Experimental study and computational intelligence on dynamic viscosity and thermal conductivity of HNTs based nanolubricant

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valéry Tusambila Wadi ◽  
Özkan Özmen ◽  
Abdullah Caliskan ◽  
Mehmet Baki Karamış
Keyword(s):  
Thermal Conductivity ◽  
Experimental Study ◽  
Dynamic Viscosity ◽  
Halloysite Nanotubes ◽  
Support Vector ◽  
Nanoparticle Concentration ◽  
Step Method ◽  
Content Type ◽  
Mean Values ◽  
Base Oil

Purpose This paper aims to evaluate the dynamic viscosity and thermal conductivity of halloysite nanotubes (HNTs) suspended in SAE 5W40 using machine learning methods (MLMs). Design/methodology/approach A two-step method with surfactant was selected to prepare nanolubricants in concentrations of 0.025, 0.05, 0.1 and 0.5 wt%. Thermal conductivity and dynamic viscosity of nanofluids were ascertained over the temperature range of 25–70 °C, with an increment step of 5 °C, using a KD2-Pro analyser device and a digital viscometer MRC VIS-8. Additionally, four different MLMs, including Gaussian process regression (GPR), artificial neural network (ANN), support vector machine (SVM) and decision tree (DT), were used for predicting dynamic viscosity and thermal conductivity by using nanoparticle concentration and temperature as input parameters. Findings According to the achieved results, the dynamic viscosity and thermal conductivity of nanolubricants mostly increased with the rise of nanoparticle concentration in the base oil. All the proposed models, especially GPR with root mean square error mean values of 0.0047 for dynamic viscosity and 0.0016 for thermal conductivity, basically showed superior ability and stability to estimate the viscosity and thermal conductivity of nanolubricants. Practical implications The results of this paper could contribute to optimising the cost and time required for modelling the thermophysical properties of lubricants. Originality/value To the best of the author’s knowledge, in this available literature, there is no paper dealing with experimental study and prediction of dynamic viscosity and thermal conductivity of HNTs-based nanolubricant using GPR, ANN, SVM and DT.

Experimental analysis and modeling of viscosity and thermal conductivity of GNPs/SAE 5W40 nanolubricant

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valéry Tusambila Wadi ◽  
Özkan Özmen ◽  
Mehmet Baki Karamış
Keyword(s):  
Thermal Conductivity ◽  
Peer Review ◽  
Dynamic Viscosity ◽  
Ann Model ◽  
Step Method ◽  
Rotational Viscometer ◽  
Content Type ◽  
Base Oil ◽  
Artificial Neural Network Ann ◽  
The Stability

Purpose The purpose of this study is to investigate thermal conductivity and dynamic viscosity of graphene nanoplatelet-based (GNP) nanolubricant. Design/methodology/approach Nanolubricants in concentrations of 0.025, 0.05, 0.1 and 0.5 Wt% were prepared by means of two-step method. The stability of nanolubricants was monitored by visual inspection and dynamic light scattering tests. Thermal conductivity and dynamic viscosity of nanolubricants in various temperatures between 25°C–70°C were measured with KD2-Pro analyser device and a rotational viscometer MRC VIS-8, respectively. A comparison between experimentally achieved results and those obtained from existing models was performed. New correlations were proposed and artificial neural network (ANN) model was used for predicting thermal conductivity and dynamic viscosity. Findings The designed nanolubricant showed good stability after at least 21 days. Thermal conductivity and dynamic viscosity increased with particles concentration. In addition, as the temperature increased, thermal conductivity increased but dynamic viscosity decreased. Compared to the base oil, maximum enhancements were achieved at 70°C with the concentration of 0.5 Wt.% for dynamic viscosity and at 55°C with the same concentration for thermal conductivity. Besides, ANN results showed better performance than proposed correlations. Practical implications This study outcomes will contribute to enhance thermophysical properties of conventional lubricating oils. Originality/value To the best of our knowledge, there is no paper related to experimental study, new correlations and modelling with ANN of thermal conductivity and dynamic viscosity of GNPs/SAE 5W40 nanolubricant in the available literature. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0088/

scholarly journals Experimental Study of Thermal Properties and Dynamic Viscosity of Graphene Oxide/Oil Nano-Lubricant

