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ÖgeAeroacoustics and aerodynamics optimization with using machine learning algorithms(Graduate School, 2025-05-26)This dissertation aims to develop an innovative optimization methodology by integrating machine learning algorithms with aeroacoustic optimization. To achieve this goal, a general optimization framework has been established, incorporating a Generative Adversarial Network (GAN) algorithm for dimensionality reduction and integrating computational tools for performance evaluation. Through this approach, a high-performance optimization tool has been developed, effectively combining aeroacoustic optimization with GAN-based methodologies. The effectiveness and efficiency of the proposed optimization methodology have been evaluated through various analyses. To this end, four different optimization tools have been developed based on the same methodological framework but incorporating different performance evaluation methods. Comparative analyses of these tools have revealed the impact of computational accuracy on the optimization process, demonstrating that higher-fidelity tools significantly enhance optimization success and efficiency. To investigate the applicability of the GAN algorithm for aeroacoustic optimization, an inviscid-based Noise-GAN optimization tool was developed, utilizing low-fidelity aerodynamic solvers without viscous effects. Due to its fast computational capabilities and reduced complexity, the tool was rapidly integrated into the methodology and tested for its potential in aeroacoustic optimization. Ten independent optimization studies were conducted under specific flight conditions, and the optimized airfoils were compared against profiles from the UIUC airfoil database. The results demonstrated that the Noise-GAN-generated profiles exhibited superior hydrodynamic and hydroacoustic performance compared to existing designs. To improve performance prediction accuracy, a viscous-based Noise-GAN optimization tool was developed. By replacing low-fidelity solvers with high-fidelity viscous solvers, this tool overcame the limitations of the inviscid-based approach. Comparative studies showed that neglecting viscous effects could misguide the optimization process, reducing efficiency and overall performance. A detailed analysis across multiple angles of attack (AoA) revealed that viscous-based Noise-GAN consistently produced high-performance profiles, whereas the inviscid-based method, while capable of generating high-performing designs, exhibited lower efficiency. To extend the methodology to three-dimensional geometries, a 3D Viscous-Based Noise-GAN optimization tool was developed. This tool facilitated the optimization of a hydrofoil at multiple AoA conditions, yielding significantly improved aerodynamic and aeroacoustic performance compared to the baseline NACA0009 profile. To assess the operational limits of the developed methodology, an optimization study was conducted on a helicopter rotor in hover under transonic flight conditions. The rotor was divided into five sections for independent 2D optimizations, and the optimized airfoils were used to construct a 3D rotor geometry. Comparative analyses with a reference rotor showed that the optimized design generated twice the thrust while maintaining a higher thrust-to-torque ratio (CT/CQ). Additionally, the new rotor exhibited a significant noise reduction of up to 14 dB in near-field observer locations, confirming its superior aeroacoustic performance. Finally, the DynStall-GAN optimization tool was developed to enhance wind turbine airfoil performance by delaying dynamic stall. Ten independent optimization cycles were performed under two different conditions, and the resulting profiles outperformed both the NACA0012 airfoil and a previously optimized profile from the literature. A comprehensive evaluation of the conducted studies confirms that the developed optimization methodology is highly effective and versatile. Its compact and adaptable structure has enabled the creation of multiple optimization tools and facilitated the investigation of various aerodynamic phenomena. With the successful implementation of five optimization studies—ranging from 2D hydrofoil optimization to 3D transonic rotor optimization—this research has yielded numerous high-performance airfoil geometries, contributing significantly to the literature on aerodynamic and aeroacoustic optimization.
