Estimation and Mapping of Ship Air Wakes using RC Helicopters as a Sensing Platform
| dc.contributor.author | Kumar, Anil | en |
| dc.contributor.committeechair | Ben-Tzvi, Pinhas | en |
| dc.contributor.committeemember | Kurdila, Andrew J. | en |
| dc.contributor.committeemember | Wicks, Alfred L. | en |
| dc.contributor.committeemember | Kochersberger, Kevin B. | en |
| dc.contributor.committeemember | Woolsey, Craig A. | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2018-04-25T08:00:34Z | en |
| dc.date.available | 2018-04-25T08:00:34Z | en |
| dc.date.issued | 2018-04-24 | en |
| dc.description.abstract | This dissertation explores the applicability of RC helicopters as a tool to map wind conditions. This dissertation presents the construction of a robust instrumentation system capable of wireless in-situ measurement and mapping of ship airwake. The presented instrumentation system utilizes an RC helicopter as a carrier platform and uses the helicopter's dynamics for spatial 3D mapping of wind turbulence. The system was tested with a YP676 naval training craft to map ship airwake generated in controlled heading wind conditions. Novel system modeling techniques were developed to estimate the dynamics of an instrumented RC helicopter, in conjunction with onboard sensing, to estimate spatially varying (local) wind conditions. The primary problem addressed in this dissertation is the reliable estimation and separation of pilot induced dynamics from the system measurements, followed by the use of the dynamics residuals/discrepancies to map the wind conditions. This dissertation presents two different modelling approaches to quantify ship airwake using helicopter dynamics. The helicopter systems were characterized using both machine learning and analytical aerodynamic modelling approaches. In the machine learning based approaches, neural networks, along with other models, were trained then assessed in their capability to model dynamics from pilot inputs and other measured helicopter states. The dynamics arising from the wind conditions were fused with the positioning estimates of the helicopter to generate ship airwake maps which were compared against CFD generated airwake patterns. In the analytical modelling based approach, the dynamic response of an RC helicopter to a spatially varying parameterized wind field was modeled using a 30-state nonlinear ordinary differential equation-based dynamic system, while capturing essential elements of the helicopter dynamics. The airwake patterns obtained from both types of approach were compared against anemometrically produced wind maps of turbulent wind conditions artificially generated in a controlled indoor environment. Novel hardware architecture was developed to acquire data critical for the operation and calibration of the proposed system. The mechatronics design of three prototypes of the proposed system were presented and performance evaluated using experimental testing with a modified YP676 naval training vessel in the Chesapeake Bay area. In closing, qualitative analysis of these systems along with potential applications and improvements are discussed to conclude this dissertation. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:14669 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/82910 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Ship Airwake | en |
| dc.subject | Helicopter Dynamics | en |
| dc.subject | Artificial Neural Networks | en |
| dc.subject | Active Particle Filters | en |
| dc.subject | Extended Kalman Filter | en |
| dc.subject | Particle Swarm Optimization | en |
| dc.subject | Indoor Motion Tracking | en |
| dc.subject | Wind Mapping | en |
| dc.subject | Wireless Telemetry | en |
| dc.title | Estimation and Mapping of Ship Air Wakes using RC Helicopters as a Sensing Platform | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |