Measurement

A non-invasive transient state measurement method for wind tunnels would be very valuable as an experimental tool. Traditional measurement techniques for transient flows, e.g., hot wire anemometry, require sensors that are placed in the flow. Alternatively, particle image velocimetry (PIV) may be used to measure transient flows non intrusively, but applying PIV requires sensors that are expensive, and it may take months to process the data. The non-invasive measurement techniques considered in this thesis utilize sensors that are imbedded into the wall of a wind tunnel, or the response of a Kevlar walled wind tunnel to obtain the pressure time histories of a transient flow. These measurements are suitable and accurate for analyzing steady state flows but the feasibility of using them on time varying flows has yet to be explored. If this method proves possible, it would be very beneficial even if it is less accurate than current invasive methods because it would give results in real time. This thesis investigates a simple
transient flow of the startup vortex of an airfoil caused by a step change in angle of attack. Based on thin airfoil theory, two models of an airfoil were created. It was determined that the response of a Kevlar wall can measure the unsteady lift of an airfoil.

It is well known that biogenic gas emissions (mainly methane and carbon dioxide) vary both spatially and temporally in peatlands. While most studies have focused on northern systems, several recent studies in tropical and subtropical peatlands (like the Everglades) have revealed the presence of areas of increased gas accumulation and emissions, or hotspots, that may be related to physical and/or biogeochemical changes within the peat's matrix. However, these studies are often limited in terms of sampling volume and resolution or are based in laboratory studies that may not be totally representative of field conditions. In this study we investigate the spatial variability (both lateral and vertical) in gas accumulation and release at the field scale, over 10 m long transects at two locations in Water Conservation Area 1 of the Florida Everglades, using an array of hydrogeophysical methods. Resulting data infers the presence of hotspots with dimensions ranging from 1-2 m in width and approximately 0.5 m tall. These areas showed high variations in biogenic gas accumulation and release an order of magnitude higher than surrounding areas and occur seasonally as the highest gas releases were observed during Florida’s wet season. This study therefore has implications for better understanding the spatial and temporal variability of biogenic gas hotspots in peat soils, and how the matrix structure affects gas accumulation and release. This study shows the importance of considering the heterogenous nature of the peat's matrix when quantifying gas fluxes in the Everglades, and particularly when using methods with small sampling volumes like gas chambers.

Most of recent studies indicate that people are negatively predisposed toward utilizing
autonomous systems. These findings highlight the necessity of conducting research
to better understand the evolution of trust between humans and growing autonomous technologies
such as self-driving cars (SDC). This research therefore presents a new approach
for real-time trust measurement between passengers and SDCs. We utilized a new structured
data collection approach along with a virtual reality (VR) SDC simulator to understand
how various autonomous driving scenarios can increase or decrease human trust and
how trust can be re-built in the case of incidental failures. To verify our methodology, we
designed and conducted an empirical experiment on 50 human subjects. The results of this
experiment indicated that most subjects could rebuild trust during a reasonable timeframe
after the system demonstrated faulty behavior. Furthermore, we discovered that the cultural
background and past trust-related experiences of the subjects affect how they lose or regain
their trust in SDCs. Our analysis showed that this model is highly effective for collecting
real-time data from human subjects and lays the foundation for more-involved future
research in the domain of human trust and autonomous driving.

Radar rainfall estimates have become a decision making tool for scientists, engineers and water managers in their tasks for developing hydrologic models, water supply planning, restoration of ecosystems, and flood control. In the present study, the utility of a power function for linking the rain gauge and radar estimates has been assessed. Mean daily rainfall data from 163 rain gauges installed within the South Florida Water Management District network have been used and their records from January 1st, 2002 to October 31st, 2007 analyzed. Results indicate that the power function coefficients and exponents obtained by using a non-linear optimization formulation, show spatial variability mostly affected by type of rainfall events occurring in the dry or wet seasons, and that the linear distance from the radar location to the rain gauge has a significant effect on the computed values of the coefficients and exponents.

