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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.

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.

This paper presents a two-layer energy management method designed for the operation of hubs within electrical and thermal smart grids. These energy hubs actively participate in both day-ahead and real time energy markets. Two-layer approach involves coordination at two distinct levels. In the first layer, the focus is on managing sources and storage equipment in collaboration with the hub operator. In the second layer, attention shifts to the interaction between the hub operator and the grid operator. The framework follows a two-stage formulation, where the first stage addresses the day-ahead operation model and the second stage pertains to real-time scheduling. In the first stage, a bi-level optimization strategy is employed. The upper level seeks to minimize the energy cost of smart grids while adhering to optimal power flow constraints, whereas the lower level aims to maximize hubs’ profit in the day-ahead energy market subject to the operational constraints of sources and storage systems represented in an energy hub model. The second stage mirrors this problem structure but uses a smaller time step and adopts the flexibility cost minimization as objective for the upper level. To simplify the bi-level optimization problem into a single-objective model, the Karush–Kuhn–Tucker (KKT) method is applied. Uncertainties of load, price of market, and renewable energy generation are modeled using the unscented transformation technique. Problem-solving is undertaken using a hybrid optimization solver that combines artificial bee colony and honey-bee mating optimization methods. Simulation results highlight the effectiveness of this approach, demonstrating its capability to enhance both economic and technical performance. Specifically, hubs achieve significant profitability and operational flexibility, leading to an 18% improvement in economic performance and a 18-27% enhancement in operational efficiency compared to traditional power flow studies.

Efficient design of proton exchange membrane fuel cells (PEMFCs) requires balancing high electrical output with thermal stability, yet the complex interactions among operating parameters make this a challenging task. Addressing this gap, this study develops an integrated predictive–optimization–decision framework that systematically models PEMFC performance, explores trade-offs, and guides application-specific design choices. The primary innovation lies in combining multi-layer perceptron neural networks (MLPNN) with metaheuristic optimization, particle swarm optimization (PSO), modified particle swarm optimization (MPSO), multi-objective Harris hawks optimization (MOHHO), and multi-objective PSO (MOPSO), followed by decision-making using the additive ratio assessment (ARAS) method. Predictive modeling results demonstrate variable-specific advantages of optimization strategies: PSO-MLPNN yielded superior accuracy for electrical power output prediction (MAPE = 0.233%), while MPSO-MLPNN achieved marginally better accuracy for cell temperature prediction (MAPE = 0.301%). Multi-objective optimization revealed the inherent trade-off between power and temperature, with MOHHO providing broader Pareto fronts and greater diversity than MOPSO. Optimal operating conditions (ST An ≈ 2.0–2.15, ST Ca ≈ 2.1–2.3, RH Ca ≈ 60–66%, T in ≈ 26 °C) enabled peak power outputs near 5300 mW while maintaining stable cell temperatures around 39.5 °C. Finally, ARAS-based decision analysis identified seven design scenarios. The scenario with balanced weights yielded a cell power output of 5205.9 mW, representing an increase of approximately 6.94% compared to the mean cell power of 4867.9 mW in the dataset. The corresponding cell temperature was 39.53 °C, which is about 20.3% lower than the mean cell temperature of 49.61 °C. These results demonstrate the proposed framework’s ability to provide flexible and application-specific design strategies, simultaneously enhancing electrical performance and maintaining thermal stability and safety.

Accurate assessment of intracranial aneurysm rupture risk, particularly in Middle Cerebral Artery (MCA) aneurysms, relies on a detailed understanding of patient-specific hemodynamic behavior. In this study, we present an integrated framework that combines Computational Fluid Dynamics (CFD) with Proper Orthogonal Decomposition (POD) and machine learning (ML) to efficiently model pulsatile blood flow using a Casson non-Newtonian fluid model, without incorporating fluid-structure interaction (FSI). Patient-specific vascular geometries were reconstructed from DICOM imaging data and simulated using ANSYS Fluent to capture key hemodynamic factors, including velocity components, pressure, wall shear stress (WSS), and oscillatory shear index (OSI). POD was applied to reduce the dimensionality of the CFD data while retaining the dominant energetic flow structures. Results showed that fewer than 10 POD modes were sufficient to capture over 99% of the energy for pressure and WSS, while OSI required significantly more modes due to its inherent complexity. Machine learning models were trained on the reduced-order features to predict hemodynamic fields across time snapshots. The hybrid POD-ML approach yielded reasonable predictions for pressure and WSS in both training and test sets, while OSI prediction accuracy decreased in the test region, indicating the need for more advanced modeling strategies. The proposed method significantly reduces computational cost while preserving critical hemodynamic information, making it well-suited for real-time or near-real-time clinical decision support. This work demonstrates the potential of combining data-driven techniques with CFD for efficient, non-invasive risk assessment and treatment planning in cerebral aneurysm management.

