The results of the fluid force acting on the cylinders suggest that the present deep-learning model has good generalization performance for systems with a larger number of cylinders.Ĭomputational fluid dynamics (CFD) simulation 1 has been hugely successful in the field of engineering. The extrapolation of the prediction to a smaller number of cylinders results in error, which can be interpreted as internal friction of the fluid. The present model accurately predicts the flow when the number of cylinders is equal to or close to that set in the training dataset. The accuracy of the predicted velocity field is investigated, focusing on the velocity profile of the fluid flow and the fluid force acting on the cylinders. The deep-learning model outputs the x- and y-components of the flow velocity field when the cylinder arrangement is input. The present paper addresses the prediction of steady flows passing many fixed cylinders using a deep-learning model and investigates the accuracy of the predicted velocity field. Shared heat transfer, multiphase flow, and fluid behavior methodologies ensure data quality and consistency between the steady-state and transient analyses.Considerable attention has been given to deep-learning and machine-learning techniques in an effort to reduce the computational cost of computational fluid dynamics simulation. In addition, where dynamic analysis is needed to add further insight, the PIPESIM-to-OLGA converter tool enables rapid conversion of models.
#PIPESIM STEADY STATE MULTIPHASE FLOW SIMULATOR SIMULATOR#
The flow assurance capabilities of the simulator enable engineers to ensure safe and effective fluid transport-from sizing of facilities, pipelines, and lift systems, to ensuring effective liquids and solids management, to well and pipeline integrity. The PIPESIM simulator offers the industry’s most comprehensive steady-state flow assurance workflows for front-end system design and production operations. Steady-state flow assurance, from concept to operations Faster simulation runtime has also been achieved for all modeling though the implementation of a new parallel network solver to spread the computational load across all processors. The interactive graphical wellbore enables rapid well model building and analysis. Networks can be built on the GIS canvas or generated automatically using a GIS shape file. The ESRI-supported GIS map canvas helps deliver true spatial representation of wells, equipment, and networks. The simulator includes advanced three-phase mechanistic models, enhancements to heat transfer modeling, and comprehensive PVT modeling options. From complex individual wells to vast production networks, the PIPESIM steady-state multiphase flow simulator enables production optimization over the complete lifecycle.Ĭontinuous innovation incorporating leading scienceįor over 30 years, the PIPESIM simulator has been continuously improved by incorporating not only the latest science in the three core areas of flow modeling-multiphase flow, heat transfer, and fluid behavior-but also the latest innovations in computing, and oil and gas industry technologies. Once these systems are brought into production, the ability to ensure optimal flow is critical to maximizing economic potential. Modern production systems require designs that ensure safe and cost-effective transportation of fluids from the reservoir to the processing facilities.
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