Background: With the rapid advancement of nanotechnology and the expansion of its applications in various fields, nanofluids have recently been proposed as a novel strategy for heat transfer operations, and extensive research has been conducted in this regard. Ternary hybrid nanofluids (HNFs) can be used in cooling electronic chips, automotive radiator, solar collectors, heat exchangers, heat pipes, and refrigeration systems. Methods: The current study investigates the efficacy of sonication time (ST) on the viscosity and thermal conductivity of a ternary HNF consisting of GO-Fe3O4-TiO2 nanoparticles (NPs) dispersed in the hydraulic oil HLP 68. Nanoparticle volume fractions (NPVFs) of 0.05-1%, STs of 30-120 min, and mixing ratios (MRs) of 1:1:1, 1:1:2, 2:1:1, and 1:2:1 are considered. Moreover, the Support Vector Regression (SVR) and Group Method of Data Handling (GMDH) machine learning algorithms are used to accurately estimate the thermal conductivity and viscosity based on the available research data. Significant findings: The outcomes indicated that the viscosity and thermal conductivity of the HNF increase with an intensification in the NPVF. The highest value of viscosity and thermal conductivity were detected for the MR of 2:1:1 and 1:1:1, respectively. The ST was found to have a significant impact on the viscosity and thermal conductivity, which is dependent on the MR and NPVF. The minimum viscosity and maximum thermal conductivity were observed at an ST of 90 min. The highest impact of ST on the viscosity and thermal conductivity was observed for an NPVF of 0.05%. By increasing the ST from 30 to 90 min, thermal conductivity increased between 10.07% (at MR = 1:2:1 and ( = 1%) to 14.82% (at MR = 1:1:1 and NPVF = 0.05%) and viscosity decreased between 1.94% (at MR = 2:1:1 and NPVF = 0.5%) to 24.27% (at MR = 1:1:1 and NPVF = 0.25%). The modeling results showed that the R2 value for thermal conductivity and viscosity is 0.9582 and 0.9832 using SVR, and 0.9531 and 0.9422 using GMDH, respectively. Additionally, the RMSE value for thermal conductivity and viscosity is 0.00282 and 73.0476 using SVR, and 0.002727 and 131.09 using GMDH, respectively.

In this paper, the rheological performance and dynamic viscosity of hybrid nanofluid containing SiO2 and multi-walled carbon nanotubes (MWCNTs) nanoparticles (90:10) with 5W30 engine oil as base fluid is experimentally evaluated under different shear rates (SRs) in the range of 50–1000 rpm. The hybrid nanofluid volume fractions (VFs) and temperatures are considered in the ranges of 0.05–1.00 vol% and 5–65 °C, respectively. It was found that the hybrid nanofluid under study behaves as a non-Newtonian fluid. In addition, the calculated power law index was lower than unity, resulting in pseudoplastic features of hybrid nanofluid in all VFs and temperatures. It was observed that the rise of nanofluid temperature from 5 to 65 °C leads to the dynamic viscosity reduction (a 93% decrease in viscosity was observed in a VF of 0.2%), while the increase of nanofluid VF brings about the dynamic viscosity elevation (By increasing VF from 0.05% to 1% at SR of 800 rpm and temperature of 25 °C, the viscosity increases by 29.21%). Based on measured data, an innovative three-variable correlation was established that can more accurately estimate the experimental data than published correlations in the literature. Moreover, the capabilities of GMDH-type neural network (NN) and response surface methodology (RSM) to predict the relative viscosity of the hybrid nanofluid were evaluated. It was concluded that both NN and RSM approaches have a superior ability to forecast the dynamic viscosity behavior of the corresponding hybrid nanofluid, having R2 values of 0.999656 and 0.9955. Furthermore, the optimization was performed and the best solution for achieving the minimum dynamic viscosity with the maximum desirability (1.00) was obtained. Eventually, the dynamic viscosity sensitivity to changes in VF, temperature, and SR was evaluated. It was observed that the dynamic viscosity sensitivity increases as the nanofluid temperature and concentration increase considering a constant SR of 800 rpm.

