Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network

Afrand, Masoud; Najafabadi, Karim Nazari; Sina, Nima; Safaei, Mohammad Reza; Kherbeet, A.Sh.; Wongwises, Somchai; Dahari, Mahidzal

doi:10.1016/j.icheatmasstransfer.2016.05.023

Cited by 169 publications

(26 citation statements)

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“…The small size of the training dataset leads to imprecise predictions of nanofluid thermophysical properties by artificial neural networks, making the conventional prediction models more suitable in many occasions [ 67 , 68 ]. The huge dataset provided by this study, 200 flow curves (25 different samples at eight temperatures), can be employed in future works to train neural networks for the prediction of nanofluids in different applications.…”

Section: Resultsmentioning

confidence: 99%

Influence of Six Carbon-Based Nanomaterials on the Rheological Properties of Nanofluids

et al. 2019

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Nanofluids, dispersions of nanosized solid particles in liquids, have been conceived as thermally-improved heat transfer fluids from their conception. More recently, they have also been considered as alternative working fluids to improve the performance of direct absorption solar thermal collectors, even at low nanoadditive concentrations. Carbon-based nanomaterials have been breaking ground in both applications as nanoadditives during the last decade due to their high thermal conductivities and the huge transformation of optical properties that their addition involves. In any application field, rheological behavior became a central concern because of its implications in the pumping power consumption. In this work, the rheological behavior of four different loaded dispersions (0.25, 0.50, 1.0, and 2.0 wt%) of six carbon-based nanomaterials (carbon black, two different phase content nanodiamonds, two different purity graphite/diamond mixtures, and sulfonic acid-functionalized graphene nanoplatelets) in ethylene glycol:water mixture 50:50 vol% have been analysed. For this purpose, a rotational rheometer with double cone geometry was employed, which included a special cover to avoid mass losses due to evaporation at elevated temperatures. The flow curves of the twenty-four nanofluids and the base fluid were obtained by varying the shear rate between 1 and 1000 s−1 for seven different temperatures in the range from 283.15 to 353.15 K. The shear-thinning behaviors identified, as well as their dependences on carbon-based nanomaterial, concentration, and temperature, were analyzed. In addition, oscillatory tests were performed for samples with the clearest Non-Newtonian response, varying the deformation from 0.1 to 1000% with constant frequency and temperature. The dependence of the behaviors identified on the employed carbon-based nanomaterial was described.

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Section: Resultsmentioning

confidence: 99%

Influence of Six Carbon-Based Nanomaterials on the Rheological Properties of Nanofluids

et al. 2019

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“…In the simulation-based optimization, achieving the best solution is done by adjusting decision variables [73][74][75]. The decision variables are chosen among a group of parameters that affect the value of each objective function; they are called effective parameters [76]. In general, the decision variables reported in Table 4 are classified into two groups: architectural and mechanical.…”

Section: Decision Variablesmentioning

confidence: 99%

The Road to Developing Economically Feasible Plans for Green, Comfortable and Energy Efficient Buildings

et al. 2021

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Owing to the current challenges in energy and environmental crises, improving buildings, as one of the biggest concerns and contributors to these issues, is increasingly receiving attention from the world. Due to a variety of choices and situations for improving buildings, it is important to review the building performance optimization studies to find the proper solution. In this paper, these studies are reviewed by analyzing all the different key parameters involved in the optimization process, including the considered decision variables, objective functions, constraints, and case studies, along with the software programs and optimization algorithms employed. As the core literature, 44 investigations recently published are considered and compared. The current investigation provides sufficient information for all the experts in the building sector, such as architects and mechanical engineers. It is noticed that EnergyPlus and MATLAB have been employed more than other software for building simulation and optimization, respectively. In addition, among the nine different aspects that have been optimized in the literature, energy consumption, thermal comfort, and economic benefits are the first, second, and third most optimized, having shares of 38.6%, 22.7%, and 17%, respectively.

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“…A mechanical ball bearing and its simple schematic As mentioned above, a new application of nanofluids is in lubrication processes such as rolling process and ball bearings. As for nanofluids rheological property in applications, Afrand et al [21] developed an optimal artificial neural network to predict the correlation of the nano-lubricant, which can be more accurate. Also, Asadi et al [22] illustrated two new highly precise correlations for predicting dynamic viscosity and thermal conductivity of the nanooil.…”

Section: Introductionmentioning

confidence: 99%

Mixed Convection Heat Transfer of Sio2-Water and Alumina-Pao Nano-Lubricants Used in a Mechanical Ball Bearing

Hatami¹,

Ali²,

Alsabery³

et al. 2021

Journal of Thermal Engineering

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In this study, the mixed convection heat transfer in a mechanical ball bearing filled with nano-lubricants were investigated theoretically. In our case, the bearing including eight balls revolving in counter clockwise while the inner shaft rotates in clockwise direction and the inner and outer walls of bearing were kept at constant hot and cold temperatures, respectively. Two kinds of nano-lubricants SiO2-water and Alumina-Polyalphaolefin (PAO) with different shapes of nanoparticles were considered. The governing equations including velocity, pressure, and temperature formulation were solved based on the Galerkin finite element method. The governing parameters such as nanoparticle volume fraction, Reynolds and Rayleigh numbers, etc., were discussed. It turns out that the average Nusselt number increases by increasing the nanoparticle volume fraction (averagely 15% for each 0.02 increase) and the oil-based nano-lubricant has greater Nusselt number than the water based one. More importantly, the Nono-rod Alumina was found to show much greater heat transfer performance (averagely 5%) than the spherical alumina nanoparticles and nano-rod Alumina-PAO has the best performance and maximum Nusselt numbers for the heat transfer.

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Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network

Cited by 169 publications

References 50 publications

Influence of Six Carbon-Based Nanomaterials on the Rheological Properties of Nanofluids

Influence of Six Carbon-Based Nanomaterials on the Rheological Properties of Nanofluids

The Road to Developing Economically Feasible Plans for Green, Comfortable and Energy Efficient Buildings

Mixed Convection Heat Transfer of Sio2-Water and Alumina-Pao Nano-Lubricants Used in a Mechanical Ball Bearing

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