Western Palm Beach County, FL is characterized by thick deposits organic soils at shallow depths. Because of their high void ratio and compressibility, these soils undergo large primary consolidation followed by extended periods of secondary compression causing excessive premature structural distress. Although soil stabilization has been largely used with remarkable results in soft, expansive and non-organic soils, limited research and practice exist in the implementation with highly organic soils. The main motivation of this research was to investigate the effects of cement stabilization on the compressibility behavior of organic rich soils, and develop mix design criteria for optimum cement contents necessary to induce the desired engineering behavior. This optimized mix design may provide guidelines for Deep Mixing Methods in organic soils.

The purpose of this research is to compare the surface dose outside the treatment area for different breast cancer irradiation modalities using Thermoluminescence Dosimeters (TLDs). Five different modalities are included in this study: Accuboost, Photon boost, Electron boost, Strut-Adjusted Volume Implant (SAVI), and Mammosite Multi-lumen (ML).Six points of interest (POI) on the breast cancer patients had been selected for the TLDs placement. Data from 25 breast cancer patients at Lynn Cancer Institute of the Boca Raton Regional Hospital were included in the study. The measured percentage ranges of the averaged doses at the six POIs for the different modalities are: Sternum 0.26% - 3.26%, Shoulder 0.33% - 2.79%, Eye 0.26% - 1.32%, Thyroid 0.20% - 2.75%, CLB 0.2% - 5.46%, Lower Abdomen 0.16% - 2.25%.

This study focuses on two main broad areas of active research on climate: climate variability and climate change and their implications on regional precipitation characteristics. All the analysis is carried out for a climate change-sensitive region, the state of Florida, USA. The focus of the climate variability analysis is to evaluate the influence of individual and coupled phases (cool and warm) of Atlantic multidecadal oscillation (AMO) and El Niäno southern oscillation (ENSO) on regional precipitation characteristics. The two oscillations in cool and warm phases modulate each other which have implications on flood control and water supply in the region. Extreme precipitation indices, temporal distribution of rainfall within extreme storm events, dry and wet spell transitions and antecedent conditions preceding extremes are evaluated. Kernel density estimates using Gaussian kernel for distribution-free comparative analysis and bootstrap sampling-based confidence intervals are used to compare warm and cool phases of different lengths. Depth-duration-frequency (DDF) curves are also developed using generalized extreme value (GEV) distributions characterizing the extremes. ... This study also introduces new approaches to optimally select the predictor variables which help in modeling regional precipitation and further provides a mechanism to select an optimum spatial resolution to downscale the precipitation projections. New methods for correcting the biases in monthly downscaled precipitation projections are proposed, developed and evaluated in this study. The methods include bias corrections in an optimization framework using various objective functions, hybrid methods based on universal function approximation and new variants.

Submerged turbines which harvest energy from ocean currents are an important potential energy resource, but their harsh and remote environment demands an automated system for machine condition monitoring and prognostic health monitoring (MCM/PHM). For building MCM/PHM models, vibration sensor data is among the most useful (because it can show abnormal behavior which has yet to cause damage) and the most challenging (because due to its waveform nature, frequency bands must be extracted from the signal). To perform the necessary analysis of the vibration signals, which may arrive rapidly in the form of data streams, we develop three new wavelet-based transforms (the Streaming Wavelet Transform, Short-Time Wavelet Packet Decomposition, and Streaming Wavelet Packet Decomposition) and propose modifications to the existing Short-TIme Wavelet Transform. ... The proposed algorithms also create and select frequency-band features which focus on the areas of the signal most important to MCM/PHM, producing only the information necessary for building models (or removing all unnecessary information) so models can run on less powerful hardware. Finally, we demonstrate models which can work in multiple environmental conditions. ... Our results show that many of the transforms give similar results in terms of performance, but their different properties as to time complexity, ability to operate in a fully streaming fashion, and number of generated features may make some more appropriate than others in particular applications, such as when streaming data or hardware limitations are extremely important (e.g., ocean turbine MCM/PHM).

This thesis describes a method to detect, localize and identify a faulty bearing in a rotating machine using narrow band envelope analysis across an array of accelerometers. This technique is developed as part of the machine monitoring system of an ocean turbine. A rudimentary mathematical model is introduced to provide an understanding of the physics governing the vibrations caused by a bearing with a raceway defect. This method is then used to detect a faulty bearing in two setups : on a lathe and in a dynamometer.