Background: Considering the importance of hydraulic oils in various tasks, such as lubrication and cooling, this study evaluated the feasibility of improving the efficiency of hydraulic systems by modifying the thermophysical properties and rheological behavior of base hydraulic oil. Methods: The rheological behavior of the hydraulic oil HLP 68 as a base fluid in the presence of a novel ternary combination of iron oxide (Fe3O4), titanium dioxide (TiO2), and graphene oxide (GO) as nano-additives were evaluated experimentally in a wide range of solid volume fractions (VFs) (0 to 1%), nanomaterial mixing ratios (MRs) (1:1:1, 2:1:1, 1:2:1 and 1:1:2), and temperatures (15 to 65 degrees C). Significant findings: Analysis of changes in dynamic viscosity versus shear rate for all MRs revealed that the THNFs have a Newtonian behavior. It was found that the highest increase in base fluid viscosity in the presence of a 1% VF of GO: Fe3O4: TiO2 is 345%, 1821%, 1763%, and 1990% for MRs of 1:1:1, 1:1:2, 1:2:1, and 2:1:1, respectively, which occurs at a temperature of 15 degrees C. Also, the maximum increase in viscosity with temperature reduction from 65 degrees C to 15 degrees C for the MRs of 1:1:1, 1:1:2, 1:2:1, and 2:1:1 was found to be 66%, 75%, 60%, and 70%, respectively, which occurs at the highest solid VF. In addition, an algorithm for optimizing the structure/training parameters of the subtractive clustering-based ANFIS system as a leading regression technique in machine learning was developed.

In the present paper, the thermal conductivity (TC) of a hydraulic oil-based nanofluid in the presence of ternary nano-additives, graphene oxide (GO), iron oxide (Fe3O4), and titanium dioxide (TiO2), is analyzed in a wide range of volume fractions (VFs), temperatures, and mixing ratios (MRs). The stability of ternary hybrid nanofluids (THNFs) and size distribution of nanomaterial is obtained through zeta potential and dynamic light scattering (DLS) tests. Zeta potential and DLS tests indicated the remarkable stability of the samples with the GO (2): Fe3O4(1): TiO2(1) MR. Analysis of the measurements revealed that the enlargement in temperature and VFs improved the TC of THNFs for all MRs (1:1:1, 2:1:1, 1:2:1, 1:1:2). The highest TC enhancement is observed at the highest temperature (65 degrees C) and VF (1%), which for the MRs of 1:1:1, 1:1:2, 1:2:1, and 2:1:1 equal to 36.04%, 26.28%, 25.95%, and 33.86%, respectively. Furthermore, considering the average TC enhancement in the presence of nano-additives for various temperatures, MRs of GO(1): Fe3O4(1): TiO2(1) and GO(1): Fe3O4(1): TiO2(2) indicated the best and worst efficiency with 30.46% and 22.01%, respectively. The RSM method is applied to provide a simple and efficient formula-based model to describe the TC of THNFs in terms of input variables. In addition, a novel genetic algorithm-based optimization of training/structure parameters of Gaussian process regression (GPR) as a leading machine learning algorithm is developed, which provided thoroughly precise outcomes (R2test = 0.9994 and R2 train = 0.9998) for the prediction of TC of THNFs. The sensitivity analysis for the present THNFs revealed that the TC sensitivity is maximized at the highest VF and temperature.

