By M. Masud K. Khan, Nur M.S Hassan
Thermofluid Modeling for Sustainable strength Applications offers a set of the latest, state of the art advancements within the software of fluid mechanics modeling to strength platforms and effort effective technology.
Each bankruptcy introduces proper theories along precise, real-life case reviews that reveal the worth of thermofluid modeling and simulation as a vital part of the engineering method.
Research difficulties and modeling recommendations throughout a number of power potency eventualities are awarded by means of specialists, assisting clients construct a sustainable engineering wisdom base.
The textual content deals novel examples of using computation fluid dynamics in terms of scorching themes, together with passive air cooling and thermal garage. it's a helpful source for teachers, engineers, and scholars project study in thermal engineering.
- Includes contributions from specialists in strength potency modeling throughout a number of engineering fields
- Places thermofluid modeling and simulation on the middle of engineering layout and improvement, with idea supported by way of specific, real-life case studies
- Features scorching issues in power and sustainability engineering, together with thermal garage and passive air cooling
- Provides a invaluable source for teachers, engineers, and scholars project examine in thermal engineering
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This would make the average velocity about 15 m s21 or less, which is more reasonable. 14, it is evident that the average velocity of the air leaving from the CRACs is around 15 m s21 or less. 5 CRAC PERFORMANCE One of the biggest issues facing data center engineers is the loss of power during normal operation of the data center. The cooling equipment, however, needs backup power if any incidents happen during operation unless generators are installed with automatic start up . Therefore, some of the CFD analysis results have been performed to analyze a CRAC failure in this study to know the consequences of this effect.
Therefore it is essential to employ both of these parameters to have efficient cooling in the data center. 8. Large temperature gradients were observed outside the racks for both cases. As can be seen from the figures, the higher temperature along the width was found to be greater than that along the 46 Thermofluid Modeling for Energy Efficiency Applications length. Moreover, a different recirculation pattern was also observed at the outside rack. Hot air exhausting into the hot aisle is forced into the cold aisle from the side as well as from the top and back of the racks.
20 Temperature profile at different iteration. (a) At the iteration of 500. (b) At the iteration of 10,000. (c) At the iteration of 25,000. (d) At the iteration of 47,033. 20(d). 06˚C). Cool air came through the inlet and cool roof temperatures surrounding the room are the main reasons behind this temperature reduction. 20 (Continued) technology, air comes through the series of buried underground pipes that was cooled by the soil in summer due to heat convection, whereas the roof temperature was cooled due to heat conduction in green roof technology.