This thesis proposes an uninterruptible power supply, UPS for high availability servers with fuel cells as its back up energy source. The system comprises a DC to DC converter designed to accommodate the fuel cellâ s wide output voltage range. A server power supply is specified, designed and simulated for use with this UPS. The UPS interfaces internal to the server power supply, instead of providing standard AC power. This topology affords enhanced protection from faults and increases overall efficiency of the system by removing power conversions. The UPS is simulated with the designed power supply to demonstrate its effectiveness.

With the increased usage of electrical equipment for various applications, the demand for quality power apart from continuous power availability has increased and hence requires the development of appropriate power conditioning system. A major factor during development of these systems is the requirement that they remain environment-friendly. This cannot be realized using the conventional systems as they use batteries and/or engine generators. Among various viable technologies, fuel cells have emerged as one of the most promising sources for both portable and stationary applications. In this thesis, a new battery less UPS system configuration powered by fuel cell is discussed. The proposed topology utilizes a standard offline UPS module and the battery is replaced by a supercapacitor. The system operation is such that the supercapacitor bank is sized to support startup and load transients and steady state power is supplied by the fuel cell. Further, the fuel cell runs continuously to supply 10% power in steady state. In case of power outage, it is shown that the startup time for fuel cell is reduced and the supercapacitor bank supplies power till the fuel cell ramps up from supplying 10% load to 100% load. A detailed design example is presented for a 200W/350VA 1- phase UPS system to meet the requirements of a critical load. The equivalent circuit and hence the terminal behavior of the fuel cell and the supercapacitor are considered in the analysis and design of the system for a stable operation over a wide range. The steady state and transient state analysis were used for stability verification. Hence, from the tests such as step load changes and response time measurements, the non-linear model of supercapacitor was verified. Temperature rise and fuel consumption data were measured and the advantages of having a hybrid source (supercapacitor in parallel with fuel cell) over just a standalone fuel cell source were shown. Finally, the transfer times for the proposed UPS system and the battery based UPS system were measured and were found to be satisfactory. Overall, the proposed system was found to satisfy the required performance specifications.

Distributed hydrogen generation from liquid hydrocarbon fuels to supply portable fuel cells presents an attractive, high energy density alternative to current battery technology. Traditional unit operation reactor design for hydrogen generation becomes inadequate with decrease in scale because of the unique challenges of size and weight minimization. To address the challenge of reactor scale-down, the concept of multifunctional reactors has emerged, in which synergistic combination of different unit operations is explored to achieve improved performance. The direct droplet impingement reactor (DDIR) studied here is based on this approach in which the liquid feed is atomized using a regularly spaced array of droplet generators with unparalleled control over droplet characteristics, followed by vaporization and reaction directly on the catalyst surface. Considering each droplet generator in the array as a unit cell, a comprehensive, first-principles model of the DDIR has been developed by considering the intimately coupled processes of 1) droplet transport, heating, evaporation, and impingement on the catalyst surface, 2) liquid reagent film formation, capillary penetration, and vaporization within the catalyst layer, and 3) gas phase heat and mass transfer and catalytic reactions. Simulations are performed to investigate the effect of reactor operating parameters on performance. Experimental validation of the model is carried out by visualizing droplet impingement and liquid film accumulation while simultaneously monitoring reaction product composition over a range of operating conditions. Results suggest an optimal unit cell shape for reaction selectivity based on a balance between reagent back diffusion and catalyst bed thermal resistance. Further, achieving a target throughput is best accomplished by adding together a larger number unit cells with optimized geometry and lower throughput (per unit cell) to more effectively spread heat and avoid hotspots at the catalyst interface. At the same time, conditions must be satisfied for ensuring droplet impingement on the catalyst surface, which become more stringent as unit cell throughput is decreased.

