Device and method to predict the onset of oscillatory instabilities in systems with turbulent flow

Patent metadata

Field	Value
Publication number	US-12025528-B2
Application number	US-202117377918-A
Country	US
Kind code	B2
Filing date	Jul 16, 2021
Priority date	Jul 16, 2020
Publication date	Jul 2, 2024
Grant date	Jul 2, 2024

Abstract

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.

First claim

What is claimed is:

1. A method of predicting and mitigating occurrence of a potentially unstable operating state of an equipment, the method comprising:
measuring, by one or more sensors in a sensor array, a signal indicative of operating state of the equipment to obtain a measured signal;
converting, by analog to digital converter the measured signal into a digital signal;
estimating, by an analyzing unit, a state tensor from the digital signal;
receiving the state tensor at a learning and prediction unit, the unit having a neural network prediction module with a dynamic model of the operating state of the equipment;
calculating a stability parameter corresponding to the received state tensor using the dynamic model;
updating the dynamic model with the state tensor;
comparing the stability parameter against a threshold; and
identifying an impending onset of instability in the operating state of the equipment, wherein the neural network is an ODE neural network and the method comprises:
a) constructing a neural network (NN) with at least 3 layers, that maps state variables p(t) of an operational state to a dynamic state variable z(t) and to its time derivative ż, wherein ż=ƒ(z,t,w);
b) defining the NN with nodes in each layer and specifying a corresponding activation function;
c) receiving weighted inputs corresponding to time series data at times t;
d) computing a sum of the weighted inputs at each node for initial time t 0 , to a final time instance t 1 for the corresponding operational states to obtain a weight matrix;
e) computing a distance in the weight matrix defined as d (i) =| w (i) −w (r) |, wherein w (i) is an averaged weight over a window of n w samples, w (r) is an average weight corresponding to an unstable state; and
f) computing a sum of distances corresponding to the weight matrix and obtaining a parameter μ 1 that varies inversely as the sum, to obtain a measure of stability of the operational state; and
comprising prior to step d), training the neural network using weights learned from a first sample data w (1) as initial guess to find weights for subsequent samples;

training the neural network on i th sample data to get corresponding weights of NN as w (i) ; and
averaging the weights over a window of n w samples, wherein

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.

2. The method of claim 1 , further comprising:
sending the updated dynamic model to the analyzing unit;
determining, by the analyzing unit a path for control and control action to
mitigate the impending stability;
sending the control action to a control unit; and
modifying, by the control unit via an actuator array, the operating state of the equipment to mitigate the impending instability.

3. The method of claim 1 , wherein in step d) the final state predicted by neural ODE, z p (t 1 ) is given by:
z p ( t 1 )=ƒ t 0 t 1 ƒ( z ( t ), t,w ) dt , wherein,

an error in the prediction is given by:
J =( z ( t 1 )− z p ( t 1 )) 2 ; and

wherein t 1 is a final time instance.

4. A method of predicting and mitigating occurrence of a potentially unstable operating state of an equipment, the method comprising:
measuring, by one or more sensors in a sensor array, a signal indicative of operating state of the equipment to obtain a measured signal;
converting, by analog to digital converter the measured signal into a digital signal;
estimating, by an analyzing unit, a state tensor from the digital signal;
receiving the state tensor at a learning and prediction unit, the unit having a neural network prediction module with a dynamic model of the operating state of the equipment;
calculating a stability parameter corresponding to the received state tensor using the dynamic model;
updating the dynamic model with the state tensor;
comparing the stability parameter against a threshold; and
identifying an impending onset of instability in the operating state of the equipment;
wherein the neural network is a hybrid convolutional neural network (CNN) and the method comprises:
providing an input adjacency matrix of nodes to construct the convolutional neural network;
providing two or more convolutional layers in the CNN with corresponding activation functions;
reconstructing a phase space corresponding to P(t) t corresponding to time series data segment of length l to obtain a recurrence plot (RP);
obtaining a recurrence matrix, R ij defined as R ij =∥X i −X j ∥∥, where X i and X j represent state points of the operational state in reconstructed phase space at i th and j th instances in time;
obtaining RPs corresponding to stable and unstable operational states;
training the convolutional neural network (CNN) using the RPs for known stable and unstable states; and
using the trained CNN to assign probabilities to unknown time series data segment as to stability of the operational state μ i .

5. The method of claim 4 , further comprising:
sending the updated dynamic model to the analyzing unit;
determining, by the analyzing unit an optimal path for control and control action to mitigate the impending stability;
sending the control action to a control unit; and
modifying, by the control unit via an actuator array, the operating state of the equipment to mitigate the impending instability.

Assignees

• Indian Institute Of Tech Madras
• Univ California
• Indian Inst Tech Madras

Inventors

• Dhadphale Jayesh
• Ruiz Eustaquio Aguilar
• Unni Vishnu Rajasekharan
• Saha Abhishek
• Nair Sujith Indusekharan

Classifications

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/02

  Neural networks · CPC title

• G06N3/045

  Combinations of networks · CPC title