Flow Control Group

・About Flow Control Group
The flow control group is studying the optimal feedback flow control system. In the system, the certain state variable is measured by sensors inside the wing of aircraft, and future flow state is predicted. And then, the optimal flow control is conducted based on the predicted flow state. Traditionally, flow control has mainly been conducted by passive flow control devices such as vortex generators. Also, flap and slat which are complicated mechanical devices are adopted in the aircraft wing, but these methods are known to be degraded that effectiveness in the out-of-design point. Therefore, in order to further improve the aerodynamic performance of the airfoil which is exposed in various conditions (mainly angle of attack), real-time and adaptive flow control are necessary.  We are also conducting fundamental research on the limit flow of compressible low Reynolds numbers for fluid control in future fluid machinery using a unique wind tunnel called the Mars wind tunnel.

・Feedback Control Team
1. Flow Field Measurement via Sparse Sensor
The present study is focused on the optimization of the measurement. It is necessary to realize the future flow state faster than the change of the flow in order to realize the optimal feedback flow control. The flow around the airplane is so fast that it is necessary to reduce the measurement points with minimizing the loss of information for fast and accurate predictions. In the measurement team,  we are studying the method to estimate the entire flow field with high accuracy from the information of limited sensor points.

2. Flow Field Estimation by Reduced Order Model
The prediction team is focused on the prediction of the future flow state in high accuracy. It is necessary to apply the control input against the future flow state, instead of applying that against the current flow state to realize the optimal feedback flow control. In the prediction team, we studying the method to construct the prediction model in high accuracy and that to predict the future flow state in real-time.  The characteristic of flow field structure is extracted by applying proper orthogonal decomposition (POD) to the time series data of the flow field obtained by particle image velocimetry (PIV). We are trying to solve the problem by constructing a lightweight prediction model that expresses the flow field by superposing the extracted modes. In addition, with a view to actual control, we are also constructing a prediction model for when a control input is applied using a plasma actuator and a model for estimating the flow velocity field from the pressure distribution on the model.

Conceptual diagram of discrete linear model construction.

3. Flow Control using Plasma Actuator
The control group is focused on the active flow control using plasma actuators. Flow separation can significantly reduce the performance of fluid machinery. Therefore, research on fluid control devices for controlling flow separation has been actively conducted for a long time. In recent years, active research has been conducted on devices such as plasma actuators and synthetic jets that have high responsiveness and can be actively controlled. In this research, we are conducting research on fluid control using plasma actuators, and we are pursuing exploration of separation control effect and control mechanism of airflow around blades, effective control parameters through visualization of aerodynamic force, pressure distribution, and flow. Recently, we are studying the control effect of a plasma actuator in a dynamic stall flow by conducting an experiment that simulates the flow around a helicopter blade whose angle of attack changes rapidly.

Schematic diagram of plasma actuator and flow separation control.

・Mars Airplane Team
Currently, Mars airplanes are being studied as a new Mars exploration method. The atmospheric environment of Mars is very different from that of the earth, and the atmospheric density is approximately 1/100 of that of the earth. As a result, the aerodynamic force obtained by the wing is reduced, and since the flight is performed at a low Reynolds number, the performance of the wing is significantly reduced. To obtain the lift required for flight under such conditions, a high-speed flight is necessary so that the Mach number during flight increases. In addition, about 95% of the Martian atmosphere is composed of carbon dioxide, which is very different from the atmospheric composition of the earth. With a common wind tunnel, it was difficult to produce a low Reynolds number and a high Mach number conditions. Therefore, our laboratory designed and developed a Martian atmospheric wind tunnel that can simulate compressible low-Reynolds number flow. The wind tunnel installed in the vacuum chamber can produce the compressible low-Reynolds-number flows, and the working gas can be replaced with any composition. Currently, we are studying the flow around a basic shape with a low Reynolds number, thin airfoil, and rotor blade using this wind tunnel.

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1. Visualization and Measurement of Flow around Basic Shape
A blunt body such as a circular cylinder or a square cylinder is considered as a simplified shape or element shape of various objects such as buildings and bridges, cables, cars, etc., and the flow field has been investigated in the wide range of Reynolds and Mach numbers. However, compressible low-Reynolds-number flow is difficult to reproduce, and the current knowledge of the flow field is poor. Therefore, returning to the basic shape, we are working to clarify the flow around the basic shape in a compressible low-Reynolds-number environment that has not been elucidated so far. The figure below shows PSD and phase difference of pressure fluctuation on cylinder surface and schlieren visualization of the wake. In a common wind tunnel test, Mach number 0.5 is a high Reynolds number flow, so the Reynolds number dependence cannot be seen. By using the Mars wind tunnel, on the other hand, Reynolds number dependencies on the position of wake vortices and the phase difference in the cylinder span direction were observed.

Schlieren visualization of flow over a circular cylinder at Mach number 0.5: (a) Re = 1000; (b) Re = 2000; (c) Re = 3000; (d) Re = 4000.
PSD and phase difference of pressure fluctuation: (a) Re = 2000; (b) Re = 3000; (c) Re = 4000; (d) Re = 5000.

2. Visualization and Measurement of flow around thin Airfoil
In low-Reynolds-number flow, it is considered that wing performance is dominated by fluid phenomena such as laminar separation, turbulent transition, and reattachment. For incompressible low-Reynolds-number flow, various studies have been conducted to improve the wing performance at low-Reynolds-number flow by using a wing shape that mimics the wings of organisms such as insects and birds (biomimetics). However, it is unclear whether performance can also be improved in compressible low-Reynolds-number flows because the flight of organisms is in the low-Mach-number region. In this research, we are investigating those phenomena through visualization of flowfield and evaluation of wing performance of lift under compressible conditions, and are conducting research toward the realization of Mars aircraft. The figure below shows the surface pressure coefficient distribution of a wing with a serrated leading edge. The low-pressure region due to the vertical vortex generated in the serrated portion is observed, and it can be seen that the low-pressure region expands as the Mach number increases.

Pressure distribution on a serrated wing with an attack angle of 6 degrees: a) Re = 11,000, M = 0.46; b) Re = 13,000, M = 0.64.

3. Measurement of Pressure Distribution on Rotor Blade
It is known that in a low Reynolds number flow, a strong leading-edge vortex is formed on the rotor blade surface and the lift is increased. Therefore, it is considered that rotor aircraft are more suitable for flight than fixed-wing aircraft in the Martian atmosphere. In our team, we are measuring the pressure distribution with a high spatial resolution by applying PSP and elucidating the mechanism of lift increase due to the separation bubble at the leading edge. The left figure shows the rotor blade model illuminated with excitation light, and the right figure shows the pressure distribution obtained by PSP measurement, which shows a low-pressure region at the leading edge (bottom of the right figure). In addition, since PSP measurement in low-pressure environments has various difficulties, we are conducting researches to improve PSP measurements in low-pressure environments, such as evaluation of optimal experimental conditions and PSP measurement with variable oxygen concentrations.

Rotor model installed in the chamber of Mars wind tunnel (left) and Pressure distribution on the blade surface measured by PSP

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