Matching Items (6)
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- All Subjects: Renewable Energy
- Genre: Masters Thesis
- Creators: Phelan, Patrick
Description
Concentrating solar thermal power systems gained a wide interest for a long time to serve as a renewable and sustainable alternate source of energy. While the optimization and modification are ongoing, focused generally on solar power systems to provide solar-electrical energy or solar-thermal energy, the production process of Ordinary Portland Cement (OPC) has not changed over the past century. A linear refractive Fresnel lens application in cement production process is investigated in this research to provide the thermal power required to raise the temperature of lime up to 623 K (350C) with zero carbon emissions for stage two in a new proposed two-stage production process. The location is considered to be Phoenix, Arizona, with a linear refractive Fresnel lens facing south, tilted 33.45 equaling the location latitude, and concentrating solar beam radiation on an evacuated tube collector with tracking system continuously rotating about the north-south axis. The mathematical analysis showed promising results based on averaged monthly values representing an average hourly useful thermal power and receiver temperature during day-light hours for each month throughout the year. The maximum average hourly useful thermal power throughout the year was obtained for June as 33 kWth m-2 with a maximum receiver temperature achieved of 786 K (513C), and the minimum useful thermal power obtained during the month of December with 27 kWth m-2 and a minimum receiver temperature of 701 K (428C).
ContributorsAlkhuwaiteem, Mohammad (Author) / Phelan, Patrick (Thesis advisor) / Shuaib, Abdelrahman (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Pavement surface temperature is calculated using a fundamental energy balance model developed previously. It can be studied using a one-dimensional mathematical model. The input to the model is changed, to study the effect of different properties of pavement on its diurnal surface temperatures. It is observed that the pavement surface temperature has a microclimatic effect on the air temperature above it. A major increase in local air temperature is caused by heating of solid surfaces in that locality. A case study was done and correlations have been established to calculate the air temperature above a paved surface. Validation with in-situ pavement surface and air temperatures were made. Experimental measurement for the city of Phoenix shows the difference between the ambient air temperature of the city and the microclimatic air temperature above the pavement is approximately 10 degrees Fahrenheit. One mitigation strategy that has been explored is increasing the albedo of the paved surface. Although it will reduce the pavement surface temperature, leading to a reduction in air temperature close to the surface, the increased pavement albedo will also result in greater reflected solar radiation directed towards the building, thus increasing the building solar load. The first effect will imply a reduction in the building energy consumption, while the second effect will imply an increase in the building energy consumption. Simulation is done using the EnergyPlus tool, to find the microclimatic effect of pavement on the building energy performance. The results indicate the cooling energy savings of an office building for different types of pavements can be variable as much as 30%.
ContributorsSengupta, Shawli (Author) / Phelan, Patrick (Thesis advisor) / Kaloush, Kamil (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2015
Description
An eco-industrial park (EIP) is an industrial ecosystem in which a group of co-located firms are involved in collective resource optimization with each other and with the local community through physical exchanges of energy, water, materials, byproducts and services - referenced in the industrial ecology literature as "industrial symbiosis". EIPs, when compared with standard industrial resource sharing networks, prove to be of greater public advantage as they offer improved environmental and economic benefits, and higher operational efficiencies both upstream and downstream in their supply chain.
Although there have been many attempts to adapt EIP methodology to existing industrial sharing networks, most of them have failed for various factors: geographic restrictions by governmental organizations on use of technology, cost of technology, the inability of industries to effectively communicate their upstream and downstream resource usage, and to diminishing natural resources such as water, land and non-renewable energy (NRE) sources for energy production.
This paper presents a feasibility study conducted to evaluate the comparative environmental, economic, and geographic impacts arising from the use of renewable energy (RE) and NRE to power EIPs. Life Cycle Assessment (LCA) methodology, which is used in a variety of sectors to evaluate the environmental merits and demerits of different kinds of products and processes, was employed for comparison between these two energy production methods based on factors such as greenhouse gas emission, acidification potential, eutrophication potential, human toxicity potential, fresh water usage and land usage. To complement the environmental LCA analysis, levelized cost of electricity was used to evaluate the economic impact. This model was analyzed for two different geographic locations; United States and Europe, for 12 different energy production technologies.
The outcome of this study points out the environmental, economic and geographic superiority of one energy source over the other, including the total carbon dioxide equivalent emissions, which can then be related to the total number of carbon credits that can be earned or used to mitigate the overall carbon emission and move closer towards a net zero carbon footprint goal thus making the EIPs truly sustainable.
Although there have been many attempts to adapt EIP methodology to existing industrial sharing networks, most of them have failed for various factors: geographic restrictions by governmental organizations on use of technology, cost of technology, the inability of industries to effectively communicate their upstream and downstream resource usage, and to diminishing natural resources such as water, land and non-renewable energy (NRE) sources for energy production.
This paper presents a feasibility study conducted to evaluate the comparative environmental, economic, and geographic impacts arising from the use of renewable energy (RE) and NRE to power EIPs. Life Cycle Assessment (LCA) methodology, which is used in a variety of sectors to evaluate the environmental merits and demerits of different kinds of products and processes, was employed for comparison between these two energy production methods based on factors such as greenhouse gas emission, acidification potential, eutrophication potential, human toxicity potential, fresh water usage and land usage. To complement the environmental LCA analysis, levelized cost of electricity was used to evaluate the economic impact. This model was analyzed for two different geographic locations; United States and Europe, for 12 different energy production technologies.
The outcome of this study points out the environmental, economic and geographic superiority of one energy source over the other, including the total carbon dioxide equivalent emissions, which can then be related to the total number of carbon credits that can be earned or used to mitigate the overall carbon emission and move closer towards a net zero carbon footprint goal thus making the EIPs truly sustainable.
