Landing configuration of a STOL commuter airplane
Commuter airplanes are typically used on short regional routes. They often operate in broken terrain of lands with underdeveloped infrastructure where they have to serve small regional airports with short landing strips.
Designers of STOL (Short Take-Off and Landing) airplanes had to reduce the landing ground distance to a minimum. To achieve the required aircraft performance, it was necessary to increase the maximum wing lift coefficient. Therefore, they had to design a new kinematic concept of a trailing-edge flap.
Due to the novelty of the concept, the performance of the new flap could not be assumed from measurements of well-known flap designs. Hence, it was necessary to understand how geometry and kinematics of the flap affect the maximum of the lift coefficient of the flapped airfoil. However, the standard methods of design exploration did not work, the development was delayed, and there was a failure to comply with deadlines.
There were 10 input parameters of the task. The standard approach of the Design Of Experiment required at least 1045 CFD computations in the Ansys FLUENT solver. In addition, achieving the quality of data interpolation and its sufficiency to mimic the real behavior of the flap aerodynamics was uncertain.
In the beginning, we coupled the UptimAI tool with the simulation software Ansys FLUENT, with no need to interfere with its code or features. Then, the UptimAI tool begun to set combinations of inputs for the FLUENT. It used the UptimAI algorithm to process simulation results into the metamodel of the flapped airfoil aerodynamics.
Important general information about the performance of the chosen flap concept was given by UptimAI histograms. The probability distribution of the resulting increment in the maximal lift coefficient had the mean value 1.7, which refers to the very well-tuned slotted flap (closer to the typical Fowler flap performance). It also showed a fair probability of an increase in the lift coefficient of the flapped airfoil by more than 2.5! Therefore, it was confirmed that the proposed flap concept has the potential to improve the airplane’s flight characteristics.
UptimAI histograms also depicted particular probability distributions of results describing the influence of each input parameter and each interaction of input parameters. This helped to detect extreme values of the lift coefficient increments and their causes. A good example is the interaction of parameters describing the position of the pivot point, the flap deflection angle, and the deflection angle of the flap cove trailing edge. All three parameters together determine the size of the slot between the flap and the wing, which turned out to be crucial for the value of the lift coefficient.
The importance of each input parameter in terms of aerodynamics was clearly shown in the UptimAI sensitivity analysis. It identified parameters emulating the additional camber of the flap (“4” – camber position, “9” – camber magnitude) as having just little influence on the lift coefficient. Concurrently they do not interact with other input parameters, thus, other criteria can be considered in their design, such as manufacturing. The same can be applied to the geometry of the flap cove entrance radius.
Details of the dependence of the flap aerodynamics regard to changes in input parameters can be examined in UptimAI increment plots. Using these, designers were able to find ranges of inputs responsible for the steep drop in values of the lift coefficient which had to be avoided in the design to prevent deterioration of the result. This approach led to the design insensitive to manufacturing tolerances and deformations under loading. Increment plots were used as well for simulation points where the CFD solver failed to converge.
- Increasing the maximum lift coefficient by up to 2.5 due to optimized flap geometry and position. The achieved properties significantly exceeded the defined expectations.
- Landing ground distance shortened by up to 20% compared to the original design with a slot flap.
- Saving 450 hours of computational time. The required number of CFD simulations (146) was only 14% of the original assumption.