应用经验模型改进 CFD-RANS 中的最大值预测:来自四个科学领域的启示

IF 2.5 4区 地球科学 Q3 ENVIRONMENTAL SCIENCES
Atmosphere Pub Date : 2024-09-16 DOI:10.3390/atmos15091124
George Efthimiou
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引用次数: 0

摘要

本研究介绍了一个经验模型,旨在预测四个湍流相关领域中随时间变化的数据的最大值:可再生能源系统中的氢气燃烧、城市小气候对文化遗产的影响、航运排放和道路车辆排放。该模型以平均值、标准偏差和积分时间尺度为基础,采用两个参数:一个是反映时间尺度敏感性的固定指数 "ν"(0.3),另一个是考虑特定应用不确定性的可变参数 "b"。该模型融入了计算流体动力学(CFD)框架,特别是雷诺平均纳维-斯托克斯(RANS)方法,解决了 RANS 方法因其固有的平均过程而在预测极端值方面的局限性。通过采用经验模型,本研究增强了 RANS 模拟预测氢气浓度峰值和最大城市风速等临界值的能力,这对于安全和可靠性评估至关重要。根据四个领域的实验数据和数值数据进行的验证显示,模型与实验数据和数值数据非常吻合,凸显了该模型在改进 CFD-RANS 预测极端事件方面的潜力。这一进展对未来的 CFD-RANS 应用具有重要意义,特别是在需要快速、可靠的最大值预测的情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of an Empirical Model to Improve Maximum Value Predictions in CFD-RANS: Insights from Four Scientific Domains
This study introduces an empirical model designed to predict the maximum values of time-dependent data across four turbulence-related fields: hydrogen combustion in renewable energy systems, urban microclimate effects on cultural heritage, shipping emissions, and road vehicle emissions. The model, which is based on the mean, standard deviation, and integral time scale, employs two parameters: a fixed exponent ‘ν’ (0.3) reflecting time scale sensitivity, and a variable parameter ‘b’ that accounts for application-specific uncertainties. Integrated into the Computational Fluid Dynamics (CFD) framework, specifically the Reynolds-Averaged Navier–Stokes (RANS) methodology, the model addresses the RANS approach’s limitation in predicting extreme values due to its inherent averaging process. By incorporating the empirical model, this study enhances RANS simulations’ ability to predict critical values, such as peak hydrogen concentrations and maximum urban wind speeds, which is essential for safety and reliability assessments. Validation against experimental and numerical data across the four fields demonstrates strong agreement, highlighting the model’s potential to improve CFD-RANS predictions of extreme events. This advancement offers significant implications for future CFD-RANS applications, particularly in scenarios demanding fast and reliable maximum value predictions.
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来源期刊
Atmosphere
Atmosphere METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
4.60
自引率
13.80%
发文量
1769
审稿时长
1 months
期刊介绍: Atmosphere (ISSN 2073-4433) is an international and cross-disciplinary scholarly journal of scientific studies related to the atmosphere. It publishes reviews, regular research papers, communications and short notes, and there is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles.
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