UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Ceramic Materials Development for Intermediate Temperature Solid Oxide Fuel Cell (IT-SOFC)(2016) Pan, Ke-Ji; Wachsman, Eric D; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Solid oxide fuel cell (SOFC) is an electrochemical device that converts chemical energy into electric power with high efficiency. Traditional SOFC has its disadvantages, such as redox cycling instability and carbon deposition while using hydrocarbon fuels. It is because traditional SOFC uses Ni-cermet as anode. In order to solve these problems, ceramic anode is a good candidate to replace Ni. However, the conductivity of most ceramic anode materials are much lower than Ni metal, and it introduces high ohmic resistance. How to increase the conductivity is a hot topic in this research field. Based on our proposed mechanism, several types of ceramic materials have been developed. Vanadium doped perovskite, Sr1-x/2VxTi1-xO3 (SVT) and Sr0.2Na0.8Nb1-xVxO3 (SNNV), achieved the conductivity as high as 300 S*cm-1 in hydrogen, without any high temperature reduction. GDC electrolyte supported cell was fabricated with Sr0.2Na0.8Nb0.9V0.1O3 and the performance was measured in hydrogen and methane respectively. Due to vanadium’s intrinsic problems, the anode supported cell is not easy. Fe doped double perovskite Sr2CoMoO6 (SFCM) was also developed. By carefully doping Fe, the conductivity was improved over one magnitude, without any vigorous reducing conditions. SFCM anode supported cell was successfully fabricated with GDC as the electrolyte. By impregnating Ni-GDC nano particles into the anode, the cell can be operated at lower temperatures while having higher performance than the traditional Ni-cermet cells. Meanwhile, this SFCM anode supported SOFC has long term stability in the reformate containing methane. During the anode development, cathode improvement caused by a thin Co-GDC layer was observed. By adding this Co-GDC layer between the electrolyte and the cathode, the interfacial resistance decreases due to fast oxygen ion transport. This mechanism was confirmed via isotope exchange. This Co-GDC layer works with multiple kinds of cathodes and the modified cell’s performance is 3 times as the traditional Ni-GDC cell. With this new method, lowering the SOFC operation temperature is feasible.Item High-Performance Computing Algorithms for Constructing Inverted Files on Emerging Multicore Processors(2012) Wei, Zheng; JaJa, Joseph F; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Current trends in processor architectures increasingly include more cores on a single chip and more complex memory hierarchies, and such a trend is likely to continue in the foreseeable future. These processors offer unprecedented opportunities for speeding up demanding computations if the available resources can be effectively utilized. Simultaneously, parallel programming languages such as OpenMP and MPI have been commonly used on clusters of multicore CPUs while newer programming languages such as OpenCL and CUDA have been widely adopted on recent heterogeneous systems and GPUs respectively. The main goal of this dissertation is to develop techniques and methodologies for exploiting these emerging parallel architectures and parallel programming languages to solve large scale irregular applications such as the construction of inverted files. The extraction of inverted files from large collections of documents forms a critical component of all information retrieval systems including web search engines. In this problem, the disk I/O throughput is the major performance bottleneck especially when intermediate results are written onto disks. In addition to the I/O bottleneck, a number of synchronization and consistency issues must be resolved in order to build the dictionary and postings lists efficiently. To address these issues, we introduce a dictionary data structure using a hybrid of trie and B-trees and a high-throughput pipeline strategy that completely avoids the use of disks as temporary storage for intermediate results, while ensuring the consumption of the input data at a high rate. The high-throughput pipelined strategy produces parallel parsed streams that are consumed at the same rate by parallel indexers. The pipelined strategy is implemented on a single multicore CPU as well as on a cluster of such nodes. We were able to achieve a throughput of more than 262MB/s on the ClueWeb09 dataset on a single node. On a cluster of 32 nodes, our experimental results show scalable performance using different metrics, significantly improving on prior published results. On the other hand, we develop a new approach for handling time-evolving documents using additional small temporal indexing structures. The lifetime of the collection is partitioned into multiple time windows, which guarantees a very fast temporal query response time at a small space overhead relative to the non-temporal case. Extensive experimental results indicate that the overhead in both indexing and querying is small in this more complicated case, and the query performance can indeed be improved using finer temporal partitioning of the collection. Finally, we employ GPUs to accelerate the indexing process for building inverted files and to develop a very fast algorithm for the highly irregular list ranking problem. For the indexing problem, the workload is split between CPUs and GPUs in such a way that the strengths of both architectures are exploited. For the list ranking problem involved in the decompression of inverted files, an optimized GPU algorithm is introduced by reducing the problem to a large number of fine grain computations in such a way that the processing cost per element is shown to be close to the best possible.