: 0

Abstract

Predicting indoor airflow distribution in multi-storey residential buildings is essential for designing energy-efficient natural ventilation systems. The indoor environment significantly impacts human health and well-being, considering the substantial time spent indoors and the potential health and safety risks faced daily. To ensure occupants’ thermal comfort and indoor air quality, airflow simulations in the built environment must be efficient and precise. This study proposes a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we investigate the viability of employing a Deep Neural Network (DNN) model for accurately forecasting indoor airflow dispersion. The quantitative results reveal the DNN’s ability to faithfully reproduce indoor airflow patterns and temperature distributions. Furthermore, DNN approaches to investigate indoor airflow in the residential building achieved an 80% reduction in the time required to anticipate testing scenarios compared with CFD simulation, underscoring the potential for efficient indoor airflow prediction. This research underscores the feasibility and effectiveness of a data-driven approach, enabling swift and accurate indoor airflow predictions in naturally ventilated residential buildings. Such predictive models hold significant promise for optimizing indoor air quality, thermal comfort, and energy efficiency, thereby contributing to sustainable building design and operation.
利用 CFD 模拟和机器学习(ML)相结合的方法,对自然通风住宅楼的室内气流分布进行数据驱动预测
预测多层住宅楼的室内气流分布对于设计节能的自然通风系统至关重要。考虑到每天在室内度过的大量时间以及面临的潜在健康和安全风险,室内环境对人类的健康和幸福有着重大影响。为确保居住者的热舒适度和室内空气质量,建筑环境中的气流模拟必须高效、精确。本研究提出了一种结合计算流体动力学(CFD)模拟和机器学习技术来预测室内气流的新方法。具体来说,我们研究了采用深度神经网络(DNN)模型准确预测室内气流扩散的可行性。定量结果显示,DNN 能够忠实再现室内气流模式和温度分布。此外,与 CFD 模拟相比,采用 DNN 方法研究住宅楼室内气流时,预测测试场景所需的时间减少了 80%,凸显了高效室内气流预测的潜力。这项研究强调了数据驱动方法的可行性和有效性,使自然通风住宅楼的室内气流预测更加迅速和准确。这种预测模型在优化室内空气质量、热舒适度和能源效率方面大有可为,从而有助于可持续建筑设计和运行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Building Physics
Journal of Building Physics 工程技术-结构与建筑技术
CiteScore
5.10
自引率
15.00%
发文量
10
审稿时长
5.3 months
期刊介绍: Journal of Building Physics (J. Bldg. Phys) is an international, peer-reviewed journal that publishes a high quality research and state of the art “integrated” papers to promote scientifically thorough advancement of all the areas of non-structural performance of a building and particularly in heat, air, moisture transfer.
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