1. AMCB-2016
Efficient modeling of nanofluid transport in microchannels using Eulerian–Lagrangian approach
Lakshmi Sirisha Maganti$, Purbarun Dhar*, T. Sundararajan$ and Sarit K. Das$ *
Department of Mechanical Engineering, $IIT Madras, * IIT Ropar, India.
Introduction
Nanofluids (NFs) in parallel microchannel heat exchangers has been recommended as a promising solution for cooling microelectronic devices.
The flow and concentration maldistribution characterized by using concentration maldistribution factor (ε) = (ϕmax-ϕmin)/ϕmax and flow maldistribution factor (ɳ) = (∆Pmax-∆ Pmin)/∆Pmax respectively.
Two different approaches used for the study
1) Discrete Phase Modeling (DPM) 2) Effective Property Model (EPM).
Present study focused on DPM.
Geometry
Validation with existing experimental data
Objective
I. Effective Property Model
To investigate effect of slip mechanisms like Brownian and Thermophoresis etc. on the NF’s thermal performance in Parallel microchannel systems numerically using two approaches. (DPM and EPM).
To show that DPM is accurate tool for modeling nanofluid in parallel microchannel systems.
The NFs are assumed to be single phase homogeneous fluids and the thermo-physical properties evaluated using effective medium correlations.
II. Discrete Phase approach
The NFs are assumed to be two phase non homogeneous fluids, having fluid as continuous phase and nanoparticles as discrete phase.
Eularian-Lagrangian approach.
Trajectory of nanoparticle determined by Newton's second law of motion.
The coupling between continuous phase and discrete phase is realized using Newton's third law.
The forces acting on particle are: Drag, Gravity, Brownian, Thermophoresis, Saffman Lift, Pressure gradient, Virtual mass
F = FD + FG + FB + FT + FL + FP + FV
Numerical Model
A 3D model of parallel microchannel U, I, Z type configurations have been developed, meshed and solved with ANSYS Fluent 14.5.
Geometry dimensions
Dh=100µm, Achannel/Aheader (Ac/Am)=0.2, Aspect ratio (H/W)=0.1, Number of channels=7.
Working fluid:
Water and Alumina (45 nm)-Water NF (5 vol. %)
Results
Maldistribution factor -DPM vs. EPM
Pressure drop of water and Alumina-water (5 vol. %) at different Re for three configurations U, I and Z.
EPM - the presence of particle migration in continuous phase cannot be incorporated. Change in ɳ with respect to concentration is only due to effective viscosity.
DPM - Change in ɳ with respect to both concentration and Re.
DPM efficiently predicts effects arising due to particle migration and diffusion
Particle distribution within channels with Re also important for design of NF based coolants
Brownian is the major slip force that governs particle maldistribution in microchannels mainly at low Re. (Vf / VB < 500).
1) Concentration distribution at inlet C-S.
a) & c) concentration distribution at inlet C-S with Brownian effect on and off b) & d) same at outlet C-S
Smart behavior of NF at high temperature zones
Particle mass concentration distribution at hot zones
Conclusions
The pressure drop across the channels is not same in vase of all 3 configurations, which is because of flow maldistribution of working fluid.
At low Reynolds numbers inclusion of nanoparticles leads to high maldistribution which is because of dominant Brownian effect.
Concentration maldistribution will not follow the Flow maldistribution since nanofluid behaves as non-homogeneous, two phase fluids.
EPM will not consider presence of particle in continuous phase so DPM is proved to be an effective tool to model nanofluid in parallel microchannel systems.
NF’s observed to be smart fluids not only in the point of just cooling but also in cooling uniformly.
Comparison of flow maldistribution (ɳ) factor and concentration maldistribution factor (ε).
Concentration maldistribution does not follow flow maldistribution
Therefore, slip forces play major role in governing the flow characteristics of nanofluids
The distribution of fluid and concentration are strong functions of flow configurations.
Z shows least flow maldistribution and independent concentration maldistribution with respect to concentration and Re.
Indian Institute of Technology Madras
AMCB-2016