Examining rheological behavior of MWCNT-TiO2/5W40 hybrid nanofluid based on experiments and RSM/ANN modeling

Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy

The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids. The goal is to minimize dynamic viscosity and maximize thermal conductivity by varying the volume fraction, temperature, and nanomaterial mixing ratio. The proposed strategy integrates machine learning, multi-objective optimization, and multi-criteria decision-making. Three machine learning techniques-GMDH-type neural network, gene expression programming, and combinatorial algorithm-are applied to model dynamic viscosity and thermal conductivity as functions of the input variables. Then, the high-performing models provide the foundation for optimization using the well-established multi-objective particle swarm optimization algorithm. Finally, the decision-making technique TOPSIS is employed to identify the most desirable points from the Pareto front, based on various design scenarios. To validate the proposed strategy, a ternary hybrid nanofluid composed of graphene oxide (GO), iron oxide (Fe₃O₄), and titanium dioxide (TiO₂) was employed as a case study. The results demonstrated that the combinatorial approach excelled in accurately modeling (R = 0.99964-0.99993). The optimization process revealed that optimal VFs span a broad range across all mixing ratios, while optimal temperatures were consistently near the maximum value (65 °C). The decision-making outcomes indicated that the mixing ratio was consistent across all design scenarios, with the volume fraction serving as the key differentiating factor.

Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving MXene-based ionanofluids applicable in PV/T solar systems

Optimization of thermophysical properties (TPPs) of MXene-based nanofluids is essential to increase the performance of hybrid solar photovoltaic and thermal (PV/T) systems. This study proposes a hybrid approach to optimize the TPPs of MXene-based Ionanofluids. The input variables are the MXene mass fraction (MF) and temperature. The optimization objectives include three TPPs: specific heat capacity (SHC), dynamic viscosity (DV), and thermal conductivity (TC). In the proposed hybrid approach, the powerful group method of data handling (GMDH)-type ANN technique is used to model TPPs in terms of input variables. The obtained models are integrated into the multi-objective particle swarm optimization (MOPSO) and multi-objective thermal exchange optimization (MOTEO) algorithms, forming a three-objective optimization problem. In the final step, the TOPSIS technique, one of the well-known multi-criteria decision-making (MCDM) approaches, is employed to identify the desirable Pareto points. Modeling results showed that the developed models for TC, DV, and SHC demonstrate a strong performance by R-values of 0.9984, 0.9985, and 0.9987, respectively. The outputs of MOPSO revealed that the Pareto points dispersed a broad range of MXene MFs (0-0.4%). However, the temperature of these optimal points was found to be constrained within a narrow range near the maximum value (75 °C). In scenarios where TC precedes other objectives, the TOPSIS method recommended utilizing an MF of over 0.2%. Alternatively, when DV holds greater importance, decision-makers can opt for an MF ranging from 0.15 to 0.17%. Also, when SHC becomes the primary concern, TOPSIS advised utilizing the base fluid without any MXene additive.

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid

This study predicts the parameters such as viscosity and thermal conductivity in silica-alumina-MWCN/water nanofluid using the artificial intelligence method and using design variables such as solid volume fraction and temperature. In this study, 6 optimization algorithms were used to predict and numerically model the μnf and TC of silica-alumina-MWCNT/water-NF. In this study, six measurement criteria were used to evaluate the estimates obtained from the coupling process of GMDH ANN with each of these 6 optimization algorithms. The results reveal that the influence of the φ is notably higher on both μnf and TC with values of 0.83 for μnf and 0.92 for TC, while Temp has a relatively weaker impact with -0.5 for μnf and 0.38 for TC. Among various algorithms, the coupling of the evolutionary algorithm NSGA II with ANN and GMDH performs best in predicting μnf and TC for the NF, with a maximum margin of deviation of -0.108 and an R2 evaluation criterion of 0.99996 for μnf and 1 for TC, indicating exceptional model accuracy. In the subsequent phase, a meta-heuristic Genetic Algorithm minimizes μnf and TC values. Four points (A, B, C, and D) along the Pareto front are selected, with point A representing the optimal state characterized by low values of φ and Temp (0.0002 and 50.8772, respectively) and corresponding target function values of 0.9988 for μnf and 0.6344 for TC. In contrast, point D represents the highest values of φ and Temp (0.49986 and 59.9775, respectively) and yields target function values of 2.382 for μnf and 0.8517 for TC. This analysis aids in identifying the optimal operating conditions for maximizing NF performance.