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2886
Author(s):  
Ramin Ranjbarzadeh ◽  
Raoudha Chaabane
Keyword(s):  
Thermal Conductivity ◽  
Experimental Study ◽  
Graphene Oxide ◽  
Cloud Point ◽  
Dynamic Viscosity ◽  
Freezing Point ◽  
Flash Point ◽  
Engine Oil ◽  
Base Oil ◽  
Nano Lubricant

This experimental study was carried out based on the nanotechnology approach to enhance the efficacy of engine oil. Atomic and surface structures of graphene oxide (GO) nanoparticles were investigated by using a field emission scanning electron microscope and X-ray diffraction. The nano lubricant was produced by using a two-step method. The stability of nano lubricant was analyzed through dynamic light scattering. Various properties such as thermal conductivity, dynamic viscosity, flash point, cloud point and freezing point were investigated and the results were compared with the base oil (Oil- SAE-50). The results show that the thermal conductivity of nano lubricant was improved compared to the base fluid. This increase was correlated with progressing temperature. The dynamic viscosity was increased by variations in the volume fraction and reached its highest value of 36% compared to the base oil. The cloud point and freezing point are critical factors for oils, especially in cold seasons, so the efficacy of nano lubricant was improved maximally by 13.3% and 12.9%, respectively, compared to the base oil. The flash point was enhanced by 8%, which remarkably enhances the usability of the oil. It is ultimately assumed that this nano lubricant to be applied as an efficient alternative in industrial systems.

Natural convection of nanofluids in a cavity: criteria for enhancement of nanofluids

2017 ◽  
Vol 27 (7) ◽  
pp. 1504-1534 ◽  
Author(s):  
M. Sabour ◽  
Mohammad Ghalambaz ◽  
Ali Chamkha
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Dynamic Viscosity ◽  
Base Fluid ◽  
Convection Heat Transfer ◽  
Governing Equations ◽  
Content Type ◽  
Separation Line ◽  
Working Temperature ◽  
Dimensional Parameters

Purpose The purpose of this study is to theoretically analyze the laminar free convection heat transfer of nanofluids in a square cavity. The sidewalls of the cavity are subject to temperature difference, whereas the bottom and top are insulated. Based on the available experimental results in the literature, two new non-dimensional parameters, namely, the thermal conductivity parameter (Nc) and dynamic viscosity parameter (Nv) are introduced. These parameters indicate the augmentation of the thermal conductivity and dynamic viscosity of the nanofluid by dispersing nanoparticles. Design/methodology/approach The governing equations are transformed into non-dimensional form using the thermo-physical properties of the base fluid. The obtained governing equations are solved numerically using the finite element method. The results are reported for the general non-dimensional form of the problem as well as case studies in the form of isotherms, streamlines and the graphs of the average Nusselt number. Using the concept of Nc and Nv, some criteria for convective enhancement of nanofluids are proposed. As practical cases, the effect of the size of nanoparticles, the shape of nanoparticles, the type of nanoparticles, the type of base fluids and working temperature on the enhancement of heat transfer are analyzed. Findings The results show that the increase of the magnitude of the Rayleigh number increases of the efficiency of using nanofluids. The type of nanoparticles and the type of the base fluid significantly affects the enhancement of using nanofluids. Some practical cases are found, in which utilizing nanoparticles in the base fluid results in deterioration of the heat transfer. The working temperature of the nanofluid is very crucial issue. The increase of the working temperature of the nanofluid decreases the convective heat transfer, which limits the capability of nanofluids in decreasing the size of the thermal systems. Originality/value In the present study, a separation line based on two non-dimensional parameters (i.e. Nc and Nv) are introduced. The separation line demonstrates a boundary between augmentation and deterioration of heat transfer by using nanoparticles. Indeed, by utilizing the separation lines, the convective enhancement of using nanofluid with a specified Nc and Nv can be simply estimated.

