Digital Twin Based on CFD Modelling for Analysis of Two-Phase Flows During Pipeline Filling–Emptying Procedures
Pipeline filling and emptying are critical hydraulic procedures involving transient two-phase air–water interactions, which can cause pressure surges and structural risks. Traditional Digital Twin models rely on one-dimensional (1D) approaches, which cannot capture air–water interactions. This study...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2643 |
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| author | Duban A. Paternina-Verona Oscar E. Coronado-Hernández Vicente S. Fuertes-Miquel Manuel Saba Helena M. Ramos |
| author_facet | Duban A. Paternina-Verona Oscar E. Coronado-Hernández Vicente S. Fuertes-Miquel Manuel Saba Helena M. Ramos |
| author_sort | Duban A. Paternina-Verona |
| collection | DOAJ |
| description | Pipeline filling and emptying are critical hydraulic procedures involving transient two-phase air–water interactions, which can cause pressure surges and structural risks. Traditional Digital Twin models rely on one-dimensional (1D) approaches, which cannot capture air–water interactions. This study integrates Computational Fluid Dynamics (CFD) models into a Digital Twin framework for improved predictive analysis. A CFD-based Digital Twin is developed and validated using real-time pressure measurements, incorporating 2D and 3D CFD models, mesh sensitivity analysis, and calibration procedures. Key contributions include a CFD-driven Digital Twin for real-time monitoring and machine learning (ML) techniques to optimise pressure surges. ML models trained with experimental and CFD data reduce reliance on computationally expensive CFD simulations. Among the 31 algorithms tested, decision trees, efficient linear models, and ensemble classifiers achieved 100% accuracy for filling processes, while k-Nearest Neighbours (KNN) provided 97.2% accuracy for emptying processes. These models effectively predict hazardous pressure peaks and vacuum conditions, confirming their reliability in optimising pipeline operations while significantly reducing computational time. |
| format | Article |
| id | doaj-art-d23aedb6e8ee4fa4a6745dfa2b3ba6ce |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d23aedb6e8ee4fa4a6745dfa2b3ba6ce2025-08-20T02:59:07ZengMDPI AGApplied Sciences2076-34172025-02-01155264310.3390/app15052643Digital Twin Based on CFD Modelling for Analysis of Two-Phase Flows During Pipeline Filling–Emptying ProceduresDuban A. Paternina-Verona0Oscar E. Coronado-Hernández1Vicente S. Fuertes-Miquel2Manuel Saba3Helena M. Ramos4School of Civil Engineering, Universidad del Sinú, Cartagena 130014, ColombiaInstituto de Hidráulica y Saneamiento Ambiental, Universidad de Cartagena, Cartagena 130001, ColombiaDepartamento de Ingeniería Hidráulica y Medio Ambiente, Universitat Politècnica de València, 46022 Valencia, SpainCivil Engineering Program, Universidad de Cartagena, Cartagena 130001, ColombiaCivil Engineering, Architecture and Environment Department, CERIS, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, PortugalPipeline filling and emptying are critical hydraulic procedures involving transient two-phase air–water interactions, which can cause pressure surges and structural risks. Traditional Digital Twin models rely on one-dimensional (1D) approaches, which cannot capture air–water interactions. This study integrates Computational Fluid Dynamics (CFD) models into a Digital Twin framework for improved predictive analysis. A CFD-based Digital Twin is developed and validated using real-time pressure measurements, incorporating 2D and 3D CFD models, mesh sensitivity analysis, and calibration procedures. Key contributions include a CFD-driven Digital Twin for real-time monitoring and machine learning (ML) techniques to optimise pressure surges. ML models trained with experimental and CFD data reduce reliance on computationally expensive CFD simulations. Among the 31 algorithms tested, decision trees, efficient linear models, and ensemble classifiers achieved 100% accuracy for filling processes, while k-Nearest Neighbours (KNN) provided 97.2% accuracy for emptying processes. These models effectively predict hazardous pressure peaks and vacuum conditions, confirming their reliability in optimising pipeline operations while significantly reducing computational time.https://www.mdpi.com/2076-3417/15/5/2643Computational Fluid Dynamics (CFD)Digital Twin (DT)two-dimensional (2D) modelsthree-dimensional (3D) modelstwo-phase flowsMachine Learning (ML) |
| spellingShingle | Duban A. Paternina-Verona Oscar E. Coronado-Hernández Vicente S. Fuertes-Miquel Manuel Saba Helena M. Ramos Digital Twin Based on CFD Modelling for Analysis of Two-Phase Flows During Pipeline Filling–Emptying Procedures Applied Sciences Computational Fluid Dynamics (CFD) Digital Twin (DT) two-dimensional (2D) models three-dimensional (3D) models two-phase flows Machine Learning (ML) |
| title | Digital Twin Based on CFD Modelling for Analysis of Two-Phase Flows During Pipeline Filling–Emptying Procedures |
| title_full | Digital Twin Based on CFD Modelling for Analysis of Two-Phase Flows During Pipeline Filling–Emptying Procedures |
| title_fullStr | Digital Twin Based on CFD Modelling for Analysis of Two-Phase Flows During Pipeline Filling–Emptying Procedures |
| title_full_unstemmed | Digital Twin Based on CFD Modelling for Analysis of Two-Phase Flows During Pipeline Filling–Emptying Procedures |
| title_short | Digital Twin Based on CFD Modelling for Analysis of Two-Phase Flows During Pipeline Filling–Emptying Procedures |
| title_sort | digital twin based on cfd modelling for analysis of two phase flows during pipeline filling emptying procedures |
| topic | Computational Fluid Dynamics (CFD) Digital Twin (DT) two-dimensional (2D) models three-dimensional (3D) models two-phase flows Machine Learning (ML) |
| url | https://www.mdpi.com/2076-3417/15/5/2643 |
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