Examining potential reductions in wind tunnel testing requirements

Hill, Raymond R.; Ahner, Darryl K.; Dillard, Douglas A.; Montgomery, Douglas C.

doi:10.1002/qre.2330

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Despite being useful for design optimization, the installation of wind tunnels is expensive and time-consuming [20,21]. Therefore, numerical models and simulations such as CFD for wind patterns or CONTAM for interior air quality are frequently employed due to their thorough, precise, and cost-effective nature, particularly in the context of buildings [5,22].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Pedestrian-Level Wind Comfort Analysis

Gür,

Karadag

2024

Buildings

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(1) Background: Artificial intelligence (AI) and machine learning (ML) techniques are being more widely employed in the field of wind engineering. Nevertheless, there is a scarcity of research on the comfort of pedestrians in terms of wind conditions with respect to building design, particularly in historic sites. (2) Objectives: This research aims to evaluate ML- and computational fluid dynamics (CFD)-based pedestrian wind comfort (PWC) analysis outputs using a novel method that relies on the sophisticated handling of image data. The goal is to propose a novel assessment method to enhance the efficiency of AI models over different urban scenarios. (3) Methodology: The stages include the analysis of climate data, CFD analysis with OpenFOAM, ML analysis using Autodesk Forma, and comparisons of the CFD and ML results using a novel image similarity assessment method based on the SSIM, MSE, and PSNR metrics. (4) Conclusions: This study effectively demonstrates the considerable potential of utilizing ML as a supplementary tool for evaluating PWC. It maintains a high degree of accuracy and precision, allowing for rapid and effective assessments. The methodology for precise comparison of two visual outputs in the absence of numerical data allows for more objective and pertinent comparisons, as it eliminates any potential distortions. (5) Recommendations: Additional research can explore the integration of ML models with climate data and different case studies, thus expanding the scope of wind comfort studies.

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Section: Introductionmentioning

confidence: 99%

Machine Learning for Pedestrian-Level Wind Comfort Analysis

Gür,

Karadag

2024

Buildings

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“…Early research on wind environments focused on wind tunnel experiments and field measurements. However, both methods are time-consuming and costly [7,8]. With the development of computer technology, computational fluid dynamics (CFD) has been increasingly used for climatic modeling of urban areas [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Generalization of Building Clusters in an Urban Wind Field Simulated by CFD

Qiu,

He,

et al. 2023

Atmosphere

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The urban climate has a critical influence on developing sustainable cities, and one important factor is the urban wind environment. Moreover, refining urban wind fields is required for the quantitative assessment of urban wind environments. Computational fluid dynamics (CFD) is a powerful tool for modeling the wind flow characteristics in urban areas. Although CFD has been widely used in various fields, its use for simulating urban wind fields has limitations because of the complexity of urban building models and the high computational workload. Accordingly, we consider the generalization parameters in the vertical and horizontal directions based on the CFD results and the building topology based on the state of the building nodes. We perform a two-dimensional generalization of building clusters, conduct spatial analysis in a geographic information system (GIS), and generate three-dimensional models. This generalization scheme is applied to Meiling Street in Jinjiang City, Fujian Province, China. The results indicate that the generalization decreases the number of buildings from 7003 to 3367 and the computation time from 11 h and 26 min to 10 h and 25 min. The computation efficiency is improved by 8.89%, with 1.85% changes in the average wind speed ratio. This scheme substantially improves the computational efficiency of urban wind field CFD simulations by reducing the geometric model’s complexity without compromising the accuracy. This strategy is suitable for simulating large-scale urban wind fields.

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“…Raymond R. Hill, Darryl K. Ahner, Douglas A. Dillard, and Douglas C. Montgomery consider wind tunnel test campaigns involving a large number of experimental data points, possibly taking a long time and consuming tremendous resources. 6 They provide a systematic approach to experimental design and analysis that has the potential to improve the efficiency and effectiveness of wind tunnel testing. A legacy wind tunnel test campaign is used, and the resulting data from that campaign are compared to data generated using smaller experimental design strategies.…”

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confidence: 99%