In the present paper, the rheological and tribological measurements of the SAE 5W30 multi-grade engine oil as a base lubricant and a novel ternary combination of molybdenum trioxide (MoO3), graphene oxide (GO), and multi-walled carbon nanotubes (MWCNTs) as nanopowders were conducted in a wide range of solid volume fractions (VF = 0-1.5%), shear rates (SR = 665-12,635 s- 1), and temperatures (T = 5-65 degrees C). As part of a two- step procedure for nanolubricant preparation, a magnetic stirrer and ultrasonic device were utilized for proper dispersion of MWCNT/GO/MoO3 in SAE 5W30. Furthermore, the nanopowders were subjected to X-ray diffraction-based structural analysis. According to diagrams of the shear stress, dynamic viscosity, and power-law index, the analyzed hybrid nanolubricant behaves as a non-Newtonian (pseudoplastic) fluid in all samples. The experimental results revealed that as the temperature drops from 65 degrees C to 5 degrees C, the viscosity of hybrid nano -lubricant increases by a maximum of 92.23% and a minimum of 83.55%. In addition, the highest increase in dynamic viscosity due to the dispersion of nanopowders was 68.54%, which was reported at 65 degrees C and an SR of 2660 s-1. Tribological experiments demonstrated that the dispersion of nanomaterials up to a VF of 0.6% reduces the wear rate and average friction coefficient. Reducing these two parameters improves engine performance, decreases fuel consumption, and reduces engine pollutants. A three-variable correlation was presented to predict dynamic viscosity, which provided reasonable accuracy (R2 = 0.9931). Moreover, an algorithm was developed to optimize the GP-based ANFIS structure, resulting in an extremely accurate model (R2 = 0.999709) for estimating the viscosity of the hybrid nanolubricants.

In the present study, the properties of ternary hybrid nanofluid (THNF) of oil (5W30) - Graphene Oxide (GO)-Silica Aerogel (SA)-multi-walled carbon nanotubes (MWCNTs) in volume fractions ( φ ) of 0.3

In this study, the rheological behavior of CeO2-CuO/10W40 hybrid nanolubricant with several volume fractions (VFs) over the range of 0.25-1.5 vol%, temperatures over the range of 5- 55 degrees C, and shear rates varying from 20 to 1000 rpm are experimentally assessed. The viscosity measurements at various shear rates (SRs), VFs, and temperatures demonstrated that the 10W40 engine oil and hybrid nanolubricant behave non-Newtonian. The experimental results show that the maximum viscosity reduction with increasing SR occurs at T = 45 degrees C and VF = 1.25 %, which its value is about 30.28 %. The experimental findings demonstrate that an increase in temperature results in reduced viscosity (between 91.84 % and 93.10 %) while the viscosity increases with increasing VF. To forecast the experimental data, two correlations (functions of three variables: temperature, VF, and SR) are presented based on experimental data using curve fitting and the response surface method (RSM). The results show that good concordance exists between experimental data and correlation results to estimate the viscosity of CeO2-CuO/10W40 hybrid nano-lubricant. Additionally, the correlation developed by the RSM is more straightforward than one derived from curve fitting. This new hybrid nano-lubricant can be used as a coolant in the automotive industry. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University.

Investigating natural convection heat transfer of nanofluids in various geometries has garnered significant attention due to its potential applications across several disciplines. This study presents a numerical simulation of the natural convection heat transfer and entropy generation process in an E-shaped porous cavity filled with nanofluids, implementing Buongiorno’s simulation model. Analyzing the behavior of individual nanoparticles, or even the entire nanofluid system at the molecular level, can be extremely computationally intensive. Symmetry is a fundamental concept in science that can help reduce this computational burden considerably. In this study, nanofluids are frequently conceived of as a combination of water and Al2O3 nanoparticles at a concentration of up to 4% by volume. A unique correlation was proposed to model the effective thermal conductivity of nanofluids. The average Nusselt number, entropy production, and Rayleigh number have been illustrated to exhibit a decreasing trend when the volume concentration of nanoparticles inside the porous cavity rises; the 4% vol. water–alumina NFs yield 17.35% less average Nu number compared to the base water.

