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dc.contributor.authorChhabra, Rupanshien_US
dc.date.accessioned2015-09-18T20:05:10Z
dc.date.available2015-09-18T20:05:10Z
dc.date.issued2015-09-15en_US
dc.identifier.othervt_gsexam:6039en_US
dc.identifier.urihttp://hdl.handle.net/10919/56580
dc.description.abstractTraditional 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.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectArtificial Neural Networken_US
dc.subjectOptimizationen_US
dc.subjectHybrid Wing Bodyen_US
dc.subjectGenetic Algorithmen_US
dc.subjectHinge Momentsen_US
dc.titleControl Power Optimization using Artificial Intelligence for Hybrid Wing Body Aircraften_US
dc.typeThesisen_US
dc.contributor.departmentAerospace and Ocean Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineAerospace Engineeringen_US
dc.contributor.committeechairKapania, Rakesh K.en_US
dc.contributor.committeememberMulani, Sameer Babasaheben_US
dc.contributor.committeememberSchetz, Joseph A.en_US


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