Matching Items (485)
Description
A novel concept for integration of flame-assisted fuel cells (FFC) with a gas turbine is analyzed in this paper. Six different fuels (CH4, C3H8, JP-4, JP-5, JP-10(L), and H2) are investigated for the analytical model of the FFC integrated gas turbine hybrid system. As equivalence ratio increases, the efficiency of the hybrid system increases initially then decreases because the decreasing flow rate of air begins to outweigh the increasing hydrogen concentration. This occurs at an equivalence ratio of 2 for CH4. The thermodynamic cycle is analyzed using a temperature entropy diagram and a pressure volume diagram. These thermodynamic diagrams show as equivalence ratio increases, the power generated by the turbine in the hybrid setup decreases. Thermodynamic analysis was performed to verify that energy is conserved and the total chemical energy going into the system was equal to the heat rejected by the system plus the power generated by the system. Of the six fuels, the hybrid system performs best with H2 as the fuel. The electrical efficiency with H2 is predicted to be 27%, CH4 is 24%, C3H8 is 22%, JP-4 is 21%, JP-5 is 20%, and JP-10(L) is 20%. When H2 fuel is used, the overall integrated system is predicted to be 24.5% more efficient than the standard gas turbine system. The integrated system is predicted to be 23.0% more efficient with CH4, 21.9% more efficient with C3H8, 22.7% more efficient with JP-4, 21.3% more efficient with JP-5, and 20.8% more efficient with JP-10(L). The sensitivity of the model is investigated using various fuel utilizations. When CH4 fuel is used, the integrated system is predicted to be 22.7% more efficient with a fuel utilization efficiency of 90% compared to that of 30%.
ContributorsRupiper, Lauren Nicole (Author) / Milcarek, Ryan (Thesis director) / Wang, Liping (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / School for Engineering of Matter,Transport & Enrgy (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Finding the optimal solution to a problem with an enormous search space can be challenging. Unless a combinatorial construction technique is found that also guarantees the optimality of the resulting solution, this could be an infeasible task. If such a technique is unavailable, different heuristic methods are generally used to improve the upper bound on the size of the optimal solution. This dissertation presents an alternative method which can be used to improve a solution to a problem rather than construct a solution from scratch. Necessity analysis, which is the key to this approach, is the process of analyzing the necessity of each element in a solution. The post-optimization algorithm presented here utilizes the result of the necessity analysis to improve the quality of the solution by eliminating unnecessary objects from the solution. While this technique could potentially be applied to different domains, this dissertation focuses on k-restriction problems, where a solution to the problem can be presented as an array. A scalable post-optimization algorithm for covering arrays is described, which starts from a valid solution and performs necessity analysis to iteratively improve the quality of the solution. It is shown that not only can this technique improve upon the previously best known results, it can also be added as a refinement step to any construction technique and in most cases further improvements are expected. The post-optimization algorithm is then modified to accommodate every k-restriction problem; and this generic algorithm can be used as a starting point to create a reasonable sized solution for any such problem. This generic algorithm is then further refined for hash family problems, by adding a conflict graph analysis to the necessity analysis phase. By recoloring the conflict graphs a new degree of flexibility is explored, which can further improve the quality of the solution.
