Matching Items (35)
Description
The friction condition is an important factor in controlling the compressing process in metalforming. The friction calibration maps (FCM) are widely used in estimating friction factors between the workpiece and die. However, in standard FEA, the friction condition is defined by friction coefficient factor (µ), while the FCM is used to a constant shear friction factors (m) to describe the friction condition. The purpose of this research is to find a method to convert the m factor to u factor, so that FEA can be used to simulate ring tests with µ. The research is carried out with FEA and Design of Experiment (DOE). FEA is used to simulate the ring compression test. A 2D quarter model is adopted as geometry model. A bilinear material model is used in nonlinear FEA. After the model is established, validation tests are conducted via the influence of Poisson's ratio on the ring compression test. It is shown that the established FEA model is valid especially if the Poisson's ratio is close to 0.5 in the setting of FEA. Material folding phenomena is present in this model, and µ factors are applied at all surfaces of the ring respectively. It is also found that the reduction ratio of the ring and the slopes of the FCM can be used to describe the deformation of the ring specimen. With the baseline FEA model, some formulas between the deformation parameters, material mechanical properties and µ factors are generated through the statistical analysis to the simulating results of the ring compression test. A method to substitute the m factor with µ factors for particular material by selecting and applying the µ factor in time sequence is found based on these formulas. By converting the m factor into µ factor, the cold forging can be simulated.
ContributorsKexiang (Author) / Shah, Jami (Thesis advisor) / Davidson, Joseph (Committee member) / Trimble, Steve (Committee member) / Arizona State University (Publisher)
Created2013
Description
The implications of a changing climate have a profound impact on human life, society, and policy making. The need for accurate climate prediction becomes increasingly important as we better understand these implications. Currently, the most widely used climate prediction relies on the synthesis of climate model simulations organized by the Coupled Model Intercomparison Project (CMIP); these simulations are ensemble-averaged to construct projections for the 21st century climate. However, a significant degree of bias and variability in the model simulations for the 20th century climate is well-known at both global and regional scales. Based on that insight, this study provides an alternative approach for constructing climate projections that incorporates knowledge of model bias. This approach is demonstrated to be a viable alternative which can be easily implemented by water resource managers for potentially more accurate projections. Tests of the new approach are provided on a global scale with an emphasis on semiarid regional studies for their particular vulnerability to water resource changes, using both the former CMIP Phase 3 (CMIP3) and current Phase 5 (CMIP5) model archives. This investigation is accompanied by a detailed analysis of the dynamical processes and water budget to understand the behaviors and sources of model biases. Sensitivity studies of selected CMIP5 models are also performed with an atmospheric component model by testing the relationship between climate change forcings and model simulated response. The information derived from each study is used to determine the progressive quality of coupled climate models in simulating the global water cycle by rigorously investigating sources of model bias related to the moisture budget. As such, the conclusions of this project are highly relevant to model development and potentially may be used to further improve climate projections.
ContributorsBaker, Noel C (Author) / Huang, Huei-Ping (Thesis advisor) / Trimble, Steve (Committee member) / Anderson, James (Committee member) / Clarke, Amanda (Committee member) / Calhoun, Ronald (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
Majority of the Sensor networks consist of low-cost autonomously powered devices, and are used to collect data in physical world. Today's sensor network deployments are mostly application specific & owned by a particular entity. Because of this application specific nature & the ownership boundaries, this modus operandi hinders large scale sensing & overall network operational capacity. The main goal of this research work is to create a mechanism to dynamically form personal area networks based on mote class devices spanning ownership boundaries. When coupled with an overlay based control system, this architecture can be conveniently used by a remote client to dynamically create sensor networks (personal area network based) even when the client does not own a network. The nodes here are "borrowed" from existing host networks & the application related to the newly formed network will co-exist with the native applications thanks to concurrency. The result allows users to embed a single collection tree onto spatially distant networks as if they were within communication range. This implementation consists of core operating system & various other external components that support injection maintenance & dissolution sensor network applications at client's request. A large object data dissemination protocol was designed for reliable application injection. The ability of this system to remotely reconfigure a network is useful given the high failure rate of real-world sensor network deployments. Collaborative sensing, various physical phenomenon monitoring also be considered as applications of this architecture.
