Thermal conductivity of non-Newtonian nanofluids: Experimental data and modeling using neural network

Hojjat, Mohammad; Etemad, S.Gh.; Bagheri, Rouhollah; Thibault, Jules

doi:10.1016/j.ijheatmasstransfer.2010.11.039

Cited by 191 publications

(73 citation statements)

References 66 publications

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“…Each dataset includes four input variables namely, methanol to oil molar ratio (mr), catalyst amount (c), temperature (T) and time (t). Scaling of the input and output variable is usually recommended [39]. Therefore, each input variable was scaled in the range {0, 1} by dividing them with their maximum values, respectively, in Table 4.…”

Section: Modeling Of Experimental Observations Through Annmentioning

confidence: 99%

Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation

et al. 2017

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This research work concerns with the transesterification of castor oil with methanol to form biodiesel. As the free fatty acid content in castor oil is more than 1%, an acid catalyst namely, H 2 SO 4 has been used for esterification. The experimental conditions were determined using central composite design method and the experiments were conducted in a 2 L working volume fully controlled reactor. The input conditions namely, catalyst concentration, methanol to oil molar ratio and temperature were varied, and % fatty acid methyl ester (FAME) content was determined. Based upon the experimental data, an ANN model has been developed which is used to predict %FAME yield for a given set of input conditions. The experimental data and the data predicted by the ANN model have been used to estimate the rate constants of a kinetic model. The ANN model predicts the % FAME yield within ±8% deviation, and the developed kinetic model shows successfully the effect of methanol to oil molar ratio on % FAME yield at 60°C and 3% (v/v) catalyst loading.

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Section: Modeling Of Experimental Observations Through Annmentioning

confidence: 99%

Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation

et al. 2017

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“…Corcione [98] The experimental results from different studies has shown that the thermal conductivity of TiO 2 nanofluids increases by increasing the volume concentration of nanoparticles but no one has clarified the upper limit or extent to which nanofluids follow this increasing trend [60,[99][100][101][102][103][104]. Figure 16 shows the variation in thermal conductivity as a function of temperature.…”

Section: Derived Model Discussionmentioning

confidence: 99%

Preparation Techniques of TiO2 Nanofluids and Challenges: A Review

et al. 2018

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Titanium dioxide (TiO 2 ) has been used extensively because of its unique thermal and electric properties. Different techniques have been used for the preparation of TiO 2 nanofluids which include single-step and two-step methods. In the natural world, TiO 2 exists in three different crystalline forms as anatase, brookite, and rutile. Nanoparticles are not used directly in many heat transfer applications, and this provides a major challenge to researchers to advance towards stable nanofluid preparation methods. The primary step involved in the preparation of nanofluid is the production of nano-sized solid particles by using a suitable technique, and then these particles are dispersed into base fluids like oil, water, paraffin oil or ethylene glycol. However, nanofluid can also be prepared directly by using a liquid chemical method or vapor deposition technique (VDT). Nanofluids are mostly used in heat transfer applications and the size and cost of the heat transfer device depend upon the working fluid properties, thus, in the past decade scientists have made great efforts to formulate stable and cost-effective nanofluids with enhanced thermophysical properties. This review focuses on the different synthesis techniques and important physical properties (thermal conductivity and viscosity) that need to be considered very carefully during the preparation of TiO 2 nanofluids for desired applications.

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“…Also, Figure 2 depicts variations of m and n by volume fraction of nanoparticles at T = 293 K. As can be seen, the nano uid has shear thinning behavior. The e ective thermal conductivity of nano uid has been adapted from the work of Hojjat et al [20]. A two-variable surface has been tted to the corresponding data.…”

Section: Thermophysical Properties Of Nano Uidmentioning

confidence: 99%

“…where k b = 1:3806488 10 23 is the Boltzmann constant, f is apparent viscosity of base uid, and d p is diameter of nanoparticles and is selected according to [19] and [20]. D t can be calculated using the McNab-Meisen relationship for the thermophoretic velocity of particles dispersed in liquids of [22], which is written as:…”

Section: Thermophysical Properties Of Nano Uidmentioning

confidence: 99%

Numerical Simulation of non-Newtonian Nanofluids Flow in Passive Injection Micromixers Using Mixture Model

2017

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Abstract. In this study, mixing of laminar non-Newtonian nano uids in an injection micromixer was numerically studied. The important and new feature of the study is using of non-Newtonian base uid in nano uid. The Titanium dioxide/0.5%wt Carboxymethyl Cellulose aqueous solution was used as nano uid. Mixture model was used for simulation of nano uid ow inside the micromixer. The governing equations were solved by nite volume method using a FORTRAN code. Also, in this paper, a new modi ed successive over-relaxation method has been introduced to considerably decrease the computation time.The results indicated that the number of injection ows and using of ba es had signi cant e ect on mixing index. It should be noted that the distance between ba es is an important parameter in mixing.

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Thermal conductivity of non-Newtonian nanofluids: Experimental data and modeling using neural network

Cited by 191 publications

References 66 publications

Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation

Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation

Preparation Techniques of TiO2 Nanofluids and Challenges: A Review

Numerical Simulation of non-Newtonian Nanofluids Flow in Passive Injection Micromixers Using Mixture Model

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