In this study, the rheological behavior and dynamic viscosity of 10W40 engine oil in the presence of ternary-hybrid nanomaterials of cerium oxide (CeO 2 ), graphene oxide (GO), and silica aerogel (SA) were investigated experimentally. Nanofluid viscosity was measured over a volume fraction range of VF = 0.25–1.5%, a temperature range of T = 5–55 °C, and a shear rate range of SR = 40–1000 rpm. The preparation of ternary-hybrid nanofluids involved a two-step process, and the nanomaterials were dispersed in SAE 10W40 using a magnetic stirrer and ultrasonic device. In addition, CeO 2 , GO, and SA nanoadditives underwent X-ray diffraction-based structural analysis. The non-Newtonian (pseudoplastic) behavior of ternary-hybrid nanofluid at all temperatures and volume fractions is revealed by analyzing shear stress, dynamic viscosity, and power-law model coefficients. However, the nanofluids tend to Newtonian behavior at low temperatures. For instance, dynamic viscosity declines with increasing shear rate between 4.51% (at 5 °C) and 41.59% (at 55 °C) for the 1.5 vol% nanofluid. The experimental results demonstrated that the viscosity of ternary-hybrid nanofluid declines with increasing temperature and decreasing volume fraction. For instance, assuming a constant SR of 100 rpm and a temperature increase from 5 to 55 °C, the dynamic viscosity increases by at least 95.05% (base fluid) and no more than 95.82% (1.5 vol% nanofluid). Furthermore, by increasing the volume fraction from 0 to 1.5%, the dynamic viscosity increases by a minimum of 14.74% (at 5 °C) and a maximum of 35.94% (at 55 °C). Moreover, different methods (COMBI algorithm, GMDH-type ANN, and RSM) were used to develop models for the nanofluid's dynamic viscosity, and their accuracy and complexity were compared. The COMBI algorithm with R 2 = 0.9995 had the highest accuracy among the developed models. Additionally, RSM and COMBI were able to generate predictive models with the least complexity.

Abstract In this study, for the first time, the effects of temperature and nanopowder volume fraction (NPSVF) on the viscosity and the rheological behavior of SAE50–SnO2–CeO2 hybrid nanofluid have been studied experimentally. Nanofluids in NPSVFs of 0.25% to 1.5% have been made by a two-step method. Experiments have been performed at temperatures of 25 to 67 °C and shear rates (SRs) of 1333 to 2932.6 s−1. The results revealed that for base fluid and nanofluid, shear stress increases with increasing SR and decreasing temperature. By increasing the temperature to about 42 °C at a NPSVF of 1.5%, about 89.36% reduction in viscosity is observed. The viscosity increases with increasing NPSVF about 37.18% at 25 °C. In all states, a non-Newtonian pseudo-plastic behavior has been observed for the base fluid and nanofluid. The highest relative viscosity occurs for NPSVF = 1.5%, temperature = 25 °C and SR = 2932.6 s−1, which increases the viscosity by 37.18% compared to the base fluid. The sensitivity analysis indicated that the highest sensitivity is related to temperature and the lowest sensitivity is related to SR. Response surface method, curve fitting method, adaptive neuro-fuzzy inference system and Gaussian process regression (GPR) have been used to predict the dynamic viscosity. Based on the results, all four models can predict the dynamic viscosity. However, the GPR model has better performance than the other models.

Abstract Hybrid nanofluids have great potential for use in thermal systems due to their improved thermal properties. In this paper, the rheological behavior of oil (5w30)- 10% multi walled carbon nanotubes (MWCNT)- 90% silicon dioxide (SiO2) is experimentally examined in the temperature range of 5°C to 65°C. The volume fractions (VFs) are in the range of 0.05 to 1 vol.% and the shear rate (SR) range is 665.5-13330 1/s. Measuring viscosity at different SRs indicated a pseudoplastic rheological behavior of the nanofluids in all VFs and temperatures. Measurement results show that the dynamic viscosity in different volume fractions is reduced when the temperature is increased from 5°C to 65°C. In addition, when the VF is increased from zero to 1%, the dynamic viscosity is augmented between 30.43% and 70.55%. Based on obtained data, a novel three-variable correlation for relative viscosity is proposed which estimates experimental results with a good accuracy. Then, the correlation results are compared to available correlations for hybrid nanofluids in the literature. Finally, a GMDH-type neural network model based on experimental data is developed to predict the relative viscosity of oil (5W30)/SiO2- MWCNT hybrid nanofluids which reveals the predictability of studied hybrid nanofluid using GMDH-type neural network.