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ÖgeDevelopment of novel green monopropellant thrusters(Graduate School, 2025-05-07)Space propulsion systems that are used for spacecraft maneuvers can be grouped into cold gas, monopropellant, and bipropellant thrusters. Monopropellant thrusters are widely used in space propulsion due to simplicity, reliability, and precision capabilities. Monopropellant thrusters commonly use a propellant that can be decomposed using catalysts. In the last few decades, hypergolic storable propellants such as hydrazine (N2H4) have been the most used propellants in space applications, however, due to their highly toxic nature, concerns about environmental and operational safety are increasing. Hydrogen peroxide (High Test Peroxide, HTP) has emerged as a promising candidate for green space propulsion applications due to its lower toxicity compared to conventional liquid propellants such as hydrazine and nitrogen tetroxide (N2O4). This study aims to optimize the performance and reliability of HTP monopropellant thrusters, focusing on catalyst bed stability, efficiency, and durability during both extended steady-state and pulse mode operations. To this end, a comprehensive thruster development process was undertaken. A series of thruster prototypes, including 10 N and 1 N thrust levels, were developed. Additionally, a monopropellant test bench, including a propellant tank, valves, and sensors, was assembled. The first phase of the study included catalyst characterization tests. During these tests, catalysts with various chemical and mechanical properties were analyzed using the pressure loss across the catalyst bed as an indicator of catalyst deterioration. Following the selection of the suitable catalyst, key operational and design parameters, including catalyst bed packing, pellet size, bed load, and HTP concentration, were investigated for their impact on steady-state performance. Results indicate that an optimal pressure drop of 1–1.5 bar across the catalyst bed provides optimal stability and durability. To evaluate transient characteristics, the effects of bed load, HTP concentration, and pre-heating temperature on thruster response times were investigated. Following the optimization process, a lifetime test consisting of six consecutive firings with an HTP throughput of 6 kg was conducted to monitor performance variations over time. Additionally, the blowdown characteristics of the thruster were analyzed to assess performance under end-of-life conditions. The experiments in this study demonstrate that HTP monopropellant thrusters are viable candidates for reliable space missions, particularly for long-duration operations such as station-keeping maneuvers. A key aspect of monopropellant thrusters is the pulse mode operation, since pulse mode is crucial for attitude control maneuvers. To analyze pulse mode characteristics, repeated short burn tests at frequencies between 0.25 Hz and 2 Hz at various duty cycles were conducted. During these tests, pulse mode characteristics, including settling time, peak thrust, peak pressure, impulse bit, and mean thrust, were measured. A smallest impulse bit of 0.16 Ns was achieved. The next phase of the study was the numerical modeling of the thrusters. A one-dimensional, adiabatic, reacting, porous media flow analysis was conducted to model the flow within the catalyst bed. The model included key aspects of the catalyst bed, such as porosity and activation energy of the catalyst. The model was validated both experimentally and using a benchmark study from the literature. To investigate the variation of catalyst parameters during the operational lifetime of the thruster, the long-duration lifetime tests of the thruster were analyzed using the numerical model. According to the results, the porosity decreased linearly over time, while activation energy increased quadratically as a function of time and propellant throughput, due to the accumulation of chemical and mechanical damage within the catalyst bed. The temperature and pressure distributions obtained from the model showed high consistency with the experimental data. The final phase of the study was designing a propulsion system, considering the storability of hydrogen peroxide within the tank. A material compatibility investigation was conducted by exposing various materials used in the propulsion system to hydrogen peroxide at elevated temperatures, as higher temperatures accelerate the decomposition reaction and shorten the required experiment duration. Results showed that the tested aluminum alloy exhibited good compatibility with the propellant. The decomposition rates obtained from the tests for each material were used for the numerical modeling of the decomposition reaction, which determined the pressure and concentration variations over a space mission. The model was further improved by incorporating the accumulation of deterioration within the catalyst bed, such as the reduction in porosity and the increase in activation energy. A significant result of the propulsion system model was the semi-self-pressurizing characteristic of hydrogen peroxide in long-duration missions. This characteristic helps maintain the thrust level over the course of a mission. A sensitivity analysis was performed to analyze the pressure, thrust, and Isp variations for different mission profiles. Hydrogen peroxide has emerged as a promising green propellant for space propulsion applications. Its high density and ease of decomposition using a catalyst pave the way for its wider adoption in space propulsion systems. The evaluation and optimization of hydrogen peroxide-based monopropellant thrusters in this study contribute to the development of propulsion systems capable of being used in a wide range of space applications.
