There is a present need for simulation and measuring the inlet swirl distortion generated by airframe/engine system interactions to identify potential degradation in fan performance and operability in a full-scale, ground testing environment. Efforts are described to address this need by developing and characterizing methods for complex, prescribed distortion patterns. A relevant inlet swirl distortion profile that mimics boundary layer ingesting inlets was generated by a novel new method, dubbed the StreamVane method, and measured in a sub scale tunnel using stereoscopic particle image velocimetry (SPIV) as a precursor for swirl distortion generation and characterization in an operating turbofan research engine. Diagnostic development efforts for the distortion measurements within the research engine paralleled the StreamVane characterization. The system used for research engine PIV measurements is described. Data was obtained in the wake of a total pressure distortion screen for engine conditions at idle and 80% corrected fan speed, and of full-scale StreamVane screen at 50% corrected fan speed. The StreamVane screen was designed to generate a swirl distortion that is representative for hybrid wing body applications and was made of Ultem*9085 using additive manufacturing. Additional improvements to the StreamVane method are also described. Data reduction algorithms are put forth to reduce spurious velocity vectors. Uncertainty estimations specific to the inlet distortion test rig, including bias error due to mechanical vibration, are made. Results indicate that the methods develop may be used to both generate and characterize complex distortion profiles at the aerodynamic interface plane, providing new information about airframe/engine integration. / Master of Science

Traditional methods of control allocation optimization have shown difficulties in exploiting the full potential of controlling a large array of control surfaces. This research investigates the potential of employing artificial intelligence methods like neurocomputing to the control allocation optimization problem of Hybrid Wing Body (HWB) aircraft concepts for minimizing control power, hinge moments, and actuator forces, while keeping the system weights within acceptable limits. The main objective is to develop a proof-of-concept process suitable to demonstrate the potential of using neurocomputing for optimizing actuation power for aircraft featuring multiple independently actuated control surfaces and wing flexibility. An aeroelastic Open Rotor Engine Integration and Optimization (OREIO) model was used to generate a database of hinge moment and actuation power characteristics for an array of control surface deflections. Artificial neural network incorporating a genetic algorithm then performs control allocation optimization for an example aircraft. The results showed that for the half-span model, the optimization results (for the sum of the required hinge moment) are improved by more than 11%, whereas for the full-span model, the same approach improved the result by nearly 14% over the best MSC Nastran solution by using the neural network optimization process. The results were improved by 23% and 27% over the case where only the elevator is used for both half-span and full-span models, respectively. The methods developed and applied here can be used for a wide variety of aircraft configurations. / Master of Science

Many futuristic aircraft such as the Hybrid Wing Body have numerous control surfaces that can result in large hinge moments, high actuation power demands, and large actuator forces/moments. Also, there is no unique relationship between control inputs and the aircraft response. Distinct sets of control surface deflections may result in the same aircraft response, but with large differences in actuation power. An Artificial Neural Network and a Genetic Algorithm were used here for the control allocation optimization problem of a Hybrid Wing Body to minimize the Sum of Absolute Values of Hinge Moments for a 2.5-G pull-up maneuver. To test the versatility of the same optimization process for different aircraft configurations, the present work also investigates its application on the Forward Swept Wing aircraft. A method to improve the robustness of the process is also presented. Constraints on the load factor and longitudinal pitch rate were added to the optimization to preserve the trim constraints on the control deflections. Another method was developed using stability derivatives. This new method provided better results, and the computational time was reduced by two orders of magnitude. A hybrid scheme combining both methods was also developed to provide a real-time estimate of the optimum control deflection schedules to trim the airplane and minimize the actuation power for changing flight conditions (Mach number, altitude and load factor) in a pull-up maneuver. Finally, the stability derivatives method and the hybrid scheme were applied for an antisymmetric, steady roll maneuver. / Master of Science / Many futuristic aircraft such as the Hybrid Wing Body have numerous control surfaces that can result in large actuation power. An Artificial Neural Network and a Genetic Algorithm were used here to minimize the actuation power on the Hybrid Wing Body. To test the versatility of the same optimization process for different aircraft configurations, the present work also investigates its application on the Forward Swept Wing aircraft. A method to improve the robustness of the process is also presented. A completely different method was developed, and it provided better results with the computational time reduced by two orders of magnitude. A hybrid scheme combining both methods was also developed to provide a real-time estimate of the optimum control deflection schedules to trim the airplane and minimize the actuation power for changing flight conditions (Mach number, altitude and load factor) in a pull-up maneuver.

Estimating the structural weight of a Hybrid Wing Body (HWB) aircraft during conceptual design has proven to be a significant challenge due to its unconventional configuration. Aircraft structural weight estimation is critical during the early phases of design because inaccurate estimations could result in costly design changes or jeopardize the mission requirements and thus degrade the concept's overall viability. The tools and methods typically employed for this task are inadequate since they are derived from historical data generated by decades of tube-and-wing style construction. In addition to the limited applicability of these empirical models, the conceptual design phase requires that any new tools and methods be flexible enough to enable design space exploration without consuming a significant amount of time and computational resources. This thesis addresses these challenges by developing a parametric and physics-based modeling and simulation (M&S) environment for the purpose of HWB structural weight estimation. The tools in the M&S environment are selected based on their ability to represent the unique HWB geometry and model the physical phenomena present in the centerbody section. The new M&S environment is used to identify key design parameters that significantly contribute to the variability of the HWB centerbody structural weight and also used to generate surrogate models. These surrogate models can augment traditional aircraft sizing routines and provide improved structural weight estimations.

The primary research objective was to develop a methodology to support conceptual design of the Hybrid Wing Body (HWB) configuration. The absence of a horizontal tail imposes new stability and control requirements on the planform, and therefore requiring greater emphasis on control authority assessment than is typical for conceptual design. This required investigations into three primary areas of research. The first was to develop a method for designing an appropriate amount of redundancy. This was motivated widely varying numbers of trailing edge elevons in the HWB literature, and inadequate explanations for these early design decisions. The method identifies stakeholders, metrics of interest, and synthesizes these metrics using the Breguet range equation for system level comparison of control surface layouts. The second area of research was the development trim analysis methods that could accommodate redundant control surfaces, for which conventional methods performed poorly. A new measure of control authority was developed for vehicles with redundant controls. This is accomplished using concepts from the control allocation literature such as the attainable moment subset and the direct allocation method. The result is a continuous measure of remaining control authority suitable for use during HWB sizing and optimization. The final research area integrated performance and control authority to create a HWB sizing environment, and investigations into how to use it for design space exploration and vehicle optimization complete the methodology. The Monte Carlo Simulation method is used to map the design space, identify good designs for optimization, and to develop design heuristics. Finally, HWB optimization experiments were performed to discover best practices for conceptual design.