In this study, the non-equilibrium Johnson and King Turbulence Model (JK model) is implemented in a three-dimensional, Navier-Stokes flow solver. The main program is a structured Euler/Navier-Stokes flow solver in which spatial discretization is accomplished by a finite volume formulation and a multigrid technique is used as a convergence accelerator. The aim is the validation of this in-house developed CFD (Computational Fluid Dynamics) tool with this enhanced enlarged capability in order to obtain a reliable flow solver that can solve flows over swept wings accurately. Various test cases were evaluated against reference solutions in order to demonstrate the accuracy of the newly implemented JK turbulence model. The selected test cases are NACA 0012 airfoil, ONERA M6 wing, DLR-F4 wing and two wings taken from the 3rd Drag Prediction Workshop. The solutions were analyzed and discussed in detail. The results show appreciably good agreement with the experimental data including force coefficients and surface pressure distributions.

In this study, the structural optimization of a cruise missile wing is accomplished for the aerodynamic loads for four different flight conditions. The flight conditions correspond to the corner points of the V-n diagram. The structural analysis and optimization is performed using the ANSYS finite element program. In order to construct the flight envelope and to find the pressure distribution in each flight condition, FASTRAN Computational Fluid Dynamics program is used. The structural optimization is performed for two different wing configurations. In the first wing configuration all the structural members are made up of aluminum material. In the second wing configuration, the skin panels are all composite material and the other members are made up of aluminum material. The minimum weight design which satisfies the strength and buckling constraints are found for both wings after the optimization analyses.

In this thesis, gas-kinetic methods for inviscid and viscous flow simulations are developed. Initially, the finite volume gas-kinetic methods are investigated for 1-D flows as a preliminary study and are discussed in detail from theoretical and numerical points of view. The preliminary results show that the gas-kinetic methods do not produce any unphysical flow phenomena. Especially the Gas-Kinetic BGK method, which takes into account the particle collisions, predicts compressible flows accurately. The Gas-Kinetic BGK method is then extended for the solution of 2-D and 3-D inviscid and viscous flows on unstructured/hybrid grids. The computations are performed in parallel. Various inviscid and viscous test cases are considered and it is shown that the Gas-Kinetic BGK method predicts both inviscid and viscous flow fields accurately. The implementation of hybrid grids for viscous flows reduces the overall number of grid cells while enabling the resolution of boundary layers. The parallel computations significantly improve the computation time of the Gas-Kinetic BGK method which, in turn, enable the method for the computation of practical aerodynamic flow problems.

A dynamic flight model of a jet trainer aircraft is developed in MATLAB-SIMULINK. Using a six degree of freedom mathematical model, non-linear simulation is used to observe the longitudinal and lateral-directional motions of the aircraft following a pilot input. The mathematical model is in state-space form and uses aircraft stability and control derivatives calculated from the aircraft geometric and aerodynamic characteristics. The simulation takes the changes in speed and altitude into consideration due to pilot input and demonstrates the non-linearity of the aircraft motion. The results from the simulation are compared with the results from flight characteristics manual of the actual aircraft to validate the mathematical model used. The simulation is carried out for a number of airspeed and altitude combinations to examine the effect of changing speed and altitude on the aircraft dynamic response.

To decrease the computational cost of genetic algorithm optimizations, surrogate models are used during optimization. Online update of surrogate models and repeated exchange of surrogate models with exact model during genetic optimization converts static optimization problems to dynamic ones. However, genetic algorithms fail to converge to the global optimum in dynamic optimization problems. To address these problems, a multiploid genetic algorithm optimization method is proposed. Multi-fidelity surrogate models are assigned to corresponding levels of fitness values to sustain the static optimization problem. Low fidelity fitness values are used to decrease the computational cost. The exact/highest-fidelity model fitness value is used for converging to the global optimum. The algorithm is applied to single and multi-objective turbine blade aerodynamic optimization problems. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size and the cost of the gas turbine engine. A 3-D steady Reynolds-Averaged Navier-Stokes solver is coupled with an automated unstructured grid generation tool. The solver is validated by using two well known test cases. Blade geometry is modelled by 37 design variables. Fine and coarse grid solutions are respected as high and low fidelity surrogate models, respectively. One of the test cases is selected as the baseline and is modified in the design process. The effects of input parameters on the performance of the multiploid genetic algorithm are studied. It is demonstrated that the proposed algorithm accelerates the optimization cycle while providing convergence to the global optimum for single and multi-objective problems.

