This thesis describes a procedure to measure the transient effects in a fuel cell air delivery system. These methods were applied to model the 20 kW automotive fuel cell system that was used in Animul H2, a fuel cell-battery hybrid sedan developed by a group of engineering students at Virginia Tech. The air delivery system included the air compressor, the drive motor for the compressor, the motor controller, and any plumbing between the fuel cell inlet and the compressor outlet. The procedure was to collect data from a series of tests of the air delivery system with no load (zero outlet pressure) and at several loads. The air compressor speed, outlet pressure, and motor controller current were measured in response to a variety of speed requests. This data was fit to transfer functions relating the compressor speed, outlet pressure, or motor controller current to the speed request. The fits were found using a least squares optimization technique. After the experimental model was developed, it was augmented with an analytical model of the rest of the fuel cell system. The mass flow of the air was determined from the air compressor speed and outlet pressure with the compressor map. The fuel cell current was found by assuming a constant stoichiometric ratio. The power out of the fuel cell was calculated from the fuel cell current and the pressure with the polarization curve. The model of the fuel cell system was implemented in Matlab/Simulink. Several open and closed loop simulations were run to test the functionality of the fuel cell system model. The gross and net powers of the fuel cell system were found as a function of the compressor operating speed. The time it took for the system to come up to power as a function of idle speed was also found. A PID controller was implemented to allow the system to track a reference power request. The key contributions of this work were to develop a method to test the air delivery system to determine the dynamics of the system, to develop a model based on these tests and some analytical knowledge of fuel cells, and to use the model to simulate the operation and control of a fuel cell system. / Master of Science

Sea level rise (SLR) is one of the most damaging impacts associated with climate change. The objective of this study is to develop a comprehensive framework to identify the spatial patterns of sea level in the historical records, project regional mean sea levels in the future, and assess the corresponding impacts on the coastal communities. The first part of the study suggests a spatial pattern recognition methodology to characterize the spatial variations of sea level and to investigate the sea level footprints of climatic signals. A technique based on artificial neural network is proposed to reconstruct average sea levels for the characteristic regions identified. In the second part of the study, a spatial dynamic system model (DSM) is developed to simulate and project the changes in regional sea levels and sea surface temperatures (SST) under different development scenarios of the world. The highest sea levels are predicted under the scenario A1FI, ranging from 71 cm to 86 cm (relative to 1990 global mean sea level); the lowest predicted sea levels are under the scenario B1, ranging from 51 cm to 64 cm (relative to 1990 global mean sea level). Predicted sea levels and SST's of the Indian Ocean are significantly lower than those of the Pacific and the Atlantic Ocean under all six scenarios. The last part of this dissertation assesses the inundation impacts of projected regional SLR on three representative coastal U.S. states through a geographic information system (GIS) analysis. Critical issues in the inundation impact assessment process are identified and discussed.