A new type of fuel cell that combines the advantages of a proton exchange membrane fuel cells and anion exchange membrane fuel cells operated with methanol is demonstrated. Two configurations: one with a high pH anode and low pH cathode (anode hybrid fuel cell (AHFC)),and another with a high pH cathode and a low pH anode (cathode hybrid fuel cell (CHFC)) have been studied in this work. The principle of operation of the hybrid fuel cells were explained. The two different hybrid cell configurations were used in order to study the effect of the electrode fabrication on fuel cell performance. Further, the ionomer content and properties such as the ion exchange capacity and molecular weight were optimized for the best performance. A comparison of the different ionomers with similar properties is carried out in order to obtain the best possible ionomer for the fuel cell. An initial voltage drop was observed at low current density in the AHFC, this was attributed to the alkaline anode and the effect of the ionomers with the new cationic groups were studied on this voltage drop was studied. These ionomers with the different cationic groups were studied in the CHFC design as well. Finally, the use of non platinum catalyst cathode with the CHFC design was also demonstrated for the first time.

Thesis (M.S.)--Tennessee Technological University, 2009. / Title from title page screen (viewed on Feb. 5, 2010). Bibliography: leaves 93-100.

Thesis (Ph.D)--Mechanical Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Andrei Fedorov; Committee Member: Srinivas Garimella; Committee Member: Timothy Lieuwen; Committee Member: William Koros; Committee Member: William Wepfer. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--Lehigh University, 2003. / Includes vita. Includes bibliographical references (leaves 110-117).

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: process based cost model; cost model; fuel cell; PBCM; multi-layer ceramics; sofc; solid oxide fuel cell. Includes bibliographical references.

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The Multi-Isotope Process Monitor is being developed at Pacific Northwest National Laboratory as a method to verify the process conditions within a nuclear fuel reprocessing facility using the gamma spectra of various process streams. The technique uses multivariate analysis techniques such as principal component analysis and partial least squares regression applied to gamma spectra collected of a process stream in order to classify the contents as belonging to a normal versus off-normal chemistry process. This approach to process monitoring is designed to function automatically, nondestructively, and in near real-time. To extend the Multi-Isotope Process Monitor, an analysis method to char- acterize spent nuclear fuel based on the reactor of origin, either pressurized or boiling water reactor, and burnup of the fuel using nuclide concentrations as input data has been developed. While the Multi-Isotope Process Monitor uses gamma spectra as input data, nuclide activities were used in this work as an initial step before Nuclide composition information was generated using ORIGEN-ARP for different fuel assembly types, initial 235U enrichments, burnup values, and cooling times. This data was used to train, tune, and test several multivariate analysis algorithms in order to compare their performance and identify the technique most suited for the analysis. To perform the classification based on reactor type, four methods were considered: k-nearest neighbors, linear and quadratic discriminant analysis, and support vector machines. Each method was optimized, and its performance on a validation set was used to determine the best method for classifying the fuel reactor class. Partial least squares was used to make burnup predictions. Three models were generated and tested: one trained on all the data, one trained for just pressurized water reactors, and one trained for boiling water reactors. Quadratic discriminant analysis was chosen as the best classifier of reactor class because of its simplicity and its potential to be extended to classify spent nuclear fuel’s fuel assembly type, i.e, more specific classes, using nuclide concentrations as input data. In the case of predicting the burnup of spent fuel using partial least squares, it was determined that making reactor-specific partial least squares models, one trained for pressurized water reactors and one trained for boiling water reactors, performed better than a single, general model that was trained for all light water reactors. Thus, the the classifier, regression algorithm, and all the necessary intermediate data processing steps were combined into a single analysis method and implemented as a Matlab function called “burnup.” This function was used to test the analysis routine on an additional set of data generated in ORIGEN-ARP. This dataset included samples with parameters that were not represented in the development data in order to ascertain the analysis method’s ability to analyze data for which it has not been explicitly trained. The algorithm was able to achieve perfect binary classification of the reactor as being a pressurized or boiling water reactor on the dataset and made burnup predictions with an average error of 0.0297%. / text