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“ACB simulation models have evolved significantly since the early 2000s [90] but their most recent iteration has noticed fewer modifications.”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [90]
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Construction of Digital Twin Geotechnical Resistance Model for Liquefaction Risk Evaluation
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Prediction Study of the Heavy Vehicle Driving State Based on Digital Twin Model
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Digital twin based comfort scenario modeling of ATO controlled train
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Generating digital twins of mesoporous silica by graph-based stochastic microstructure modeling
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CFD MODEL AS A DIGITAL TWIN OF THE RADIATION STATE OF THE NEW SAFE CONFINEMENT OF THE CHERNOBYL NPP
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Zhao, P., & Sun, B. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1875/1/012003 · Cited by 7
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Song, X.-Y., Jiang, T., Schlegel, S., & Westermann, D. (2019) · IEEE PES Innovative Smart Grid Technologies Conference Europe · doi:10.1109/isgteurope.2019.8905551 · Cited by 6
Construction and Optimization of Digital Twin Model for Hardware Production Line
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Transactions on Computational Science XXXVIII
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Elkarii, M., Bouallou, C., & Ratnani, A. (2020) · Chemical engineering transactions · doi:10.3303/cet2081127 · Cited by 3
Informationsmodelle mit intelligenter Auswertung für den Digitalen Zwilling
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Informationsmodelle mit intelligenter Auswertung für den Digitalen Zwilling
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Ruan, H., Xie, H., Wang, J., Liao, J., Sun, L., Gao, M., & Li, C. (2021) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2021.101331
Flagged first, then by citations
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Cited by
Sorted by citations
Digital twin in healthcare: Recent updates and challenges
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Advancements and challenges of digital twins in industry
Tao, F., Zhang, H., & Zhang, C. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00603-w · Cited by 303
The Application of Artificial Intelligence and Big Data in the Food Industry
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Digital twin: Data exploration, architecture, implementation and future
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Metadata
- Authors
- Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P.
- Affiliations
- Beihang University (CN)
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Qi, Qinglin
- Venue
- Journal of Manufacturing Systems
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 64
- Pages
- 372-389
- ISSN
- 0278-6125 (ISSN-L)
- OpenAlex
- W4285098908
- Cited by
- 1,255
- References
- 141
- Citations by year
- 2026: 290 · 2025: 437 · 2024: 347 · 2023: 166 · 2022: 15
- FWCI
- 110.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Manufacturing Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China (52005024, G-YZ3N)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
- Keywords
- digital twin, digital twin modeling, enabling technologies, digital transformation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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