Numerical Study of Flow and Heat Transfer with ZnO-Water Nanofluid in Flattened Tubes
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Mark Wing Tsan Lee
Abstract
The usage of nanofluids and modification of tube geometry are the two most prominent heat transfer enhancement methods employed to improve the performance of thermal devices. In this work, the combined effect of these methods has been studied by CFD modelling of developing and Graetz laminar flow in flattened tubes with ZnO – water nanofluid. For the purpose of comparison, simulation with water and circular tube has also been carried out. Performance evaluation has been done using PEC, PER and entropy generation. Results reveal that tube flattening has more pronounced effect on both heat transfer and flow compared to that of nanofluid. An optimum tube flattening in terms of aspect ratio and nanofluid concentration has also been identified for this kind of flow. Flattened tube with aspect ratio 6 with 1 % ZnO-water nanofluid has been found to yield the highest entropy generation reduction of 13.24 %
Nomenclature
- Greek Symbols
- Α
Thermal diffusivity (m2/s)
- β
Fraction of liquid volume travelling with a particle
- γ
Ratio of nanolayer thermal conductivity to particle conductivity
- δ
Distance between nanoparticles (m)
- Є
Aspect ratio of geometry cross-sectional height to width
- μ
Dynamic viscosity (Pa.s)
- ρ
Density (kg/m3)
Volume fraction
- χ
Ratio of the nanolayer thickness to the particle radius
- ψ
Particle sphericity
- τ
Dimensionless wall and fluid temperature difference
- λ
Dimensionless length of circular tube
- φ
Total dimensionless entropy
- Latin Symbols
- A
Area (m2)
- AR
Aspect ratio
- c
Correction factor
- C
Specific heat capacity (J/ kg K)
- C1
Constant
- Cf
Skin friction coefficient
- d
Diameter
- Ec
Eckart number
- f
Darcy’s friction factor
- fr
Friction coefficient
- h
Convective heat transfer coefficient (W/m2K)
- k
Thermal conductivity (W/mK)
- K
Boltzmann constant, 1.381 × 10−23 J/K
- kpe
Equivalent thermal conductivity (W/mK)
- L
Length of channel (m)
Mass flow rate (kg/s)
- n
Empirical shape factor
- Nu
Nusselt number
- OS
Occupied space (m2)
- P
Perimeter
- PEC
performance evaluation criterion
- PER
performance enhancement ratio
- ΔP
Pressure drop (Pa)
- Pr
Prandtl number
- Q
Heat transfer (W)
- q
Heat flux (W/m2)
- Re
Reynolds number
- Sgen
Entropy generation
- T
Temperature (K)
- To
Reference temperature, 273 K
- u
Fluid velocity (m/s)
- V
Volume (m3)
- v̇
Volumetric flow rate (m3)
- W
Pumping power (W)
- Subscripts
- b
Bulk
- bf
Base fluid
- c
Cross-sectional
- f
Fluid
- h
Hydraulic
- in
Inlet
- LMTD
Logarithmic Mean Temperature Difference
- max
Maximum value
- nf
Nanofluid
- out
Outlet
- p
Nanoparticle
- s
Surface
- w
water
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© 2019 Walter de Gruyter GmbH, Berlin/Boston
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Artikel in diesem Heft
- Research Articles
- An Analysis of Increasing the Purity of Ethylene Production in the Ethylene Fractionation Column by the Genetic Algorithm
- Designing of IMC-PID Controller for Higher-order Process Based on Model Reduction Method and Fractional Coefficient Filter with Real-time Verification
- Short Communication
- Change in Distillation Column Control Philosophy Using Dynamic Simulation
- Research Articles
- Level Control of Coupled Tank System Based on Neural Network Techniques
- Numerical Study of Flow and Heat Transfer with ZnO-Water Nanofluid in Flattened Tubes
- ASU Simulation in Separating Air Components by Refrigeration Distillation (Oxygen and Nitrogen) Using ASPEN HYSYS (Case Study: SIAD Company)
- Process Optimization for the Production of Ferric Sulfate Coagulant by the Oxidation of Ferrous sulfate with Hydrogen Peroxide
- Comparative Analysis of Machine Learning Models for Predictive Control of the Cyclopentadine Production Process
