Thermophysical Properties of Nanofluid s (Density, Specific Heat and Thermal Conductivity)

1. Thermophysical Properties of Nanofluids(Density, Specific Heat and Thermal Conductivity) M.Reza Azizian Supervisor: Prof. Dr. Hikmet Ş. Aybar Co-Supervisor: Asst.Prof. Dr. Tuba Okutucu Mechanical Engineering Department May 21, 2008 http://me.emu.edu.tr/mreza/current_research.htm

2. Introduction • Heat transfer in cooling processescan be found in many industrial areas. • The conventional methods to increase cooling rates: 1- Extended surfaces such as fins 2- Increasing flow rates • These conventional methods have their own limitations: 1- Fins: undesirable increase in the size of thermal management system 2- Increasing flow rates: increases pumping power • There is an immediate need for new and innovative concepts to achieve ultra high performance cooling. • Nanofluids are promising to meet and enhance the challenges.

3. The thermal conductivity characteristics of ordinary heat transfer fluids are not adequate to meet our requirements. • The thermal conductivity characteristics of ordinary heat transfer fluids are about two orders of magnitude less efficient in conducting heat compared to metals. • Because a solid metal has a larger thermal conductivity than a base fluid, suspending metallic solid fine particles into the base fluid is expected to enhance the thermal conductivity of that fluid. • Scattering solid particles (micrometer or even mm dimension)into liquids to improve the physical properties of liquids is hardly new, it has been well known for 100 years, since the idea can be traced back to James Clerk Maxwell’s theoretical work (Maxwell, 1873). • These micrometer or mm dimension solids in base fluid cause problems such as sedimentation, clogging, abrasion, and increase in pressure drop.

4. Nanofluids • Nanofluids have a potential to reduce such problems. • Nanofluids, a name conceived by Dr.Choi, in Argonne National Laboratory, to describe a fluid consisting of solid nanoparticles with size less than 100nm suspended on it with solid volume fractions typically less than 4%.

5. Base fluid: water, organic liquid • Nanoparticle size: 1-100 nm • Nanoparticle materials: oxides (e.g., Al2O3, ZrO2, SiO2, CuO), metals (e.g., Au, Cu), Carbon nanotubes • Nanofluid can be produced by two techniques: two-step technique and the single-step technique. • Two-step technique: The two step method starts with producing nanoparticle by one of the physical or chemical processes (e.g., evaporation and inert-gas condensation processing), and proceeds to disperse them into a base fluid; most of the nanofluids are produced by two step method. • Single-step technique: The single step simultaneously makes and disperses the nanoparticles directly into a base fluid; best for metallic nanofluids. • Very recently an interesting scheme:the synthesis of metal nanoparticles in deionized water, using multi-beam laser ablation in liquids, is reported by Argonne National Laboratory, where the nanoparticle size and distribution will be controlled by the laser parameters.

6. - ZrO2 in water that produce with two - Cu nanoparticles produced by Step method direct evaporation into ethylene glycol

7. Density and Specific heat • Calculation of the effective density and effective specific heat of nanofluid is straightforward. • They can be estimated based on the physical principle of the mixture rule these results are in very good agreement with some experimental data. • For this propose we define a nanofluid as a mixture consisting of a continuous base fluid component called “matrix” and a discontinuous solid component called “particles”, the subscript “m” represents the base fluid matrix and the subscript “p” represents particles in base fluid.

8. Thermal Conductivity (Experimental) • Before suggesting a theoretical model for thermal conductivity let’s first look at the parameters that affect the thermal conductivity of nanofluids from experiments. • According to the report of Argonne National Laboratory, eight parameters affect the thermal conductivity of nanofluids, they got these results from about 124 researchers experiments.These effects are: 1- Particle volume concentration 2- Particle materials 3- Particle size 4- Particle shape 5- Base fluid material 6- Temperature 7- Additive 8- Acidity

9. - Effect of Particle Volume Concentration From the experimental results the general trend is clear: thermal conductivity enhancement increases with increase in particle volume concentration. (Al2O3 in water) • Effect of Particle Material The thermal conductivity ratio is seen to increase faster for metals than oxide particles. (Particles in ethylene glycol)

10. Effect of Particle Size The trend in this part is not obvious. Most of the researchers report thatlarger particle diameters produce larger enhancement in thermal conductivity but in some cases the experiments show indicate the opposite. A consistent trend appears where larger particle diameters produce a large enhancement in thermal conductivity: Al2O3 in water • Effect of Particle Shape All of the results indicate that elongated particles are superior to spherical for thermal conductivity enhancement. e.g. SiC in water

