Modern aircraft design is greatly impacted by environmental considerations, such as noise and carbon emissions, fuel efficiency improvement, and carbon-neutral growth, and the rise of fuel prices has also made fuel efficiency a vital element in aircraft design. The aviation sector has used many strategies to meet these goals, and this thesis aims to develop a morphing wing concept based on the Morphing Leading Edge (MLE) and Morphing Trailing Edge (MTE) to increase the aerodynamic efficiency of an aircraft. Therefore, an aerodynamic and structural optimization for the UAS-S45 airfoil using morphing wing technology was performed at the Research Laboratory in Active Controls, Avionics, and AeroServoElasticity (LARCASE).
This includes an optimization framework, which combines an optimization algorithm, airfoil parameterization techniques, and an aerodynamic solver. To obtain the optimized wing shapes, different parameterization methods, such as the Bezier-Parsec (BP), Class Shape Transformation (CST), and Makima methods, and optimization algorithms, such as the Coupled Particle Swarm Optimization (PSO)-Pattern search (PS), Genetic Algorithm, and Black Window Optimization (BWO) were implemented. The effects of various parameterization techniques and algorithm choices on computing time and the results of aerodynamic optimization are investigated. Several objective functions, such as drag minimization, lift-to-drag maximization, and maximizing aerodynamic endurance performance, were used to optimize the airfoil shapes. An aerodynamic solver was coupled with an in-house MATLAB-based optimization framework. The parameterization methods were integrated with the two-dimensional aerodynamic solver known as XFoil, and the Transition (y - Reꝋ) turbulence model was used to validate the results.
The optimization results revealed that Morphing Leading Edge (MLE) and Morphing Trailing Edge (MTE) wings improve their aerodynamic performance at different flight conditions. Such as, the lift coefficient of the MLE airfoil was increased by 21%, and the maximum lift coefficient was increased to 9.6%. In addition, a 3-degree delay for the stall angle of attack was observed. The MTE configurations also showed a significant improvement in aerodynamic performance. The MTE airfoils increased the maximum lift coefficient to 8.13%, and the endurance was maximized by increasing CL3/2/CD by 10.25%. Overall, the morphing wing potentially increased the maximum lift coefficient, delayed the separation of the boundary layer, and increased the stall angle of attack.
In addition, a preliminary structural optimization was conducted to assist in the design and analysis of a morphing leading-edge structure using the parametric sensitivity analysis. The correlation among the design variables, such as composite laminate thickness, material properties, ply orientation, number of plies, etc., and the parameters for two failure criteria, maximum stress failure, and Tsai-Wu failure, were evaluated. The investigation helped in obtaining the important variables which are significant for the morphing wing design. The correlation and determination matrices along with scatter plots were obtained for all the design parameters, and therefore, these results were used to design the composite morphing leading-edge wing.
Furthermore, the use of morphing wing technology as a stall control technique was studied because dynamic stall control occurs in all aerospace applications. The need for dynamic stall control is crucial for UAVs and military aircraft, as they require a high level of maneuverability. The design and implementation of the Dynamically Morphing Leading Edge (DMLE) technology were comprehensively investigated for different morphing parameters, such as deflection frequency and amplitude, and the morphing starting time. It was effective in controlling the formation and development of leading-edge vortex and flows separation, thus preventing the airfoil from experiencing dynamic stall.
| Date | 4 Apr 2023 |
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| Original language | American English |
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| Awarding Institution | - École de technologie supérieure
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| Supervisor | Ruxandra Botez (Supervisor) & Tony Wong (Co-supervisor) |
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