Date of Award

2018

Degree Type

Thesis

Degree Name

Master of Science in Ocean Engineering

Department

Ocean Engineering

First Advisor

Jason Dahl

Abstract

In the field of ocean exploration and marine vehicle design there is a heavy reliance on the use of acoustic and visual sensors for mapping, maneuvering, and obstacle avoidance. In low light regions, constricted spaces, or areas where water is turbid, however, passive systems similar to the lateral line of fish can be advantageous. Using a set of measurements like these, a digital twin could be constructed such that it represents the true physical system for use in mapping, control system design, or tracking the loading of a structure over time. For moving bodies in the presence of solid boundaries such as a vehicle operating at the ocean floor or a vessel in shallow waters, it is critically important to properly estimate the local boundary conditions for use with a digital twin.

The following study implements a viscous numerical simulation as a model for object identification. The presented methods include a classification schema via machine learning for a variety of wall shapes and sizes, as well as a general boundary estimation method using basis splines and an Unscented Kalman filter. At the cost of generality, the classifier is shown to be capable of identifying shape and size with relatively high accuracy especially as the number of available classes increases.

The basis spline method uses an Unscented Kalman filter and modeled pressure as a proxy to determine the locations of control points which govern boundary conditions. The method is shown to work for a single and multiple protrusions from a wall, with a weighted estimate approach developed which outperforms the standard formulation of the Unscented Filter when accounting for multiple measurement locations along the length of the foil body. Performance of the method as well as the sensitivity to the design of the system are discussed, and future work is presented.

Available for download on Saturday, July 27, 2019

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