LEE- Uçak ve Uzay Mühendisliği-Yüksek Lisans

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http://hdl.handle.net/11527/19474

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Konu "Aerodynamics" ile LEE- Uçak ve Uzay Mühendisliği-Yüksek Lisans'a göz atma
  • Öge
    Aerodynamic and structural optimization a male class unmanned aerial vehicle wing with genetic algorithm
    (Graduate School, 2023) Ün, Kağan ; Yıldız, Kaan ; 511191209 ; Aeronautical and Astronautical Engineering Programme
    In this thesis, a genetic algorithm based airfoil, planform and wing structure design is utilized for a 1500-2000 kg class fixed wing reconnaissance MALE (Medium Altitude Long Endurance) type UAV (Unmanned Aerial Vehicle). Due to their mission descriptions, these UAV are generally designed to have long range and high loiter time. For these reasons, an aircraft to be designed for observation purposes must have high aerodynamic efficiency and high fuel load ratios (low empty mass) to maximize the range and loiter time. To achieve high aerodynamic efficiency, these aircraft have high wing span ratios, and their wings contain specially designed airfoils. These airfoils are generally designed to have high lift-to-drag ratios. On the other hand, due to long wing structures of high-span aircraft, the wings of such aircraft have high bending forces especially during maneuvers. Therefore, design of airfoils of such aircraft have a compromise between fitting the ideal lightweight inner skeleton for the wing and providing ideal aerodynamics. For these reasons, the wing profile located at the root of the wing is generally chosen to be thicker than the profile used at the tips of the same wing. To simplify the design process, each airfoil are constructed from two Bezier curves that create centreline and thickness distribution of airfoils. Control points of the Bezier curves are the most of the input parameters of the genetic algorithm program. From sets of control points, the airfoils are created. Then, the airfoils are analysed in XFoil by the interface of the function made in MATLAB. After analysis of airfoils, a planform that uses these root-tip airfoils is tested for having sufficiently high lift and low drag for the cruise altitude, cruise speed and cruise power. Then the airfoilplanform combination that pass the basic requirements are sorted by their maximum lift-to-drag ratios. The airfoil-planform combinations with higher maximum lift-todrag ratios are selected for creating the next generation, and the cycle continues. When the maximum number of generations are achieved, the best airfoil-planform combination of the last generation is selected as the best candidate. The fitness criterion of this first phase is the lift-to-drag ratio of the airfoil-planform combination. After the winner airfoil-planform combination is created, inner structure optimization process for the wing begins. Inner structure of the planform consists of four ribs and a twin-box spar structure made of 7068 aluminium alloy, the strongest commercial aluminium alloy available. The lift force and torsion moment of each wing segment is transferred to the spar by the ribs of the wing. The cross section of the spar consists of two closed cells with the support of four stiffeners and eight flanges. Vertical walls have thickness of 2.5 mm, while upper and lower walls have thickness of 1.5 mm. The flanges have cross section value of 400 mm2 , and are set to the upper and lower ends of vertical walls, filling the corners of each cells. The stiffeners have cross section value of 200 mm2 , and are set to the middle of the upper and lower walls, in between the vertical walls. While the stiffeners and flanges carry the tensile and compressive loads, the walls primarily carry the shear loads. To simplify the structural analysis, several assumptions are made. The main spar is assumed as a serial combination of smaller spar sections with constant cross sections. The stresses on cross sections are analysed with structural idealization method. The cross section is assumed as collections of idealized shear force carrying panels and normal force carrying area members called booms. At first, the effective areas at the positions of booms are found by adding effective boom area of wall sections to the actual stiffener or flange areas. From the effective areas and positions of each boom, area moments of inertia and bending moment centre are found. From area moments of inertia of the section and applied bending moment, the compressive and tensile forces of each boom are calculated. After the calculation of tensile and compressive forces, shear forces on the walls are calculated from the area and wall thicknesses of each cell, torsion moment inflicted on section, and compressive and tensile forces of each boom. After the stress calculations are made for each section, a selection process is carried out to control the stresses on the cross sections on each wing rib. If the stresses on the wing at any point is larger than safe limits, the spar is considered as infeasible specimen. Otherwise the fitness value for the spar is compared to the other successful specimens and the fittest specimen is chosen for each generation to create new specimens. The weight of the spar is the fitness value for the second phase. At the end of the process, the ideal cross section is obtained and the program finishes working.
