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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:

Airfoils optimization based on deep reinforcement learning to improve the aerodynamic performance of rotors
Jiaqi Liu, Rongqian Chen, Jinhua Lou, et al.
Aerospace Science and Technology (2023) Vol. 143, pp. 108737-108737
Closed Access | Times Cited: 12

Showing 12 citing articles:

An amphibious propeller design optimization framework based on deep neural network surrogate model
Zhaolong Dang, Ming-Yu Wu, Xianjun He, et al.
Aerospace Science and Technology (2025), pp. 109967-109967
Closed Access
Flow field prediction and optimization of non-axisymmetric aero-engine nacelles based on deep learning
Guocheng Tao, Yang Liu, Jiahuan Cui
Aerospace Science and Technology (2025), pp. 109990-109990
Closed Access
Recent Advancements in Morphing Applications: Architecture, Artificial Intelligence Integration, Challenges, and Future Trends- A Comprehensive Survey
Md. Najmul Mowla, Davood Asadi, Tahir Durhasan, et al.
Aerospace Science and Technology (2025) Vol. 161, pp. 110102-110102
Open Access
Aerodynamic optimization of wind turbine blades via surrogate-assisted deep reinforcement learning
Wang Xiao, Haibin Li, Hexi Baoyin, et al.
Physics of Fluids (2025) Vol. 37, Iss. 4
Closed Access
A data‐driven Bayesian approach for optimal dynamic product transitions
Antonio Flores‐Tlacuahuac, Luis Fabián Fuentes‐Cortés
AIChE Journal (2024) Vol. 70, Iss. 6
Open Access | Times Cited: 2
A gradient aerodynamic optimization method based on deep learning
Hao Wu, Rongqian Chen, Jinhua Lou, et al.
Physics of Fluids (2024) Vol. 36, Iss. 5
Closed Access | Times Cited: 2
General framework for unsteady aerodynamic prediction of airfoils based on deep transfer learning
Jinhua Lou, Rongqian Chen, Jiaqi Liu, et al.
Aerospace Science and Technology (2024) Vol. 155, pp. 109606-109606
Closed Access | Times Cited: 1
Development of a Performance-Based Design Technique for an Axial-Flow Fan Unit Using Airfoil Cascades Based on the Blade Strip Theory
Seo-Yoon Ryu, Cheolung Cheong, Jong Wook Kim, et al.
Applied Sciences (2024) Vol. 14, Iss. 2, pp. 804-804
Open Access
Application of Penalty-Free SQP Method in Powered Descent Guidance Problem
韦杰 徐
Journal of Aerospace Science and Technology (2024) Vol. 12, Iss. 01, pp. 53-62
Closed Access
Study on the Rapid Improvement Method of Fatigue Life Reliability for Aero-Engine Single Crystal Turbine Blades: Casting Orientation Design Technology
Yanchao Zhao, Yanqiu Yang, Yanchao Zhao, et al.
(2024)
Closed Access
RANS representation of transition and separation over a low-Re number blade section at high angle of attack
Luca Pagliarini, Raymond J. Corsini, Enrico Stalio, et al.
Journal of Physics Conference Series (2024) Vol. 2766, Iss. 1, pp. 012086-012086
Open Access
Multi-physical fields prediction model for turbine cascades based on physical information neural networks
Lele Li, Weihao Zhang, Ya Li, et al.
Aerospace Science and Technology (2024) Vol. 155, pp. 109709-109709
Closed Access
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