ContributorsFernando, M. S. R (Author) / Dasgupta, Partha (Thesis advisor) / Bhattacharya, Amiya (Thesis advisor) / Gupta, Sandeep (Committee member) / Arizona State University (Publisher)
Created2013
Description
Wireless technologies for health monitoring systems have seen considerable interest in recent years owing to it's potential to achieve vision of pervasive healthcare, that is healthcare to anyone, anywhere and anytime. Development of wearable wireless medical devices which have the capability to sense, compute, and send physiological information to a mobile gateway, forming a Body Sensor Network (BSN) is considered as a step towards achieving the vision of pervasive health monitoring systems (PHMS). PHMS consisting of wearable body sensors encourages unsupervised long-term monitoring, reducing frequent visit to hospital and nursing cost. Therefore, it is of utmost importance that operation of PHMS must be reliable, safe and have longer lifetime. A model-based automatic code generation provides a state-of-art code generation of sensor and smart phone code from high-level specification of a PHMS. Code generator intakes meta-model of PHMS specification, uses codebase containing code templates and algorithms, and generates platform specific code. Health-Dev, a framework for model-based development of PHMS, uses code generation to implement PHMS in sensor and smart phone. As a part of this thesis, model-based automatic code generation was evaluated and experimentally validated. The generated code was found to be safe in terms of ensuring no race condition, array, or pointer related errors in the generated code and more optimized as compared to hand-written BSN benchmark code in terms of lesser unreachable code.
ContributorsVerma, Sunit (Author) / Gupta, Sandeep (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2013
Description
Increasing computational demands in data centers require facilities to operate at higher ambient temperatures and at higher power densities. Conventionally, data centers are cooled with electrically-driven vapor-compressor equipment. This paper proposes an alternative data center cooling architecture that is heat-driven. The source is heat produced by the computer equipment. This dissertation details experiments investigating the quantity and quality of heat that can be captured from a liquid-cooled microprocessor on a computer server blade from a data center. The experiments involve four liquid-cooling setups and associated heat-extraction, including a radical approach using mineral oil. The trials examine the feasibility of using the thermal energy from a CPU to drive a cooling process. Uniquely, the investigation establishes an interesting and useful relationship simultaneously among CPU temperatures, power, and utilization levels. In response to the system data, this project explores the heat, temperature and power effects of adding insulation, varying water flow, CPU loading, and varying the cold plate-to-CPU clamping pressure. The idea is to provide an optimal and steady range of temperatures necessary for a chiller to operate. Results indicate an increasing relationship among CPU temperature, power and utilization. Since the dissipated heat can be captured and removed from the system for reuse elsewhere, the need for electricity-consuming computer fans is eliminated. Thermocouple readings of CPU temperatures as high as 93°C and a calculated CPU thermal energy up to 67Wth show a sufficiently high temperature and thermal energy to serve as the input temperature and heat medium input to an absorption chiller. This dissertation performs a detailed analysis of the exergy of a processor and determines the maximum amount of energy utilizable for work. Exergy as a source of realizable work is separated into its two contributing constituents: thermal exergy and informational exergy. The informational exergy is that usable form of work contained within the most fundamental unit of information output by a switching device within a CPU. Exergetic thermal, informational and efficiency values are calculated and plotted for our particular CPU, showing how the datasheet standards compare with experimental values. The dissertation concludes with a discussion of the work's significance.
