Made available in DSpace on 2014-06-11T19:35:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2002Bitstream added on 2014-06-13T20:46:45Z : No. of bitstreams: 1 ribeiro_ja_dr_guara.pdf: 976132 bytes, checksum: 3d4e175f5aca733de9311c6483ffc385 (MD5) / Com a evolução tecnológica e maior competitividade entre as empresas, aliadas as maiores exigências do mercado consumidor, tornou-se imprescindível, na avaliação e aceitação de secadoras, a necessidade de se fabricar equipamentos que operem com maior rendimento e menor consumo de energia. Tais fatos motivam a estudar e analisar o desempenho de sistemas suspensos de secagem de uso doméstico, com relação às características de funcionamento e eficiência no processo de desumidificação de tecidos, pois os parâmetros que influenciam o desempenho dos secadores e os mecanismos de secagem devem ser conhecidos nos graus de precisão requeridos para cada aplicação. Os parâmetros avaliados neste trabalho foram os seguintes: conteúdos de umidade do tecido na entrada e na saída do secador, condições de transferência de calor, temperatura, a velocidade, a direção e a umidade absoluta do fluxo do meio de fornecimento de calor, e a distribuição de temperatura superficial do tecido. As previsões teóricas são comparadas com os resultados experimentais obtidos em uma bancada de testes onde, conforme configurações apresentadas, simularam-se as condições reais de trabalho do sistema. Foram selecionados os tecidos mais utilizados para manufatura de vestuário: Jeans índigo, Flanela, Cotton-Lycra, Brim, Tergal, Viscose e Linho. / Due to the technologic evolution and a higher competition among the companies, with the bigger requirements of the consuming market, it turned out necessary the manufacturing of equipment with higher performance and lower energy consumption. These aspects are motivation for studying and analyzing the performance of suspended household drying systems, regarding to their functioning features and efficiency on the cloth dehumidification process, since the parameters which influence the dryer performance and the drying mechanisms must be well known on the required accuracy degrees for each application. The parameters evaluated by this thesis were: cloth moisture contents at the dryer input and output; heat transfer conditions; speed, direction and absolute humidity of the heat supplying mean; and the cloth surface temperature distribution. The theoretical predictions are compared to the experimental results obtained from a test bench, where real system work conditions were simulated. The most utilized kinds of cloth for clothing manufacturing were selected, such as: blue-jeans, flannel, cotton-spandex, canvas, polyester, viscose rayon and linen.

Companies in the US and the UK are frantically looking for ways to prevent future exorbitant data centre costs. South Africa does not have the space shortages, nor the high energy costs that companies in the US and UK experience. Hence, data centre energy efficiency is currently not a priority for South African businesses. However, things are about to change. Rising electricity costs and high growth rates in electricity consumption will motivate companies to consider implementing energy efficiency initiatives in their data centres. The purpose of the research begins at an assessment of the level of awareness of the energy consumption of data centres. This report looks at what the perceptions are of the factors that could cause fluctuations in energy usage at the data centre. The report further explores how South African companies are addressing potential fluctuations in usage, and what the motivation is for action taken. The methodology was a series of telephonic, semi-structured interviews with available and relevant stakeholders in data centre energy management. The interviewees were sourced from ICT companies in South Africa identified by the Market Research Foundation in the publication “Top ICT companies in South Africa 2005/6”, attendees of the ITWEB Green IT Summit 2008, and further referrals from these interviews. / Dissertation (MBA)--University of Pretoria, 2010. / Gordon Institute of Business Science (GIBS) / unrestricted

This paper explains typical knee wall construction and insulation in homes in Indiana and demonstrates the energy inefficiencies caused by such methods. Literature research gives examples of possible opportunities for changing the standard methods of knee wall construction and insulation. The methodology of the study is explained, as is energy intensity (EI) — the main evaluation tool used in this paper. EI allows a researcher to compare homes of different sizes in different locations. Overall, the study shows that homes whose knee walls have an air barrier backing use less energy than homes without knee walls. Additionally, as expected, homes with improperly constructed knee walls use the most energy. Finally, the paper contains recommendations about what methods might be used to change the standard practices involved in building and insulating knee walls. / Department of Urban Planning

Thesis (M. Sc.)--University of Hong Kong, 1997. / Includes bibliographical references (leaf 71-74).

No description available.

This study generalizes the applications of learning curve theory. It extends the simple power learning model in two ways: (1) by extending the model to include other sift variables, the extensive learning model; (2) by generalizing the functional relationship to give greater flexibility in modelling the learning curve, the translog learning model. Through empirical analyses of gross domestic product, total energy consumption, petroleum consumption, and petroleum products consumption, different learning curve models are explored and compared.

The vision of the future Internet of Things is posing new challenges due to gigabytes of data being generated everyday by millions of sensors, actuators, RFID tags, and other devices. As the volume of data is growing dramatically, so is the demand for performance enhancement. When it comes to this big data problem, much attention has been given to cloud computing and virtualization for their almost unlimited resource capacity, flexible resource allocation and management, and distributed processing ability that promise high scalability and availability. On the other hand, the variety of types and nature of data is continuously increasing. Almost without exception, data centers supporting cloud based services are monitored for performance and security and the resulting monitoring data needs to be stored somewhere. Similarly, billions of sensors that are scattered throughout the world are pumping out huge amount of data, which is handled by a database. Typically, the monitoring data consists time series, that is numbers indexed by time. To handle this type of time series data a distributed time series database is needed.   Nowadays, many database systems are available but it is difficult to use them for storing and managing large volumes of time series data. Monitoring large amounts of periodic data would be better done using a database optimized for storing time series data. The traditional and dominant relational database systems have been questioned whether they can still be the best choice for current systems with all the new requirements. Choosing an appropriate database for storing huge amounts of time series data is not trivial as one must take into account different aspects such as manageability, scalability and extensibility. During the last years NoSQL databases have been developed to address the needs of tremendous performance, reliability and horizontal scalability. NoSQL time series databases (TSDBs) have risen to combine valuable NoSQL properties with characteristics of time series data from a variety of use-cases.   In the same way that performance has been central to systems evaluation, energy-efficiency is quickly growing in importance for minimizing IT costs. In this thesis, we compared the performance of two NoSQL distributed time series databases, OpenTSDB and InfluxDB, based on the energy consumed by them in different scenarios, using the same set of machines and the same data. We evaluated the amount of energy consumed by each database on single host and multiple hosts, as the databases compared are distributed time series databases. Individual analysis and comparative analysis is done between the databases. In this report we present the results of this study and the performance of these databases based on energy consumption.

No description available.

Thesis (Ph. D.)--Michigan State University, 1990. / Includes bibliographical references (leaves 158-162).

Thesis (Ph.D.)--Washington State University, May 2006. / Includes bibliographical references (p. 340-363).