In the present work, an efficient active flow control strategy in eliminating vortex-induced vibration of a cylinder at Re = 100 has been explored by two machine learning frameworks, from active learning to reinforcement learning. Specifically, an adaptive control scheme by a pair of jets placed on the poles of the cylinder as actuators has been discovered. In the active learning framework, a Gaussian progress regression surrogate model is used to predict vibration amplitude of the cylinder using a limited number of numerical simulations by combining the Bayesian optimization algorithm with specified control actions while in the reinforcement learning framework, soft actor-critic deep reinforcement learning algorithm is adopted to construct a real-time control system. The results have shown that the triangle control agent in the active learning framework can reduce the vibration amplitude of the cylinder from A = 0.6 to A = 0.43. The real-time control in the reinforcement learning framework can successfully suppress the vibration amplitude to 0.11, which is decreased by 82.7%. By comparison, there are some similarities in the amplitude and phase of the action trajectories between two intelligent learning frameworks. They both aim at keeping track of the antiphase between the position and the action, which will restrain the cylinder at a low-amplitude vibration. The underlying physics shows that the jet will contain suction in the stage of vortex generation and injection in the stage of vortex shedding. The current findings have provided a new concept to the typical flow control problem and make it more practical in industrial applications.

A novel data-driven nonlinear reduced-order modeling framework is proposed for unsteady fluid–structure interactions (FSIs). In the proposed framework, a convolutional variational autoencoder model is developed to determine the coordinate transformation from a high-dimensional physical field into a reduced space. This enables the efficient extraction of nonlinear low-dimensional manifolds from the high-dimensional unsteady flow field of the FSIs. The sparse identification of a nonlinear dynamics (SINDy) algorithm is then used to identify the dynamical governing equations of the reduced space and the vibration responses. To investigate and validate the effectiveness of the proposed framework for modeling and predicting unsteady flow fields in FSI problems, the two-dimensional laminar vortex shedding of a fixed cylinder is considered. Furthermore, the proposed data-driven nonlinear reduced-order modeling framework is applied to the three-dimensional vortex-induced vibration of a flexible cylinder. Using the SINDy model to analyze the vibration responses, the dynamics of the flexible cylinder are found to be correlated with the flow wake patterns, revealing the underlying FSI mechanism. The present work is a significant step toward the establishment of machine learning-based nonlinear reduced-order models for complex flow phenomena, the discovery of underlying unsteady FSI physics, and real-time flow control.

For annular combustors, the interactions between different modal structures (longitudinal, azimuthal spinning, standing, and slanted) remain a challenge in ongoing studies of thermoacoustic instabilities. The present work develops an annular combustor named TurboCombo, similar to the multiple-injector annular combustor named MICCA, but with a convergent turbine section connected to the combustor outlet. A Helmholtz solver provides a proper estimate of the modal frequencies and reveals the spatial structures for both two rigs. The experimental results present the consistencies of frequency domains between the acoustic pressure signals and the hydroxyl (OH^*) radicals emission signal. The high-speed flame images are processed by the dynamic mode decomposition method, statistically showing longitudinal, azimuthal spinning, or standing nature in different cases. The stability maps of the two combustors show slight differences under atmospheric conditions. In the TurboCombo rig, there are two types of interaction behaviors between the 1/4 wave longitudinal mode of the chamber and the 1st order azimuthal mode of the plenum. Unlike the prevalent “dual-mode coexistence” characteristics, an interesting behavior of “intermittent switching” is revealed, which manifests as conspicuous two-way jumps between the two modes with different frequencies. Such intermittent switching occurs randomly between two stable limit cycles in a long-time observation without any external excitation, but is driven by the turbulence-induced stochastic process. Moreover, there still exist some weak azimuthal oscillations while the longitudinal mode is dominated. The standing or spinning bias of the azimuthal mode also features intermittent transition, corresponding to the occasions dominated by longitudinal mode or azimuthal mode, respectively.