Loading...
  • Home
  • Search Results
1811-1812 of 4327 Papers

HEAT TRANSFER AUGMENTATION FOR THE CAR RADIATOR BY USING NANOFLUID IN

Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN MECHANICAL AND ROBOTICS ENGINEERING
Author(s): ADNAN M. HUSSEIN , G.L. MING , K. KADIRGAMA , R. A. BAKAR

Abstract: The adding solid nanoparticles to liquids are significant topics to augment heat transfer for many industrial applications in the last years. This article included the friction factor and forced convection heat transfer of SiO2 nanoparticle suspended to water as a base fluid into a car radiator experimentally. Four different concentrations of nanofluids in the range of 1 to 4vol. % have been used. The flow rate changed in the range of 1 to 5 LPM to get Reynolds number with the range of 250 to 2000. The results showed that the friction factor decreases with an increase in flowrate and increase with increasing in volume concentration. Furthermore, the inlet temperature to the radiator has insignificantly affected to the friction factor. Likewise, the heat transfer increases with increasing in flowrate, nanofluid volume concentration and inlet temperature. Meanwhile, application of SiO2 nanofluid with low concentrations can enhance heat transfer rate up to 30% as a comparison with pure wa

  • Publication Date: 09-Mar-2014
  • DOI: 10.15224/978-1-63248-002-6-118
  • Views: 0
  • Downloads: 0

PREDICTIVE DATA ANALYSIS IN CLOUD USING BIG DATA ANALYTIC TECHNIQUES

Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTER SCIENCE AND ELECTRONICS ENGINEERING
Author(s): R.POORVADEVI , S.RAJALAKSHMI , S.RAMAMOORTHY

Abstract: All the companies are nowadays migrating their applications towards cloud environment, because of the huge reduce in the overall investment and greatest flexibility provided by the cloud. The Cloud provides the larger volume of space for the storage and different set of services for all kind of applications to the cloud customers. There is not much delay and major changes required at the client level. The large amount of user data and application results stored on the cloud environment, will automatically make the data analysis and prediction process very difficult on the different clusters of cloud. It is always difficult to process, whenever a user required to analyze the data stored on the cloud as well as frequently used service by other cloud customers for the same set of query on the cloud environment.The existing data mining techniques are insufficient to analyze those huge data volumes and identify the frequent services accessed by the cloud users. In this proposed scheme we ar

  • Publication Date: 09-Mar-2014
  • DOI: 10.15224/978-1-63248-000-2-32
  • Views: 0
  • Downloads: 0