An experimental study on the effect of diameter on thermal conductivity and dynamic viscosity of Fe/water nanofluids

2014 ◽  
Vol 119 (3) ◽  
pp. 1817-1824 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Seyfolah Saedodin ◽  
Somchai Wongwises ◽  
Davood Toghraie
Keyword(s):  
Thermal Conductivity ◽  
Experimental Study ◽  
Dynamic Viscosity

scholarly journals Impact of Copper Nanoparticle Addition on Thermo Physical Properties of Different Base Fluids

2019 ◽  
Vol 8 (4) ◽  
pp. 4192-4195
Keyword(s):  
Thermal Conductivity ◽  
Ethylene Glycol ◽  
Physical Properties ◽  
Specific Heat ◽  
Propylene Glycol ◽  
Copper Nanoparticle ◽  
Nanoparticle Concentration ◽  
Step Method ◽  
Effect Of Copper ◽  
Thermo Physical Properties

In the present work, thermo physical properties of different base fluids (Water, Ethylene Glycol, Propylene Glycol) by suspending various concentrations of copper nanoparticle was evaluated. Initially copper based nanofluid was prepared by two-step method and the concentration of copper nanoparticle was varied at 0.15, 0.2, 0.25 and 0.3 volume. %. The effect of copper nanoparticle concentration on thermo physical properties was evaluated. The result shows that the density, thermal conductivity and viscosity of all the chosen base fluids (Water, Ethylene Glycol, and Propylene Glycol) were increased; however the specific heat of these base fluids decreases while increasing the copper nanoparticle concentration.

scholarly journals Development of Simple-to-Use Predictive Models to Determine Thermal Properties of Fe2O3/Water-Ethylene Glycol Nanofluid

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 18 ◽  
Author(s):  
Mohammad Hossein Ahmadi ◽  
Ali Ghahremannezhad ◽  
Kwok-Wing Chau ◽  
Parinaz Seifaddini ◽  
Mohammad Ramezannezhad ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thermophysical Properties ◽  
Predictive Models ◽  
Dynamic Viscosity ◽  
Least Square ◽  
Support Vector ◽  
Mass Ratio ◽  
Artificial Neural

Thermophysical properties of nanofluids play a key role in their heat transfer capability and can be significantly affected by several factors, such as temperature and concentration of nanoparticles. Developing practical and simple-to-use predictive models to accurately determine these properties can be advantageous when numerous dependent variables are involved in controlling the thermal behavior of nanofluids. Artificial neural networks are reliable approaches which recently have gained increasing prominence and are widely used in different applications for predicting and modeling various systems. In the present study, two novel approaches, Genetic Algorithm-Least Square Support Vector Machine (GA-LSSVM) and Particle Swarm Optimization- artificial neural networks (PSO-ANN), are applied to model the thermal conductivity and dynamic viscosity of Fe2O3/EG-water by considering concentration, temperature, and the mass ratio of EG/water as the input variables. Obtained results from the models indicate that GA-LSSVM approach is more accurate in predicting the thermophysical properties. The maximum relative deviation by applying GA-LSSVM was found to be approximately ±5% for the thermal conductivity and dynamic viscosity of the nanofluid. In addition, it was observed that the mass ratio of EG/water has the most significant impact on these properties.

Thermal Conductivity Behavior of Yttria Nanofluids

2012 ◽  
Author(s):  
Salim J. S. Masharqa ◽  
Waka Tesfai ◽  
Pawan K. Singh ◽  
Matteo Chiesa ◽  
Youssef Shatilla
Keyword(s):  
Thermal Conductivity ◽  
Size Distributions ◽  
Conductivity Measurements ◽  
Nanoparticle Concentration ◽  
Particle Size Distributions ◽  
Step Method ◽  
Cooling Systems ◽  
Conductivity Enhancement ◽  
Acoustic Spectroscopy ◽  
Dependent Viscosity

In this paper the effect of nanoparticle concentration and temperature on the thermal conductivity of Yttria-Ethylene glycol nanofluid has been investigated. In addition, the effect of aging on the viscosity and the thermal conductivity of these nanofluids also have been studied. The nanofluids were prepared by two-step method, and particle size distributions were characterized using acoustic spectroscopy. It was found that the thermal conductivity of Yttria nanofluids increases beyond the classical Hamilton-Crosser model. Moreover, the enhancement in the thermal conductivity of this nanofluid showed high temperature dependence behavior. For instance at 3.0% by volume particles loading, the thermal conductivity enhancement increased from 16.6% at 26 °C to 27.0% at 59 °C, making these nanofluids attractive and effective for cooling systems that operates at high temperatures. Finally, time dependent viscosity and thermal conductivity measurements showed stable behavior for 16 days of study demonstrating the good stability of these nanofluids.

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