Neural Network Training via 2D Mesh Reduction for Real-Time CFD
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Solution Overview
Problem
Real-time simulation of complex multiphase fluid flows in industrial processes, such as those in the oil and gas industry, is challenging due to the complexity and computational resource requirements, making conventional numerical models impractical for real-time applications.
Innovation Solution
A deep-learning neural network (DNN) is trained using simplified data from computational fluid dynamics (CFD) simulations, reducing the number of nodal points and dimensions to enable fast and realistic real-time multiphase flow simulations by focusing on specific areas of interest within the domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional numerical models are used to simulate multiphase fluid flows, then simulation accuracy is improved, but computational time and resource requirements increase significantly
Solution Approach 1:
The patent pre-processes complex 3D CFD simulation data into simplified 2D representations that capture essential flow characteristics. This preliminary transformation enables the neural network to learn from reduced-complexity data, achieving real-time prediction capabilities while maintaining accuracy. The 2D mesh generation and data simplification performed beforehand allow fast inference without requiring full 3D simulations during real-time operations.
Solution Approach 2:
The patent creates simplified 2D copies or representations of the complex 3D fluid flow data. Instead of processing the full 3D CFD results, the system generates 2D mesh representations that preserve critical flow information in a reduced dimensionality format. These 2D copies serve as efficient training data for the neural network, enabling real-time predictions without the computational burden of full 3D simulations.
2Measurement precision
If detailed numerical models are used to simulate multiphase flows in complex fluid systems, then simulation precision is improved, but device complexity and computing resources required increase
Solution Approach 1:
The patent transforms 3D CFD simulation data into 2D representations by generating 2D meshes from the 3D domains. This dimensionality reduction preserves essential flow characteristics while dramatically reducing data complexity and computational requirements. The 2D meshes capture critical spatial relationships and flow patterns needed for accurate predictions without requiring full 3D computational resources.
3Adaptability or versatility
If conventional neural network modeling techniques are used with multiple layers of neurons, then multi-scale fluid flow structures are accounted for, but understanding requirements and practical implementation become more complex
Solution Approach 1:
The patent applies local quality by using 2D mesh representations that focus computational effort on capturing essential local flow characteristics. Instead of requiring complex multi-layer neural networks to model all scales, the 2D meshes provide localized flow information that simplifies the neural network's learning task. This approach accounts for multi-scale structures through targeted 2D representations rather than through network architecture complexity.
Data Source
AI summary
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.