Examining rheological behavior of CeO2-GO-SA/10W40 ternary hybrid nanofluid based on experiments and COMBI/ANN/RSM modeling

In this study, the rheological behavior and dynamic viscosity of 10W40 engine oil in the presence of ternary-hybrid nanomaterials of cerium oxide (CeO₂), 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, CeO₂, 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 R² = 0.9995 had the highest accuracy among the developed models. Additionally, RSM and COMBI were able to generate predictive models with the least complexity.

Numerical Analysis of the Effect of Nanoparticles Size and Shape on the Efficiency of a Micro Heatsink

In this paper, two novel micro heat sinks (MHSs) were designed and subjected to thermal analysis using a numerical method. The fluid used was Boehmite alumina-water nanofluid (NFs) with high volume fractions (VOFs). Studies were conducted to determine the influence of a variety of nanoparticle (NP) shapes, such as platelet brick, blade, cylinder, and Os. The heatsink (HS) was made of copper, and the NFs entered it through the middle and exited via four outlets at the side of the HS. The finite element method was used to simulate the NFs flow and heat transfer in the HSs. For this purpose, Multi Physics COMSOL software was used. The maximum and middle values of HS temperature (T-MAX and T-Mid), thermal resistance (TH-R), heat transfer coefficient (h), FOM, etc., were studied for different NP shapes, and with Reynolds numbers (Re) of 300, 1000, and 1700, and VOFs of 0, 3, and 6%. One of the important outcomes of this work was the better thermal efficiency of the HS with rectangular fins. Moreover, it was discovered that a rise in Re increased the heat transfer. In general, adding NPs with high VOFs to MHSs is not appropriate in terms of heat. The Os shape was the best NP shape, and the platelet shape was the worst NP shape for high NPVOF. When NPs were added to an MHS, the temperature of the MHS dropped by an average of 2.8 or 2.19 K, depending on the form of the pin-fins contained inside the MHS (circular or square). The addition of NPs in the MHS with circular and square pin-fins enhanced the pressure drop by 13.5% and 13.3%, respectively, when the Re = 1700.

The effect of different parameters on ability of the proposed correlations for the rheological behavior of SiO2 -MWCNT (90:10)/SAE40 oil-based hybrid nano-lubricant and presenting five new correlations

In this study, after investigating the rheological behavior and viscosity of SiO₂-MWCNT (90:10)/SAE40 oil-based hybrid nano-lubricant, the theory was studied by response surface methodology (RSM). Experimental results show that viscosity enhancement decreases significantly at a concentration of 0.0625% to -9.22% and at a concentration of 1% with + 48.23% the highest increase of viscosity enhancement is observed. But the main purpose of this study is to present the new two- and three-variable correlation relationships and compare them with each other. The effectiveness of the experimental correlation for predicting the viscosity is investigated. According to the results of the RSM method, in the relations of two variables, the presence of an independent variable of temperature with the volume fraction (φ) has a greater effect compared to using the variable of shear rate (SR) along the φ and increases the accuracy of the mathematical relation.

Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO2-BC). At the same time, the turbine design and optimization process for the sCO2-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO2 power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO2 are implemented by coupling the code with NIST’s Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio (rs4r3), hub ratio (rs4r3), speed ratio (νs) and inlet flow angle (α3) and are investigated numerically through 3D-RANS simulations. The generated CFD data is then used to train a deep neural network (DNN). Finally, the trained DNN model is employed as a fitting function in the multi-objective genetic algorithm (MOGA) to explore the optimized design parameters for the turbine’s rotor geometry. Moreover, the off-design performance of the optimized turbine geometry is computed and reported in the current study. Results suggest that the employed multifaceted approach reduces computational time and resources significantly and is required to completely understand the effects of various turbine design parameters on its performance and sizing. It is found that sCO2-turbine performance parameters are most sensitive to the design parameter speed ratio (νs), followed by inlet flow angle (α3), and are least receptive to shroud ratio (rs4r3). The proposed turbine design methodology based on the machine learning algorithm is effective and substantially reduces the computational cost of the design and optimization phase and can be beneficial to achieve realistic and efficient design to the turbine for sCO2-BC.

Study on the Effect of Hole Size of Trombe Wall in the Presence of Phase Change Material for Different Times of a Day in Winter and Summer

In this article, a numerical study is performed on a Trobme wall in a tropical city for two seasons, summer and winter. A 1×1.5 m Trobme wall with a thickness of 15 cm is designed and analyzed. A 1-inch-diameter tube filled with PCM is used to enhance efficiency. The wall is analyzed at different times of the day for the two cold and hot seasons for different sizes of wall holes in the range of 70 to 17.5 cm when the wall height is 20 cm. A fluid simulation software is employed for the simulations. The problem variables include different hours of the day in the two cold and hot seasons, the presence or absence of PCM, as well as the size of the wall hole. The results of this simulation demonstrate that the maximum outlet temperature of the Trobme wall occurs at 2 P.M. Using PCM on the wall can allow the wall to operate for longer hours in the afternoon. However, the use of PCM reduces the outlet wall temperature in the morning. The smaller the size of the wall hole, the more air can be expelled from the wall.

