LEE Uçak ve Uzay Mühendisliği Lisansüstü Programı
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ÖgeA study on optimization of a wing with fuel sloshing effects(Graduate School, 20220124) Vergün, Tolga ; Doğan, Vedat Ziya ; 511181206 ; Aeronautics and Astronautics Engineering ; Uçak ve Uzay MühendisliğiIn general, sloshing is defined as a phenomenon that corresponds to the free surface elevation in multiphase flows. It is a movement of liquid inside another object. Sloshing has been studied for centuries. The earliest work [48] was carried out in the literature by Euler in 1761 [17]. Lamb [32] theoretically examined sloshing in 1879. Especially with the development of technology, it has become more important. It appears in many different fields such as aviation, automotive, naval, etc. In the aviation industry, it is considered in fuel tanks. Since outcomes of sloshing may cause instability or damage to the structure, it is one of the concerns about aircraft design. To prevent its adverse effect, one of the most popular solutions is adding baffles into the fuel tank. Still, this solution also comes with a disadvantage: an increase in weight. To minimize the effects of added weight, designers optimize the structure by changing its shape, thickness, material, etc. In this study, a NACA 4412 airfoilshaped composite wing is used and optimized in terms of safety factor and weight. To do so, an initial composite layup is determined from current designs and advice from literature. When the design of the initial system is completed, the system is imported into a transient solver in the Ansys Workbench environment to perform numerical analysis on the time domain. To achieve more realistic cases, the wing with different fuel tank fill levels (25%, 50%, and 75%) is exposed to aerodynamic loads while the aircraft is rolling, yawing, and dutch rolling. The aircraft is assumed to fly with a constant speed of 60 m/s (~120 knots) to apply aerodynamic loads. Resultant force for 60 m/s airspeed is applied onto the wing surface by 1Way FluidStructure Interaction (1Way FSI) as a distributed pressure. Using this method, only fluid loads are transferred to the structural system, and the effect of wing deformation on the fluid flow field is neglected. Once gravity effects and aerodynamic loads are applied to the wing structure, displacement is defined as the wing is moving 20 deg/s for 3 seconds for all types of movements. On the other hand, fluid properties are described in the Ansys Fluent environment. Fluent defines the fuel level, fluid properties, computational fluid dynamics (CFD) solver, etc. Once both structural and fluid systems are ready, system coupling can perform 2Way FluidStructure Interaction (2Way FSI). Using this method, fluid loads and structural deformations are transferred simultaneously at each step. In this method, the structural system transfers displacement to the fluid system while the fluid system transfers pressure to the structural system. After nine analyses, the critical case is determined regarding the safety factor. Critical case, in which system has the lowest minimum safety factor, is found as 75% filled fuel tank while aircraft dutch rolling. After the determination of the critical case, the optimization process is started. During the optimization process, 1Way FSI is used since the computational cost of the 2Way FSI method is approximately 35 times that of 1Way FSI. However, taking less time should not be enough to accept 1Way FSI as a solution method; the deviation of two methods with each other is also investigated. After this investigation, it was found that the variation between the two methods is about 1% in terms of safety factors for our problem. In the light of this information, 1Way FSI is preferred to apply both sloshing and aerodynamic loads onto the structure to reduce computational time. After method selection, thickness optimization is started. Ansys Workbench creates a design of experiments (DOE) to examine response surface points. Latin Hypercube Sampling Design (LHSD) is preferred as a DOE method since it generates noncollapsing and spacefilling points to create a better response surface. After creating the initial response surface using Genetic Aggregation, the optimization process is started using the MultiObjective Genetic Algorithm (MOGA). Then, optimum values are verified by analyzing the optimum results in Ansys Workbench. When the optimum results are verified, it is realized that there is a notable deviation in results between optimized and verified results. To minimize the variation, refinement points are added to the response surface. This process is kept going until variation comes under 1%. After finding the optimum results, it is noticed that its precision is too high to maintain manufacturability so that it is rounded into 1% of a millimeter. In the end, final thickness values are verified. As a result, optimum values are found. It is found that weight is decreased from 100.64 kg to 94.35 kg, which means a 6.3% gain in terms of weight, while the minimum safety factor of the system is only reduced from 1.56 to 1.54. At the end of the study, it is concluded that a 6.3% reduction in weight would reflect energy saving.