Packed-bed rock thermal energy storage for concetrated solar power: enhancement of storage time and system efficiency
- Authors: Maidadi, Mohaman Bello
- Date: 2013
- Subjects: Solar thermal energy , Energy storage , Reliability (Engineering)
- Language: English
- Type: Thesis , Masters , MTech
- Identifier: vital:9639 , http://hdl.handle.net/10948/d1020914
- Description: Solar thermal energy harvesting is a promising solution to offset the electricity demands of a growing population. The use of the technology is however still limited and this can most likely be attributed to the capital cost and also the intermittent nature of solar energy which requires incorporation of a storage system. To make the technology more attractive and effective, cheap means of harvesting solar energy and the development of efficient and inexpensive thermal energy storage devices will improve the performance of solar energy systems and the widespread use of solar energy. Heat storage in a packed-bed rock with air as the working fluid presents an attractive and simple solution for storing solar thermal energy and it is recommended for solar air heaters. A packed-bed rock storage system consists of rocks of good heat capacity packed in a storage tank. The working fluid (air) flows through the bed to transfer its energy. The major concern of the design for a packed-bed rock thermal storage system is to maximize the heat transfer and minimise the pressure drop across the storage tank and hence the pumping power. The time duration the stored energy can be preserved and the air flow wall effect through the bed are the common complications encountered in this system. This study presents an experimental and analytical analysis of a vacuum storage tank with the use of expanded perlite for high temperature thermal energy storage in a packed-bed of rocks. Dolerite rocks are used as the storage medium due to their high heat capacity and as they are locally available. To minimise the pressure drop across the tank, moderate rock sizes are used. The tank contains baffles, allowing an even spread of air to rock contact through the entire tank, therefore improving heat transfer. There is a good correlation between the predicted and the actual results (4 percent) which implies that the baffles incorporated inside the vacuum tank forces the air through the entire tank, thereby resulting in an even lateral temperature distribution across the tank. The investigation of heat loss showed that a vacuum with expanded perlite is a viable solution to high temperature heat storage for an extended period. The research also focuses on the investigation of a proposed low cost parabolic trough solar collector for an air heating system as shown in Figure (1.3). The use of a standard solar geyser evacuated tube (@R130 each) has cost benefits over the industry standard solar tubes normally used in concentrating solar power systems. A mathematical was developed to predict the thermal performance of proposed PTC and it was found that the measured results compared well with the predictions. The solar energy conversion efficiency of this collector is up to 70 percent. This research could impact positively on remote rural communities by providing a source of clean energy, especially for off-grid applications for schools, clinics and communication equipment. It could lead to a significant improvement in the cost performance, ease of installation and technical performance of storage systems for solar heating applications.
- Full Text:
- Date Issued: 2013
- Authors: Maidadi, Mohaman Bello
- Date: 2013
- Subjects: Solar thermal energy , Energy storage , Reliability (Engineering)
- Language: English
- Type: Thesis , Masters , MTech
- Identifier: vital:9639 , http://hdl.handle.net/10948/d1020914
- Description: Solar thermal energy harvesting is a promising solution to offset the electricity demands of a growing population. The use of the technology is however still limited and this can most likely be attributed to the capital cost and also the intermittent nature of solar energy which requires incorporation of a storage system. To make the technology more attractive and effective, cheap means of harvesting solar energy and the development of efficient and inexpensive thermal energy storage devices will improve the performance of solar energy systems and the widespread use of solar energy. Heat storage in a packed-bed rock with air as the working fluid presents an attractive and simple solution for storing solar thermal energy and it is recommended for solar air heaters. A packed-bed rock storage system consists of rocks of good heat capacity packed in a storage tank. The working fluid (air) flows through the bed to transfer its energy. The major concern of the design for a packed-bed rock thermal storage system is to maximize the heat transfer and minimise the pressure drop across the storage tank and hence the pumping power. The time duration the stored energy can be preserved and the air flow wall effect through the bed are the common complications encountered in this system. This study presents an experimental and analytical analysis of a vacuum storage tank with the use of expanded perlite for high temperature thermal energy storage in a packed-bed of rocks. Dolerite rocks are used as the storage medium due to their high heat capacity and as they are locally available. To minimise the pressure drop across the tank, moderate rock sizes are used. The tank contains baffles, allowing an even spread of air to rock contact through the entire tank, therefore improving heat transfer. There is a good correlation between the predicted and the actual results (4 percent) which implies that the baffles incorporated inside the vacuum tank forces the air through the entire tank, thereby resulting in an even lateral temperature distribution across the tank. The investigation of heat loss showed that a vacuum with expanded perlite is a viable solution to high temperature heat storage for an extended period. The research also focuses on the investigation of a proposed low cost parabolic trough solar collector for an air heating system as shown in Figure (1.3). The use of a standard solar geyser evacuated tube (@R130 each) has cost benefits over the industry standard solar tubes normally used in concentrating solar power systems. A mathematical was developed to predict the thermal performance of proposed PTC and it was found that the measured results compared well with the predictions. The solar energy conversion efficiency of this collector is up to 70 percent. This research could impact positively on remote rural communities by providing a source of clean energy, especially for off-grid applications for schools, clinics and communication equipment. It could lead to a significant improvement in the cost performance, ease of installation and technical performance of storage systems for solar heating applications.
- Full Text:
- Date Issued: 2013
Forecasting solar cycle 24 using neural networks
- Authors: Uwamahoro, Jean
- Date: 2009
- Subjects: Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5468 , http://hdl.handle.net/10962/d1005253 , Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Description: The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.
- Full Text:
- Date Issued: 2009
- Authors: Uwamahoro, Jean
- Date: 2009
- Subjects: Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5468 , http://hdl.handle.net/10962/d1005253 , Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Description: The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.
- Full Text:
- Date Issued: 2009
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