ContributorsHaywood, Anna (Author) / Phelan, Patrick E (Thesis advisor) / Herrmann, Marcus (Committee member) / Gupta, Sandeep (Committee member) / Trimble, Steve (Committee member) / Myhajlenko, Stefan (Committee member) / Arizona State University (Publisher)
Created2014
Description
The energy consumption of data centers is increasing steadily along with the associ- ated power-density. Approximately half of such energy consumption is attributed to the cooling energy, as a result of which reducing cooling energy along with reducing servers energy consumption in data centers is becoming imperative so as to achieve greening of the data centers. This thesis deals with cooling energy management in data centers running data-processing frameworks. In particular, we propose ther- mal aware scheduling for MapReduce framework and its Hadoop implementation to reduce cooling energy in data centers. Data-processing frameworks run many low- priority batch processing jobs, such as background log analysis, that do not have strict completion time requirements; they can be delayed by a bounded amount of time. Cooling energy savings are possible by being able to temporally spread the workload, and assign it to the computing equipments which reduce the heat recirculation in data center room and therefore the load on the cooling systems. We implement our scheme in Hadoop and performs some experiments using both CPU-intensive and I/O-intensive workload benchmarks in order to evaluate the efficiency of our scheme. The evaluation results highlight that our thermal aware scheduling reduces hot-spots and makes uniform temperature distribution within the data center possible. Sum- marizing the contribution, we incorporated thermal awareness in Hadoop MapReduce framework by enhancing the native scheduler to make it thermally aware, compare the Thermal Aware Scheduler(TAS) with the Hadoop scheduler (FCFS) by running PageRank and TeraSort benchmarks in the BlueTool data center of Impact lab and show that there is reduction in peak temperature and decrease in cooling power using TAS over FCFS scheduler.
ContributorsKole, Sayan (Author) / Gupta, Sandeep (Thesis advisor) / Huang, Dijiang (Committee member) / Varsamopoulos, Georgios (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis seeks to further explore off-design point operation of gas turbines and to examine the capabilities of GasTurb 12 as a tool for off-design analysis. It is a continuation of previous thesis work which initially explored the capabilities of GasTurb 12. The research is conducted in order to: 1) validate GasTurb 12 and, 2) predict off-design performance of the Garrett GTCP85-98D located at the Arizona State University Tempe campus. GasTurb 12 is validated as an off-design point tool by using the program to predict performance of an LM2500+ marine gas turbine. Haglind and Elmegaard (2009) published a paper detailing a second off-design point method and it includes the manufacturer's off-design point data for the LM2500+. GasTurb 12 is used to predict off-design point performance of the LM2500+ and compared to the manufacturer's data. The GasTurb 12 predictions show good correlation. Garrett has published specification data for the GTCP85-98D. This specification data is analyzed to determine the design point and to comment on off-design trends. Arizona State University GTCP85-98D off-design experimental data is evaluated. Trends presented in the data are commented on and explained. The trends match the expected behavior demonstrated in the specification data for the same gas turbine system. It was originally intended that a model of the GTCP85-98D be constructed in GasTurb 12 and used to predict off-design performance. The prediction would be compared to collected experimental data. This is not possible because the free version of GasTurb 12 used in this research does not have a module to model a single spool turboshaft. This module needs to be purchased for this analysis.
ContributorsMartinjako, Jeremy (Author) / Trimble, Steve (Thesis advisor) / Dahm, Werner (Committee member) / Middleton, James (Committee member) / Arizona State University (Publisher)
Created2014
Optimization of the implementation of renewable resources in a municipal electric utility in Arizona
Description
A municipal electric utility in Mesa, Arizona with a peak load of approximately 85 megawatts (MW) was analyzed to determine how the implementation of renewable resources (both wind and solar) would affect the overall cost of energy purchased by the utility. The utility currently purchases all of its energy through long term energy supply contracts and does not own any generation assets and so optimization was achieved by minimizing the overall cost of energy while adhering to specific constraints on how much energy the utility could purchase from the short term energy market. Scenarios were analyzed for a five percent and a ten percent penetration of renewable energy in the years 2015 and 2025. Demand Side Management measures (through thermal storage in the City's district cooling system, electric vehicles, and customers' air conditioning improvements) were evaluated to determine if they would mitigate some of the cost increases that resulted from the addition of renewable resources.