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ÖgeExperimental investigation of gust response in flapping wing aerodynamics(Graduate School, 2025-03-26)This PhD thesis explores the generation and characterization of strong periodic transverse gusts for Micro Air Vehicle applications and investigates their effects on fixed, pitching-up, and plunging wings. The study aims to enhance our understanding of gust impacts on flapping wing aerodynamics. The literature is reviewed focusing on the theory of unsteady aerodynamics and the historical development of experimental gust generation methods, as well as the aerodynamic mechanisms of pitching-up and plunging wing. The experiments are conducted in the closed-circuit, large-scale, free-surface water channel located in the Trisonic Laboratories at the Department of Aeronautics and Astronautics Engineering of Istanbul Technical University. A flat plate made of Plexiglas is used as the gust generator. A connection rod connects the flat plate to a servo motor, which provides the pitching motion with a servo controller. This pitching motor is placed on a linear table, which provides the plunging motion with a servo motor as well. The motion system is controlled by a Labview VI code providing the motion signal and synchronization with the measurement systems. For the gust response experiments, a NACA0012 airfoil is mounted on an identical motion system from its quarter chord at the downstream of the gust generator. There are two chords of distance between the mounting axes of the gust generator and the airfoil. A rectangular end plate with an outward bevel is suspended from the top of the channel to reduce the free-surface effects and increase the aspect ratio of the wings acting as a symmetry plane. A Digital Particle Image Velocimetry system is used to acquire the vector fields in the wake of the gust generator. The flow is illuminated by a dual cavity Nd:Yag laser, and the water is seeded with silver-coated glass spheres of 10μm diameter. Two cameras are positioned under the water channel. The two double-frame images from the two cameras are stitched using an in-house code. The resulting PIV images are interrogated using a double frame, cross-correlation technique with a window size of 64×64 pixels and 50% overlapping in each direction. Force and moments acting on the NACA0012 airfoil are measured using a six-component Force-Torque sensor. The sensor is attached to the vertical cantilevered mounting beam of the test model, oriented with its cylindrical z-axis normal to the pitch-plunge plane. The data acquisition is accomplished by a separate Labview VI. All measurements are obtained with a 10kHz sampling rate, then the data is down-sampled to 100Hz with block averaging to smooth the signal. The flat-plate gust generator that moves with periodic functions in pitch and plunge axes produces distinct vortices to simulate transverse quasi-sinusoidal gusts. The motion parameters are targeted for the generation of strong gusts. The gust characterization methodology by in-depth spectral analysis of the wake is the pivotal point of this study to produce gusts with the desired intensity and characteristics. Instead of a point or a line in the wake, this approach examines the whole flowfield in the wake of the gust generator, where the airfoil will be present at the next phase of the study. The spectral analysis consists of an FFT analysis of the velocity field to obtain the predominant frequency of the gust, followed by auto-spectral and cross-spectral analysis. The three gust types selected after this process are validated by vorticity plots and velocity profiles. Quantitative gust characterization parameters are calculated for these gusts, which are named as A, B, and C. It has been observed that Gust A and C exhibit approximately twice the amplitude compared to Gust B, while Gust A has a frequency that is twice as high as that of Gust B and C. Additionally, it has been noted that Gust A and B slightly increase the average velocity in the streamwise direction. Gust encounter investigation begins with a steady flow analysis of a NACA0012 airfoil at a Reynolds number of 10000 to establish a baseline for gust cases, which reveals continuous lift generation even at high angles of attack. Fixed-wing experiments are conducted at angle of attack values from zero to 45 degrees under gust conditions. The results confirm the quasi-sinusoidal nature of the selected gusts, with Gusts A and B enhancing lift at higher angles of attack due to LEV attachment during positive transverse velocity perturbation peaks. In contrast, Gust C, with its distributed smaller vorticities, does not replicate this effect. Analytical lift predictions using the Sears formula, compared with experimental data, show a good correlation up to 30 degrees of AoA, although some overprediction in lift delay occurs due to complex flow interactions. Gust encounter of a pitching-up wing with a ramp motion of two different angular velocities (7.5 deg/s and 45 deg/s) is explored. The encounter timing significantly influences the forces on the wing, with faster maneuvers causing more drastic changes due to gust timing. Gusts A and B exhibit more pronounced effects during fast maneuvers, consistent with the RMS plots from fixed-wing experiments. For plunging wing experiments, steady flow conditions are evaluated at four flapping frequencies, generating drag-producing, zero-drag, and thrust-producing wakes. Especially resonant frequencies of plunge motion with gust frequencies result in significant thrust variations, influenced by gust timing. Although high plunge frequencies mitigate gust effects, due to producing large forces of lift and thrust, gusts still produce notable variations compared to lower frequencies. In conclusion, this study contributes valuable insights into gust generation and characterization for MAVs. The frequency and timing of the gust, as well as the characteristics of its vortices are found to be important parameters of the gust encounters with flapping wings. Further studies are recommended to refine analytical models and explore additional experimental gust encounter scenarios for an enhanced understanding of MAV aerodynamics under gusty conditions.