ContributorsNayeri, Peyman (Author) / Colbourn, Charles (Thesis advisor) / Konjevod, Goran (Thesis advisor) / Sen, Arunabha (Committee member) / Stanzione Jr, Daniel (Committee member) / Arizona State University (Publisher)
Created2011
Description
Reverse engineering gene regulatory networks (GRNs) is an important problem in the domain of Systems Biology. Learning GRNs is challenging due to the inherent complexity of the real regulatory networks and the heterogeneity of samples in available biomedical data. Real world biological data are commonly collected from broad surveys (profiling studies) and aggregate highly heterogeneous biological samples. Popular methods to learn GRNs simplistically assume a single universal regulatory network corresponding to available data. They neglect regulatory network adaptation due to change in underlying conditions and cellular phenotype or both. This dissertation presents a novel computational framework to learn common regulatory interactions and networks underlying the different sets of relatively homogeneous samples from real world biological data. The characteristic set of samples/conditions and corresponding regulatory interactions defines the cellular context (context). Context, in this dissertation, represents the deterministic transcriptional activity within the specific cellular regulatory mechanism. The major contributions of this framework include - modeling and learning context specific GRNs; associating enriched samples with contexts to interpret contextual interactions using biological knowledge; pruning extraneous edges from the context-specific GRN to improve the precision of the final GRNs; integrating multisource data to learn inter and intra domain interactions and increase confidence in obtained GRNs; and finally, learning combinatorial conditioning factors from the data to identify regulatory cofactors. The framework, Expattern, was applied to both real world and synthetic data. Interesting insights were obtained into mechanism of action of drugs on analysis of NCI60 drug activity and gene expression data. Application to refractory cancer data and Glioblastoma multiforme yield GRNs that were readily annotated with context-specific phenotypic information. Refractory cancer GRNs also displayed associations between distinct cancers, not observed through only clustering. Performance comparisons on multi-context synthetic data show the framework Expattern performs better than other comparable methods.
ContributorsSen, Ina (Author) / Kim, Seungchan (Thesis advisor) / Baral, Chitta (Committee member) / Bittner, Michael (Committee member) / Konjevod, Goran (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation studies routing in small-world networks such as grids plus long-range edges and real networks. Kleinberg showed that geography-based greedy routing in a grid-based network takes an expected number of steps polylogarithmic in the network size, thus justifying empirical efficiency observed beginning with Milgram. A counterpart for the grid-based model is provided; it creates all edges deterministically and shows an asymptotically matching upper bound on the route length. The main goal is to improve greedy routing through a decentralized machine learning process. Two considered methods are based on weighted majority and an algorithm of de Farias and Megiddo, both learning from feedback using ensembles of experts. Tests are run on both artificial and real networks, with decentralized spectral graph embedding supplying geometric information for real networks where it is not intrinsically available. An important measure analyzed in this work is overpayment, the difference between the cost of the method and that of the shortest path. Adaptive routing overtakes greedy after about a hundred or fewer searches per node, consistently across different network sizes and types. Learning stabilizes, typically at overpayment of a third to a half of that by greedy. The problem is made more difficult by eliminating the knowledge of neighbors' locations or by introducing uncooperative nodes. Even under these conditions, the learned routes are usually better than the greedy routes. The second part of the dissertation is related to the community structure of unannotated networks. A modularity-based algorithm of Newman is extended to work with overlapping communities (including considerably overlapping communities), where each node locally makes decisions to which potential communities it belongs. To measure quality of a cover of overlapping communities, a notion of a node contribution to modularity is introduced, and subsequently the notion of modularity is extended from partitions to covers. The final part considers a problem of network anonymization, mostly by the means of edge deletion. The point of interest is utility preservation. It is shown that a concentration on the preservation of routing abilities might damage the preservation of community structure, and vice versa.
ContributorsBakun, Oleg (Author) / Konjevod, Goran (Thesis advisor) / Richa, Andrea (Thesis advisor) / Syrotiuk, Violet R. (Committee member) / Czygrinow, Andrzej (Committee member) / Arizona State University (Publisher)
Created2011
Description
Production from a high pressure gas well at a high production-rate encounters the risk of operating near the choking condition for a compressible flow in porous media. The unbounded gas pressure gradient near the point of choking, which is located near the wellbore, generates an effective tensile stress on the porous rock frame. This tensile stress almost always exceeds the tensile strength of the rock and it causes a tensile failure of the rock, leading to wellbore instability. In a porous rock, not all pores are choked at the same flow rate, and when just one pore is choked, the flow through the entire porous medium should be considered choked as the gas pressure gradient at the point of choking becomes singular. This thesis investigates the choking condition for compressible gas flow in a single microscopic pore. Quasi-one-dimensional analysis and axisymmetric numerical simulations of compressible gas flow in a pore scale varicose tube with a number of bumps are carried out, and the local Mach number and pressure along the tube are computed for the flow near choking condition. The effects of tube length, inlet-to-outlet pressure ratio, the number of bumps and the amplitude of the bumps on the choking condition are obtained. These critical values provide guidance for avoiding the choking condition in practice.
