Chapter V. Flow Control Applications
6. Other applications
This section presents other applications realised with my colleagues from the Civil Aircraft Unit. This illustrates the variety of active flow control applications on civil aircraft which are studied today. Of course, this is a just part of the possible application studied at ONERA. There are other units in the Aerodynamics, Aeroelasticity and Acoustics Department which study the application of flow control on turbomachinery, helicopter blades and fuselage, UCAV, ducts, re-duction of turbulent skin friction by riblets or blowing, missiles, projectiles, launchers, jet noise reduction, etc. (see Aerospace Lab n°6 (www.aerospacelab-journal.org) for an overview).
Flow control on a flap by passive with VGs: joint
optimisa-tion of flap settings and VG parameters (in JTI Clean Sky
SFWA with F.
Moëns [42])
Reference
Opt. Settings +VG Opt. Settings
Active flow control at the wing tip (AFLoNext Europe-an project with J.-L.
Hantrais-Gervois)
Active flow control at the engine/wing junction (in AFLoN-ext European project
with D. Hue [32])
Alpha
CL
-5 0 5 10 15
1.5 2 2.5 3 3.5
Settings + VG Optimised : VG on Settings + VG Optimised : VG off Reference
Settings Optimised DLR F15 Experiments (Alpha corrected)
VG not efficient at high a.o.a.
DP2
DP1 +27.25%
+8.9%
Passive flow control on main landing
gear doors (in AFLoNext European
project [55])
Active flow by flu-idic VGs on a na-celle in crosswind conditions (ENO-VAL European pro-ject, with M. Costes
and F. Sartor)
Active flow control on a vertical tail plane (in progress)
85
Perspectives
In the continuity of my previous work, my research activities on active flow control and buffet understanding will continue in the following years. Nonetheless, a new research topic will also start in 2018 on turbulence modelling. Concerning the actuators, as explained in the actuator section, a piezoelectric synthetic jet is currently in development. For the first time, a synthetic jet with a sonic peak velocity should be available. Its application to control the separation at the en-gine/wing junction during a flight test on the DLR A320 is a possible opportunity in the frame-work of JTI Clean Sky 2 European project. I would also like to study ejectors to decrease mass flow rate requirements. Ejectors consist in a nozzle which induces a secondary flow by entrain-ment. Entrainment ratios up to 100% are observed in the literature. This could allow dividing by two the mass flow rate requirements which is not negligible. Ejectors are already used by Avi Seifert in his sweeping jets as explained in the corresponding section of the manuscript. Concern-ing low speed flow control applications, a vertical tail plane (VTP) equipped with synthetic jet actuators will be tested next year in the ONERA L1 wind tunnel. The enhancement of the VTP effectiveness would allow reducing its size and consequently drag in cruise. In terms of perspec-tives, parametric studies to define the flow control parameters (location, blowing angle, etc.) are time consuming and not very interesting. Today, stability tools allow determining the sensitivity of the flow to a modification of the boundary conditions at the wall for example. This has been studied during an internship. The figure below shows, for the flow around a cylinder, the sensi-tivity of the unstable eigenvalue (vortex shedding mode) to a modification of the boundary condi-tion (blowing or succondi-tion) on the wall. These tools have to be developed in the future to determine the optimal location, angle of the blowing and possibly new ideas of actuation.
Concerning the buffet phenomenon, the PhD thesis of E. Paladini currently in progress aims at a better understanding of the 3D turbulent buffet. The objective is for the first time to perform a global stability analysis of this 3D flow. This is a challenging objective in terms of computing resources since the size of the matrix whose eigenvalues are searched is proportional to the square of the mesh size. Then, a simple steady RANS simulation (used as baseflow) will allow predicting the 3D buffet frequency like it has been done for the 2D phenomenon by Crouch [19]. Since it has been shown that the 3D buffet phenomenon is linked to the appearance of buffet cells or stall cells, a PhD thesis will start at the end of 2017 in collaboration with Polytechnique Montréal on the study of these stall cells at low and high speeds with in parallel an application to stall predic-tion. Global stability analysis should allow predicting the spanwise wavelength of these stall cells.
Linked with this PhD, a research project on the study of the stall phenomenon will start in 2018 for four years. Wind tunnel tests will be performed to collect data on the wavelengths of these stall cells. Another research project will start in 2018 on buffet for three years. The objective is to study the transition from the 2D to the 3D buffet thanks to a variable sweep model. It has been explained in the buffet section of this manuscript that the buffet frequency increases with the sweep angle but this has to be explained. Unsteady pressure sensitive paint (PSP) will be used to follow the spanwise convection of the stall cells. The objective is to generate a reference database for the validation of numerical simulations. In parallel, a post-doc will start in 2018 on a DNS of 3D buffet. The main difficulty of this computation comes from the spanwise wavelength of the buffet cells which is quite large (roughly 1.5 times the chord length). So, if one wants to simulate at least 4 wavelengths, it leads to a huge mesh of 30×109 cells.
A new research topic will start in 2018 on turbulence modelling. The main turbulence mod-els used today in CFD (Spalart-Allmaras, k-ω) have been developed thirty years ago. It is well known that these linear eddy-viscosity models suffer from some limitations in separated zones for example where turbulence is anisotropic and out of equilibrium. Today, with the increase in com-puting resources, more and more DNS or LES flow are available in the literature. With the adjoint
of the Navier-Stokes equation, it is possible to find for each cell the eddy-viscosity minimising the error with respect to a DNS or LES field. This gives an optimal eddy viscosity field but it is also possible to compute corrective terms to a turbulence model or to the Boussinesq relation. In paral-lel, during the last decade, there has been a huge development of machine learning. Like the work done by Duraisamy [52], it is possible to train a neural network to predict a corrected eddy-viscosity field obtained from local variables like the strain-rate, the velocity, its gradient, the wall distance, etc. If this neural network is trained on a large number of test cases which explore a lot of flow states, it will be able to predict corrective terms for the current linear eddy-viscosity mod-els to improve them in regions of the flow where they are not accurate.
Real part of the sensitivity to boundary conditions reduced to the sensitivity to the two dimension-al vector (ρU, ρV).
87
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