Development of a Mechanistic and Data-Driven Model for Multiphase Flow Leak Detection in Pipeline
Notice bibliographique
Résumé
Abstract Prompt and reliable detection of pipeline leaks is vital for human safety, the economy, the environment, and corporate reputation. However, a simple mechanistic model for accurately predicting leak characteristics in different flow regimes is lacking. To fill this gap, a novel methodology was used to develop a multiphase flow leak detection model using only inlet and outlet parameters. The gas-liquid two-phase leak mass flow rate, location, and size are computed through iterative processes in the upstream and downstream sections of the leak. Data sets were generated for a wide range of geometric (3 – 5 inch pipe diameter, 2000 – 10000 feet pipe length, 500 – 1500 feet leak location, 0.2 – 3 inch leak opening diameter), hydrodynamic (Newtonian, air, CO2, N2) and operating conditions (0.3 – 0.628 outlet liquid fraction). These data sets were utilized to develop contour plots and a data-driven model using statistical analysis based solely on the inlet and outlet parameters. The results indicate that a change in total mass flow rate and pressure in the inlet and the outlet section of the leak can be a good indicator for determining the location and size of the leak. The effect of different pressure constraints, pipe length, pipe diameter, two-phase fluid rheology, leak diameter, leak location, outlet liquid volume fraction, and flowing liquid hold-up on leak size, pressure, and flow rate is analyzed. Decreasing the liquid fraction in the outlet section of the leak leads to a slight increase (6% average) in the inlet mass flow rate and a significant decrease (50% average) in the outlet mass flow rate for fixed pressure constraints, resulting in an increased leak flow rate, pressure, and density. Similarly, longer pipe lengths, bigger pipe diameters, heavier gas phase, and lower liquid fraction at the outlet have higher leak pressure for the same leak locations due to higher leak flow rate. Furthermore, contour plots revealed that identifying a leak near the pipe inlet is easier, although determining its size remains challenging. On the other hand, detecting a leak near the pipe outlet is more difficult, but assessing its size is comparatively easier. The developed data-driven showed good agreement with different literature data sets with a MAPE of less than 20%. The mechanistic model's key advantage lies in its reliance on fundamental equations and physics, making it applicable to various operating conditions for field applications. Moreover, the data-driven model is straightforward and accurate, eliminating the need for complex simulations. This study has the potential to assist industries in determining leak location, size, and pressure using only the inlet and outlet parameters, without requiring multiple sensors along pipelines.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».