Control of Dynamic Flow Fields

Dear Colleagues,

For several decades, the control of dynamic flow fields has been investigated by experiments and numerical simulations, but applications-based advanced flow control such as active and reactive flow control, which seems to be more effective, has been limited. There have been difficulties owing to (1) a lack of knowledge of complex fluid dynamics, (2) the high cost of simulations for the state-space model, and (3) the absence of appropriate flow sensor and control devices that work with small latency. However, recently high-fidelity experiments and numerical simulations have been made available for (1) a more detailed understanding of the flow fields to address and (2) reduced order modeling or machine learning to run the model simulating the simplified flow that the model has been developed to resolve. In addition to these resolutions, (3) the appropriate flow control devices with small latency, such as plasma actuators and synthetic jets, have become available. Based on the three advances mentioned above, now is a great opportunity to advance research on the control of dynamic flow fields. To accelerate the efforts on the control of dynamic flow fields, we would like to organize the Special Issue "Control of Dynamic Flow Fields", in Energy. This Special Issue welcomes, but is not limited to, papers related to any of the three kinds of efforts for flow control:

(1) A detailed analysis of dynamic flow control based on high-fidelity experiments and numerical simulations, such as

• Advanced measurements, such as dynamic particle image velocimetry, for controlled flow fields;
• High-fidelity simulations, such as direct numerical simulations or large-eddy simulations for controlled flow fields.

(2) Reduced order modeling or machine learning to run advanced flow control algorithms such as

• Modal decompositions for modeling controlled flow fields based on the data-driven approach;
• Discourteous Galerkin projection for modeling based on the analytical approach;
• Machine learning such as deep neural networks for flow control algorithms.

(3) Dynamic flow control results using advanced flow control devices, such as plasma actuators such as

• Active flow control using plasma actuators;
• Active flow control using synthetic jets.

Prof. Dr. Taku Nonomura
Guest Editor

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