Smart Boiling Control via Image-Based Heat Flux Prediction
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Solution Overview
Problem
Current methods for controlling flow boiling are inefficient due to the complexity of bubble dynamics, which limits the ability to actively control boiling systems with synchronized image analysis, and existing technologies fail to fully connect image data with boiling physics using machine learning-based systems.
Innovation Solution
A data-driven learning framework that uses convolutional neural networks and object detection algorithms to extract hierarchical and physics-based features from boiling images, enabling the prediction of boiling heat characteristics and control of flow boiling systems through real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to control flow boiling, then the control process is simple, but the control efficiency is low and the system cannot actively respond to boiling conditions
Solution Approach 1:
The patent replaces traditional mechanical control systems with a machine learning-based vision system. The system uses deep learning models to analyze boiling images and automatically determine optimal control parameters, substituting complex mechanical control mechanisms with intelligent software-based control that achieves higher efficiency without physical complexity
Solution Approach 2:
The system enables the boiling system to self-regulate by automatically analyzing its own state through image processing and adjusting control parameters independently. The machine learning model continuously monitors boiling conditions and autonomously optimizes control settings without external intervention, achieving active control with simplified operational complexity
2Measurement precision
If machine learning methods are implemented to connect image data with boiling physics, then prediction accuracy improves, but computational resource consumption increases
Solution Approach 1:
The patent pre-trains deep learning models offline using extensive boiling data and physics knowledge before deployment. This preliminary training phase captures complex boiling physics patterns, allowing the deployed system to make accurate predictions with minimal real-time computational resources. The model structure and parameters are optimized in advance to balance accuracy and efficiency
Solution Approach 2:
The system uses pre-trained models and transfer learning approaches where knowledge from extensive training datasets is copied into the deployed system. This allows accurate boiling predictions without requiring the deployed system to process vast amounts of data in real-time, significantly reducing operational energy consumption while maintaining high prediction accuracy
3Speed
If real-time image analysis is performed to control boiling systems, then control responsiveness improves, but processing time for large datasets increases
Solution Approach 1:
The patent segments the image analysis process into distinct processing stages using convolutional neural network layers. Each layer extracts specific features at different levels of abstraction, allowing parallel processing of different image regions and features. This segmented approach enables real-time analysis by breaking down complex processing into manageable, concurrently executable units
Solution Approach 2:
The system implements periodic sampling of boiling images rather than continuous analysis of every frame. By analyzing images at optimized intervals and using the deep learning model to predict intermediate states, the system maintains high control responsiveness while significantly reducing total processing time and computational load
Data Source
AI summary
Systems and methods for real-time boiling analysis and decision making in accordance with embodiments of the invention are illustrated. One embodiment includes a method for real-time smart boiling analysis. The method includes steps for receiving a set of one or more boiling images, identifying a set of bubble characteristics from the set of boiling images using a first model, identifying a set of image features from the set of boiling images using a second model, predicting a set of boiling heat characteristics based on the identified set of bubble characteristics, and controlling a flow boiling system based on the predicted set of boiling heat characteristics.