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ÖgeVibration analysis of rotating beam structures made of functionally graded materials in a thermal environment by generalized differential quadrature method(Graduate School, 2024-10-18)In recent years, there has been increasing curiosity in rotating aerospace structures such as gas turbine blades, helicopter rotary wings, wind turbine blades, tilt-rotor propellers, and flexible appendages of space vehicles due to various problems up to the chosen material type, boundary conditions, rotational speed, geometric properties, and environmental effects that have been studied for the best solution. To overcome these problems and prevent possible catastrophic failures, various analyses like vibration, buckling, static of structures, fatigue, and thermal are performed through engineering analysis software, user-written scripts based on numerical and semi-analytical methods, or specific test equipment. Then, the structure is improved by changing design parameters and operating conditions at the beginning of the design process if any unwanted cases occur. In specific design problems, user-written scripts are generally preferred as engineering analysis tools to obtain more accurate and precise results in a short time. The existence of this wide research field causes many researchers to turn their attention to the topics related to such types of rotating structures able to be modeled as beam elements. One of the extensively studied topics realized under free vibration analysis is the computation of dynamic characteristics, which is a critical design and performance evaluation criteria designating the life of a structure, operating limits, and stability. Therefore, many numerical methods have been attempted to analyze and avoid possible resonance cases by calculating the dynamic characteristics of rotating beam structures accurately. Of these numerical approaches, DQM is first introduced by Bellman et al. to solve nonlinear partial differential equations accurately by expressing them as a set of algebraic equations. This technique uses weighting coefficients to approximate the derivatives of a function at a point and employs a weighted linear sum of the function values at all discrete points. In many areas of engineering problems, it presents satisfactory results for the well-optimized spacing of grid points and well-determined weighting coefficients by using suitable functions. Being computationally less expensive, easy implementation of non-classical boundary conditions, less memory requirements, simple algorithm building, derivation of new numerical methods by combining element-based methods to solve complex geometries, etc. are some reasons why preferred by many researchers. The first objective of this study is to develop a mathematical model for a rotating double-tapered beam with a flexible root i.e. elastic restraints on the root of the beam attached to a rigid hub and present a numerical solution algorithm based on DQM to compute the dynamic characteristics of the beam. As a beam model, the Euler-Bernoulli beam theory is employed to model the system easily with a reasonable result in the first part of free vibration analysis. Using DQM, the governing differential equations of beam and boundary conditions are transformed into a set of linear algebraic equations written in the matrix form. Both of them are defined in different matrices, and boundary conditions are entered by updating corresponding rows of the system matrix created for the governing differential equations, which gives great flexibility to use various nonclassical support types defined as mixed-type partial or ordinary differential equations. Differently from previous studies based on DQM, the effect of rotary inertia, setting angle, and linear changes in taper ratios on dynamic characteristics are investigated. Also, the effect of hub radius and rotational speed are presented akin to previous research findings. To validate the solution method, the obtained results are compared with other studies in the open literature. The second objective is to investigate the elevated temperature and material effects on the mechanical behavior of the beam structures made of functionally graded materials, including shear deformations. Under a uniform temperature distribution, the variations in natural frequencies and mode shapes are investigated for temperature-dependent material properties. The findings of the second part of the solution present that the gradational composition of material, thermal loads, and shear deformation have a significant effect on the dynamic characteristics of the beam structures exposed to elevated temperatures. To overcome the softening effect of high temperatures, the composition of the ceramic-metal mixtures must be determined accurately by employing meshless numerical approaches such as DQM. To sum up, a comprehensive study about free vibration analysis of beam structures used in the aerospace industry has been presented through the thesis, providing assessments of their vibration behavior. Understanding the structural dynamics of these structures is vital in sectors like aerospace, energy, and manufacturing.
