Blended Wing Body Concept Development with Open Rotor Engine Intergration

The purpose of this study is to perform a systems analysis of a Blended Wing Body (BWB) open rotor concept at the conceptual design level. This concept will be utilized to estimate overall noise and fuel burn performance, leveraging recent test data. This study will also investigate the challenge of propulsion airframe installation of an open rotor engine on a BWB configuration. Open rotor engines have unique problems relative to turbofans. The rotors are open, exposed to flow conditions outside of the engine. The flow field that the rotors are immersed in may be higher than the free stream flow and it may not be uniform, both of these characteristics could increase noise and decrease performance. The rotors sometimes cause changes in the flow conditions imposed on aircraft surfaces. At high power conditions such as takeoff and climb out, the stream tube of air that goes through the rotors contracts rapidly causing the boundary layer on the body upper surface to go through an adverse pressure gradient which could result with separated airflow. The BWB / Open Rotor configuration must be designed to mitigate these problems

Artificial Intelligence Based Control Power Optimization on Tailless Aircraft

Traditional methods of control allocation optimization have shown difficulties in exploiting the full potential of controlling large arrays of control devices on innovative air vehicles. Artificial neutral networks are inspired by biological nervous systems and neurocomputing has successfully been applied to a variety of complex optimization problems. This project investigates the potential of applying neurocomputing to the control allocation optimization problem of Hybrid Wing Body (HWB) aircraft concepts to minimize control power, hinge moments, and actuator forces, while keeping system weights within acceptable limits. The main objective of this project 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. A Nastran aeroservoelastic finite element model is used to generate a learning database of hinge moment and actuation power characteristics for an array of flight conditions and control surface deflections. An artificial neural network incorporating a genetic algorithm then uses this training data to perform control allocation optimization for the investigated aircraft configuration. The phase I project showed that optimization results for the sum of required hinge moments are improved by more than 12% over the best Nastran solution by using the neural network optimization process

Fed cattle price discovery: Issues and considerations

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