Bislug flow in circular and noncircular channels and the role of interface stretching on energy dissipation

The area of microfluidics has increased in popularity with such fields as MEMS, microreactors, microscaleheat exchangers, etc. A comprehensive understanding of dissipation mechanisms for fluid flow in microchannels is required to accurately predict the behavior in these small systems. Tests were performed using a constant pressure potential created by two immiscible fluids juxtaposed in a microchannel. This study focused on the flow and dissipation mechanisms in round and square microchannels. There are four major dissipation mechanisms in slug flow; wall shear, dissipation at the contact line, menisci interaction and the stretching of the interface. A force balance between the internal driving potential, viscous drag and interface stretching was used to develop a model for the prediction of the velocity of a bislug in a microchannel. Interface stretching is a dissipation mechanism that has been included due to the unique system properties and becomes increasingly more important as the bislug decreases in length.

Computational fluids domain reduction to a simplified fluid network

The primary goal of this project is to demonstrate the practical use of data mining algorithms to cluster a solved steady-state computational fluids simulation (CFD) flow domain into a simplified lumped-parameter network. A commercial-quality code, “cfdMine” was created using a volume-weighted k-means clustering that that can accomplish the clustering of a 20 million cell CFD domain on a single CPU in several hours or less. Additionally agglomeration and k-means Mahalanobis were added as optional post-processing steps to further enhance the separation of the clusters. The resultant nodal network is considered a reduced-order model and can be solved transiently at a very minimal computational cost. The reduced order network is then instantiated in the commercial thermal solver MuSES to perform transient conjugate heat transfer using convection predicted using a lumped network (based on steady-state CFD). When inserting the lumped nodal network into a MuSES model, the potential for developing a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track temperatures near specific objects (such as equipment in vehicles).

Experimental investigation of regular fluids and nanofluids during flow boiling in a single microchannel at different heat fluxes and mass fluxes

The dissipation of high heat flux from integrated circuit chips and the maintenance of acceptable junction temperatures in high powered electronics require advanced cooling technologies. One such technology is two-phase cooling in microchannels under confined flow boiling conditions. In macroscale flow boiling bubbles will nucleate on the channel walls, grow, and depart from the surface. In microscale flow boiling bubbles can fill the channel diameter before the liquid drag force has a chance to sweep them off the channel wall. As a confined bubble elongates in a microchannel, it traps thin liquid films between the heated wall and the vapor core that are subject to large temperature gradients. The thin films evaporate rapidly, sometimes faster than the incoming mass flux can replenish bulk fluid in the microchannel. When the local vapor pressure spike exceeds the inlet pressure, it forces the upstream interface to travel back into the inlet plenum and create flow boiling instabilities. Flow boiling instabilities reduce the temperature at which critical heat flux occurs and create channel dryout. Dryout causes high surface temperatures that can destroy the electronic circuits that use two-phase micro heat exchangers for cooling. Flow boiling instability is characterized by periodic oscillation of flow regimes which induce oscillations in fluid temperature, wall temperatures, pressure drop, and mass flux. When nanofluids are used in flow boiling, the nanoparticles become deposited on the heated surface and change its thermal conductivity, roughness, capillarity, wettability, and nucleation site density. It also affects heat transfer by changing bubble departure diameter, bubble departure frequency, and the evaporation of the micro and macrolayer beneath the growing bubbles. Flow boiling was investigated in this study using degassed, deionized water, and 0.001 vol% aluminum oxide nanofluids in a single rectangular brass microchannel with a hydraulic diameter of 229 µm for one inlet fluid temperature of 63°C and two constant flow rates of 0.41 ml/min and 0.82 ml/min. The power input was adjusted for two average surface temperatures of 103°C and 119°C at each flow rate. High speed images were taken periodically for water and nanofluid flow boiling after durations of 25, 75, and 125 minutes from the start of flow. The change in regime timing revealed the effect of nanoparticle suspension and deposition on the Onset of Nucelate Boiling (ONB) and the Onset of Bubble Elongation (OBE). Cycle duration and bubble frequencies are reported for different nanofluid flow boiling durations. The addition of nanoparticles was found to stabilize bubble nucleation and growth and limit the recession rate of the upstream and downstream interfaces, mitigating the spreading of dry spots and elongating the thin film regions to increase thin film evaporation.