Mehdi Mehrabi

A Review of Thermal Conductivity Models for Nanofluids

Nanofluids, as new heat transfer fluids, are at the center of attention of researchers, while their measured thermal conductivities are more than for conventional heat transfer fluids. Unfortunately, conventional theoretical and empirical models cannot explain the enhancement of the thermal conductivity of nanofluids. Therefore, it is important to understand the fundamental mechanisms as well as the important parameters that influence the heat transfer in nanofluids. Nanofluids’ thermal conductivity enhancement consists of four major mechanisms: Brownian motion of the nanoparticle, nanolayer, clustering, and the nature of heat transport in the nanoparticles. Important factors that affect the thermal conductivity modeling of nanofluids are particle volume fraction, temperature, particles size, pH, and the size and property of nanolayer. In this paper, each mechanism is explained and proposed models are critically reviewed. It is concluded that there is a lack of a reliable hybrid model that includes all mechanisms and influenced parameters for thermal conductivity of nanofluids. Furthermore, more work needs to be conducted on the nature of heat transfer in nanofluids. A reliable database and experimental data are also needed on the properties of nanoparticles.

Adaptive Neuro-Fuzzy Modeling of the Thermal Conductivity of Alumina-Water Nanofluids

By using on Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as experimental data, a model was established for the prediction of the thermal conductivity ratio of alumina (Al2O3)-water nanofluids. In the ANFIS the target parameter was the thermal conductivity ratio, and the nanoparticle volume concentration, temperature and Al2O3 nanoparticle size were considered as the input (design) parameters. In the development of the model, the empirical data was divided into train and test sections. The ANFIS network was instructed by eighty percent of the experimental data and the remaining data (twenty percent) were considered for benchmarking. The results which were obtained by the proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) model were in good agreement with the experimental results.© 2012 ASME

Heat transfer and fluid flow optimization of titanium dioxide–water nanofluids in a turbulent flow regime

Abstract In this study, the convection heat transfer and pressure drop of titanium dioxide–water nanofluids were modeled by applying the fuzzy C-means adaptive neuro-fuzzy inference system approach for a completely developed turbulent flow based on experimentally obtained training and test datasets. Two models were proposed based on the effective parameters; one model was developed for the Nusselt number considering the effects of the Reynolds number, Prandtl number, nanofluid volume concentration and average nanoparticle diameter. Another model was suggested for the pressure drop of the nanofluid as a function of the Reynolds number, nanofluid volume concentration, and average nanoparticle diameter. The results of these two proposed models were compared with experimental data as well as the existing correlations in the literature. The validity of the proposed models was benchmarked by statistical criteria. Moreover, a modified non-dominated sorting genetic algorithm multiobjective optimization technique was applied to obtain the optimum design points, and the final result was shown in a Pareto front.

The effect of geometrical characteristics of wavy strip turbulator and thermodynamic properties of fluid on exergy loss and heat transfer in a tube in tube heat exchanger

ABSTRACT The present work conducts an experimental investigation into the influence of flow, thermodynamic and geometrical characteristics of the wavy strip on exergy loss and dimensionless exergy loss in a tube in tube heat exchanger. The working fluid is water with hot water passing the inner tube and cold water passing annulus. Wavy strips with four different angles and three widths were investigated experimentally. The result of exergy loss and dimensionless exergy loss for various conditions is presented and on the basis of curve fitting, three empirical correlations are suggested to predict dimensionless exergy loss in a double tube heat exchanger.

Humidification and Dehumidification Processes: Advantages and Disadvantages

Desalination systems based on the Humidification Dehumidification (HDH) process come in a large variety of forms. They all operate in very similar manners, all requiring the evaporation of salt water and the condensation of pure, distilled water, in the same way clouds are formed every day. As engineers we must pursue the processes that have the best possible efficiency for the specific situation we find ourselves in. The purpose of this paper is to explore the various Humidification Dehumidification desalination processes and thermodynamically compare them with one another in order to determine which one of these processes has the most potential in specific environmental situations. This paper explores the advantages and disadvantages of all HDH processes as well as compare them with more common desalination methods like reverse osmosis. An in depth study of available literature is conducted, listed and explained regarding these HDH processes and their specific characteristics, focusing on the key points of efficiency, limiting factors and environmental effects. In some cases these processes, especially those utilizing the open air cycle or solar collectors, can be severely affected by environmental situations. The environmental situations can include high humidity environments, high temperature environments, high rainfall environments and of course the polar opposites of these situations and any combination of them. The paper takes into account these environmental effects and makes recommendations based on different environmental situations that can be found around the world. The HDH processes can obtain the energy they need to operate from various sources. Recommendations are also made with this in mind. Recommendations are also made regarding possible processes and specific design areas, which, in the in the writers opinion, should be the focus of improvements made by future designers are noted. Considering the efficiency limiting areas that cause bottlenecks in the processes, the writer prescribes possible ways to limit the effects of these bottleneck areas. The writer’s own recommendations regarding possible processes and possible improvements are also stated, paying attention to the recommendations already found in literature.Copyright

Application of Genetic Algorithm-Polynomial Neural Network for Modelling Heat Transfer and Fluid Flow Characteristics of a Double-Pipe Heat Exchanger

In this paper a genetic algorithm-polynomial neural network approach is used in order to model the effect of important parameters on heat transfer as well as fluid flow characteristics for a double-pipe helical heat exchanger by using numerical-certified results. In this way, overall heat transfer coefficient (Uo), inner and annular pressure drop (ΔPin, ΔPan) are modeled with respect to the variation of inner and annular dean number, inner and annular Prandtl number, and pitch of coil which are defined as input (design) variables. The numerical-certified data was randomly divided into test and train sections which the former is used for benchmark. The GA-PNN structure was instructed by 75 percent of the numerical-validated data. 25 percent of the primary data which had been considered for testing procedure were entered into GA-PNN proposed models and results were compared by statistical criteria.Copyright