TU Berlin

Collaborative Research Centre 1029SFB1029: B06


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B06: Fast control of unsteady effects in turbomachinery

Principal investigators:
Prof. R. King ()

Scientist:  Karl Neuhäuser, M.Sc. ()
phone       (030) 314 23401

Scientist:  Benjamin Fietzke, M.Sc. ()
phone       (030) 314 29753


The assistance of CRC partners by providing control solutions for complex flow control tasks will be the primary role of project B06 during the second phase. Due to the successful utilization of Iterative Learning Controllers (ILCs) within the aero- and fluiddynamic experiments of the first phase, the theoretical and practical focus will be kept on these control algorithms further on.  The knowledge and experience in implementing various ILC algorithms collected during the first phase at the linear compressor cascade set-ups will be exploited for the development of a flow control system for the annular cascade set-up of partner B01. In this experiment, a configuration of binary (opened/closed) solenoid valves, instead of directional-proportional valves, is used as actuators which generates a significantly more difficult control task. It is the objective of this project to modify an ILC algorithm accordingly. Newly developed control strategies will be tested at the B06 wind tunnel first. Furthermore, B06 is responsible for the optimization of actuation patterns to improve impingement cooling and the design of a film cooling control system in cooperation with B03 and B05, respectively.

1st Funding period 2012 - 2016


The subproject pursues two superordinate research objectives. One focuses on the development of iterative learning control methods to control processes with dual-periodic nature, e.g. the fluid dynamics inside an impeller, where you will find periodic patterns in the velocity field of the flow along time and the circumferential coordinates of the machine. The other emphasis is the utilization of iterative learning control to experimental setups analyzing the unsteady and periodically perturbed flow around generic aerodynamic profiles, especially the stator blades in a linear compressor cascade, a three-dimensional compressor test rig and a rotor-stator-tandem-configuration. The measures of automatic-control shall ensure the stable operation of the compressor in a Pulsed Combustion Engine

Fig. 1: Qualitative increase of the incidence angle i for a compressor blade by the impact of upstream wakes and the throttling by the PDE, with imax1 > imax2


A closed-loop active flow control strategy to reduce the velocity deficit of the wake of a compressor stator blade has been developed.
The unsteady stator-rotor interaction, caused by the incoming stator wakes, generates fast changes of the rotor blade loading, affecting the stability and the performance of the overall compressor. Negative effects will be seen likewise when unsteady combustion concepts, such as a pulsed detonation (PDE), produce upstream disturbances. The combined effect of the wakes and the PDE would result in very high aerodynamic peak loads for the compressor blades. Therefore, an efficient compressor design is only partly possible, since it has to cover these critical loads. A reliable active flow control (AFC) of the stator wake is a way to handle this issue. It could be used in the future to refill the wakes wherever the compressor is throttled to mitigate the aerodynamic peak load. This is qualitatively shown in Figure 1.

Fig. 2: Low-speed compressor cascade test rig with trailing edge actuation system.

Therefore, investigations on wake manipulation with trailing-edge blowing were carried out on a new low-speed cascade test rig, see Figure 1. Detailed information about the wake profile is obtained by five-hole probe measurements in a plane downstream of the cascade for the natural and the actuated flow at a Reynolds number of 600,000. These measurements show a significant reduction of the wake velocity deficit for the investigated actuator geometry with an injection mass flow of less than 1 % of the passage mass flow.

Based on these results a position in the wake was chosen which is representative for the actuation impact on the velocity deficit. There, a hot-wire-probe measurement serves as the controlled variable. A family of linear dynamic black-box models was identified from experimental data to account for nonlinear and unmodelled effects. Static nonlinearitiy was compensated for by a Hammerstein model to reduce the model uncertainty and get a higher controller performance. To handle off-design conditions, a robust controller working in a range of Reynolds numbers from 500,000 to 700,000 was synthesized. The task of the controller is to rapidly regulate the controlled variable to a reference velocity by changing the blowing amplitude. The synthesized robust controller was successfully tested in closed-loop experiments with good results in reference tracking at a high bandwidth.

Iterative Learning Control (ILC)

ILC (animated)

One focus of project B06 is the application of optimal ILC algorithms to periodic active flow control problems. The compressor stator cascade developed by project B01 is the plant to be controlled. The objective of the ILC is to decrease/reject the impact of a periodically working disturbance generator on the cascade flow by means of sidewall and blade actuation. The periodic disturbances of the individual passage flows are generated by a damper flap device that is located downstream of the trailing edges of the blades. These mimic the throttling effect of periodically closed combustion tubes in a pulsed detonation engine.

The research considers both, time-domain and frequency-domain algorithms. Robustness aspects are of great importance for the considered application and are therefore one the project’s major scopes. Furthermore, the considered periodic active flow control problem is constrained. By formulating the iteration variant optimization problem as an constrained quadratic program the task can be solved by means of a computationally efficient active-set optimization algorithm.

Project B06 pursues the development of iterative learning control methods to control processes with dual-periodic nature, e.g., the fluid dynamics inside an impeller, where you will find periodic patterns in the velocity field of the flow along time and the circumferential coordinates of the machine. In the example, the flow inside each of the impellers passages is considered as one single process. The overall learning rate shall now be enhanced not only from period to period but also by learning from each other, i.e., by learning from process to process. Especially in the beginning of a new repetitive control task, e.g., if a new disturbance pattern appears, there is great potential in quickening the learning process.


Steinberg, S., M. Staats, W. Nitsche and R. King. Comparison of iterative learning and repetitive control applied to a compressor stator cascade. In R. King, ed., Active Flow and Combustion Control 2014, vol 127 of NNFM, 39-54. Springer International Publishing, 2015.

Steinberg, S., M. Staats, W. Nitsche and R. King. “Iterative learning active flow control applied to a compressor stator cascade with periodic
disturbances,” in Submitted to: Proceedings of the ASME Turbo Expo 2015, Montreal, Canada , Paper No. GT2015-42008, 2015.


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