The growing demand for high-efficiency, compact heat exchangers in various industrial applications necessitates innovative solutions for thermal performance enhancement. This study aims to numerically investigate the thermal-hydraulic performance of a shell-and-double-helical-coil heat exchanger by integrating annular fins on the shell side. We systematically explore the influence of two critical geometric parameters: the number of annular fins (120, 160, and 200) and the fin outer-to-inner diameter ratio (Do, fin/Di, fin, varied across 1.25, 1.50, and 1.75). A three-dimensional computational fluid dynamics (CFD) model was utilized to rigorously analyze the average convective heat transfer coefficient (h), Nusselt number (Nu), pressure drop (ΔP), and overall thermal performance (η) across a wide range of Reynolds numbers. The results demonstrate that the addition of annular fins significantly augments heat transfer due to enhanced fluid mixing and the generation of secondary flow structures. Specifically, the 200-fin configuration achieved a thermal performance (η) of approximately 2.1 at Re = 2000, representing a 110% improvement over the unfinned case. Furthermore, varying the fin diameter ratio revealed that the 1.75 ratio yielded the highest thermal performance, reaching approximately 2.2 at Re = 2000, a 120% enhancement over the unfinned coil. These findings underscore the critical role of fin geometry in improving the thermo-hydraulic performance of compact heat exchangers.

Fuzzy methods using linguistic expressions and fuzzy numbers can provide a more accurate examination of manufacturing systems where data is not clear. Researchers expanded fuzzy control charts (CCs) using fuzzy linguistic statements and investigated the current process efficiency index to evaluate the performance, precision, and accuracy of the production process in a fuzzy state. Compared to nonfuzzy data mode, fuzzy linguistic statements provided decision makers with more options and a more accurate assessment of the quality of products. The fuzzy index of the actual process efficiency analyzed the process by considering mean, target value, and variance of the process simultaneously. Inspection of household water meters in Ha'il, Saudi Arabia showed the actual process index values were less than 1, indicating unfavorable production conditions. Fuzzy methods enhance the accuracy and effectiveness of statistical quality control in real-world systems where precise information may not be readily available. In addition, to provide a new perspective on the comparison of urban water and sewage systems, the results obtained from fuzzy-CC were compared with various machine learning methods such as artificial neural network and M5 model tree, in order to identify and understand their respective advantages and limitations.

Convective heat transfer and entropy generation in a 3D closed cavity, equipped with adiabatic-driven baffle and filled with CNT (carbon nanotube)-water nanofluid, are numerically investigated for a range of Rayleigh numbers from 103 to 105. This research is conducted for three configurations; fixed baffle (V = 0), rotating baffle clockwise (V+) and rotating baffle counterclockwise (V−) and a range of CNT concentrations from 0 to 15%. Governing equations are formulated using potential vector vorticity formulation in its three-dimensional form, then solved by the finite volume method. The effects of motion direction of the inserted driven baffle and CNT concentration on heat transfer and entropy generation are studied. It was observed that for low Rayleigh numbers, the motion of the driven baffle enhances heat transfer regardless of its direction and the CNT concentration effect is negligible. However, with an increasing Rayleigh number, adding driven baffle increases the heat transfer only when it moves in the direction of the decreasing temperature gradient; elsewhere, convective heat transfer cannot be enhanced due to flow blockage at the corners of the baffle.

Solar photovoltaic (PV) energy production is important in reducing global energy crises since it is transportable, scalable, and highly customizable dependent on the needs of the industry or end-user. In addition, compared to other renewable resources, photovoltaic systems can produce electricity without moving parts and have a long lifespan. Nevertheless, solar photovoltaic (PV) systems provide intermittent output electricity with a nonlinear output voltage. Due to this intermittent availability, PV installations are facing significant challenges. As a result, in PV power systems, a Maximum Power Point Tracker (MPPT), a power extraction mechanism, is required to assure maximum power delivery at any given moment. The main objective of this work is to study the MPPT method of extracting the maximum power from photovoltaic modules under different solar irradiation and temperatures. Several MPPT methods have been developed for photovoltaic systems to achieve MPP, depending on weather conditions and applications, ranging from simple to more complex methods. Among these methods, five techniques have been presented and compared that are P&O perturbation and observation method, INC incremental conductance method, the ANN neural network method, the open circuit voltage based neural network method FVCO, and the neural network method at the base of FCC (short circuit current).

Thermal performance and phase-change dynamics in a channel having a cavity equipped with a heater and phase-change material (PCM)-packed bed (PB) region are analyzed during nanoliquid convection under an inclined magnetic field. Curvature of the upper wall above the PCM zone is also considered by using the finite element method. Impacts of curvature of the upper wall (between 0.01H and 0.6H, H-channel height), strength of magnetic field (MGF) (Hartmann number between 0 and 40), height (between 0.1H and 0.4H) and number (between 5 and 17) of heaters on the thermal performance and phase-change dynamics are studied. In the interior and wall near regions of the PCM-PB, the curvature effects become opposite, while phase completion time (tF) rises by about 42% at the highest radius of the curvature. Imposing MGF and increasing its strength has positive impacts on the phase change and thermal performance. There is a reduction in tF by about 45.2% and 41.8% when MGF is imposed at Ha = 40 for pure fluids and nanofluids. When thermal performance for all different cases is compared, using MGF+nanofluid+PCM provides the most favorable case. When the reference case (only pure fluid without MGF and PCM) is used, including nanoparticles results in an improvement of 33.7%m while it is further increased to 71.1% when PCM-PB is also installed. The most favorable case by using MGF, nanofluid and PCM-PB results in thermal performance improvement of about 373.9% as compared to the reference configuration.