Monitoring Stormwater Redistribution Into Low Resistivity Soils Using Noninvasive Geophysical Techniques

Water held in the unsaturated zone is important for agriculture and construction and is replenished by infiltrating rainwater. Monitoring the soil water content of clay soils using ground-penetrating radar (GPR) has not been researched, as clay soils cause attenuation of GPR signal. In this study, GPR common-midpoint soundings (CMPs) are used in the clayey soils of the Miller Run floodplain to monitor changes in the soil water content (SWC) before and after rainfall events. GPR accomplishes this task because increases in water content will increase the dielectric constant of the subsurface material, and decrease the velocity of the GPR wave. Using an empirical relationship between dielectric constant and SWC, the Topp relation, we are able to calculate a SWC from these velocity measurements. Non-invasive electromagnetics, resistivity, and seismic were performed, and from these surveys, the layering at the field site was delineated. EM characterized the horizontal variation of the soil, allowing us to target the most clay rich area. At the CMP location, resistivity indicates the vertical structure of the subsurface consists of a 40 cm thick layer with a resistivity of 100 ohm*m. Between 40 cm and 1.5 m is a layer with a resistivity of 40 ohm*m. The thickness estimates were confirmed with invasive auger and trenching methods away from the CMP location. GPR CMPs were collected relative to a July 2013 and September 2013 storm. The velocity observations from the CMPs had a precision of +/- 0.001 m/ns as assessed by repeat analysis. In the case of both storms, the GPR data showed the expected relationship between the rainstorms and calculated SWC, with the SWC increasing sharply after the rainstorm and decreasing as time passed. We compared these data to auger core samples collected at the same time as the CMPs were taken, and the volumetric analysis of the cores confirmed the trend seen in the GPR, with SWC values between 3 and 5 percent lower than the GPR estimates. Our data shows that we can, with good precision, monitor changes in the SWC of conductive soils in response to rainfall events, despite the attenuation induced by the clay.

Analysis and Prediction of Transient Opacity Spikes Using Dimensional Modeling

Dimensional modeling, GT-Power in particular, has been used for two related purposes-to quantify and understand the inaccuracies of transient engine flow estimates that cause transient smoke spikes and to improve empirical models of opacity or particulate matter used for engine calibration. It has been proposed by dimensional modeling that exhaust gas recirculation flow rate was significantly underestimated and volumetric efficiency was overestimated by the electronic control module during the turbocharger lag period of an electronically controlled heavy duty diesel engine. Factoring in cylinder-to-cylinder variation, it has been shown that the electronic control module estimated fuel-Oxygen ratio was lower than actual by up to 35% during the turbocharger lag period but within 2% of actual elsewhere, thus hindering fuel-Oxygen ratio limit-based smoke control. The dimensional modeling of transient flow was enabled with a new method of simulating transient data in which the manifold pressures and exhaust gas recirculation system flow resistance, characterized as a function of exhaust gas recirculation valve position at each measured transient data point, were replicated by quasi-static or transient simulation to predict engine flows. Dimensional modeling was also used to transform the engine operating parameter model input space to a more fundamental lower dimensional space so that a nearest neighbor approach could be used to predict smoke emissions. This new approach, intended for engine calibration and control modeling, was termed the "nonparametric reduced dimensionality" approach. It was used to predict federal test procedure cumulative particulate matter within 7% of measured value, based solely on steady-state training data. Very little correlation between the model inputs in the transformed space was observed as compared to the engine operating parameter space. This more uniform, smaller, shrunken model input space might explain how the nonparametric reduced dimensionality approach model could successfully predict federal test procedure emissions when roughly 40% of all transient points were classified as outliers as per the steady-state training data.