11. - Effect of Base Fluid Material The results show increased thermal conductivity enhancement for poorer (lower thermal conductivity) heat transfer fluid. The results show the least enhancement for water, which is the best heat transfer fluid with the highest thermal conductivity of the fluids compared. (Al2O3 in fluids) • Effect of Temperature All experiments show increased thermal conductivity enhancement with increased temperature. (Al2O3 in water)

12. Effect of Additives Experiments have used fluid additives in an attempt to keep nanoparticles in suspension and to prevent them from agglomerating. The thermal conductivity enhancement improved by using the additive. (Cu in ethylene glycol) • Effect of Acidity (PH) Limited studies have been published on the effect of fluid acidity on the thermal conductivity enhancement of nanofluids. But the general trend is that acidity increases the thermal conductivity enhancement. (Al2O3 in water)

13. Thermal Conductivity (Theoretical Modeling) • Maxwell was one of the first to analytically investigate conduction through suspended particles. • This equation and other equations for thermal conductivity e.g., Hamilton and Crosser, and Rayleigh predict thermal conductivity reasonably well for dilute mixtures of relatively large particles in fluids. • When we go to the nanoscale we have to consider some effects that do not exist in large scales. To improve the predictions we consider: 1- Effect of nanoparticle-matrix interfacial layer 2- Effect of nanoparticle Brownian motion 3- Effect of nanoparticle cluster/aggregate

14. Effect of Nanoparticle Interfacial layer • Liquid molecules close to a solid surface are known to form layered structures. • According to the work of Yu, et al. 2000 the layered molecules are in an intermediate physical state between a solid and bulk liquid. • With these solid like liquid layers, the nanofluid structure consists of solid nanoparticles, solid-like liquid layer, and a bulk liquid. • The solid-like nanolayer acts as a thermal bridge between a solid nanoparticle and a bulk liquid and so is key to enhancing thermal conductivity.

15. In this figure you can see the thermal conductivity enhancement by modified Maxwell equation according to the interfacial layer assumption, for copper-particle-in-ethylene-glycol suspension. • A three to eight fold increase in thermal conductivity of nanofluids compared to the enhancement without considering the nanolayer occurs when nanoparticles are smaller than the critical radius 5nm. - This finding suggests that adding smaller (<10 nm diameter) particles could be potentially better than addinglarger-size nano-particles.

16. Effect of Brownian Motion • Because of the size of nanoparticles the Brownian motion of nanoparticles will be another potential factor in calculating the thermal conductivity of nanofluids. • To develop such a theory for the thermal conductivity of nanofluids it is assumed that energy transport in nanofluids involve four modes: 1- Collision between base fluid molecules. 2- Thermal diffusion in nanoparticles. 3- Collision between nanoparticles. 4- Thermal interaction of dynamic or dancing nanoparticles with base fluid molecules.

17. The S.P. Jang and U.S. Choi model is able to predict a particle-size and temperature-dependent conductivity of nanofluids, while no existing theories explain or predict these effects. • The S.P. Jang and U.S. Choi model predicts the experimental data for temperature dependency with excellent agreement.In contrast, conventional theories with motionless nanoparticles fail to predict this behaviour (horizontal dashed line). • The Maxwell model predicts decreasing nanofluid conductivity with decreasing particle size, but according to S.P. Jang and U.S. Choi model when the particle size decreases the random motion is larger and the convectionlike effect becomes dominant.

18. Effect of Particle Clustering • Sometimes nanofluids are in form of clusters when the concentration is high or when the time is increased. • It is accepted that heat transfer is a surface phenomenon and the thermal energy interaction takes place at the surface of nanoparticles. • When the particles get agglomerated, the effective surface area to volume ratio decreases, thus reducing the effective area of thermal interaction of particles causing a decrease in the thermal conductivity of the fluid.

19. Mesh like structure observed, in water based CuO nanofluid of 0.1 vol% after sonication for (a) 20min, (b) 60min and (c) 70 min. • It can be seen that the structure formation begin only after 60 min from the sonication(vibration of liquid with ultrasonic bath).

20. Conclusion • A review of experimental studies clearly shows a relatively large chaos and randomness in the published data. • Our review on theoretical models indicates that a clear understanding of the main mechanism(s) involved in thermal transport phenomena in nanofluids is not established yet. • In our project we have a plan to apply the formula for thermal conductivity of gas mixtures to nanofluids.