Matching Items (31)

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Enhancing the cooling capacity of roof ponds using polyethylene band filter

Description

With the desire of high standards of comfort, huge amount of energy is being consumed to maintain the indoor environment. In US building consumes 40% of the total primary energy while residential buildings consume about 21%. A large proportion of

With the desire of high standards of comfort, huge amount of energy is being consumed to maintain the indoor environment. In US building consumes 40% of the total primary energy while residential buildings consume about 21%. A large proportion of this consumption is due to cooling of buildings. Deteriorating environmental conditions due to excessive energy use suggest that we should look at passive designs and renewable energy opportunities to supply the required comfort. Phoenix gets about 300 days of clear sky every year. It also witnesses large temperature variations from night and day. The humidity ratio almost always stays below the 50% mark. With more than six months having outside temperatures more than 75 oF, night sky radiative cooling promise to be an attractive means to cool the buildings during summer. This technique can be useful for small commercial facilities or residential buildings. The roof ponds can be made more effective by covering them with Band Filters. These band filters block the solar heat gain and allow the water to cool down to lower temperatures. It also reduces the convection heat gain. This helps rood ponds maintain lower temperatures and provide more cooling then an exposed pond. 50 μm Polyethylene band filter is used in this study. Using this band filter, roof ponds can be made up to 10% more effective. About 45% of the energy required to cool a typical residential building in summer can be saved.

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Date Created
2013

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Optimization of complex thermal-fluid processes

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First, in a large-scale structure, a 3-D CFD model was built to simulate flow and temperature distributions. The flow patterns and temperature distributions are characterized and validated through spot measurements. The detailed understanding of them then allows for optimization of

First, in a large-scale structure, a 3-D CFD model was built to simulate flow and temperature distributions. The flow patterns and temperature distributions are characterized and validated through spot measurements. The detailed understanding of them then allows for optimization of the HVAC configuration because identification of the problematic flow patterns and temperature mis-distributions leads to some corrective measures. Second, an appropriate form of the viscous dissipation term in the integral form of the conservation equation was considered, and the effects of momentum terms on the computed drop size in pressure-atomized sprays were examined. The Sauter mean diameter (SMD) calculated in this manner agrees well with experimental data of the drop velocities and sizes. Using the suggested equation with the revised treatment of liquid momentum setup, injection parameters can be directly input to the system of equations. Thus, this approach is capable of incorporating the effects of injection parameters for further considerations of the drop and velocity distributions under a wide range of spray geometry and injection conditions. Lastly, groundwater level estimation was investigated using compressed sensing (CS). To satisfy a general property of CS, a random measurement matrix was used, the groundwater network was constructed, and finally the l-1 optimization was run. Through several validation tests, correct estimation of groundwater level by CS was shown. Using this setup, decreasing trends in groundwater level in the southwestern US was shown. The suggested method is effective in that the total measurements of registered wells can be reduced down by approximately 42 %, sparse data can be visualized and a possible approach for groundwater management during extreme weather changes, e.g. in California, was demonstrated.

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Date Created
2015

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Analysis methods for post occupancy evaluation of energy-use in high performance buildings using short-term monitoring

Description

The green building movement has been an effective catalyst in reducing energy demands of buildings and a large number of `green' certified buildings have been in operation for several years. Whether these buildings are actually performing as intended, and if

The green building movement has been an effective catalyst in reducing energy demands of buildings and a large number of `green' certified buildings have been in operation for several years. Whether these buildings are actually performing as intended, and if not, identifying specific causes for this discrepancy falls into the general realm of post-occupancy evaluation (POE). POE involves evaluating building performance in terms of energy-use, indoor environmental quality, acoustics and water-use; the first aspect i.e. energy-use is addressed in this thesis. Normally, a full year or more of energy-use and weather data is required to determine the actual post-occupancy energy-use of buildings. In many cases, either measured building performance data is not available or the time and cost implications may not make it feasible to invest in monitoring the building for a whole year. Knowledge about the minimum amount of measured data needed to accurately capture the behavior of the building over the entire year can be immensely beneficial. This research identifies simple modeling techniques to determine best time of the year to begin in-situ monitoring of building energy-use, and the least amount of data required for generating acceptable long-term predictions. Four analysis procedures are studied. The short-term monitoring for long-term prediction (SMLP) approach and dry-bulb temperature analysis (DBTA) approach allow determining the best time and duration of the year for in-situ monitoring to be performed based only on the ambient temperature data of the location. Multivariate change-point (MCP) modeling uses simulated/monitored data to determine best monitoring period of the year. This is also used to validate the SMLP and DBTA approaches. The hybrid inverse modeling method-1 predicts energy-use by combining a short dataset of monitored internal loads with a year of utility-bills, and hybrid inverse method-2 predicts long term building performance using utility-bills only. The results obtained show that often less than three to four months of monitored data is adequate for estimating the annual building energy use, provided that the monitoring is initiated at the right time, and the seasonal as well as daily variations are adequately captured by the short dataset. The predictive accuracy of the short data-sets is found to be strongly influenced by the closeness of the dataset's mean temperature to the annual average temperature. The analysis methods studied would be very useful for energy professionals involved in POE.

