Adekunle Omolade Adelaja

Achieving optimal thermal comfort for individuals using prosthetic devices remains a challenge despite advances in prosthetic technology. Understanding and predicting the thermal behavior inside the prosthetic socket are crucial for improving user comfort and device performance. In this study, the thermal conditions within a transtibial prosthetic socket were analyzed using Computational Fluid Dynamics (CFD) with the ANSYS Fluent software. The simulation accounted for heat transfer by conduction, convection, and metabolic heat generation to estimate temperature distribution across the residual limb tissues. The results showed that the skin temperature near bony regions was lower than that over muscular areas. Under basal (rest) conditions, the region with higher metabolic activity (muscular tissue) exhibited a maximum temperature of 33.5 °C, while areas of lower activity reached 30.2 °C. During walking—a light physical activity—the overall temperature change relative to the basal condition was modest, with the largest difference being less than 2 °C. These findings indicate that local metabolism strongly influences thermal distribution, and CFD provides a reliable non-invasive tool for investigating thermal comfort in lower limb prostheses.

Exploring the dynamics of nonlinear nanofluidic flow-induced vibrations, this work focuses on single-walled branched carbon nanotubes (SWCNTs) operating in a thermal–magnetic environment. Carbon nanotubes (CNTs), renowned for their exceptional strength, conductivity, and flexibility, are modeled using Euler–Bernoulli beam theory alongside Eringen’s nonlocal elasticity to capture nanoscale effects for varying downstream angles. The intricate interactions between nanofluids and SWCNTs are analyzed using the Differential Transform Method (DTM) and validated through ANSYS simulations, where modal analysis reveals the vibrational characteristics of various geometries. To enhance predictive accuracy and system stability, machine learning algorithms, including XGBoost, CATBoost, Random Forest, and Artificial Neural Networks, are employed, offering a robust comparison for optimizing vibrational and thermo-magnetic performance. Key parameters such as nanotube geometry, magnetic flux density, and fluid flow dynamics are identified as critical to minimizing vibrational noise and improving structural stability. These insights advance applications in energy harvesting, biomedical devices like artificial muscles and nanosensors, and nanoscale fluid control systems. Overall, the study demonstrates the significant advantages of integrating machine learning with physics-based simulations for next-generation nanotechnology solutions.

In this study, the numerical analysis of conjugate heat transfer of laminar flow in a functionally graded hollow cylinder (FGHC) made of metal/ceramic for a two‐dimensional fluid and wall conduction subject to Newton boundary condition is considered. The fluid and FGHC energy equations are coupled through the continuity of temperature and heat flux at the inner wall‐fluid interface while the outer surface is subject to convective heat transfer. The continuity, momentum, and energy equations of the fluid are discretized using the finite volume approach. The effects of fluid and functionally graded material parameters, such as volume fraction index, volume composition, time history, wall‐to‐fluid thermal diffusivity ratio, wall‐to‐fluid thermal conductivity ratio, Biot number, Peclet number, and Prandtl number are investigated on the temperature field in the FGHC. The result shows that on account of the inhomogeneity of the material property, the volume fraction index has a significant effect on the other parameters and the temperature variation along the thickness. The lower the volume fraction index, the higher the inner wall (metal side) temperature, and the temperature gradient along the thickness. However, except for the variation in the wall‐to‐fluid thermal conductivity ratio, the lower the volumetric fraction, the lower the outer wall (ceramic side) temperature distribution.