Numerical investigation on the effect of four constant temperature pipes on natural cooling of electronic heat sink by nanofluids: A multifunctional optimization

In the present study, natural-convective heat transfer along with the effects of radiation of aluminum/water nano-fluid between two blades of a heat sink, which is under the impact of a uniform magnetic-field, is studied numerically. The space between two blades of the heat sink is considered as a two-dimensional square enclosure. In the square cavity, there are four pipes with constant temperature T_h with a circular cross section. The RSM method is used to optimize the geometric parameters of the pipes. The results show that the heat transfer rate from the pipes and the irreversibility generation augment and the Bejan number reduces by augmenting the Rayleigh number. The heat transfer intensified 7% and 16% by doubling of the aspect ratio of the pipes at the Rayleigh number of 10³ and 10⁶, respectively. As the distance between constant-temperature pipes intensified, Nusselt number augments. As the horizontal enclosure rotates 90°, i.e., it becomes a vertical enclosure, the heat transfer decreases by 22% and total irreversibility decreases by 21%. The optimum physical conditions of the pipes are is in the diameter of 0.15 and 0.25 of distance from each other to have maximum heat transfer and the minimum irreversibility generation.     

Thermohydraulic performance and effectiveness of a mini shell and tube heat exchanger working with a nanofluid regarding effects of fins and nanoparticle shape

The influence of varied nanoparticle shapes on thermal–hydraulic efficacy of a boehmite nanofluid (BNF) in a mini shell and tube heat exchanger (MSTHX) in the cases of with and without fin is investigated. The five nanoparticle shapes with 90 °C inlet temperature at Reynolds number of 500 for the warm fluid side, and four Reynolds numbers of 500, 1000, 1500, and 2000 with 20 °C inlet temperature for the cold fluid side are considered. The warm fluid is the nanofluid that moves in the tube side, whilst the cool fluid is common water inside the shell side. With elevating Reynolds number, the heat transfer rate (q), overall heat transfer coefficient (U), pressure drop, effectiveness, and number of transfer unit (NTU) increase, while the performance index reduces. By increasing the Reynolds number from 500 to 2000 in the nanofluid having the oblate spheroid nanoparticles, the effectiveness rises 20%, and the performance index reduces 21.7%. The BNF with the platelet additives results in the largest q, whilst the smallest pressure loss is achieved for the Os-shaped additives. Also, the heat transfer rate, U, effectiveness, NTU, performance index, and pressure loss for the MSTHX with fin are larger than those for the MSTHX without fin.     

The preparation of surfactant-free highly dispersed ethylene glycol-based aluminum nitride-carbon nanofluids for heat transfer application

In the present study, aluminum nitride-carbon (AlN-C) nanocomposites are synthesized through a green, facile and inexpensive mechanochemical route. Well-dispersed nanofluids are prepared by milling of nanocomposite in ethylene glycol (EG) without using any surfactants/ dispersants. The resulting nanofluids have an excellent stability with no obvious sedimentation for at least three months. The results confirm the in-situ polymerization of EG on AlN surface and the formation of hyperbranched glycerol upon milling which in turn stabilizes the particles through a steric effect. The working nanofluids with very low loadings of up to 0.22 vol% of powder exhibit an enhanced heat transfer coefficient (h) of about 24% compared to that of the base fluid in a laminar flow regime (Re = 160). Brownian motion and boundary layer thinning are known as the main mechanisms, causing for this enhancement.     

Experimental study on heat transfer enhancement of nanofluid flow through helical tubes

Fluid flow and heat transfer characteristics of nanofluids flowing through helically coiled tubes under uniform heat flux condition are studied experimentally. The turbulent flow of two different kinds of nanofluids, i.e. Ag-water and SiO₂-water, are examined. Three different helically coiled tubes along with straight ones are constructed to investigate the effects of geometrical parameters such as pitch circle diameter and helical pitch as well as nanoparticle volume concentration. The viscosity and thermal conductivity of nanofluids are determined experimentally in different volume fractions and temperatures. The range of Reynolds number is from 8900 to 11970. The experimental outcomes show that using nanoparticles in coiled tubes can be more effective in improving the heat transfer rate than the straight tube. Empirical correlations are extracted based on experimental data to predict the Nusselt number and friction factor of turbulent nanofluids flow through helically coiled tubes.     

A stochastic model for thermal transport of nanofluid in porous media: Derivation and applications

In this paper, a stochastic thermal transport model is developed for nanofluid flowing through porous media. This model incorporates the influences of nanoparticle migration on convective heat transfer of the colloidal solution. We show that Lévy flight movement patterns of nanoparticles result in the derived model using fractional derivative for the diffusion term. The new thermal transport model is then applied to the mixed convective problem which is solved using finite difference method. Numerical results show that the smaller values of Lévy index $\gamma$ lead to larger Nusselt numbers, thus the occurrence of long jumps for nanoparticles increases the heat transport of nanofluids. The effects of other involved physical parameters are also presented and discussed.     

