Physics-Informed GNN Gas Explosion Prediction in Obstructed Environments
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Solution Overview
Problem
Conventional methods for predicting gas explosions in complex urban environments with spatial obstacles have low accuracy and fail to consider physical interactions between congestion, flame, and blast wave propagation, leading to significant prediction discrepancies.
Innovation Solution
A physics-informed GNN model is used to optimize gas explosion predictions by incorporating geometric models with spatial obstacles, training on simulation datasets, and adjusting target pressure values to enhance accuracy and efficiency, while a VR-based emergency training system provides immersive training scenarios for emergency response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction methods (CFD and empirical model combined method, machine learning method) are used for gas explosion prediction, then the prediction process can be completed, but the prediction accuracy is very low in scenes involving complex spatial obstacles
Solution Approach 1:
The patent combines physics-based explosion propagation models with graph neural network (GNN) approaches to create a hybrid prediction system. The physics model provides fundamental explosion mechanics while the GNN handles complex spatial obstacle interactions, merging the strengths of both conventional physics methods and machine learning to achieve high accuracy in complex urban environments.
Solution Approach 2:
The patent introduces a graph neural network as an intermediary between the physics-based explosion model and the complex urban environment. The GNN processes spatial obstacle information and mediates the interaction between explosion propagation and urban geometry, enabling accurate predictions without requiring overly complex conventional models.
2Reliability
If machine/deep learning approaches are used for real-time gas explosion prediction, then the prediction can be performed, but the methods lack explicit consideration of physical interactions between congestion, flame, and blast wave propagation
Solution Approach 1:
The patent merges physics-based explosion propagation models with graph neural networks to create a hybrid system that maintains both the physical interpretability of conventional models and the computational efficiency of machine learning. This combination ensures reliable predictions by explicitly considering physical interactions while handling complex spatial patterns.
3Productivity
If conventional prediction methods are used for gas explosion in urban scenes with spatial obstacles, then the prediction can be performed, but the calculation time is long and processing efficiency is low
Solution Approach 1:
The patent replaces parts of the conventional mechanical physics calculation system with a graph neural network-based computational approach. The GNN processes spatial obstacle data and explosion propagation patterns through neural network computations, which are generally faster and more efficient than traditional CFD simulations, thereby reducing prediction time while maintaining accuracy.
Data Source
AI summary
A method for predicting gas explosion. The method includes: obtaining a geometric model with spatial obstacles; determining a simulation dataset of the gas explosion based on the geometric model, where the simulation dataset is a dataset including process data of multiple gas explosion scenes in the geometric model, and a state vector of a spatial grid node in the simulation dataset includes a first moment, coordinate information and a target pressure value; optimizing, using the simulation dataset, a physics-informed GNN to obtain an optimized physics-informed GNN; and predicting the gas explosion in an obstructed gas explosion scenario using the optimized physics-informed GNN. This method is applied to use optimized physics-informed GNN to realize spatiotemporal second-level prediction of overpressure distribution and explosion wave propagations occurring in the obstructed gas explosion scenario, enhance the accuracy, the reliability and the efficiency of gas explosion prediction in the obstructed gas explosion scenario.