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. / Ph. D. / Ship airwake is a trail of wind turbulence left behind the superstructure of cruising naval vessels and are considered as a serious safety concern for aviators during onboard operations. Prior knowledge of the airwake distribution around the ship can alert pilots of possible hazards ahead of time and mitigate operational risks during the launch and recovery of the aircraft on the flight deck.
This dissertation presents a novel application of Remote Control (RC) helicopters as tools to measure and map ship airwake. This dissertation presents two approaches to extract wind conditions from helicopter dynamics: (1) using machine learning based modeling, and (2) using analytic aerodynamic modeling-based estimation. Machine Learning is a modern engineering tool to model and simulate any system using experimental data alone. Under the machine learning based approach, the helicopter’s response to pilot inputs was modeled using multiple algorithms, with experimental flight data collected the absence of the ship airwake. With an assumption of capturing all the aerodynamic effects with the machine learning algorithms, the deviations in the dynamics estimates during testing environment were used to characterize and map ship airwake. In contrast to the machine learning model, the analytical approach modeled all critical aerodynamic processes of the RC helicopter as functions of pilot inputs and wind conditions using well defined physics laws, thus eliminating any need for training data. This approach predicts wind conditions on the basis of the model’s capability to match the estimates of helicopter dynamics to the actual measurements.
Both presented approaches were tested on wind conditions created in indoor and outdoor environments. The performance of the proposed system was evaluated in experimental testing with a modified YP676 naval training vessel in the Chesapeake Bay area. The dissertation also presents the mechatronic design details of the novel hardware prototypes and subsystems used in the various studies and experiments. Finally, qualitative analysis of these systems along with their potential applications and improvements are discussed to conclude this dissertation.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/82910 |
| Date | 24 April 2018 |
| Creators | Kumar, Anil |
| Contributors | Mechanical Engineering, Ben-Tzvi, Pinhas, Kurdila, Andrew J., Wicks, Alfred L., Kochersberger, Kevin B., Woolsey, Craig A. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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