Abstract: Embodiments herein provide a system (100) to estimate the amplitude of oscillations in a turbulent flow system (102) that exhibits oscillatory instabilities. The system (100) comprises of a sensor (102A) mounted on the turbulent flow system (102) to detect an oscillatory variable in the system obtaining a signal, a signal conditioner (104) that conditions the signal from the sensor, an amplitude estimator (110) that estimates the amplitude of the limit cycle oscillations, and also predict the proximity of the system to the oscillatory instability, a processor (108) connected to the amplitude estimator (110) to compare the predicted oscillation amplitude with a threshold value. The amplitude is estimated by estimating the spectral measure of the time series signal obtained from the system.

Abstract: A system for early detection of onset of oscillatory instabilities in practical devices is described. The system consists of a measuring device (102), an instability detection unit (104) and a control unit (106). The measuring device (102) is configured to generate signals corresponding to the dynamics happening inside the practical device. The instability detection unit (104) along with an amplitude estimation unit (130) is configured to diagnose the stability of the practical device from the signals that are generated by the measuring device (102). Further, the control unit (106) is configured to control various operating parameters in the practical device based on the information obtained from the instability detection unit (104).

Abstract: A system and method for optimizing passive control strategies of oscillatory instabilities in turbulent systems using finite-time Lyapunov exponents are disclosed. The method includes receiving data from one or more measuring devices connected to the turbulent flow system incorporating a control strategy in the flow field. One or more flow characteristics are determined from the data obtained from the measuring devices. The method involves computing critical dynamics from backward time finite-time Lyapunov exponent (FTLE) field based on the one or more flow characteristics. Next, one or more regions of critical dynamics associated with impending oscillatory instabilities in the turbulent flow system are identified. The identified region of critical dynamics is disrupted the control the onset of oscillatory instabilities in the turbulent flow system.

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.

Abstract: Embodiments herein provide a system (100) to estimate the amplitude of oscillations in a turbulent flow system (102) that exhibits oscillatory instabilities. The system (100) comprises of a sensor (102A) mounted on the turbulent flow system (102) to detect an oscillatory variable in the system obtaining a signal, a signal conditioner (104) that conditions the signal from the sensor, an amplitude estimator (110) that estimates the amplitude of the limit cycle oscillations, and also predict the proximity of the system to the oscillatory instability, a processor (108) connected to the amplitude estimator (110) to compare the predicted oscillation amplitude with a threshold value. The amplitude is estimated by estimating the spectral measure of the time series signal obtained from the system.

Abstract: A system for early detection of onset of oscillatory instabilities in practical devices is described. The system consists of a measuring device (102), an instability detection unit (104) and a control unit (106). The measuring device (102) is configured to generate signals corresponding to the dynamics happening inside the practical device. The instability detection unit (104) along with an amplitude estimation unit (130) is configured to diagnose the stability of the practical device from the signals that are generated by the measuring device (102). Further, the control unit (106) is configured to control various operating parameters in the practical device based on the information obtained from the instability detection unit (104).

Abstract: A system and method for optimizing passive control strategies of oscillatory instabilities in turbulent systems using finite-time Lyapunov exponents are disclosed. The method includes receiving data from one or more measuring devices connected to the turbulent flow system incorporating a control strategy in the flow field. One or more flow characteristics are determined from the data obtained from the measuring devices. The method involves computing critical dynamics from backward time finite-time Lyapunov exponent (FTLE) field based on the one or more flow characteristics. Next, one or more regions of critical dynamics associated with impending oscillatory instabilities in the turbulent flow system are identified. The identified region of critical dynamics is disrupted the control the onset of oscillatory instabilities in the turbulent flow system.

Abstract: A method and system for determining one or more precursors to control blowout in a combustor is provided. The method includes obtaining a time series signal corresponding to a dynamic state variable of the combustor. The method includes detecting one or more precursor based on an analysis of the time series signal using one or more parameters to control blowout in the combustor. One or more parameters include a Hurst exponent estimation, a Burst count estimation, and a recurrence quantification based estimation.

Abstract: A method for detecting onset of oscillatory instability in a device is described. The method includes obtaining a symbolic time series of a time series signal corresponding to a dynamic state variable of the device. The method further includes detecting the onset of oscillatory instability in the device based on the symbolic time series.

Abstract: A method for detecting onset of oscillatory instability in a device is described. The method includes obtaining a symbolic time series of a time series signal corresponding to a dynamic state variable of the device. The method further includes detecting the onset of oscillatory instability in the device based on the symbolic time series.

Abstract: A method and system for determining one or more precursors to control blowout in a combustor is provided. The method includes obtaining a time series signal corresponding to a dynamic state variable of the combustor. The method includes detecting one or more precursor based on an analysis of the time series signal using one or more parameters to control blowout in the combustor. One or more parameters include a Hurst exponent estimation, a Burst count estimation, and a recurrence quantification based estimation.