Efficient mixing under laminar flow conditions remains a critical challenge in microfluidic systems because molecular diffusion alone is generally insufficient to achieve rapid and homogeneous species transport. In this study, the influence of obstacle orientation on mixing performance in passive micromixers was systematically investigated through numerical simulations. Inclined straight obstacles with orientation angles of 15°, 30°, 45°, and 60° were incorporated into microchannels under both leaky and leak-free configurations. Flow and concentration fields were solved using COMSOL Multiphysics, and the resulting mixing efficiencies and times were quantitatively evaluated. It was found that the introduction of inclined obstacles substantially enhanced mixing performance relative to a simple unobstructed microchannel. Superior mixing behavior was consistently achieved in the leak-free configuration, where stronger flow perturbations and more pronounced recirculation zones were generated within the central mixing region. For the leak-free configuration, mixing efficiency was observed to increase with decreasing obstacle angle. In contrast, no monotonic relationship between obstacle angle and mixing performance was identified for the leaky configuration. Among all investigated designs, the 15° obstacle configuration exhibited the highest overall performance, achieving mixing efficiencies of approximately 92% and nearly 100% in the leaky and leak-free configurations, respectively. To further evaluate the influence of geometric scale, the microchannel length was doubled for the 45° configuration. Enhanced concentration uniformity and reduced mixing time were achieved in the extended leaky microchannel, whereas no improvements were observed in the corresponding leak-free design. These findings demonstrate that obstacle orientation and channel configuration exert a strong influence on microscale transport phenomena and mixing enhancement. The proposed obstacle-based passive micromixer design provides an effective and energy-efficient strategy for improving mixing performance in microfluidic devices and offers valuable design guidelines for applications in biomedical analysis, chemical processing, and lab-on-a-chip systems.

Photovoltaic technology has become one of the most promising approaches for sustainable electricity generation; however, its performance is strongly influenced by environmental conditions, including dust accumulation and operating temperature. In this study, the combined effects of dust deposition and panel tilt angle on the thermal behavior and electrical performance of photovoltaic modules were experimentally investigated under controlled artificial illumination generated by light bulbs. Five irradiance levels (100, 200, 300, 400, and 500 W/m²) were employed to simulate different operating conditions, while various dust loading levels and panel tilt angles were systematically evaluated. The results demonstrated that the tilt angle significantly affected photovoltaic performance. As the dust loading increased, the panel's temperature rose. Although electrical power generation was successfully achieved through the bulbs, the output remained substantially low. Dust deposition reduced the amount of incident solar radiation reaching the photovoltaic modules, thereby decreasing electrical power generation and energy conversion efficiency. These findings provide valuable experimental evidence for optimizing photovoltaic system installation and maintenance in dusty environments and contribute to the development of more efficient and sustainable photovoltaic energy systems.

This paper presented a computational investigation of micromagnetorotation (MMR) and thermal transport characteristics in steady two-dimensional Williamson nanofluid flow over a stretching sheet. The mathematical model incorporated the non-Newtonian behavior of the Williamson fluid together with micromagnetorotational effects, magnetic field influence, viscous dissipation, and heat transfer mechanisms. By employing appropriate similarity transformations, the governing partial differential equations were reduced to a system of coupled nonlinear ordinary differential equations (ODEs). The resulting boundary-value problem was solved numerically using a shooting technique combined with the Runge–Kutta method. The effects of key physical parameters—including the Williamson parameter, magnetic parameter, MMR parameter, micropolar parameter, Prandtl number, and Eckert number—on the velocity, microrotation, and temperature distributions were examined in detail. The numerical results revealed that increasing the Williamson parameter suppressed the fluid velocity and enhanced non-Newtonian resistance within the boundary layer. Higher magnetic field strength reduced the momentum boundary-layer thickness due to the Lorentz force, while MMR significantly altered the rotational dynamics of fluid microelements. Furthermore, thermal transport was enhanced by viscous dissipation, leading to higher temperature distributions and reduced heat transfer rates at the surface. Variations in the skin-friction coefficient and local Nusselt number were reported. The current findings provided useful insights into the design and optimization of thermal systems involving non-Newtonian nanofluids subjected to micromagnetic rotational effects.

The performance of cold thermal energy storage systems is often limited by the low thermal conductivity of phase change materials (PCMs), which delays solidification and reduces charging efficiency. In the present study, the synergistic effects of hybrid nanofluids, porous metal foam, and thermal radiation on the solidification behavior of a PCM-based cold thermal energy storage unit incorporating elliptical and triangular cooling boundaries were numerically investigated. A transient numerical model was developed using the Galerkin finite element method and was coupled with an implicit time-integration scheme and adaptive mesh refinement to accurately resolve temperature evolution and the moving solid–liquid interface during the freezing process. The numerical framework was validated against benchmark results available in the literature, and excellent agreement was achieved. It was found that the addition of hybrid nanoparticles reduced the total solidification time by approximately 5.19%. When thermal radiation was incorporated, the freezing duration was further shortened by nearly 34.35%. The most pronounced enhancement was obtained through the incorporation of porous metal foam, for which the solidification time was reduced by approximately 75.25% as a result of the substantial augmentation of conductive heat transfer pathways within the PCM. Under the combined application of all enhancement mechanisms, the total freezing time was reduced by up to 84.59% relative to the baseline configuration. These findings demonstrate that the integration of porous structures, radiative cooling, and hybrid nanofluids represents an effective strategy for overcoming the thermal limitations of conventional PCM-based cold thermal energy storage systems and provides valuable design guidance for the development of high-efficiency thermal energy storage technologies in industrial cooling and sustainable energy systems.

Mobile photovoltaic (PV) power systems provide a flexible electricity supply for remote locations, emergency operations, and other off-grid applications. Their practical operation requires continuous assessment of power conversion performance under changing environmental conditions. This study investigates the efficiency, power output, and operational reliability of a mobile PV system through an Internet of Things (IoT)-enabled monitoring platform and a multilayer perceptron (MLP) model. Solar irradiance, panel temperature, voltage, current, power, and battery state of charge (SOC) were recorded under outdoor operating conditions, yielding approximately 1,600 observations. An MLP with two hidden layers was trained using the Levenberg–Marquardt algorithm, and the data were divided into training and testing subsets at a ratio of 80:20. Operational reliability was evaluated by comparing measured and predicted power outputs against statistically defined control limits. The PV panel achieved an average operating efficiency of approximately 15%, whereas the efficiency of the solar charge controller (SCC) reached 60%. For the normalized dataset, the MLP produced mean squared error (MSE) values of 0.002402 and 0.001951, root mean squared error (RMSE) values of 0.049012 and 0.044173, mean absolute error (MAE) values of 0.033774 and 0.027760, and $R^2$ values of 0.964491 and 0.970248 for PV and controller power, respectively. The predicted outputs remained within the established control limits throughout the observation period. These findings indicate that the proposed framework can support real-time power-performance assessment and the early identification of abnormal operating conditions in mobile PV systems.