ContributorsGupta, Vaibhav (Author) / Calhoun, Ronald J (Thesis advisor) / Dooley, Kevin (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2014
Description
In recent years, 40% of the total world energy consumption and greenhouse gas emissions is because of buildings. Out of that 60% of building energy consumption is due to HVAC systems. Under current trends these values will increase in coming years. So, it is important to identify passive cooling or heating technologies to meet this need. The concept of thermal energy storage (TES), as noted by many authors, is a promising way to rectify indoor temperature fluctuations. Due to its high energy density and the use of latent energy, Phase Change Materials (PCMs) are an efficient choice to use as TES. A question that has not satisfactorily been addressed, however, is the optimum location of PCM. In other words, given a constant PCM mass, where is the best location for it in a building? This thesis addresses this question by positioning PCM to obtain maximum energy savings and peak time delay. This study is divided into three parts. The first part is to understand the thermal behavior of building surfaces, using EnergyPlus software. For analysis, a commercial prototype building model for a small office in Phoenix, provided by the U.S. Department of Energy, is applied and the weather location file for Phoenix, Arizona is also used. The second part is to justify the best location, which is obtained from EnergyPlus, using a transient grey box building model. For that we have developed a Resistance-Capacitance (RC) thermal network and studied the thermal profile of a building in Phoenix. The final part is to find the best location for PCMs in buildings using EnergyPlus software. In this part, the mass of PCM used in each location remains unchanged. This part also includes the impact of the PCM mass on the optimized location and how the peak shift varies. From the analysis, it is observed that the ceiling is the best location to install PCM for yielding the maximum reduction in HVAC energy consumption for a hot, arid climate like Phoenix.
ContributorsPrem Anand Jayaprabha, Jyothis Anand (Author) / Phelan, Patrick (Thesis advisor) / Wang, Robert (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2018
Description
This work describes an approach for distance computation between agents in a
multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-
Arrival (AoA) data and local trajectory data. Each agent in the swarm is able
to discretely determine distance and bearing to every other neighbor agent in the
swarm. From this information, I propose a lightweight method for sensor coverage
of an unknown area based on the work of Sameera Poduri. I also show that this
technique performs well with limited calibration distances.
multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-
Arrival (AoA) data and local trajectory data. Each agent in the swarm is able
to discretely determine distance and bearing to every other neighbor agent in the
swarm. From this information, I propose a lightweight method for sensor coverage
of an unknown area based on the work of Sameera Poduri. I also show that this
technique performs well with limited calibration distances.
ContributorsMulford, Philip (Author) / Das, Jnaneshwar (Thesis advisor) / Takahashi, Timothy (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2020
Description
The operating temperature of photovoltaic (PV) modules has a strong impact on the expected performance of said modules in photovoltaic arrays. As the install capacity of PV arrays grows throughout the world, improved accuracy in modeling of the expected module temperature, particularly at finer time scales, requires improvements in the existing photovoltaic temperature models. This thesis work details the investigation, motivation, development, validation, and implementation of a transient photovoltaic module temperature model based on a weighted moving-average of steady-state temperature predictions.
This thesis work first details the literature review of steady-state and transient models that are commonly used by PV investigators in performance modeling. Attempts to develop models capable of accounting for the inherent transient thermal behavior of PV modules are shown to improve on the accuracy of the steady-state models while also significantly increasing the computational complexity and the number of input parameters needed to perform the model calculations.
The transient thermal model development presented in this thesis begins with an investigation of module thermal behavior performed through finite-element analysis (FEA) in a computer-aided design (CAD) software package. This FEA was used to discover trends in transient thermal behavior for a representative PV module in a timely manner. The FEA simulations were based on heat transfer principles and were validated against steady-state temperature model predictions. The dynamic thermal behavior of PV modules was determined to be exponential, with the shape of the exponential being dependent on the wind speed and mass per unit area of the module.
The results and subsequent discussion provided in this thesis link the thermal behavior observed in the FEA simulations to existing steady-state temperature models in order to create an exponential weighting function. This function can perform a weighted average of steady-state temperature predictions within 20 minutes of the time in question to generate a module temperature prediction that accounts for the inherent thermal mass of the module while requiring only simple input parameters. Validation of the modeling method presented here shows performance modeling accuracy improvement of 0.58%, or 1.45°C, over performance models relying on steady-state models at narrow data intervals.
This thesis work first details the literature review of steady-state and transient models that are commonly used by PV investigators in performance modeling. Attempts to develop models capable of accounting for the inherent transient thermal behavior of PV modules are shown to improve on the accuracy of the steady-state models while also significantly increasing the computational complexity and the number of input parameters needed to perform the model calculations.
The transient thermal model development presented in this thesis begins with an investigation of module thermal behavior performed through finite-element analysis (FEA) in a computer-aided design (CAD) software package. This FEA was used to discover trends in transient thermal behavior for a representative PV module in a timely manner. The FEA simulations were based on heat transfer principles and were validated against steady-state temperature model predictions. The dynamic thermal behavior of PV modules was determined to be exponential, with the shape of the exponential being dependent on the wind speed and mass per unit area of the module.
The results and subsequent discussion provided in this thesis link the thermal behavior observed in the FEA simulations to existing steady-state temperature models in order to create an exponential weighting function. This function can perform a weighted average of steady-state temperature predictions within 20 minutes of the time in question to generate a module temperature prediction that accounts for the inherent thermal mass of the module while requiring only simple input parameters. Validation of the modeling method presented here shows performance modeling accuracy improvement of 0.58%, or 1.45°C, over performance models relying on steady-state models at narrow data intervals.
ContributorsPrilliman, Matthew (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2020