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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Using Machine Learning to Predict Wind Flow in Urban Areas
Nir Benmoshe, Eyal Fattal, Bernd Leitl, et al.
Atmosphere (2023) Vol. 14, Iss. 6, pp. 990-990
Open Access | Times Cited: 14

Showing 14 citing articles:

Assessment of the dispersion of pollutants from automobile exhaust, taking into account relative humidity, pavement temperature, wind direction and speed, which varies depending on the time of day
Alibek Issakhov, Aizhan Abylkassymova
International Communications in Heat and Mass Transfer (2024) Vol. 159, pp. 108140-108140
Closed Access | Times Cited: 8
A review of surrogate-assisted design optimization for improving urban wind environment
Yihan Wu, Steven Jige Quan
Building and Environment (2024) Vol. 253, pp. 111157-111157
Closed Access | Times Cited: 6
Machine Learning for Pedestrian-Level Wind Comfort Analysis
Miray Gür, İlker Karadağ
Buildings (2024) Vol. 14, Iss. 6, pp. 1845-1845
Open Access | Times Cited: 4
A novel airflow zonal model for urban microclimate modelling at the block scale
Flavia Barone, Lucie Merlier, Mathias Bouquerel, et al.
Journal of Building Performance Simulation (2025), pp. 1-26
Closed Access
Accelerating urban street canyon wind flow predictions with deep learning method
Wai-Chi Cheng, Tzung‐May Fu
Building Simulation (2025)
Closed Access
Large-scale building-level electricity consumption estimation for multiple building types: A Case study from Dongguan, China
Geng Liu, Jinpei Ou, Yuebing Zheng, et al.
Sustainable Cities and Society (2025), pp. 106224-106224
Closed Access
Urban Multi-Scale Building Energy Modeling (BEM) and Computational Fluid Dynamics (CFD) Integration: Tools, Strategies and Accuracy for Energy Microclimate Analysis
Weihao Huang, Q. Xu, Bolun Zhao, et al.
Journal of Building Engineering (2025), pp. 112041-112041
Closed Access
Deep Learning for Urban Wind Prediction: An Mlp-Mixer Approach with 3d Encoding
Adam Clarke, Knut Erik Teigen Giljarhus, Luca Oggiano, et al.
(2025)
Closed Access
Predicting Wind Comfort in an Urban Area: A Comparison of a Regression- with a Classification-CNN for General Wind Rose Statistics
Jennifer Werner, Dimitri Nowak, Franziska Hunger, et al.
Machine Learning and Knowledge Extraction (2024) Vol. 6, Iss. 1, pp. 98-125
Open Access | Times Cited: 3
The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges
Radostin Mitkov, Mariya Pantusheva, Dessislava Petrova‐Antonova, et al.
ISPRS annals of the photogrammetry, remote sensing and spatial information sciences (2024) Vol. X-4/W4-2024, pp. 131-136
Open Access | Times Cited: 3
Optimisation of city structures with respect to high wind speeds using U-Net models
Dimitri Nowak, Jennifer Werner, Quentin Parsons, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108812-108812
Open Access | Times Cited: 3
Fast estimation of airflow distribution in an urban model using generative adversarial networks with limited sensing data☆
Chaoyi Hu, Hideki Kikumoto, Bingchao Zhang, et al.
Building and Environment (2023) Vol. 249, pp. 111120-111120
Closed Access | Times Cited: 7
A data-driven model to determine the infiltration characteristics of air curtains at building entrances
Linye Song, Cong Zhang, Jing Hua, et al.
Physics of Fluids (2023) Vol. 35, Iss. 11
Closed Access | Times Cited: 5
Adversarial image-to-image model to obtain highly detailed wind fields from mesoscale simulations in urban environments
Jaime Milla-Val, Carlos Montañés, Norberto Fueyo
Building and Environment (2024), pp. 112123-112123
Open Access | Times Cited: 1
MLP-mixer-based deep learning network for pedestrian-level wind assessment
Adam Clarke, Knut Erik Teigen Giljarhus, Luca Oggiano, et al.
Environmental Data Science (2024) Vol. 3
Open Access
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