Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/303838EF-62DD-4BE7-8E89-4634758A23A7 | - |
| Αναγνωριστικό | https://doi.org/10.3390/app122311997 | - |
| Αναγνωριστικό | https://www.mdpi.com/2076-3417/12/23/11997 | - |
| Γλώσσα | en | - |
| Μέγεθος | 13 pages | en |
| Τίτλος | Review of computational mechanics, optimization, and machine learning tools for digital twins applied to infrastructures | en |
| Δημιουργός | Stavroulakis Georgios | en |
| Δημιουργός | Σταυρουλακης Γεωργιος | el |
| Δημιουργός | Charalampidi Varvara | en |
| Δημιουργός | Χαραλαμπιδη Βαρβαρα | el |
| Δημιουργός | Koutsianitis Panagiotis | en |
| Δημιουργός | Κουτσιανιτης Παναγιωτης | el |
| Εκδότης | MDPI | en |
| Περίληψη | This review discusses the links between the newly introduced concepts of digital twins and more classical finite element modeling, reduced order models, parametric modeling, inverse analysis, machine learning, and parameter identification. The purpose of this article is to demonstrate that development, as almost always is the case, is based on previously developed tools that are currently exploited since the technological tools for their implementation are available and the needs of their usage appear. This fact has rarely been declared clearly in the available literature. The need for digital twins in infrastructures arises due to the extreme loadings applied on energy-related infrastructure and to the higher importance that fatigue effects have. Digital twins promise to provide reliable and affordable models that accompany the structure throughout its whole lifetime, make fatigue and degradation prediction more reliable, and support effective predictive maintenance schemes. | en |
| Τύπος | Ανασκόπηση | el |
| Τύπος | Review | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2023-08-25 | - |
| Ημερομηνία Δημοσίευσης | 2022 | - |
| Θεματική Κατηγορία | Digital twins | en |
| Θεματική Κατηγορία | Parametric modeling | en |
| Θεματική Κατηγορία | Analysis | en |
| Θεματική Κατηγορία | Industrial internet of things | en |
| Θεματική Κατηγορία | Big data | en |
| Θεματική Κατηγορία | Data analytics | en |
| Θεματική Κατηγορία | Artificial intelligence | en |
| Θεματική Κατηγορία | Predictive maintenance | en |
| Θεματική Κατηγορία | Damage prediction | en |
| Βιβλιογραφική Αναφορά | G. E. Stavroulakis, B. G. Charalambidi, and P. Koutsianitis, “Review of computational mechanics, optimization, and machine learning tools for digital twins applied to infrastructures,” Appl. Sci., vol. 12, no. 23, Nov. 2022, doi: 10.3390/app122311997. | en |
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