ContributorsYuan, Jing (Author) / Chen, Kangping (Thesis advisor) / Wang, Liping (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The primary function of the medium access control (MAC) protocol is managing access to a shared communication channel. From the viewpoint of transmitters, the MAC protocol determines each transmitter's persistence, the fraction of time it is permitted to spend transmitting. Schedule-based schemes implement stable persistences, achieving low variation in delay and throughput, and sometimes bounding maximum delay. However, they adapt slowly, if at all, to changes in the network. Contention-based schemes are agile, adapting quickly to changes in perceived contention, but suffer from short-term unfairness, large variations in packet delay, and poor performance at high load. The perfect MAC protocol, it seems, embodies the strengths of both contention- and schedule-based approaches while avoiding their weaknesses. This thesis culminates in the design of a Variable-Weight and Adaptive Topology Transparent (VWATT) MAC protocol. The design of VWATT first required answers for two questions: (1) If a node is equipped with schedules of different weights, which weight should it employ? (2) How is the node to compute the desired weight in a network lacking centralized control? The first question is answered by the Topology- and Load-Aware (TLA) allocation which defines target persistences that conform to both network topology and traffic load. Simulations show the TLA allocation to outperform IEEE 802.11, improving on the expectation and variation of delay, throughput, and drop rate. The second question is answered in the design of an Adaptive Topology- and Load-Aware Scheduled (ATLAS) MAC that computes the TLA allocation in a decentralized and adaptive manner. Simulation results show that ATLAS converges quickly on the TLA allocation, supporting highly dynamic networks. With these questions answered, a construction based on transversal designs is given for a variable-weight topology transparent schedule that allows nodes to dynamically and independently select weights to accommodate local topology and traffic load. The schedule maintains a guarantee on maximum delay when the maximum neighbourhood size is not too large. The schedule is integrated with the distributed computation of ATLAS to create VWATT. Simulations indicate that VWATT offers the stable performance characteristics of a scheduled MAC while adapting quickly to changes in topology and traffic load.
ContributorsLutz, Jonathan (Author) / Colbourn, Charles J (Thesis advisor) / Syrotiuk, Violet R. (Thesis advisor) / Konjevod, Goran (Committee member) / Lloyd, Errol L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Tesla turbo-machinery offers a robust, easily manufactured, extremely versatile prime mover with inherent capabilities making it perhaps the best, if not the only, solution for certain niche applications. The goal of this thesis is not to optimize the performance of the Tesla turbine, but to compare its performance with various working fluids. Theoretical and experimental analyses of a turbine-generator assembly utilizing compressed air, saturated steam and water as the working fluids were performed and are presented in this work. A brief background and explanation of the technology is provided along with potential applications. A theoretical thermodynamic analysis is outlined, resulting in turbine and rotor efficiencies, power outputs and Reynolds numbers calculated for the turbine for various combinations of working fluids and inlet nozzles. The results indicate the turbine is capable of achieving a turbine efficiency of 31.17 ± 3.61% and an estimated rotor efficiency 95 ± 9.32%. These efficiencies are promising considering the numerous losses still present in the current design. Calculation of the Reynolds number provided some capability to determine the flow behavior and how that behavior impacts the performance and efficiency of the Tesla turbine. It was determined that turbulence in the flow is essential to achieving high power outputs and high efficiency. Although the efficiency, after peaking, begins to slightly taper off as the flow becomes increasingly turbulent, the power output maintains a steady linear increase.
