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Researchers developed a data-driven framework using autoencoders and neural networks to model turbulent flows that oscillate at multiple frequencies simultaneously. The method extracts representative oscillators from flow field data and accurately predicts the complex oscillatory behavior of turbulent systems over extended time periods. The approach was successfully validated on three-dimensional supersonic turbulent flow over a cavity, where it captured dominant large-scale flow features and physical characteristics of the turbulent system.
Why it matters
This advancement enables simplified modeling of previously intractable turbulent flows with multiple frequencies, opening new possibilities for understanding and controlling complex fluid dynamics in aerospace applications, such as supersonic cavity flows that cause structural vibrations and acoustic problems in aircraft.
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⚠️ Preprint – Noch nicht peer-reviewed
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Abstract: The complex dynamics of high-dimensional oscillatory flows can be simplified using phase-reduction analysis, providing a deeper understanding of the flow response to external perturbations. Although phase-based modeling and analysis have been utilized in recent studies on oscillatory fluid flows, their usage is often limited to single-frequency flows due to difficulties in addressing chaotic characteristics, emerging from multiple dominant frequencies with broadband spectra in turbulent flows. In order to overcome this limitation, we propose a data-driven framework that models the dynamics of turbulent flows with multiple dominant frequencies based on a set of oscillators. The representative oscillators are extracted from the flow field data by training specially designed autoencoders. The oscillator dynamics are modeled through a machine-learning technique using neural networks to accurately predict the multi-frequency oscillatory behavior of turbulent flows. We verify the oscillator-based model of the oscillatory turbulent flows by applying the proposed data-driven method to the three-dimensional supersonic turbulent flow over a cavity. We show that the oscillators extracted from the spanwise-averaged pressure field represent the dominant large-scale flow features and reflect the physical characteristics of the turbulent cavity flow. The data-driven oscillator dynamics model with observation-based correction accurately forecasts the oscillatory behavior of the turbulent cavity flow for a long period. The proposed data-driven method for reduced-order modeling of turbulent flows with oscillators will enable deeper investigations of perturbation dynamics and control of turbulent flows.
Source: Data-driven oscillator model for turbulent flows with multiple dominant frequencies