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This paper assesses the contribution of TiO2 nanoparticles on thermal performance of a 0.5 mass% aqueous solution of carboxymethyl cellulose (CMC) in a square channel. In this regard, a neural network model is firstly developed for modeling of power law index, consistency index, and thermal conductivity of the aqueous solution of TiO2/CMC-water non-Newtonian nanofluid in terms of the nanoparticle concentration and temperature. Then, an attempt is made to evaluate the friction factor and heat transfer coefficient relative values. According to the results, it is found that the friction factor ratio is directly proportional to the temperature and nanoparticle content, while it inversely varies relative to the shear rate. Moreover, heat transfer coefficient ratio is improved at elevated nanoparticle content, and this improvement is much more profound at higher temperature conditions. For practical purposes, the nanofluid hydrothermal performance index is examined since the addition of nanoparticles increases both heat transfer and friction factor. The corresponding data disclose that the performance index is directly proportional to the nanoparticle content, especially at decreased shear rate and elevated temperature conditions. The application of TiO2/CMC-water nanofluid is found to be more favorable for applications with elevated shear rate conditions. [ABSTRACT FROM AUTHOR]

The costly and time-consuming determination of thermophysical properties of nanofluids through the experimental analysis leads the current investigations to use the soft computing methods like correlating, artificial neural network (ANN) and genetic algorithm. In this study, the application of ANN, empirical correlations and genetic algorithm for modeling and multi-criteria optimization of the thermophysical properties of clove-treated MWCNTs nanofluid which has been synthesized through a facile and eco-friendly procedure has been investigated. In this contribution, totally 6 structures are assessed: networks including one and two hidden layers with 2, 4, and 6 neurons. From assessment of the ANN, it is found that the network including two hidden layers with 4 neurons in every layer results in the least difference between the network outputs and the experimental data, providing the best performance. It is concluded that the optimal ANN model is a more precise and accurate way to predict the thermal conductivity and viscosity of environmentally friendly C-MWCNT/water nanofluid compared to empirical correlations obtained from non-linear regression method. Moreover, the evolutionary algorithm has been implemented for achieving the optimal conditions to maximize the thermal conductivity and to minimize the viscosity of nanofluid. In this regard, based on real-world engineering experience, the final optimal solutions opted from several distinguished procedures of decision-making including the Bellman-Zadeh, TOPSIS and LINMAP approaches has been investigated in parallel. The results of this study revealed that the obtained outputs using TOPSIS and LINMAP procedures are the closest to ideal solution. [ABSTRACT FROM AUTHOR]

Abstract It has been more than two decades since the discovery of nanofluids-mixtures of common liquids and solid nanoparticles less than 100 nm in size. As a type of colloidal suspension, nanofluids are typically employed as heat transfer fluids due to their favorable thermal and fluid properties. There have been numerous numerical studies of nanofluids in recent years (more than 1000 in both 2016 and 2017, based on Scopus statistics). Due to the small size and large numbers of nanoparticles that interact with the surrounding fluid in nanofluid flows, it has been a major challenge to capture both the macro-scale and the nano-scale effects of these systems without incurring extraordinarily high computational costs. To help understand the state of the art in modeling nanofluids and to discuss the challenges that remain in this field, the present article reviews the latest developments in modeling of nanofluid flows and heat transfer with an emphasis on 3D simulations. In part I, a brief overview of nanofluids (fabrication, applications, and their achievable thermo-physical properties) will be presented first. Next, various forces that exist in particulate flows such as drag, lift (Magnus and Saffman), Brownian, thermophoretic, van der Waals, and electrostatic double layer forces and their significance in nanofluid flows are discussed. Afterwards, the main models used to calculate the thermophysical properties of nanofluids are reviewed. This will be followed with the description of the main physical models presented for nanofluid flows and heat transfer, from single-phase to Eulerian and Lagrangian two-phase models. In part II, various computational fluid dynamics (CFD) techniques will be presented. Next, the latest studies on 3D simulation of nanofluid flow in various regimes and configurations are reviewed. The present review is expected to be helpful for researchers working on numerical simulation of nanofluids and also for scholars who work on experimental aspects of nanofluids to understand the underlying physical phenomena occurring during their experiments. [ABSTRACT FROM AUTHOR]