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ÖgeMulti agent planning under uncertainty using deep Q-networks(Graduate School, 2024-04-29)The extensive popularity of commercial unmanned aerial vehicles has drawn great attention from the e-commerce industry due to their suitability for last-mile delivery. However, the organization of multiple aerial vehicles efficiently to deliver the given set of goods within the existence of no-fly zones, numerous warehouses, limited fuel, and uncertainties are still a problem for traditional algorithms. The main challenge of planning is scalability, since the planning space grows exponentially with respect to the number of agents, and it is not efficient to let human-level supervisors structure the problem for such large-scale settings. With the recent advancements in deep reinforcement learning, algorithms such as Deep Q Networks (DQN), had unprecedented success in solving single-agent decision-making problems. Extension of these algorithms to multi-agent problems such as multi-drone delivery is very limited due to scalability issues. This work proposes an approach that improves the performance of DQN on multi-agent drone delivery problems by utilizing state decompositions for lowering the problem complexity, curriculum learning for handling the exploration complexity of delivery environments, and genetic algorithms (GA) for searching efficient packet-drone matching across the combinatorial solution space. The performance of the proposed method is shown in a multi-agent delivery by drone problem that has $10$ agents and $\approx10^{77}$ state-action pairs. Comparative simulation results are provided to demonstrate the merit of the proposed method. Compared with the conventional DQN schemes, and recently developed utility decomposition techniques, the proposed genetic algorithm-aided multi-agent DRL outperformed the rest in terms of scalability and convergent behavior. The prior techniques become intractable quickly at a large number of agents within the context of delivery by drone problem. The basic DQN algorithm fails to find a solution for three agents in a 10x10 drone delivery scenario within a reasonable number of steps, but the deep correction method successfully converges after approximately 1 million Bellman updates. Furthermore, applying the deep correction method increases the learning capacity to five agents and converges around 35 million Bellman updates. However, using this method does not lead to convergence with ten agents in a manageable way. With powerful computing resources, it becomes clear that while single-agent models set an initial computational standard, increasing the number of agents introduces complexity, as seen through immediate convergence difficulties in a three-agent DQN setup. Although there is promise with three- and five-agent configurations using Deep Correction, the model with ten agents exceeds the threshold for convergence within 24 hours, emphasizing the delicate balance between agent quantity and computational feasibility. The utilization of drone delivery simulation presents intricate challenges, including restricted airspace, fuel limitations, and the pick-and-place scenario. The study demonstrates that employing a method involving packet distribution through genetic algorithms effectively minimizes the complexity in resolving tasks for 10 agents within 5.74 minutes. Subsequently, the reduced problem is handled by deep Q-network inference models with Curriculum Learning and Prioritized Experience Replay, achieving execution times measured in milliseconds. This two-fold approach skillfully learns the dynamic nature of delivery problems without requiring prior domain knowledge input amid uncertain environmental conditions prone to altering actions. Furthermore, visual evidence at various time steps during execution illustrates how integrating GA-based packet distribution empowers the proposed base DQN model with Curriculum Learning and PER framework to tackle scenarios involving 10 agents – an accomplishment deemed unattainable by other explored solutions within reasonable time frames and computational resources. In conclusion, the combination of deep reinforcement learning and genetic algorithms provides a promising approach for efficient and effective delivery with multi-agent drones under uncertainty.