Pipe Blockage Prediction Using Segmented CFD and Machine Learning
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Solution Overview
Problem
Computational fluid dynamics (CFD) analysis techniques face challenges in real-time prediction and require significant computational resources, making them inefficient for large-scale fluid flow analysis, especially in constructing digital twins.
Innovation Solution
A pipe blockage prediction method that generates multiple simulation models using CFD analysis and machine learning, combining first and second simulation models to create a third simulation model for predicting pipe blockages in various sections, allowing for rapid and accurate fluid flow phenomenon prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CFD analysis technique is used to predict fluid flow, then prediction accuracy is improved, but computational time and resource requirements increase exponentially
Solution Approach 1:
The patent divides the pipe system into multiple discrete sections and creates separate simulation models for each section. This segmentation allows the system to perform localized predictions rather than analyzing the entire pipe network globally, significantly reducing computational time while maintaining prediction accuracy for each specific section.
Solution Approach 2:
The patent pre-generates multiple simulation models covering different pipe sections and conditions before actual prediction is needed. These pre-computed models are stored and can be quickly applied to new scenarios, eliminating the need to perform full CFD analysis in real-time and thus reducing computational time requirements.
2Measurement precision
If CFD analysis technique is used to predict fluid flow, then prediction accuracy is improved, but computational resource requirements increase
Solution Approach 1:
By segmenting the pipe system into discrete sections and creating dedicated simulation models for each, the patent reduces the overall computational burden. Each segment requires fewer computational resources than a full-system model, allowing accurate predictions with lower energy and computational resource consumption.
Solution Approach 2:
The patent creates multiple simplified simulation models that replicate the behavior of different pipe sections. These copied models can be stored and reused without requiring additional computational resources during actual prediction tasks, maintaining accuracy while reducing ongoing resource requirements.
3Productivity
If multiple simulation models are generated using CFD and machine learning, then prediction speed is improved, but model complexity increases
Solution Approach 1:
The patent segments the complex prediction task into multiple simpler models, each handling a specific pipe section. This division reduces the complexity of individual models while collectively providing comprehensive coverage, enabling faster predictions without requiring any single model to be overly complex.
Solution Approach 2:
The patent creates simulation models that can be applied universally across different pipe sections with similar characteristics. These multi-functional models can handle various prediction scenarios within their domain, reducing the need for highly specialized complex models for each individual case and thereby reducing overall system complexity.
Data Source
AI summary
A pipe blockage prediction method includes generating a first simulation model by performing a first simulation based on pipe information and fluid information including a flow rate and pressure of a fluid in a pipe, generating a second simulation model by performing a second simulation that is different from the first simulation, based on the pipe information and the fluid information, generating a third simulation model through machine learning, based on the first simulation model and the second simulation model, and predicting pipe blockage for each of a plurality of sections of the pipe based on the third simulation model.