ContributorsPeshlakai, Aaron (Author) / Phelan, Patrick (Thesis advisor) / Trimble, Steve (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2012
Description
Semiconductor scaling technology has led to a sharp growth in transistor counts. This has resulted in an exponential increase on both power dissipation and heat flux (or power density) in modern microprocessors. These microprocessors are integrated as the major components in many modern embedded devices, which offer richer features and attain higher performance than ever before. Therefore, power and thermal management have become the significant design considerations for modern embedded devices. Dynamic voltage/frequency scaling (DVFS) and dynamic power management (DPM) are two well-known hardware capabilities offered by modern embedded processors. However, the power or thermal aware performance optimization is not fully explored for the mainstream embedded processors with discrete DVFS and DPM capabilities. Many key problems have not been answered yet. What is the maximum performance that an embedded processor can achieve under power or thermal constraint for a periodic application? Does there exist an efficient algorithm for the power or thermal management problems with guaranteed quality bound? These questions are hard to be answered because the discrete settings of DVFS and DPM enhance the complexity of many power and thermal management problems, which are generally NP-hard. The dissertation presents a comprehensive study on these NP-hard power and thermal management problems for embedded processors with discrete DVFS and DPM capabilities. In the domain of power management, the dissertation addresses the power minimization problem for real-time schedules, the energy-constrained make-span minimization problem on homogeneous and heterogeneous chip multiprocessors (CMP) architectures, and the battery aware energy management problem with nonlinear battery discharging model. In the domain of thermal management, the work addresses several thermal-constrained performance maximization problems for periodic embedded applications. All the addressed problems are proved to be NP-hard or strongly NP-hard in the study. Then the work focuses on the design of the off-line optimal or polynomial time approximation algorithms as solutions in the problem design space. Several addressed NP-hard problems are tackled by dynamic programming with optimal solutions and pseudo-polynomial run time complexity. Because the optimal algorithms are not efficient in worst case, the fully polynomial time approximation algorithms are provided as more efficient solutions. Some efficient heuristic algorithms are also presented as solutions to several addressed problems. The comprehensive study answers the key questions in order to fully explore the power and thermal management potentials on embedded processors with discrete DVFS and DPM capabilities. The provided solutions enable the theoretical analysis of the maximum performance for periodic embedded applications under power or thermal constraints.
ContributorsZhang, Sushu (Author) / Chatha, Karam S (Thesis advisor) / Cao, Yu (Committee member) / Konjevod, Goran (Committee member) / Vrudhula, Sarma (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2012
Description
This work summarizes the development of a dynamic measurement platform in a cryostat to measure sample temperature response to space-like conditions and the creation a MATLAB theoretical model to predict sample temperature responses in the platform itself. An interesting variable-emittance sample called a Fabry-Perot emitter was studied for its thermal homeostasis behavior using the two developments. Using the measurement platform, it was shown that there was no thermal homeostatic behavior demonstrated by the sample at steady state temperatures. Theoretical calculations show other ways to demonstrate the cooling homeostasis behavior through time-varying heat inputs. Factors within the system such as heat loss and thermal mass contributed to an inhibited sample performance in the platform. Future work will have to be conducted, not only to verify the findings of the initial experiments but also to improve the measurement platform and the theoretical model.
ContributorsBoman, Neal D (Author) / Wang, Liping (Thesis director) / Taylor, Syndey (Committee member) / Mechanical and Aerospace Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
A thermochromic mid-infrared filter is designed, where a spectrally-selective transmittance peak exists while vanadium dioxide layers are below their transition temperature but broad opaqueness is observed below the transition temperature. This filter takes advantage of interference effects between a silicon spacer and insulating vanadium dioxide to create the transmittance peak and the drastic optical property change between insulating and metallic vanadium dioxide. The theoretical performance of the filter in energy dissipation and thermal camouflaging applications is analyzed and can be optimized by tuning the thicknesses of the thin-film layers.
ContributorsChao, Jeremy (Author) / Wang, Liping (Thesis director) / Taylor, Sydney (Committee member) / Mechanical and Aerospace Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05