Effective representation of complex three-dimensional simulation results for real-time operations
US12061980B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 26, 2017 |
| Grant date | Aug 13, 2024 |
| Priority date | — |
| Expiry date | Jun 6, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2113/08
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
System and methods for training neural network models for real-time flow simulations are provided. Input data is acquired. The input data includes values for a plurality of input parameters associated with a multiphase fluid flow. The multiphase fluid flow is simulated using a complex fluid dynamics (CFD) model, based on the acquired input data. The CFD model represents a three-dimensional (3D) domain for the simulation. An area of interest is selected within the 3D domain represented by the CFD model. A two-dimensional (2D) mesh of the selected area of interest is generated. The 2D mesh represents results of the simulation for the selected area of interest. A neural network is then trained based on the simulation results represented by the generated 2D mesh.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.