Accuracy of the finite difference sensitivity calculations are improved by calculating the optimum finite difference interval sizes. In an aerodynamic inverse design algorithm, a compressor cascade geometry is perturbed by shape functions and finite differences sensitivity derivatives of the flow variables are calculated with respect to the base geometry flow variables. Sensitivity derivatives are used in an optimization code and a new airfoil is designed verifying given design characteristics. Accurate sensitivities are needed for optimization process. In order to find the optimum finite difference interval size, a method is investigated. Convergence error estimation techniques in iterative solutions and second derivative estimations are investigated to facilitate this method. For validation of the method, analytical sensitivity calculations of Euler equations are used and several applications are performed. Efficiency of the finite difference sensitivity calculations is improved by parallel computing. Finite difference sensitivity calculations are independent tasks in an inverse aerodynamic design algorithm and can be computed separately. Sensitivity calculations are performed on parallel processors and computing time is decreased.

In this thesis, structural optimization study is performed by using three different methods. In the first method, optimization is performed using MSC.NASTRAN Optimization Module, a commercial structural analysis program. In the second method, optimization is performed using the optimization code prepared in MATLAB and MSC.NASTRAN as the solver. As the third method, optimization is performed by using the optimization code prepared in MATLAB and analytical equations as the solver. All three methods provide certain advantages in the solution of optimization problems. Therefore, within the context of the thesis these methods are demonstrated and the interface codes specific to the programs used in this thesis are explained in detail. In order to compare the results obtained by the methods, the verification study has been performed on a cantilever beam with rectangular cross-section. In the verification study, the height and width of the cross-section of the beam are taken as the two design parameters. This way it has been possible to show the design space on the two dimensional graph, and it becomes easier to trace the progress of the optimization methods during each step. In the last section structural optimization of a multi-element wing torque box has been performed by the MSC.NASTRAN optimization module. In this section geometric property optimization has been performed for constant tip loading and variable loading along the wing span. In addition, within the context of shape optimization optimum rib placement problem has also been solved.

The Multiple Cooling Multi-Objective Simulated Annealing Algorithm is used for the conceptual design optimization of a supersonic Unmanned Combat Aerial Vehicle (UCAV). Single and multiobjective optimization problems are addressed while limiting performance requirements between desired bounds to obtain viable aircraft configurations. A conceptual aircraft design code was prepared for planned but flexible combat missions. The results demonstrate that the optimization technique employed is an effective tool for the conceptual design of aircrafts.

In this thesis, adaptive neural network controllers are designed for a high subsonic cruise missile. Two autopilot designs are included in the study using adaptive neural networks, namely an altitude hold autopilot designed for the longitudinal channel and a directional autopilot designed for heading control. Aerodynamic coefficients are obtained using missile geometry / a 5-Degree of Freedom (5-DOF) simulation model is obtained, and linearized at a single trim condition. An inverted model is used in the controller. Adaptive Neural Network (ANN) controllers namely, model inversion controllers with Sigma-Pi Neural Network, Single Hidden Layer Neural Network and Background Learning implemented Single Hidden Layer Neural Network, are deployed to cancel the modeling error and are applied for the longitudinal and directional channels of the missile. This approach simplifies the autopilot designing process by combining a controller with model inversion designed for a single flight condition with an on-line learning neural network to account for errors that are caused due to the approximate inversion. Simulations are performed both in the longitudinal and directional channels in order to demonstrate the effectiveness of the implemented control algorithms. The advantages and drawbacks of the implemented neural network based controllers are indicated.

Spray is an efficient tool in the usage whose primary objectives are to obtain droplets with increased liquid surface area and more dispersed liquid over a larger volume. The determination of spray characteristics has been a topic of extensive research recently. In the present investigation, the flow structure of a spray issuing from an oil burner nozzle was determined in a parametrical manner. The main tool in the experimental research is the Interferometric Particle Imaging (IPI) configuration. This method exploits the interference between light reflected from and refracted through individual transparent spray droplets which are illuminated by a laser light sheet in a wide angle forward-scatter region. Based on a scattering theory, the droplet diameter of spray particles can be related to the light pattern scattered from that particle. In addition, using double-framed images also enables the calculation of velocities associated with these particles. In this way, as a representation of spray structure, the droplet size and velocity distributions were obtained prior to a change in the primary parameters of liquid flow e.g. surface tension, viscosity, density and the injection pressure. The evolution of spray characteristics in space were also examined by conducting measurements in different radial and axial locations relative to spray centerline.