In this study, the rheological behavior and dynamic viscosity of 5 W30 engine oil based ZnO-MWCNT (30:70) hybrid nanofluid with various volume fractions (VFs) ranging from 0.05 to 1 vol%, temperatures in the range of 5–55 °C, and shear rates altering from 50 to 1000 rpm are experimentally evaluated. The measured viscosity values at different shear rates (SRs) and temperatures revealed that the hybrid nanofluid under study has a non-Newtonian behavior. For example, with increasing SR from 50 to 300 rpm, the maximum viscosity reduction (= 25.6%) occurs at temperature of 5 °C and VF of 1%, while with increasing SR from 700 to 1000 rpm, the minimum viscosity reduction (= 7.6%) occurs at temperature of 55 °C and VF of 0.05%. Moreover, the power law index is observed less than unity indicating pseudoplastic behavior of hybrid nanofluid under study in all VFs and temperatures. The experimental results show that the increase of the VF of nanoparticles leads to the elevated viscosity. By raising the nanofluid temperature, the viscosity of hybrid nanofluid reduces, while engine oil without additives has a high viscosity at elevated temperatures. As an example, considering constant SR of 300 rpm, the increase of temperature from 5 to 55 °C leads to the 78.4% and 85.5% viscosity reduction for the hybrid nanofluid with VF of 0.5% and 0.75%, respectively. In order to predict the experimental data, a three-variable correlation (depending on the temperature, VF of nanoparticles, and SR) and artificial neural network modeling are developed. It is concluded that an excellent agreement exists between the experimental data, correlation results, and predicted data by neural network, however, neural network modeling demonstrated its capability to estimate the viscosity of 5 W30 engine oil based ZnO-MWCNT hybrid nanofluid more precisely rather than the proposed correlation. Hence, the neural network modeling is highly recommended for prediction of viscosity of the hybrid nanofluid under study.

Purpose This paper aims to study the thermal and thermo-hydraulic performances of ferro-nanofluid flow in a three-dimensional trapezoidal microchannel heat sink (TMCHS) under uniform heat flux and magnetic fields. Design/methodology/approach To investigate the effect of direction of Lorentz force the magnetic field has been applied: transversely in the x direction (Case I);transversely in the y direction (Case II); and parallel in the z direction (Case III). The three-dimensional governing equations with the associated boundary conditions for ferro-nanofluid flow and heat transfer have been solved by using an element-based finite volume method. The coupled algorithm has been used to solve the velocity and pressure fields. The convergence is reached when the accuracy of solutions attains 10–6 for the continuity and momentum equations and 10–9 for the energy equation. Findings According to thermal indicators the Case III has the best performance, but according to performance evaluation criterion (PEC) the Case II is the best. The simulation results show by increasing the Hartmann number from 0 to 12, there is an increase for PEC between 845.01% and 2997.39%, for thermal resistance between 155.91% and 262.35% and ratio of the maximum electronic chip temperature difference to heat flux between 155.16% and 289.59%. Also, the best thermo-hydraulic performance occurs at Hartmann number of 12, pressure drop of 10 kPa and volume fraction of 2%. Research limitations/implications The embedded electronic chip on the base plate generates heat flux of 60 kW/m2. Simulations have been performed for ferro-nanofluid with volume fractions of 1%, 2% and 3%, pressure drops of 10, 20 and 30 kPa and Hartmann numbers of 0, 3, 6, 9 and 12. Practical implications The authors obtained interesting results, which can be used as a design tool for magnetohydrodynamics micro pumps, microelectronic devices, micro heat exchanger and micro scale cooling systems. Originality/value Review of the literature indicated that there has been no study on the effects of magnetic field on thermal and thermo-hydraulic performances of ferro-nanofluid flow in a TMCHS, so far. In this three dimensional study, flow of ferro-nanofluid through a trapezoidal heat sink with five trapezoidal microchannels has been considered. In all of previous studies, in which the effect of magnetic field has been investigated, the magnetic field has been applied only in one direction. So as another innovation of the present research, the effect of applying magnetic field direction (transverse and parallel) on thermo-hydraulic behavior of TMCHS is investigated.

