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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:

Modelling of wind turbine gear stages for Digital Twin and real-time virtual sensing using bond graphs
Felix C. Mehlan, Eilif Pedersen, Amir R. Nejad
Journal of Physics Conference Series (2022) Vol. 2265, Iss. 3, pp. 032065-032065
Open Access | Times Cited: 10

Showing 10 citing articles:

Digital Twin Based Virtual Sensor for Online Fatigue Damage Monitoring in Offshore Wind Turbine Drivetrains
Felix C. Mehlan, Amir R. Nejad, Zhen Gao
Journal of Offshore Mechanics and Arctic Engineering (2022) Vol. 144, Iss. 6
Open Access | Times Cited: 33
On the modeling errors of digital twins for load monitoring and fatigue assessment in wind turbine drivetrains
Felix C. Mehlan, Amir R. Nejad
Wind energy science (2025) Vol. 10, Iss. 2, pp. 417-433
Open Access
Kinematic and Dynamic Modeling of Mechanical Systems towards Digital Twins
Chiara Nezzi, Veit Gufler, Renato Vidoni, et al.
Results in Engineering (2025), pp. 104874-104874
Open Access
Virtuelle Sensoren für die Messung von Hauptwellenlasten und Ermüdungsschäden im Antriebstrang von Windenergieanlagen
Felix C. Mehlan, Jonathan Keller, Amir R. Nejad
Forschung im Ingenieurwesen (2023) Vol. 87, Iss. 1, pp. 207-218
Open Access | Times Cited: 10
Industrial digital twins in offshore wind farms
Evi Elisa Ambarita, Anniken Karlsen, Francesco Scibilia, et al.
Energy Informatics (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 3
Digital Twin-Based Approach for a Multi-Objective Optimal Design of Wind Turbine Gearboxes
Carlos Llopis‐Albert, Francisco Rubio, Carlos Devece, et al.
Mathematics (2024) Vol. 12, Iss. 9, pp. 1383-1383
Open Access | Times Cited: 2
A Review of Digital Twinning for Rotating Machinery
Vamsi Inturi, Bidisha Ghosh, G. R. Sabareesh, et al.
Sensors (2024) Vol. 24, Iss. 15, pp. 5002-5002
Open Access | Times Cited: 2
Health Assessment for RUL Prediction of Machinery Components Using Low-Sampling Temporal Signals: A Condensed Image Coding Approach
Danyang Han, Diyin Tang, Jinsong Yu, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-12
Closed Access | Times Cited: 5
PUDT: Plummeting Uncertainties in Digital Twins for Aerospace Applications using Deep Learning Algorithms
Shitharth Selvarajan, Hariprasath Manoharan, Achyut Shankar, et al.
Future Generation Computer Systems (2023) Vol. 153, pp. 575-586
Open Access | Times Cited: 5
Digital Twin-Based Approach for a Multi-objective Optimal Design of Wind Turbine Gearboxes
Carlos Llopis‐Albert, Francisco Rubio, Carlos Devece, et al.
(2024)
Open Access
Intelligent digital twin – machine learning system for real-time wind turbine wind speed and power generation forecasting
Eamonn Tuton, Xinhui Ma, Nina Dethlefs
E3S Web of Conferences (2023) Vol. 433, pp. 01008-01008
Open Access | Times Cited: 1
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