FLOW BOILING - A SIMPLE CORRELATION FOR CONVECTIVE HEAT TRANSFER COMPONENT

Abstract In air conditioning systems, lubricating oil leaves the compressor and circulates through the other system components. This lubricant acts as a contaminant affecting heat transfer in heat exchangers. The literature indicated that mixtures of refrigerants and nanolubricants, that is, nanoparticles dispersed in the lubricant oils, have potentials to augment heat transfer exchange effectiveness. However, the nanoparticle mechanisms leading to such heat transfer changes are still unclear and not well included in the models. In this work, an existing single-phase forced flow convective heat transfer model, originally developed for water-based nanofluids, was modified to include the effects of diffusion and mass balance of different shape nanoparticles within the laminar sublayer and turbulent layer of the flow. A new physics-based superposition heat transfer model for saturated two-phase flow boiling of refrigerant and nanolubricants was also developed by integrating the modified forced flow convective heat transfer model and a semi-empirical pool boiling model for nanolubricants. The new model included the several physical effects that influenced heat transfer, such as slip mechanisms at the nanoparticles and base fluid interface and its influence on the laminar sublayer thickness, momentum transfer from the nanoparticles to the growing bubbles, and formation of lubricant excess concentration at the tube surface and its influence on bubble growth and tube wetting. The new model was validated for single-phase convective heat transfer and two-phase flow boiling of refrigerant R410A with two nanolubricants, having nonspherical ZnO nanoparticles and spherical Al2O3 nanoparticles.

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Flow regimes and convective heat transfer of refrigerant flow boiling in ultra-small clearance microgaps

International Journal of Heat and Mass Transfer ◽

10.1016/j.ijheatmasstransfer.2016.12.056 ◽

2017 ◽

Vol 108 ◽

pp. 1702-1713 ◽

Cited By ~ 12

Author(s):

Mohamed H. Nasr ◽

Craig E. Green ◽

Peter A. Kottke ◽

Xuchen Zhang ◽

Thomas E. Sarvey ◽

...

Keyword(s):

Heat Transfer ◽

Convective Heat Transfer ◽

Convective Heat ◽

Flow Boiling ◽

Flow Regimes ◽

Small Clearance

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Characterization of Twisted-Tape-Induced Helical Swirl Flows for Enhancement of Forced Convective Heat Transfer in Single-Phase and Two-Phase Flows

Journal of Thermal Science and Engineering Applications ◽

10.1115/1.4023935 ◽

2013 ◽

Vol 5 (2) ◽

Cited By ~ 17

Author(s):

Raj M. Manglik ◽

Arthur E. Bergles

Keyword(s):

Heat Transfer ◽

Convective Heat Transfer ◽

Convective Heat ◽

Single Phase ◽

Flow Boiling ◽

Heated Surface ◽

Heat Fluxes ◽

Twisted Tape ◽

Forced Convective Heat Transfer ◽

Swirl Flows

By generating helical swirling motion inside a tube with a twisted-tape insert, forced convective heat transfer is significantly enhanced. The primary mechanism entails imparting a centrifugal force component to the longitudinal fluid motion, which superimposes secondary circulation over the main axial flow to promote cross-stream mixing. Based on experimental flow visualization and computational modeling of single-phase laminar flows, a fundamental scaling of the cross-sectional vortex structure and a parametric analysis of the primary enhancement mechanisms in single-phase flows are delineated. Heat transfer coefficient and friction factor correlations for both laminar and turbulent regimes are presented, and the damping effect of swirl on the transition region is highlighted. In flow boiling with net vapor generation, tape-twist-induced helical swirl pushes liquid droplets from the core to the wall to enhance heat transfer and delay dryout. In subcooled boiling, the radial pressure gradient due to the swirl promotes vapor removal from the heated surface to retard vapor blanketing and accommodate higher heat fluxes. The scaling and phenomenological descriptions of the underlying vapor-liquid transport in these different boiling modes and regimes are presented along with any available predictive correlations.

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