Iran with 300 sunny days in more than two thirds of its land is among the countries with high potential of solar energy. Nevertheless, to date no research has been conducted on status of solar exergy in Iran. In this study, in order to expand the perception of solar energy quality and to compensate the lack of research on solar radiation exergy in Iran, long term meteorological and solar data of eight capital provinces of Iran with five different climatic conditions are utilized. These properly distributed stations include Urmia, Bushehr, Isfahan, Ilam, Kerman, Mashhad, Zahedan and Zanjan. The monthly average daily solar radiation exergy on a horizontal surface for each station is obtained first, then it is recognized that the ratio of exergy to energy is almost independent of the month, the climatic condition and the geographical location; thus, can be considered 0.87 for the whole Iran. For predicting the solar exergy at every station, five empirical models with linear, quadratic, cubic, exponential and power functional forms, all dependent only on relative sunshine duration, are calibrated. Then, eight statistical indicators are utilized to evaluate the performance of the established models for every capital province. The best models recognized for Urmia, Bushehr, Isfahan, Ilam, Kerman, Mashhad, Zahedan and Zanjan have cubic, power, exponential, exponential, linear, quadratic, power and cubic functional forms, respectively. These models are simple and easy to apply and can be also utilized for other places with similar climatic classification and conditions.

In the present work, for the first time, gas flow with considering slip velocity and temperature jump boundary condition is studied in a heat sink consisting of rectangular fins and microchannels with calculating conjugated heat transfer. In this paper, helium gas flow with Knudsen number between 0.048 to 0.06 has been studied. Heat flux applied to the bottom of the aluminum heat sink is 500W/m2. The governing equation for fluid flow has been discretized using second-order upwind method and solved with using the Coupled algorithm in Ansys-Fluent commercial software. Results show that inlet and local Knudsen numbers decrease with increasing pressure ratio and also local Poiseuille number decreases with increasing inlet Knudsen number. Also, with increasing inlet Knudsen number (reduction of pressure ratio), first the average Nusselt number decreases and then increases. In this case, the average Nusselt number decreases about 54.4% with increasing Knudsen number from 0.006 to 0.024 and the average Nusselt number increases with increasing Knudsen number from 0.024 to 0.048. With increasing Knudsen number, thermal resistance increases continuously. The results show that with increasing inlet Knudsen number, slip and temperature jump coefficients increase. Review History: Received: Revised: Accepted: Available Online:

In this study, the turbulent natural convection of Ag-water nanofluid in a tall, inclined enclosure has been investigated. The main objective of this study is finding the optimized angle of the enclosure with operational boundary condition in cooling from ceiling utilizing the computational fluid dynamics-artificial neural network (CFD-ANN) hybrid method, which has not been noticed in previous studies. To achieve this, we proposed two approaches. First, the simulations have been done with a deviation angle of 0 to 90 degrees by using water and Ag-water nanofluid. And second, a new prediction approach is proposed based on radial basis function artificial neural networks (RBF-ANN) to predict the mean Nusselt number and entropy generation with the variation of Rayleigh numbers, deviation angles, and volume fractions as inputs. The results from the first approach indicate that the Rayleigh number has a considerable function in the determination of optimized angle. The results from the second approach, which used the first approach simulation results as training data set, could predict the mean Nusselt number and entropy generation with 1.4577e(-022) and 1.552e(-015) mean square error, respectively. Moreover, a new set of data for Rayleigh numbers, deviation angles, and volume fractions were used to test the performance of the prediction model, which shows promising and superior prospects for RBF-ANN.

