Neural Network Reconstruction for Low-Memory CFD Data Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current CFD simulation data processing is limited by large data file sizes requiring significant memory and specialized software, which is costly and complex to operate, posing challenges for engineers.
Innovation Solution
Training a neural network model with CFD simulation data to generate a file that reconstructs simulation data, allowing for simplified and efficient data processing without large files, supporting post-processing functions and user-friendly query methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If CFD simulation data is stored and processed using traditional methods, then comprehensive simulation data can be obtained, but large data file sizes require significant memory and specialized software, increasing system complexity and cost
Solution Approach 1:
The patent creates a neural network model that learns from CFD simulation data and generates simplified representations of fluid flow patterns. This copying approach allows the system to retain essential simulation information while reducing data complexity, enabling engineers to query and analyze results without requiring the full original simulation datasets or specialized CFD software.
Solution Approach 2:
The patent replaces traditional mechanical CFD simulation processes with a neural network-based computational approach. Instead of running complex fluid dynamics equations through specialized software, the system uses trained neural network models to quickly generate simulation results, substituting the mechanical computation system with an AI-based system that requires minimal specialized equipment.
2Measurement precision
If traditional CFD simulation data processing is used, then accurate simulation results can be obtained, but specialized software and significant memory resources are required, increasing operational cost
Solution Approach 1:
The patent extracts essential fluid flow information from complex CFD simulation data by training neural network models on representative simulation results. The model learns and stores only the critical patterns and relationships, allowing accurate prediction of fluid flow characteristics without requiring access to the complete original simulation datasets or intensive computational resources for each query.
Solution Approach 2:
The patent transforms the representation of simulation data by changing from raw CFD field data to encoded neural network parameters. This parameter transformation compresses the information while preserving accuracy, enabling the system to maintain simulation precision while using minimal memory and computational resources compared to traditional methods.
3Adaptability or versatility
If CFD simulation data is processed using specialized software, then comprehensive analysis can be performed, but the software is costly and complex to operate, reducing ease of use
Solution Approach 1:
The patent implements a self-service system where the neural network model automatically processes simulation data without requiring users to operate complex specialized software. The system handles data processing, analysis, and visualization automatically based on simple user queries, eliminating the need for users to learn and operate sophisticated CFD post-processing tools while maintaining comprehensive analysis capabilities.
Data Source
AI summary
Illustrative embodiments include a method, an electronic device, and a program product for processing simulation data of computational fluid dynamics (CFD). A method in one embodiment includes: training, based on acquired CFD simulation sample data, a neural network model to obtain a trained neural network model, wherein the CFD simulation sample data includes: a CFD simulation condition sample value, sample data of an input parameter, and simulation sample data of an output parameter at the CFD simulation condition sample value; and generating a file associated with the trained neural network model, wherein the file includes a network parameter value of the trained neural network model, and the file is used for reconstructing the neural network model to provide CFD simulation data. According to the method in embodiments of the present disclosure, the trained neural network model can automatically provide CFD simulation data.