Geophysical Analysis of Seasonal Montandon Gravel Ridge Water Table Fluctuation and Moisture Gradient Variation due to Storm Events

The purpose of this research project is to continue exploring the Montandon Long-Term Hydrologic Research Site(LTHR) by using multiple geophysical methods to obtain more accurate and precise information regarding subsurface hydrologic properties of a local gravel ridge,which are important to both the health of surrounding ecosystems and local agriculture. Through using non-invasive geophysical methods such as seismic refraction, Direct Current resistivity and ground penetrating radar (GPR) instead of invasive methods such as boreholedrilling which displace sediment and may alter water flow, data collection is less likely to bias the data itself. In addition to imaging the gravel ridge subsurface, another important researchpurpose is to observe how both water table elevation and the moisture gradient (moisture content of the unsaturated zone) change over a seasonal time period and directly after storm events. The combination of three types of data collection allows the strengths of each method combine together and provide a relatively strongly supported conclusions compared to previous research. Precipitation and geophysical data suggest that an overall increase in precipitation during the summer months causes a sharp decrease in subsurface resistivity within the unsaturated zone. GPR velocity data indicate significant immediate increase in moisture content within the shallow vadose zone (< 1m), suggesting that rain water was infiltrating into the shallow subsurface. Furthermore, the combination of resistivity and GPR results suggest that the decreased resistivity within the shallow layers is due to increased ion content within groundwater. This is unexpected as rainwater is assumed to have a DC resistivity value of 3.33*105 ohm-m. These results may suggest that ions within the sediment must beincorporated into the infiltrating water.

Assessing the Validity of Brain Glucose Concentration Measurement Using Microdialysis

Brain functions, such as learning, orchestrating locomotion, memory recall, and processing information, all require glucose as a source of energy. During these functions, the glucose concentration decreases as the glucose is being consumed by brain cells. By measuring this drop in concentration, it is possible to determine which parts of the brain are used during specific functions and consequently, how much energy the brain requires to complete the function. One way to measure in vivo brain glucose levels is with a microdialysis probe. The drawback of this analytical procedure, as with many steadystate fluid flow systems, is that the probe fluid will not reach equilibrium with the brain fluid. Therefore, brain concentration is inferred by taking samples at multiple inlet glucose concentrations and finding a point of convergence. The goal of this thesis is to create a three-dimensional, time-dependent, finite element representation of the brainprobe system in COMSOL 4.2 that describes the diffusion and convection of glucose. Once validated with experimental results, this model can then be used to test parameters that experiments cannot access. When simulations were run using published values for physical constants (i.e. diffusivities, density and viscosity), the resulting glucose model concentrations were within the error of the experimental data. This verifies that the model is an accurate representation of the physical system. In addition to accurately describing the experimental brain-probe system, the model I created is able to show the validity of zero-net-flux for a given experiment. A useful discovery is that the slope of the zero-net-flux line is dependent on perfusate flow rate and diffusion coefficients, but it is independent of brain glucose concentrations. The model was simplified with the realization that the perfusate is at thermal equilibrium with the brain throughout the active region of the probe. This allowed for the assumption that all model parameters are temperature independent. The time to steady-state for the probe is approximately one minute. However, the signal degrades in the exit tubing due to Taylor dispersion, on the order of two minutes for two meters of tubing. Given an analytical instrument requiring a five μL aliquot, the smallest brain process measurable for this system is 13 minutes.

Design and Preliminary Results of an Atmospheric-Pressure Model Gas Turbine Combustor Utilizing Varying CO2 Doping Concentration in CH4 to Emulate Biogas Combustion

Utilization of biogas can provide a source of renewable energy in both heat and power generation. Combustion of biogas in land-based gas turbines for power generation is a promising approach to reducing greenhouse gases and US dependence on foreign-source fossil fuels. Biogas is a byproduct from the decomposition of organic matter and consists primarily of CH4 and large amounts of CO2. The focus of this research was to design a combustion device and investigate the effects of increasing levels of CO2 addition to the combustion of pure CH4 with air. Using an atmospheric-pressure, swirl-stabilized dump combustor, emissions data and flame stability limitations were measured and analyzed. In particular, CO2, CO, and NOx emissions were the main focus of the combustion products. Additionally, the occurrence of lean blowout and combustion pressure oscillations, which impose significant limitations in operation ranges for actual gas turbines, was observed. Preliminary kinetic and equilibrium modeling was performed using Cantera and CEA for the CH4/CO2/Air combustion systems to analyze the effect of CO2 upon adiabatic flame temperature and emission levels. The numerical and experimental results show similar dependence of emissions on equivalence ratio, CO2 addition, inlet air temperature, and combustor residence time. (C) 2014 Elsevier Ltd. All rights reserved.