Using Machine Learning to Predict Wind Flow in Urban Areas

Benmoshe, Nir; Fattal, Eyal; Leitl, Bernd; Arav, Yehuda

doi:10.3390/atmos14060990

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…By leveraging machine learning, researchers and practitioners can develop surrogate models or emulators that approximate the results of full-scale CFD simulations with reduced computational time. BenMoshe et al (2023) present a machine learning model that predicts the wind flow patterns and showcase its capabilities by examining the wind conditions in Tel Aviv, Israel. Reduced order models (ROMs) also offer an alternative approach to accelerating CFD simulations by capturing the essential dynamics and behaviour of the high-fidelity, complex models.…”

Section: Implementation Constraintsmentioning

confidence: 99%

The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges

Mitkov,

Pantusheva,

Petrova-Antonova

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The rapid urbanization trend has led to complex challenges in managing urban environments, ranging from energy consumption to environmental quality and public health. In response, the concept of city digital twins has emerged, offering a promising approach to understanding and managing urban complexity. City digital twins utilize real-time data and simulations to create virtual replicas of urban environments, enabling stakeholders to analyze, optimize, and make informed decisions about various aspects of city life. Computational Fluid Dynamics (CFD) plays a crucial role within city digital twins, offering capabilities to simulate wind flow dynamics, air quality, and pollutant dispersion within urban environments. This paper explores the opportunities and challenges of integrating CFD within city digital twins. Opportunities include optimizing urban planning and design processes, enhancing environmental quality, and improving public health outcomes. However, challenges such as data integration and quality, implementation constraints, computational complexity, and communication of results must be addressed to realize the full application potential of CFD in urban environments. Despite these challenges, the integration of CFD within city digital twins holds promise for creating more livable, sustainable, and resilient cities in the face of urbanization and climate change.

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Section: Implementation Constraintsmentioning

confidence: 99%

The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges

Mitkov,

Pantusheva,

Petrova-Antonova

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Ref. [72] employed machine learning methods-specifically neural networks and regression trees-to examine the impact of urban elements such as buildings and streets on wind flow. Their research, which utilized an ML algorithm trained with gathered data, emphasized that flow patterns are impacted by local attributes such as surrounding building features and cell height.…”

Section: Ann Rbf Neural Network (Rbfnn)mentioning

confidence: 99%

“…However, their ML model had several limitations, such as its emphasis on flat surfaces and its ability to only consider a single wind direction. The authors recommended that future research should encompass different topographies and wind directions [72]. In another study, Ref.…”

Section: Ann Rbf Neural Network (Rbfnn)mentioning

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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“…These types of models are called image-to-image models. Another approach could be to use simpler models like the k-nearest neighbors method described in [17] or the random forest classifier described in [18]. Further models used in this context are, for example, artificial neural networks, Gaussian regression processes, support vector machines, ensemble methods (like gradient boosting regression trees), and fuzzy neural networks (see [19]).…”

Section: Introductionmentioning

confidence: 99%

Predicting Wind Comfort in an Urban Area: A Comparison of a Regression- with a Classification-CNN for General Wind Rose Statistics

Werner,

Nowak,

Hunger

et al. 2024

MAKE

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Wind comfort is an important factor when new buildings in existing urban areas are planned. It is common practice to use computational fluid dynamics (CFD) simulations to model wind comfort. These simulations are usually time-consuming, making it impossible to explore a high number of different design choices for a new urban development with wind simulations. Data-driven approaches based on simulations have shown great promise, and have recently been used to predict wind comfort in urban areas. These surrogate models could be used in generative design software and would enable the planner to explore a large number of options for a new design. In this paper, we propose a novel machine learning workflow (MLW) for direct wind comfort prediction. The MLW incorporates a regression and a classification U-Net, trained based on CFD simulations. Furthermore, we present an augmentation strategy focusing on generating more training data independent of the underlying wind statistics needed to calculate the wind comfort criterion. We train the models based on different sets of training data and compare the results. All trained models (regression and classification) yield an F1-score greater than 80% and can be combined with any wind rose statistic.

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Using Machine Learning to Predict Wind Flow in Urban Areas

Cited by 9 publications

References 32 publications

The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges

The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges

Machine Learning for Pedestrian-Level Wind Comfort Analysis

Predicting Wind Comfort in an Urban Area: A Comparison of a Regression- with a Classification-CNN for General Wind Rose Statistics

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