The applications of nanofluids to improve the performance of thermal behavior have been the focus of significant research in recent years. Scientists have focused in the interaction of magnetic field with hybrid nanoparticles applications. It explores the flow and thermal behavior of an engine oil based Maxwell hybrid nanofluid containing dispersed titanium dioxide (TiO2) and ferric oxide (Fe2O3) nanoparticles. The fluid between two rotating disks is analysis while accounts for mixed convection, magnetic field, and quadratic thermal radiation. The work addresses a gap in the understanding of viscoelastic fluid behavior, magnetohydrodynamics influences, and the associated heat transfer processes, offering useful insights for improving energy systems, electronic cooling, and magnetic fluid based technologies. To transform the governing nonlinear partial differential equations into dimensionless coupled ODEs via using von Kármán similarity approach. The resulting ODEs system is solved numerically with shooting technique through with Runge–Kutta method. The investigation evaluates how major parameters affect the system, including the Reynolds number, magnetic field strength, and slip boundary condition, along with their influence on axial and radial velocity distributions and temperature profiles. From the present study, we have concluded that hybrid nanofluid gives us more satisfactory results than unitary nanofluid. The results show that the Maxwell hybrid nanofluid (TiO2 + Fe2O3/EO) enhances the heat transfer rate by up to 10.6340 %. As engine oil is commonly used in engineering and industrial applications, this enhancement in thermal transfer can help to achieve a higher thermal management, an enhanced machine performance and to minimize the wasted useful energy. A key contribution of the present analysis lies in developing a Maxwell hybrid nanofluid model for TiO2–Fe2O3/ engine oil flow between two coaxial rotating disks, incorporating the coupled effects of nonlinear thermal radiation, Cattaneo–Christov heat flux, mixed convection, heat generation, viscous dissipation, and convective boundary conditions.
Growing environmental concerns and the push to reduce fossil fuel dependence have driven the search for cleaner, more sustainable transportation fuels. Biodiesel, produced from renewable and non-edible sources, offers a viable alternate to diesel used in daily life. Although this research investigates emission and performance parameters of a four-stroke diesel engine using blends of biodiesel enhanced with Magnesium Oxide (MgO) nanoparticles. This biodiesel was derived from “Jatropha Curcas oil” and blended with commercial diesel and ethanol as binders. Various proportions of MgO were added to different blends to evaluate their impact. At 3,500 rpm, “DJE09MgO125” showed a 13.5 % increase in both torque and brake power. “DJE01MgO25” recorded the highest volumetric efficiency gain of 9.7 % at 2,900 rpm, while “DJE09MgO125” improved brake thermal efficiency by 10.6 % at 3,200 rpm. In terms of emissions, “DJE01MgO25” achieved the maximum CO reduction of 39.88 % at 3,500 rpm in vol%. “DJE03MgO125” reduced CO2 emissions by 4.71 % in vol% at the same speed, and “DJE06MgO125” cut hydrocarbon (HC) emissions reported in ppm by 60 % at 2,600 rpm. “DJE09MgO125” also delivered the greatest NOx reduction reported in ppm, with a 29.57 % decrease at 2,900 rpm. These results highlight the dual benefit of MgO nanoparticles in biodiesel blends, offering notable improvements in engine performance including torque, power, and efficiency alongside significant reductions in CO, CO2, HC, and NOx emissions. Overall, MgO proves to be an effective additive for enhancing the combustion and environmental performance of biodiesel-fueled engines.
Nanomaterials play a key role in nanoscale devices, including oscillators, signaling systems, and drug delivery platforms. This work investigates the van der Waals interactions between carbon nanotubes and molybdenum disulfide nanotubes using a continuum model based on the 6–12 Lennard-Jones potential. The continuum approach provides an analytically tractable, physically transparent, and computationally efficient alternative for systematically exploring nanotube configurations. Two aspects are examined: the suction energy governing the encapsulation of a carbon nanotube into a semi-infinite molybdenum disulfide nanotube and the equilibrium radial offset of a confined carbon nanotube. Analytical expressions for the interaction energy and axial force are derived and evaluated for representative armchair and zigzag nanotube systems. The results show that encapsulation is energetically favorable when the intertube gap approaches the equilibrium van der Waals spacing, whereas insufficient radial clearance produces strong repulsive interactions that prevent insertion. In addition, the equilibrium radial offset decreases as the intertube gap narrows, approaching a nearly concentric configuration at optimal spacing. The proposed continuum model offers an efficient predictive framework for identifying stable carbon nanotube–molybdenum disulfide nanotube configurations and complements computationally demanding atomistic simulations.
This investigation examines the three-dimensional magnetohydrodynamic flow of an Eyring–Powell nanofluid with reactive transport, modeled using the Cattaneo–Christov heat and mass flux theory. The aim is to investigate the combined effects of the magnetic field, rotation, and reactive parameters on flow, heat, and mass transfer, and to validate the predictive capability of artificial neural networks experimentally. The governing equations are solved numerically using MATLAB’s bvp4c solver, and the resulting data are used to train artificial neural networks based on the Levenberg–Marquardt algorithm. It is observed that increasing the magnetic parameter decreases velocity by about 12–18 %, whereas increasing the rotation factor increases velocity by 9–14 % owing to Coriolis forces. Temperature drops by 10–13 % with increased heat generation, while concentration gradients decrease by 8–11 % with an increase in the Schmidt number. The ANN model achieves the lowest MSE of 6.81 × 10−9 in close agreement with numerical predictions. Model validation against benchmark cases confirms the model’s accuracy. The results provide new insights into advanced machine learning integrated with reactive nanofluid modeling for industrial, environmental, and biomedical thermal systems.
To investigate the influence of sand ratio on the axial compressive behavior of geopolymer concrete incorporating non-sintered aggregates under freeze-thaw cycles, nine prismatic specimens with sand ratios of 0.45, 0.55, and 0.65 were prepared. Rapid freeze-thaw tests and uniaxial compression tests were conducted to obtain the complete stress-strain curves. The results indicate that, after 25, 50, and 75 freeze-thaw cycles, crack propagation accelerates significantly during monotonic loading, and the failure mode becomes increasingly brittle and fragmented. With the increase in freeze-thaw cycles, the axial compressive strength and elastic modulus decrease markedly, whereas the peak strain and ultimate strain increase. Based on the Guo Zhenhai model, regression analysis was performed to modify the ascending parameter a and descending parameter b. A full stress-strain constitutive model for geopolymer concrete with non-sintered aggregates was established. The predicted results agree well with the experimental data, demonstrating the accuracy and applicability of the proposed model.