  • Öge
    Quantitative analysis of aircraft aerodynamic derivatives using the least squares method in a six degrees of freedom flight simulation environment
    (Graduate School, 2024-08-21) Altınışık, Furkan ; Acar, Hayri ; 511201165 ; Aeronautical and Astronautical Engineering
    This thesis presents an in-depth analysis of aircraft aerodynamic derivatives using the Least Squares Method (LSM) within a six degrees of freedom (6-DOF) flight simulation environment. The primary objective is to evaluate and compare the performance of Ordinary Least Squares (OLS) and Recursive Least Squares (RLS) methods in estimating aerodynamic parameters under various flight conditions, including ideal, turbulent, and error-induced scenarios. A detailed 6-DOF flight simulation model was developed using data from the SIAI Marchetti S211 aircraft. This model integrates various subsystems, including equations of motion, aerodynamics, engine dynamics, and atmospheric conditions. The Newton-Raphson method was employed to maintain steady-state conditions, ensuring the aircraft's trim state was accurately represented. For solving the differential equations derived from the equations of motion, the Runge-Kutta method was chosen due to its robustness and accuracy in handling the nonlinearities associated with flight dynamics in the simulation model. The aerodynamic forces and moments were linearized using the small disturbance theorem, which simplifies the complex nonlinear equations into a more manageable linear form. This linearization allowed for the formulation of force and moment coefficients as functions of aerodynamic derivatives. These derivatives, critical for understanding the aircraft's behavior, were estimated using both OLS and RLS methods. Realistic flight data was simulated under various conditions, including ideal scenarios without any disturbances, scenarios with atmospheric turbulence, and scenarios with systematic sensor errors. The Dryden turbulence model was used to simulate realistic atmospheric disturbances, providing a continuous representation of turbulence that affects the aircraft during flight. Systematic sensor errors were introduced to understand their impact on the accuracy of parameter estimation. The OLS method provided single-step parameter estimates by processing all data points simultaneously, making it straightforward and computationally efficient. In contrast, the RLS method updated parameter estimates incrementally as new data became available. This dynamic approach allowed the RLS method to adapt to changes over time, making it particularly suitable for real-time applications where system characteristics may vary. Performance metrics such as the $R^2$ statistic and standard deviation were used to evaluate the estimation accuracy. These metrics provided quantitative measures of how well the estimated parameters matched the true values, with the $R^2$ statistic indicating the proportion of variance explained by the model and the standard deviation providing a measure of the estimation precision. The analysis revealed that both OLS and RLS methods produced accurate results under ideal and turbulent conditions. The presence of atmospheric turbulence did not significantly affect the estimation accuracy, as the average error introduced by the turbulence was zero. This robustness highlights the effectiveness of LSM in handling real-world flight data with environmental disturbances. However, when systematic sensor errors were introduced, both OLS and RLS methods showed biased estimation results. The bias was evident in the deviation of the estimated aerodynamic derivatives from their true values, underscoring the importance of accurate and error-free measurement data for reliable parameter estimation. Further analysis demonstrated that increasing the sampling frequency improved the performance of the RLS method. At higher frequencies, such as 50 kHz, the RLS estimates converged more closely to the true values, even in the presence of systematic sensor errors. This improvement is attributed to the reduced information loss in higher frequency sampling, which captures more details and variations in the data that might be missed at lower frequencies. This finding suggests that higher sampling rates can effectively mitigate the adverse effects of sensor errors on parameter estimation. The design of control surface inputs was identified as a crucial factor influencing the accuracy of aerodynamic parameter estimation. Optimal input design, which involves selecting appropriate control surface deflections, ensured accurate estimation results. Conversely, non-optimal inputs led to discrepancies between the estimated and true values. This emphasizes the need for carefully designed excitation maneuvers during flight tests to obtain reliable aerodynamic data. The RLS method demonstrated particular advantages in dynamic environments due to its ability to update estimates in real-time. This adaptive capability allowed it to maintain accuracy even when the system characteristics changed over time. However, the OLS method exhibited slightly better performance at lower frequencies, showing less sensitivity to variations in sampling rates. Both methods showed distinct strengths, with OLS excelling in stable, low-frequency scenarios and RLS proving superior in dynamic, high-frequency conditions. The theoretical expected value formulas for the parameter estimates were validated using the simulation model outputs. This validation confirmed the presence of bias when systematic errors were introduced and reinforced the high accuracy of estimates under both ideal and turbulent conditions. In conclusion, this thesis provides a comprehensive evaluation of OLS and RLS methods for estimating aerodynamic derivatives in a 6-DOF flight simulation environment. The findings demonstrate the robustness of these methods under various flight conditions, highlight the impact of systematic sensor errors, and underscore the importance of optimal input design and high-frequency data sampling under linear database.