In the present study, the natural convective heat transfer in the turbulent flow of water/CuO nanofluid with volumetric radiation and magnetic field inside a tall enclosure has been numerically investigated. The thermophysical properties of nanofluid have been considered variable with temperature and the effects of Brownian motion of nanoparticles have been considered. The main objective of this work is an investigation of the effect of using water/CuO nanofluid and presence of magnetic field on turbulent natural convection in three types of enclosures (vertical, inclined, and horizontal) by considering the volumetric radiation. The governing equations on turbulent flow domain under the influence of the magnetic field and by considering the combination of volumetric radiation and natural convection have been solved by a coupled algorithm. For validating the present research, a comparison has been carried out with the laminar natural convection flow under the influence of the magnetic field and radiation effects and also, the natural turbulent convection flow of previous studies and a proper coincidence has been achieved. The results indicated that by increasing volume fraction and Hartmann number the average Nusselt number enhances and reduces, respectively. By adding 1% CuO nanoparticles to the base fluid, heat transfer improves from 10.59% to 17.05%. However, by increasing the volume fraction from 1% to 4%, heat transfer improves from 1.35% to 4.90%. By increasing Hartmann number from 0 to 600, heat transfer reduces from 9.29% to 22.07%. Also, the results show that the ratio of deviation angle of the enclosure to the horizontal surface has considerable effects on heat transfer performance. Therefore, in similar conditions, the inclined enclosure with a deviation angle of 45 degrees compared to the vertical and horizontal enclosure has better thermal performance.

Simultaneous using of MEMS (Micro Electro Mechanical Systems) and nanotechnology systems in the cooling of micro-scale electrical equipment has attracted researchers in recent years. In the present study, cooling of medical equipment with electronic board is discussed. For this purpose, water and water-diamond nanofluid with a volume fraction of 1%, 2%, 3% and 4% are used as a coolant of micro-scale cooling system. Coolants are pumped into heat sink at pressures of 5, 15, 25 and 35 kPa. The electronic chip on the board is embedded in the base plate of heat sink and generates uniform heat flux of 85kW/m(2). The governing equations have been solved using finite volume method based on finite element. The results show that utilizing water-diamond nanofluid compared to water improves the cooling process so that utilizing water-diamond nanofluid with volume fraction of 4% improves the cooling process between 4.46% and 7.22%. Moreover, increasing pressure drop from 5 kPa to 35 kPa improves cooling indexes between 17.86% and 25.52%. Moreover, designing radial basis function artificial neural network shows good agreement between numerical simulation and predicted results.

Most of numerical studies on microchannel heat sinks (MCHS) performed up to now are for a two-dimensional domain using constant properties of the coolant and solid part. In this study, laminar fluid flow and heat transfer of variable properties water in a trapezoidal MCHS, consisted of five trapezoidal microchannels, are studied. The three dimensional solution domains include both the flow field and the complete MCHS silicon made solid parts with variable conductivity. Four entry/exit configurations and three pressure drops of 5, 10 and 15 kPa are assumed. The results indicate that the A-type heat sink, for which the entry and exit are placed horizontally at the center of the north and the south walls, has a better heat transfer performance, smaller thermal resistance and provides more uniform solid temperature distribution. For pressure drop of 15 kPa, temperature-dependent properties of water increases the heat transfer between 2.73% and 3.33%, decreases the thermal resistance between 3.46% and 5.55 % and decreases the ratio of difference between the maximum and minimum substrate temperatures to the heat flux, θ, between 3.42% and 11.15%. Also by assuming temperature-dependent conductivity of silicon, the heat transfer increases between 0.75% and 2.58%, the thermal resistance decreases between 1.15% and 4.97 % and θ decreases between 2.41% and 6.49%.