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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.

Non-Newtonian fluids are also widely used in a variety of scientific, engineering, and industrial domains, including the petroleum sector and polymer technologies. They are vital in the development of drag-reducing agents, damping and braking systems, food manufacturing, personal protective equipment, and the printing industry. Fluid movement and transport via porous materials draw a lot of attention; they are important in science and technology. Porous media appear in a variety of high-speed phenomena and devices, including catalytic converters, condensers, and gas turbines. Due to above physical significance, the influence of solar radiation and Lorentz forces on the behavior of non-Newtonian second-grade fluids in a Darcy-Forchheimer porous medium at a stagnation point is tackled in this study on both assisting and opposing flow regimes. A study on thermal diffusion or the Soret effect and diffusion-thermo or the Dufour effects are included in the research. Mathematical models are developed for the current situation and translated into a set of ordinary differential equations that are solved using MATLAB’s bvp4c. The data reveal that raising the second-grade fluid reduces the velocity profile while increasing the temperature and concentration profiles in both assisting and opposing flows. In both flowing regimes, increasing the porous medium parameter increases velocity while decreasing temperature. The descending trends in the velocity profiles with respect to the Forchheimer and Prandtl numbers occurs for both assisting and opposing flows. The assisting flow shows higher profiles, values compared to the opposing flow. Results show that increasing second- grade fluid parameter causes the increase in skin friction, Nusselt number and Sherwood number. The results of the current modeled problems are compared with already published results and it has been concluded that there is sufficient agreement between both of them, indicating the validity and accuracy of the present results.

A new way to compute the English Language Mathematical Model (ELMM) is described in this paper using a neural network hybridized with a genetic algorithm-based Sequential Quadratic Programming (SQP) optimization technique. The proposed GA-SQP-NN model for the ELMM is not just standard hybridization, it is for nonlinear linguistic learning processes when GA is used for global search and SQP for local refinement within the framework of a neural-network mapping structure. Another novelty is the use of error-based statistical measurements (TIC, MAD, RMSE) in the optimization cycle that creates convergence and precision than a GA-SQP. The results of the comparative measurements using the Lobatto method show that GA-SQP-NN is more reliable, more accurate, and more flexibly. There are also multi-run statistical verifications that prove the GA-SQP-NN model is stable. Therefore, the GA-SQP-NN structure is a usable and efficacious solver of complex mathematical models in the language acquisition area.

In this paper, we apply stochastic differential equations with the Wiener process to investigate the soliton solutions of the Chaffee–Infante (CI) equation. The CI equation, a fundamental model in mathematical physics, explains concepts such as wave propagation and diffusion processes. Exact soliton solutions are obtained through the application of the modified extended tanh (MET) method. The obtained wave figures in 3D, 2D, and contour are highly localized and determine an individual frequency shift under the behavior of sharp peak, periodic wave, and singular soliton. The MET method shows to be a valuable analytical tool for obtaining soliton solutions, essential for understanding the dynamics of nonlinear wave phenomena. Numerical simulations enable us to explore soliton solutions in two and three dimensions, shedding light on their properties over time. Our results have wide applications in various domains, including stochastic processes and nonlinear dynamics, impacting advancements in physics, engineering, finance, biology, and beyond.

This study provides valuable insight into developing more accurate blood-flow models for targeted drug delivery and therapeutic heat management in stenosed arteries by focusing on the synergistic effects of electrokinetic forces and thermal-chemical interactions. The aim is to investigate electroosmotic flow and endothermic/exothermic chemical reactions within a constricted artery by incorporating the Cattaneo–Christov (CC) heat flux model into a Carreau ternary hybrid nanofluid framework. The governing equations are solved computationally using the BVP4C solver. The main results indicate that the increase of the zeta potential (electrokinetic effect) causes a substantial reduction of the wall shear stress, which lowers energy losses and improves overall blood flow efficiency. In addition, at high electroosmotic parameter the fluid is accelerated, and an enhancement of drug delivery precision and therapeutic effectiveness occur. The model also predicts a modest ~ 7% increase in drag force on the arterial wall under these conditions. Conclusion: Integrating electrokinetic forces and thermal-chemical effects into blood-flow modeling significantly improves flow efficiency and targeted delivery in stenotic arteries, highlighting a promising strategy for optimizing nanoparticle-based treatments.