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Date Created
2011

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Analysis of the impact of urban heat island on energy consumption of buildings in Phoenix

Description

The Urban Heat Island (UHI) has been known to have been around from as long as people have been urbanizing. The growth and conglomeration of cities in the past century has caused an increase in the intensity and impact of

The Urban Heat Island (UHI) has been known to have been around from as long as people have been urbanizing. The growth and conglomeration of cities in the past century has caused an increase in the intensity and impact of Urban Heat Island, causing significant changes to the micro-climate and causing imbalances in the temperature patterns of cities. The urban heat island (UHI) is a well established phenomenon and it has been attributed to the reduced heating loads and increased cooling loads, impacting the total energy consumption of affected buildings in all climatic regions. This thesis endeavors to understand the impact of the urban heat island on the typical buildings in the Phoenix Metropolitan region through an annual energy simulation process spanning through the years 1950 to 2005. Phoenix, as a representative city for the hot-arid cooling-dominated region, would be an interesting example to see how the reduction in heating energy consumption offsets the increased demand for cooling energy in the building. The commercial reference building models from the Department of Energy have been used to simulate commercial building stock, while for the residential stock a representative residential model prescribing to IECC 2006 standards will be used. The multiyear simulation process will bring forth the energy consumptions of various building typologies, thus highlighting differing impacts on the various building typologies. A vigorous analysis is performed to see the impact on the cooling loads annually, specifically during summer and summer nights, when the impact of the 'atmospheric canopy layer' - urban heat island (UHI) causes an increase in the summer night time minimum and night time average temperatures. This study also shows the disparity in results of annual simulations run utilizing a typical meteorological year (TMY) weather file, to that of the current recorded weather data. The under prediction due to the use of TMY would translate to higher or lower predicted energy savings in the future years, for changes made to the efficiencies of the cooling or heating systems and thermal performance of the built-forms. The change in energy usage patterns caused by higher cooling energy and lesser heating energy consumptions could influence future policies and energy conservation standards. This study could also be utilized to understand the impacts of the equipment sizing protocols currently adopted, equipment use and longevity and fuel swapping as heating cooling ratios change.

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Date Created
2011

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Adaptive operation decisions for a system of smart buildings

Description

Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of CO2 emissions, which is more than any other industry sectors.

Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of CO2 emissions, which is more than any other industry sectors. As a result, the initiative smart building which aims to not only manage electrical consumption in an efficient way but also reduce the damaging effect of greenhouse gases on the environment has been launched. Another important technology being promoted by government agencies is the smart grid which manages energy usage across a wide range of buildings in an effort to reduce cost and increase reliability and transparency. As a great amount of efforts have been devoted to these two initiatives by either exploring the smart grid designs or developing technologies for smart buildings, the research studying how the smart buildings and smart grid coordinate thus more efficiently use the energy is currently lacking. In this dissertation, a "system-of-system" approach is employed to develop an integrated building model which consists a number of buildings (building cluster) interacting with smart grid. The buildings can function as both energy consumption unit as well as energy generation/storage unit. Memetic Algorithm (MA) and Particle Swarm Optimization (PSO) based decision framework are developed for building operation decisions. In addition, Particle Filter (PF) is explored as a mean for fusing online sensor and meter data so adaptive decision could be made in responding to dynamic environment. The dissertation is divided into three inter-connected research components. First, an integrated building energy model including building consumption, storage, generation sub-systems for the building cluster is developed. Then a bi-level Memetic Algorithm (MA) based decentralized decision framework is developed to identify the Pareto optimal operation strategies for the building cluster. The Pareto solutions not only enable multiple dimensional tradeoff analysis, but also provide valuable insight for determining pricing mechanisms and power grid capacity. Secondly, a multi-objective PSO based decision framework is developed to reduce the computational effort of the MA based decision framework without scarifying accuracy. With the improved performance, the decision time scale could be refined to make it capable for hourly operation decisions. Finally, by integrating the multi-objective PSO based decision framework with PF, an adaptive framework is developed for adaptive operation decisions for smart building cluster. The adaptive framework not only enables me to develop a high fidelity decision model but also enables the building cluster to respond to the dynamics and uncertainties inherent in the system.