Laminar pulsating flow of nanofluids in a circular tube with isothermal wall

In this study, two-dimensional pulsating flow of nanofluids through a pipe with isothermal walls is numerically investigated. In order to solve Navier–Stokes and energy equations, the finite volume approach with collocated grid is employed. The momentum interpolation technique of Rhie and Chow is applied in SIMPLE algorithm. Pulsating flows have potential for research as many aspects of such flows are still unclear and require further investigation because of many upcoming applications. In the past decade, nanofluids have attracted much interest because of their reported superior thermal performance and many potential applications. Pulsation in nanofluid is a new idea in case of fluid mechanics and heat transfer. The authors have not found any records similar to the present study. One of the advantages of using pulsation in nanofluid is a delay in nanoparticles sedimentation process. The simulation performed at with different pulse parameters (Amplitude, Strouhal and Reynolds numbers) and volume fractions of nanoparticles for unsteady flow. Increasing both the frequency and amplitude leads to a slight increase in Nusselt number but by increasing Reynolds and volume fraction, more rate of heat transfer is observed.     

Investigation of pool boiling of nanofluids using artificial neural networks and correlation development techniques

The nucleate pool boiling heat transfer characteristics of TiO₂ nanofluids are investigated to determine the important parameters' effects on the heat transfer coefficient and also to have reliable empirical correlations based on the neural network analysis. Nanofluids with various concentrations of 0.0001, 0.0005, 0.005, and 0.01 vol.% are employed. The horizontal circular test plate, made from copper with different roughness values of 0.2, 2.5 and 4 μm, is used as a heating surface. The artificial neural network (ANN) training sets have the experimental data of nucleate pool boiling tests, including temperature differences between the temperatures of the average heater surface and the liquid saturation from 5.8 to 25.21 K, heat fluxes from 28.14 to 948.03 kW m^− 2. The pool boiling heat transfer coefficient is calculated using the measured results such as current, voltage, and temperatures from the experiments. Input of the ANNs are the 8 numbers of dimensional and dimensionless values of the test section, such as thermal conductivity, particle size, physical properties of the fluid, surface roughness, concentration rate of nanoparticles and wall superheating, while the outputs of the ANNs are the heat flux and experimental pool boiling heat transfer coefficient from the analysis. The nucleate pool boiling heat transfer characteristics of TiO₂ nanofluids are modeled to decide the best approach, using several ANN methods such as multi-layer perceptron (MLP), generalized regression neural network (GRNN) and radial basis networks (RBF). Elimination process of the ANN methods is performed together with the copper and aluminum test sections by means of a 4-fold cross validation algorithm. The ANNs performances are measured by mean relative error criteria with the use of unknown test sets. The performance of the method of MLP with 10-20-1 architecture, GRNN with the spread coefficient 0.7 and RBFs with the spread coefficient of 1000 and a hidden layer neuron number of 80 are found to be in good agreement, predicting the experimental pool boiling heat transfer coefficient with deviations within the range of ± 5% for all tested conditions. Dependency of output of the ANNs from input values is investigated and new ANN based heat transfer coefficient correlations are developed, taking into account the input parameters of ANNs in the paper.     

Natural convection of nanoparticle–water mixture near its density inversion in a rectangular enclosure

Natural convection of mixture of nanoparticles and water near its density maximum in a rectangular enclosure is studied numerically. A non-Boussinesq homogenous model is used in mathematical formulations of governing equations. The finite volume method is used to solve the governing equations. The results are presented graphically in the form of streamlines, isotherms and velocity vectors and are discussed for various nanoparticle volume fractions. It is observed that flow and temperature field is affected significantly in the presence of nanoparticles. The average heat transfer rate considering a non-Boussinesq temperature-dependent density (inversion of density) is lower than considering a Boussinesq temperature-dependent density. The average Nusselt number increases with an increase of nanoparticle volume fraction. It is observed that the density inversion of water leaves strong effects on fluid flow and heat transfer due to the formation of bi-cellular structure. The properties of nanoparticles also affect the fluid flow and heat transfer.     

非晶炭包裹铜纳米粒子在导热流体中的应用

采用氩电弧等离子体法制备不同粒径的炭包铜纳米粒子,研究其抗氧化性能、在水介质中的分散性能和导热性能.结果表明:炭层的保护作用赋予铜晶体更高的抗氧化性能;非晶炭层的特殊结构可通过双氧水化学处理使其表面产生羧基和羟基,提高其在水介质中的分散性能;炭包铜粒径越小,纳米流体导热性能越好.     

Numerical study of mixed convection in an inclined two sided lid driven cavity filled with nanofluid using two-phase mixture model

Mixed convection of a nanofluid consisting of water and SiO2 in an inclined enclosure cavity has been studied numerically. The left and right walls are maintained at different constant temperatures while upper and bottom insulated walls are moving lids. Two-phase mixture model has been used to investigate the thermal behaviors of the nanofluid for various inclination angles of enclosure ranging from θ = − 60° to θ = 60°, volume fraction from 0% to 8%, Richardson numbers varying from 0.01 to 100 and constant Grashof number 10⁴. The governing equations are solved numerically using the finite-volume approach. Results are presented in the form of streamlines, isotherms, distribution of nanoparticles and average Nusselt number. In addition, effects of solid volume fraction of nanofluids on the hydrodynamic and thermal characteristics have been investigated. The results reveal that addition of nanoparticles enhances heat transfer in the cavity remarkably and causes significant changes in the flow pattern. Besides, effect of inclination angle is more pronounced at higher Richardson numbers.