Control Power Optimization using Artificial Intelligence for Hybrid Wing Body Aircraft
| dc.contributor.author | Chhabra, Rupanshi | en |
| dc.contributor.committeechair | Kapania, Rakesh K. | en |
| dc.contributor.committeemember | Mulani, Sameer Babasaheb | en |
| dc.contributor.committeemember | Schetz, Joseph A. | en |
| dc.contributor.department | Aerospace and Ocean Engineering | en |
| dc.date.accessioned | 2015-09-18T20:05:10Z | en |
| dc.date.available | 2015-09-18T20:05:10Z | en |
| dc.date.issued | 2015-09-15 | en |
| dc.description.abstract | 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. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:6039 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/56580 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Artificial Intelligence | en |
| dc.subject | Artificial Neural Network | en |
| dc.subject | Optimization | en |
| dc.subject | Hybrid Wing Body | en |
| dc.subject | Genetic Algorithm | en |
| dc.subject | Hinge Moments | en |
| dc.title | Control Power Optimization using Artificial Intelligence for Hybrid Wing Body Aircraft | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Aerospace Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |