[Personal PhD Project] 

Recently, more and more of the sensing tasks, usually performed by various aircraft configurations, are being transferred to the domain of unmanned aerial vehicles (UAVs). Therefore, the number of different UAV manufacturers is rapidly increasing, providing different products of novel configurations. Unlike with manned aircraft, these products have very fast prototyping cycles, meaning that it can be manufactured faster than in-depth detailed analysis of its performance is done. Consequently, their performance can be measured in real life.

But this is not always that easy. New flow phenomena appear when the small UAVs are compared to their manned counterparts. Low Reynolds number flow, increased sensitivity to turbulence and different component drag ratios are all topics that were not as significant for bigger aircraft. Wind tunnels can be used to study the steady parts of these effects, but their availability is very limited. Therefore, performance measurement of UAVs has to be done in-flight, limiting the available environmental conditions and sensors. 

In addition to the above, aerodynamic design process for UAVs also poses some difficulties. Most commonly, low-order panel methods like Vortex Lattice Method or a Doublet Lattice Method are used for estimating aerodynamic coefficients for UAVs. These methods are well capable of predicting lift characteristics of lifting surfaces and induced drag. However, the total drag, for which viscous air effects and separation regions need to be evaluated, is rarely properly accounted for. This is sometimes mediated with use of the parasite drag build-up tools. Namely either the equivalent skin-friction method or the component build-up method would be used. However, both methods are based on previously collected drag data of built and tested manned aircraft with no specific application to UAVs in mind. Therefore it raises questions if these methods are applicable to the new design space.

The problems described above display the unknowns when one is to design an efficient UAV. To try and solve these problems, I raise the following research questions:

1. What is needed to accurately measure the drag of the UAV in-flight?

2. What workflows, procedures and tools are needed to analyze the flight test data of UAVs efficiently and accurately?

3. How well can the common low order tools predict the drag of a UAV? How well do they compare to the higher order tools?

I use data analysis and system identification tools together with the data from projects FLEXOP and FLIPASED to validate my methods and draw conclusions. 

Milestones reached so far:

• I developed a workflow for analyzing and sorting the collected data from UAV flight test campaigns.

• I designed and implemented a thrust measurement system for a pylon-mounted jet engine of a UAV.

• I have performed preliminary drag analysis for different components of a UAV using system identification methods.

• I have ran a wind tunnel test campaign to acquire validation data.

The draft thesis is expected to be finished in the second half of 2024.

[Research Projects]

FLEXOP (Flutter Free Flight Envelope Expansion for Economical Performance Improvement) was a European H2020 project initiated in 2015. The project aimed to investigate various fields within multidisciplinary aircraft design, primarily developing tools for aeroelastic tailoring and aeroservoelastic control. A strong focus was placed on flutter modelling and control using Active Flutter Suppression (AFS) technologies. An unmanned aircraft was designed, built, and tested to demonstrate the validity of the tools. 

The follow-up project FLIPASED (Flight Phase Adaptive Aero-Servo-Elastic Aircraft Design Methods) with an international consortium of Technical University of Munich (TUM), the Institute for Computer Science and Control from Hungary (SZTAKI), the German Aerospace Center (DLR), and ONERA from France was started end of 2019 and lasted until June 2023. The project aimed at continuing the goals of FLEXOP while further expanding them to incorporate flutter control, gust response, and active load-shape control. 

The demonstrator design, carried out during the FLEXOP project, concluded with a swept 7m wingspan, 65kg aircraft with a sleek fuselage and a V-Tail. The aircraft was equipped with a 300N jet turbine mounted on a pylon.

Three different sets of wings were manufactured. The first, stiff wing pair was to be used to set the aircraft's baseline. The second pair was more flexible and designed to flutter at a specific airspeed of 56m/s by tuning the first symmetric wing bending and wing torsion modes. The third pair was aeroelastically tailored with unbalanced composite laminates to passively alleviate wing loads. The first full demonstrator was integrated in 2019, followed by ground vibration tests (GVTs) and a taxi test in May of the same year. The demonstrator was then named T-FLEX. 

As the flutter wing was ready to be flown, an accident occurred during flight test 23. As a result, the demonstrator burned down. Consequently, the second fuselage had to be integrated.

6 months after the crash, the new demonstrator was already hanging in the DLR’s GVT hall - with reduced total weight, improved radio control system, and increased fuel capacity. The newly rebuilt demonstrator received a new name P-FLEX where the P stands for phoenix – the mythical bird resurrecting from its ashes. 

The two projects resulted in 37 flight tests. The test campaigns began with covering the topics of complex UAV operations, baseline controller design, experimental aerodynamics and system identification. These flights led to the culmination of the projects, where, for the first time, the conventional flutter boundary was passed by using active flutter suppression on an unmanned subscale demonstrator in real-life conditions. 

Personal contributions:

• Flight test planning, flight test data preparation and analysis, 

• Aerodynamic modelling,

• Assembly and ground testing of the subscale demonstrators.

Flight test data is freely available here.

Related media articles:

Aerospace Testing International 

DLR Press Release after a successful flutter suppression test 

Aviation Week 

[Masters Project - Link to the thesis]

One of the important developments in the capabilities of some post-WWII period fighter airplanes is supermaneuverability, allowing the pilot to change the heading faster and decrease the maneuver space requirements. This is achievable with flight at very high angles of attack, where delta wings are known to have superiority over conventional wing designs. In addition to this, active flow control is known to increase the post-stall capabilities of delta wings even further. 

The current report investigates flow changes on a sharp edged delta wing due to unsteady leading-edge blowing with different actuation frequencies. Constant frequency and two spatially varying frequency configurations were considered. Experimental force and particle image velocimetry (PIV) measurements were done on a half model delta wing with 12 blowing slots located on the leading-edge. From force measurements it was noted that up to 40% increase in lift is possible with actuation at post-stall angles of attack. Litle or no gain was seen at stall and pre-stall regimes. From PIV measurements, change in flow structure was discovered for stall regime, with actuation forming separate flow structures and not transporting the actuation momentum well into the main flow structure. For post-stall regime, where the biggest changes in force coefficients were observed, the actuation completely reshaped the flow, recreating a structure similar to the one seen in stall region angles of attack.

[Personal Project]

"Does shaving legs really make you faster?" is the eternal question within the sport of cycling. Motivated by this question, I performed outside tests with myself as the subject. I collected power data for 5 configurations: 1 - Hairy legs; 2 - Shaved legs; 3 - Shaved legs, aerosocks; 4 - Shaved legs, aerosocks and a camelback; 5 - Shaved legs, aerosocks, aero position. I wrote an algorithm which identifies the wind speed for the data and the drag coefficient for each configuration.

The resulting answer: yes, it does make you faster by 4%. Another 4% can be gained by using aerodynamic socks. But the biggest gains can be expected by optimizing one's position on the bike.

The results agree well with the available wind tunnel data.