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Created

Date Created
2012

Arntzen, Charles

Description

Charlie Arntzen joined ASU in August 2000 as the Florence Ely Nelson Presidential Endowed Chair and retired in 2016 from the School of Life Sciences and Biodesign Institute. Charlie was the founding Director of the Biodesign Institute.
Important ASU stories

Charlie Arntzen joined ASU in August 2000 as the Florence Ely Nelson Presidential Endowed Chair and retired in 2016 from the School of Life Sciences and Biodesign Institute. Charlie was the founding Director of the Biodesign Institute.
Important ASU stories include:
1) the creation of the Biodesign Institute,
2) the design and operation of the Biodesign labs,
3) the development of ZMapp to fight Ebola,
4) The New American University - a discussion of the importance of collaboration, and
5) several comments about Presidents Coor and Crow and Provost Glick

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Created

Date Created
2019-05-08

Lewis, William

Description

Bill Lewis, Vice Provost for Computing, came to ASU in 1966 to join the Industrial Engineering faculty. He retired in 2008. Important interview stories include ones involving: FOUNDING PROGRAMS (computer science); COMPUTERS (student information system, computing support); TEMPE CAMPUS (1966,

Bill Lewis, Vice Provost for Computing, came to ASU in 1966 to join the Industrial Engineering faculty. He retired in 2008. Important interview stories include ones involving: FOUNDING PROGRAMS (computer science); COMPUTERS (student information system, computing support); TEMPE CAMPUS (1966, changes); DEPARTMENTS (Industrial Engineering); PEOPLE (Lee P Thompson, Milt Glick); and BUILDINGS (Computer Commons).

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Created

Date Created
2010-01-22

Borovansky, Vladimir

Description

Vladimir Borovansky, ASU Noble Library Research librarian, joined ASU in May 1968. Interesting stories include:
1) the development of the NOBLE LIBRARY,
2) GOING DIGITAL - progression in the use of digital searching from offline searches to ARPANET to Google,

Vladimir Borovansky, ASU Noble Library Research librarian, joined ASU in May 1968. Interesting stories include:
1) the development of the NOBLE LIBRARY,
2) GOING DIGITAL - progression in the use of digital searching from offline searches to ARPANET to Google,
3), the creation of a Patent Depository Library and
4) an important trait of being a research librarian, i.e., know your audience

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Created

Date Created
2015-02-19

Glick, Milton

Description

Dr. Milton Glick grew up wanting to follow in his father’s footsteps and become a jeweler. However, his father had other plans for him and insisted that he attend college. Milt received his undergraduate degree in Chemistry from Augustana College

Dr. Milton Glick grew up wanting to follow in his father’s footsteps and become a jeweler. However, his father had other plans for him and insisted that he attend college. Milt received his undergraduate degree in Chemistry from Augustana College in his hometown of Rock Island, IL. He went on to receive his PhD in Chemistry from the University of Wisconsin-Madison. He spent 2 years as a Post-Doctoral student at Cornell University before joining the faculty of Wayne State University. From there, he went to serve as Dean at the University of Missouri and then Provost at Iowa State University, serving as interim President in his final year. In 1991 he joined the administration of ASU as Provost and remained here for 15 years. He spent almost 5 years as President of the University of Nevada, Reno before unexpectedly passing away of a stroke in April 2011.

In this interview, Milt talks about his goals of improving the quality of the faculty at ASU from being the “ordinary” that he found when he arrived to becoming the “extraordinary”. He attributes his success in improving faculty salaries as one aspect of achieving this goal. He talks about the challenges ASU had living in the shadows of the greatness of the University of Arizona and overcoming those to where the UofA now looks up to ASU! Milt also talks about his role as the “Zen master of managing limited budgets” during his years at ASU. And he speaks of the special relationship he had with now President Michael Crow, from his years at Iowa State, to using Michael as a consultant and mentor to him in his role as Provost at ASU and finally to having Dr. Crow as his “boss”. Throughout the interview, Milt stressed his love for ASU and mentioned that ASU was “more than just a destination for sunlight.”

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Created

Date Created
2010-03-06

McNeill, Connie

Description

Connie McNeill came to ASU in the summer of 1976 as head of the payroll project and retired in the summer of 2006 as the West Campus Assistant Vice Provost for Information Technology. Interesting stories include:
1) her role in

Connie McNeill came to ASU in the summer of 1976 as head of the payroll project and retired in the summer of 2006 as the West Campus Assistant Vice Provost for Information Technology. Interesting stories include:
1) her role in developing both academic and administrative computing services on the Tempe and West Campuses
2) bringing PC’s onto the Tempe Campus
3) the “Space Wars”
4) short stories about Roland Haden and Darel Eschbach

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Created

Date Created
2012-02-29