Central composite rotatable design (CCRD) and artificial neural networks (ANN) have been applied to optimize the performance of nanofluid systems. In this regard, the performance was evaluated by measuring the stability and thermal conductivity ratio based on the critical independent variables such as temperature, particle volume fraction and the pH of the solution. A total of 20 experiments were accomplished for the construction of second-order polynomial equations for both target outputs. All the influential factors, their mutual effects and their quadratic terms were statistically validated by analysis of variance (ANOVA). According to the results, the predicted values were in reasonable agreement with the experimental data as more than 96% and 95% of the variation could be predicted by the respective models for zeta potential and thermal conductivity ratio. Also, ANN proved to be a very promising method in comparison with CCD for the purpose of process simulation due to the complexity involved in generalization of the nanofluid system.

/pdf/prediction-of-stability-and-thermal-conductivity-of-384ut38t9p.pdf

PREDICTION OF STABILITY AND THERMAL CONDUCTIVITY OF SnO2NANOFLUID VIA STATISTICAL METHOD AND AN ARTIFICIAL NEURAL NETWORK

Seven important parameters (temperature, concentration, average primary particle size, pH of nanofluid, density of nanoparticle, elapsed time, and sonication time) which are responsible for the change of the thermal conductivity of nanofluids were experimentally investigated on CuO/water nanofluid and were statistically surveyed by a factorial design method. In order to investigate the main effects and their interactions on the thermal conductivity ratio, a fractional factorial design (FFD) with three other experiments at the center of the design for analysis of variance were applied. Also, the factorial model was statistically validated by analysis of variance (ANOVA). The predicted responses was compared with the experimental ones. Generally, the predicted values were in reasonable agreement with the experimental data, further confirming the high predictability of the models.

Identification of the Key Variables on Thermal Conductivity of CuO Nanofluid by a Fractional Factorial Design Approach

For more than 20 years, the use of nanofluids to enhance heat transfer in microchannel heat sinks (MCHSs) has been the subject of a large number of scientific articles. Despite the great potentialities reported in several works, the presence of controversial results and the lack of understanding of heat transfer enhancement mechanisms prevent further advancement in the use of nanofluids as coolants. This article reviews the scientific literature focused on several aspects of nanofluids that have a role in the heat transfer enhancement within the MCHSs: nanofluid stability, thermal conductivity, and particle clustering, as well as the particle–surface interactions, i.e., abrasion, erosion, and corrosion. We also include the most relevant works on the convective heat transfer and MCHSs operated with nanofluids in our review.

/pdf/experiments-on-single-phase-nanofluid-heat-transfer-2cmaxz6p.pdf

Experiments on Single-Phase Nanofluid Heat Transfer Mechanisms in Microchannel Heat Sinks: A Review

Rod bundle thermal-hydraulics experiment with water and water-Al2O3 nanofluid for small modular reactor

A review on prediction of thermo physical properties of heat transfer nanofluids using intelligent techniques

Theoretical Investigation Heat Transfer Mechanisms in Nanofluids and the Effects of Clustering on Thermal Conductivity

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Theoretical Investigation Heat Transfer Mechanisms in Nanofluids and the Effects of Clustering on Thermal Conductivity