Applying Artificial Neural Network and Response Surface Method to Forecast the Rheological Behavior of Hybrid Nano-Antifreeze Containing Graphene Oxide and Copper Oxide Nanomaterials

In this study, the efficacy of loading graphene oxide and copper oxide nanoparticles into ethylene glycol-water on viscosity was assessed by applying two numerical techniques. The first technique employed the response surface methodology based on the design of experiments, while in the second technique, artificial intelligence algorithms were implemented to estimate the GO-CuO/water-EG hybrid nanofluid viscosity. The nanofluid sample’s behavior at 0.1, 0.2, and 0.4 vol.% is in agreement with the Newtonian behavior of the base fluid, but loading more nanoparticles conforms with the behavior of the fluid with non-Newtonian classification. Considering the possibility of non-Newtonian behavior of nanofluid temperature, shear rate and volume fraction were effective on the target variable and were defined in the implementation of both techniques. Considering two constraints (i.e., the maximum R-square value and the minimum mean square error), the best neural network and suitable polynomial were selected. Finally, a comparison was made between the two techniques to evaluate their potential in viscosity estimation. Statistical considerations proved that the R-squared for ANN and RSM techniques could reach 0.995 and 0.944, respectively, which is an indication of the superiority of the ANN technique to the RSM one.

Statistical modeling for bioconvective tangent hyperbolic nanofluid towards stretching surface with zero mass flux condition

This article presents the implementation of a numerical solution of bioconvective nanofluid flow. The boundary layer flow (BLF) towards a vertical exponentially stretching plate with combination of heat and mass transfer rate in tangent hyperbolic nanofluid containing microorganisms. We have introduced zero mass flux condition to achieve physically realistic outcomes. Analysis is conducted with magnetic field phenomenon. By using similarity variables, the partial differential equation which governs the said model was converted into a nonlinear ordinary differential equation, and numerical results are achieved by applying the shooting technique. The paper describes and addresses all numerical outcomes, such as for the Skin friction coefficients (SFC), local density of motile microorganisams (LDMM) and the local number Nusselt (LNN). Furthermore, the effects of the buoyancy force number, bioconvection Lewis parameter, bioconvection Rayleigh number, bioconvection Pecelt parameter, thermophoresis and Brownian motion are discussed. The outcomes of the study ensure that the stretched surface has a unique solution: as Nr (Lb) and Rb (Pe) increase, the drag force (mass transfer rate) increases respectively. Furthermore, for least values of Nb and all the values of Nt under consideration the rate of heat transfer upsurges. The data of SFC, LNN, and LDMM have been tested utilizing various statistical models, and it is noted that data sets for SFC and LDMM fit the Weibull model for different values of Nr and Lb respectively. On the other hand, Frechet distribution fits well for LNN data set for various values of Nt.

Exploring the nanomechanical concepts of development through recent updates in magnetically guided system

This article outlines an analytical analysis of unsteady mixed bioconvection buoyancy-driven nanofluid thermodynamics and gyrotactic microorganisms motion in the stagnation domain of the impulsively rotating sphere with convective boundary conditions. To make the equations physically realistic, zero mass transfer boundary conditions have been used. The Brownian motion and thermophoresis effects are incorporated in the nanofluid model. Magnetic dipole effect has been implemented. A system of partial differential equations is used to represent thermodynamics and gyrotactic microorganisms motion, which is then transformed into dimensionless ordinary differential equations. The solution methodology is involved by homotopy analysis method. The results obtained are based on the effect of dimensionless parameters on the velocity, temperature, nanoparticles concentration and density of the motile microorganisms profiles. The primary velocity increases as the mixed convection and viscoelastic parameters are increased while it decreases as the buoyancy ratio, ferro-hydrodynamic interaction and rotation parameters are increased. The secondary velocity decreases as viscoelastic parameter increases while it increases as the rotation parameter increases. Temperature is reduced as the Prandtl number and thermophoresis parameter are increased. The nanoparticles concentration is increased as the Brownian motion parameter increases. The motile density of gyrotactic microorganisms increases as the bioconvection Rayleigh number, rotation parameter and thermal Biot number are increased.