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.

In the context of energy and the environment and considering the increasing energy demand in industrial processes and for thermal comfort, enhancing system performance and optimizing operations are essential. This paper presents a comprehensive analysis of the adsorption -desorption isotherms of silica gels, which are widely used desiccant materials. Accurate characterization of the adsorption and desorption behaviors is pivotal for assessing their performance across various applications. In this study, we examined multiple isothermal equations describing the interaction between silica gel and water vapor. Our findings highlight the critical importance of the adsorption capacity at low temperatures, as confirmed by experimental results from a climatic wind tunnel. The experimental results indicate that the isosteric heat of adsorption (ΔH ads ) decreases with increasing moisture content, suggesting a progressive reduction in the energy required for water uptake as adsorption progresses. Furthermore, we demonstrate that the adsorption and desorption isotherms follow distinct paths, highlighting a divergence from hypothetical models. Two empirical correlations linking water content to temperature and relative humidity for both adsorption and desorption processes were developed, showing good agreement with experimental results compared to existing correlations in the literature and illustrating the gap between theoretical and experimental findings. This research enhances our understanding of the adsorption properties of silica gel and provides the groundwork for its effective application in areas such as atmospheric water generation and air conditioning systems, among others.

The growing applications of the hybrid nanofluids in drug delivery, cancer treatment, and other fields of science and engineering encouraged researchers and scientists to pay their attention towards such important fluid flow problems. This motivated the authors to carry out the novel current study, which is confined to fluid flow and heat transfer mechanisms in the Maxwell hybrid nanofluid based on the AA7072-AA7075 nanoparticles are suspended in methanol. The moving thin needle of variable thickness embedded in a porous medium. The impact of the Lorentz force applied perpendicular to the flow direction is considered to control the boundary layer flow and boundary layer thickness. The equations for the problem in partial differential equations are transformed to ordinary differential equations with an appropriate similarity transformation for a similar solution with the help of the bvp4c solver. The results show that increasing the volume fraction of the nanoparticles enhances the thermal effects of the fluid. Further, the increasing Lorentz force controls the flow speed, leading to control of the boundary layer thickness. The Maxwell fluid parameter also influences the fluid speed and thermal efficiency of the fluid. Results reflect that the skin friction coefficient reduces and the Nusselt number or heat transfer rate increases with increasing Prandtl number. For better visualization of the flow pattern, the streamlines and isotherms are plotted. The validity of the current solution is ensured by the comparison of the current results with already published results in the theoretical study. Overall, it is concluded that the combined effects of the time relaxation, magnetic field, and porosity of the medium on the moving thin needle of variable thickness increase the thermal performance of the hybrid nanofluid based on the AA7072-AA7075/Methanol. The results are asymptotic.

The thermal performance enhancement of a vertical helical coil heat exchanger using distilled water-based copper oxide-graphene hybrid nanofluid has been analyzed experimentally. Accordingly, the focus of this study is the preparation of CuO-Gp (80-20%) hybrid nanoparticles-based suspensions with various mass fractions (0% ≤ wt ≤ 1%). The volume flow rate is ranged from 0.5 L·min−1 to 1.5 L·min−1 to keep the laminar flow regime (768 ≤ Re ≤ 1843) and the supplied hot fluid’s temperature was chosen to equal 50 °C. To ensure the dispersion and avoid agglomeration an ultrasound sonicator is used and the thermal conductivity is evaluated via KD2 Pro Thermal Properties Analyzer. It has been found that the increment in nanoparticles mass fraction enhances considerably the thermal conductivity and the thermal energy exchange rate. In fact, an enhancement of 23.65% in the heat transfer coefficient is obtained with wt = 0.2%, while it is as high as 79.68% for wt = 1%. Moreover, increasing Reynolds number results in a considerable augmentation of the heat transfer coefficient.