Neural ODE Instability Prediction for Turbulent Flow Systems
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Solution Overview
Problem
Systems with turbulent fluid flow often experience undesirable oscillatory instabilities, such as thermoacoustic, aeroacoustic, and aeroelastic instabilities, which cause increased wear and tear and potential damage due to positive feedback loops between oscillations in subsystems, posing challenges for prognosis and mitigation.
Innovation Solution
A system comprising a sensor array and actuator array, utilizing a neural network-based learning and prediction unit to estimate a state tensor from measured signals, identify impending instability, and implement control actions to mitigate oscillatory instabilities through actuation, such as cooling holes, staged fuel injectors, or counterweights, to promote stable operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to detect oscillatory instabilities, then the system structure remains simple, but the detection precision and reliability are insufficient to predict instabilities before they occur
Solution Approach 1:
The system segments the monitoring task into multiple specialized components: sensor array for data collection, analog-to-digital converter for signal processing, state tensor estimator for mathematical modeling, and learning/prediction units for analysis. This segmentation enables high detection precision while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediate processing layers between simple sensing and instability prediction: state tensor estimation acts as an intermediary that transforms raw sensor data into meaningful system state representations, enabling accurate prediction without requiring direct complex modeling at the sensor level.
2Reliability
If no prediction system is implemented, then the device complexity remains low, but the equipment reliability and protection from damage are insufficient
Solution Approach 1:
The system performs preliminary actions by continuously estimating state tensors and predicting future system states before actual instabilities occur. The learning unit identifies precursors and warning signs early, enabling preventive control actions that protect equipment reliability before damage can occur.
Solution Approach 2:
The patent implements closed-loop feedback where prediction results inform control decisions, and control outcomes feed back into the prediction model for continuous improvement. This feedback mechanism enhances reliability by dynamically adjusting control actions based on real-time predicted system behavior.
3Stability of the object's composition
If complex control actions are implemented to mitigate instabilities, then the stability maintenance is effective, but the ease of operation and system simplicity are reduced
Solution Approach 1:
The learning and prediction units operate autonomously to identify instabilities and trigger appropriate control actions without requiring manual intervention. The system serves itself by automatically adjusting control parameters based on predicted system behavior, maintaining stability while simplifying operation for users.
Solution Approach 2:
The system maintains stability by dynamically changing control parameters based on predicted system states. The controller adjusts operational parameters in real-time according to prediction outputs, achieving effective stability control through automated parameter optimization rather than complex mechanical adjustments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively predicts and mitigates oscillatory instabilities by identifying potential unstable states and implementing control actions, reducing the risk of damage to equipment by maintaining stable operating conditions.
Implementation Method 1
a sensor array comprising at least one sensor of a piezoelectric pressure transducer, a microphone or a strain gauge to measure analog signals
Implementation Method 2
a sensor array comprising at least one sensor of a piezoelectric pressure transducer, a microphone or a strain gauge to measure analog signals
Data Source
AI summary
Disclosed are devices and methods of detecting and mitigating oscillatory instabilities in systems with turbulent flow, such as thermoacoustic, aeroacoustic or aeroelastic equipment. The system includes a sensor array for measuring one or more parameters of an operating equipment S, and an analysis and prediction unit. The analysis and prediction unit is configured to estimate a state tensor to identify a state of the equipment indicating stable operation or impending oscillatory instability. The system further includes an actuator array configured to implement a control action to promote stable operation of the equipment. Methods for robust prediction of the state of stability are also disclosed. A neural ordinary differential equation (ODE) method of predicting stability or instability is disclosed, involving forming a neural network that incorporates an equation characteristic of the operational state. The invention further discloses a hybrid convolutional neural network based prediction method for stability.