利用物理信息神经网络分析通风和渗透率:空间运行和气象因素的影响

IF 7.1 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
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引用次数: 0

摘要

本研究介绍了物理信息神经网络(PINN)模型的开发和应用情况,该模型可利用长期观测数据估算通风率和渗透率,以应对动态变化的空间运行和气象条件带来的挑战。研究的核心问题是:如何在考虑这些时变因素的同时准确估算通风率?传统的示踪气体方法需要进行大量测量,才能准确描述动态空间运行和不同气象条件下的换气率(ACR)。我们的 PINN 模型整合了这些波动因素,可以更精确地分析它们对 ACR 的瞬时影响。我们采用夏普利相加解释(SHAP)来解释每个影响因素的敏感性和贡献。我们的研究结果表明,门窗状态对空间运行有显著影响,而风速和风向则是影响最大的气象因素。与单独影响相比,打开的门窗之间的相互作用会导致更高的通风率。与风有关的因素导致 ACR 变化超过 200%,其中相对于办公室窗户的风向起着至关重要的作用。此外,外部温度和室内外温差也与 ACR 密切相关。然而,其局限性包括缺乏室外二氧化碳测量数据,以及假设室内二氧化碳水平一致,这可能会影响准确性。由于所研究空间的特殊性,推广性也受到了限制。未来的工作应纳入室外二氧化碳数据和多个空间,以提高模型的适用性。这项研究有助于优化通风策略,从而提高室内空气质量和能源效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of ventilation and infiltration rates using physics-informed neural networks: Impact of space operation and meteorological factors
This study presents the development and application of a Physics-Informed Neural Network (PINN) model to estimate ventilation and infiltration rates using long-term observation data, addressing the challenge of dynamically varying space operations and meteorological conditions. A central research equestion is: How can we accurately estimate ventilation rates while accounting for these time-varying factors? Traditional tracer gas methods require numerous measurements to accurately characterize air change rates (ACR) under dynamic space operations and varying meteorological conditions. Our PINN model integrates these fluctuating factors, providing a more precise analysis of their transient effects on ACR. We employed Shapley Additive Explanations (SHAP) to interpret the sensitivity and contributions of each influencing factor. Our findings indicate that the state of windows and doors significantly affects spatial operations, while wind speed and direction are the most impactful meteorological factors. The interaction between open windows and doors results in higher ventilation rates compared to their individual effects. Wind-related factors cause ACR variations exceeding 200 %, with the wind direction relative to the office window playing a crucial role. Additionally, external temperature and indoor-outdoor temperature differences show a strong correlation with ACR. However, limitations include the lack of outdoor CO2 measurements and the assumption of uniform indoor CO2 levels, which may affect accuracy. Generalizability is also limited due to the specificity of the space studied. Future work should incorporate outdoor CO2 data and multiple spaces to enhance model applicability. This study contributes to optimizing ventilation strategies for better indoor air quality and energy efficiency.
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来源期刊
Building and Environment
Building and Environment 工程技术-工程:环境
CiteScore
12.50
自引率
23.00%
发文量
1130
审稿时长
27 days
期刊介绍: Building and Environment, an international journal, is dedicated to publishing original research papers, comprehensive review articles, editorials, and short communications in the fields of building science, urban physics, and human interaction with the indoor and outdoor built environment. The journal emphasizes innovative technologies and knowledge verified through measurement and analysis. It covers environmental performance across various spatial scales, from cities and communities to buildings and systems, fostering collaborative, multi-disciplinary research with broader significance.
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