Open Access Helper

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:

Data-driven models and digital twins for sustainable combustion technologies
Alessandro Parente, N. Swaminathan
iScience (2024) Vol. 27, Iss. 4, pp. 109349-109349
Open Access | Times Cited: 9

Showing 9 citing articles:

Data-driven multifidelity surrogate models for rocket engines injector design
José Felix Zapata Usandivaras, Michaël Bauerheim, Bénédicte Cuenot, et al.
Data-Centric Engineering (2025) Vol. 6
Open Access
Development of mass, energy, and thermodynamics constrained steady-state and dynamic neural networks for interconnected chemical systems
Angan Mukherjee, Debangsu Bhattacharyya
Chemical Engineering Science (2025), pp. 121506-121506
Closed Access
Digital twin for smart manufacturing equipment: modeling and applications
Xuehao Sun, Fengli Zhang, Jinjiang Wang, et al.
The International Journal of Advanced Manufacturing Technology (2025)
Closed Access
Remaining useful life prediction method of centrifugal pump rolling bearings based on digital twins
Shaohao Zhou, L. Zhang, Xiaoming Yang, et al.
Research Square (Research Square) (2025)
Closed Access
Interpretable large-scale belief rule base for complex industrial systems modeling with expert knowledge and limited data
Zheng Lian, Zhijie Zhou, Changhua Hu, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102852-102852
Closed Access | Times Cited: 3
Challenges and opportunities for the application of digital twins in hard-to-abate industries: a review
Muhammad Azam Hafeez, Alberto Procacci, Axel Coussement, et al.
Resources Conservation and Recycling (2024) Vol. 209, pp. 107796-107796
Closed Access | Times Cited: 2
Integrating data assimilation and sparse sensing for updating a digital twin of a semi-industrial furnace
Laura Donato, M. Mustafa Kamal, Alberto Procacci, et al.
Proceedings of the Combustion Institute (2024) Vol. 40, Iss. 1-4, pp. 105284-105284
Closed Access
Continuous simulation method and case study based on AMEsim and Python
Tianyu Xia, Jianyong Zuo, Jingxian Ding, et al.
(2024), pp. 785-788
Closed Access
A multi-fidelity framework for developing digital twins of combustion systems from heterogeneous data: Application to ammonia combustion
Aysu Özden, Matteo Savarese, Lorenzo Giuntini, et al.
Proceedings of the Combustion Institute (2024) Vol. 40, Iss. 1-4, pp. 105608-105608
Closed Access
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