In the 2015 simulation, wind energy was less expensive than solar to integrate to the supply mix. When five percent of the utility's energy requirements in 2015 are met by wind, this caused a 3.59% increase in the overall cost of energy. When that five percent is met by solar in 2015, it is estimated to cause a 3.62% increase in the overall cost of energy. A mix of wind and solar in 2015 caused a lower increase in the overall cost of energy of 3.57%. At the ten percent implementation level in 2015, solar, wind, and a mix of solar and wind caused increases of 7.28%, 7.51% and 7.27% respectively in the overall cost of energy.
In 2025, at the five percent implementation level, wind and solar caused increases in the overall cost of energy of 3.07% and 2.22% respectively. In 2025, at the ten percent implementation level, wind and solar caused increases in the overall cost of energy of 6.23% and 4.67% respectively.
Demand Side Management reduced the overall cost of energy by approximately 0.6%, mitigating some of the cost increase from adding renewable resources.
In the 2015 simulation, wind energy was less expensive than solar to integrate to the supply mix. When five percent of the utility's energy requirements in 2015 are met by wind, this caused a 3.59% increase in the overall cost of energy. When that five percent is met by solar in 2015, it is estimated to cause a 3.62% increase in the overall cost of energy. A mix of wind and solar in 2015 caused a lower increase in the overall cost of energy of 3.57%. At the ten percent implementation level in 2015, solar, wind, and a mix of solar and wind caused increases of 7.28%, 7.51% and 7.27% respectively in the overall cost of energy.
In 2025, at the five percent implementation level, wind and solar caused increases in the overall cost of energy of 3.07% and 2.22% respectively. In 2025, at the ten percent implementation level, wind and solar caused increases in the overall cost of energy of 6.23% and 4.67% respectively.
Demand Side Management reduced the overall cost of energy by approximately 0.6%, mitigating some of the cost increase from adding renewable resources.
ContributorsCadorin, Anthony (Author) / Phelan, Patrick (Thesis advisor) / Calhoun, Ronald (Committee member) / Trimble, Steve (Committee member) / Arizona State University (Publisher)
Created2014
Description
With the ever-increasing demand for high-end services, technological companies have been forced to operate on high performance servers. In addition to the customer services, the company's internal need to store and manage huge amounts of data has also increased their need to invest in High Density Data Centers. As a result, the performance to size of the data center has increased tremendously. Most of the consumed power by the servers is emitted as heat. In a High Density Data Center, the power per floor space area is higher compared to the regular data center. Hence the thermal management of this type of data center is relatively complicated.
Because of the very high power emission in a smaller containment, improper maintenance can result in failure of the data center operation in a shorter period. Hence the response time of the cooler to the temperature rise of the servers is very critical. Any delay in response will constantly lead to increased temperature and hence the server's failure.
In this paper, the significance of this delay time is understood by performing CFD simulation on different variants of High Density Modules using ANSYS Fluent. It was found out that the delay was becoming longer as the size of the data center increases. But the overload temperature, ie. the temperature rise beyond the set-point became lower with the increase in data center size. The results were common for both the single-row and the double-row model. The causes of the increased delay are accounted and explained in detail manner in this paper.
Because of the very high power emission in a smaller containment, improper maintenance can result in failure of the data center operation in a shorter period. Hence the response time of the cooler to the temperature rise of the servers is very critical. Any delay in response will constantly lead to increased temperature and hence the server's failure.
In this paper, the significance of this delay time is understood by performing CFD simulation on different variants of High Density Modules using ANSYS Fluent. It was found out that the delay was becoming longer as the size of the data center increases. But the overload temperature, ie. the temperature rise beyond the set-point became lower with the increase in data center size. The results were common for both the single-row and the double-row model. The causes of the increased delay are accounted and explained in detail manner in this paper.
ContributorsRamaraj, Dinesh Balaji (Author) / Gupta, Sandeep (Thesis advisor) / Hermann, Marcus (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2015