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.

The Impact of Foreign Direct Investment on Economic Growth

Over the past four decades, the number of democracies in the world has increased exponentially. This project considers how democracy and FDI affect economic growth as well as whether the impact of FDI depends on the level of democracy in a country. Thus, I explore two major research questions: 1) Whether increased FDI speeds up economic growth, controlling for political regime type, urbanization and other developmental indicators; and 2) Whether an increase in political freedom helps or hinders economic growth, and specifically whether the impact of FDI varies depending on the political regime in the recipient country. To examine these questions, this paper used data from 150 countries over a period between 1980 and 2010 and utilized several models, testing variables such as institutions, agglomerations, urbanization, FDI and type of political regime, among others, for their impact on economic growth. I found that FDI does have a positive impact on economic growth, and that this impact is often magnified when it interacts with other relevant factors. I also found that, after controlling for other variables, FDI inflows do not have a different impact on economic growth in autocracies than they do in democracies. This may be partially explained by autocratic outliers such as China and the OPEC states, which have recently experienced rapid export-led growth. This suggests that factors such as education could have a greater impact on a country¿s economic growth than does its political system.