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This paper presents an innovative IoT-enabled solution for the real-time digitization of traditional chart recorders using a Raspberry Pi and the MPU6050 accelerometer. The proposed system harnesses modern IoT communication protocols to enable accurate pressure monitoring, remote data access, and real-time analysis, addressing the limitations of conventional paper-based systems. A key contribution of this work is the development of the first mathematical model for translating mechanical needle displacement in chart recorders into electrical signals, offering a robust theoretical foundation for precise signal conversion. Experimental results validate the system’s ability to accurately capture rapid pressure changes, demonstrating its suitability for demanding industrial applications, particularly in the oil and gas sector. The system’s performance was evaluated in various scenarios, showcasing its resilience to environmental noise, effective real-time data transmission (with latency as low as 130 ms), and significant noise reduction (up to 95%) through advanced filtering techniques. Furthermore, the system demonstrated a high level of accuracy in pressure measurements, with a maximum error of just 0.3 KPSI after filtering, confirming its reliability for precision monitoring. In addition to its technical capabilities, the proposed system supports paperless operation, significantly reducing operational costs and enhancing environmental sustainability. By eliminating the need for consumables such as paper and ink, the system offers a cost-effective and scalable solution. These results underscore the transformative potential of the system in modernizing industrial pressure monitoring, offering a scalable, precise, and environmentally sustainable alternative to traditional chart recorders. This work also lays the groundwork for future advancements in IoT-based sensing, predictive maintenance, and automation technologies in industrial settings.

Additive manufacturing has been prominent for making complicated polymeric structures, with PLA being preferred for its biodegradability, ease of use, and wide 3D printing compatibility. The present study aims to explore the effects of graphene-integrated adhesive on shear properties, failure modes, and vibrational response of adhesively joined bonded joints prepared with 3D printed short glass fibre-reinforced polylactic acid (PLA) adherents. Field emission scanning electron microscopy (FESEM) was used to analyse fracture surfaces, while artificial neural networks (ANN) predicted failure modes using a backpropagation algorithm. Tensile testing of bulk samples indicated that samples printed with 0º raster orientation have higher tensile strength (30.7 MPa) than samples printed with 45º (26.7 MPa) and 90º (23.4 MPa) raster orientations. Shear test results demonstrate that incorporating 1.0 wt.% of graphene into the adhesive enhances the adhesive joint’s shear properties, leading to a 19.51% increase in shear strength compared to neat samples. The free vibrational analysis avowed that the addition of graphene up to 1.0 wt.% increases natural frequencies due to improved stiffness from its well-dispersed state within the epoxy matrix. Furthermore, the failure load was accurately predicted using an artificial neural network trained on data from stress–strain curves. The R 2 value of 0.9861 indicates that the results are reliable and show a good correlation. Thereby, this study demonstrated how graphene-integrated epoxy adhesives enhance the mechanical and vibrational properties of adhesively bonded lap joints prepared with 3D printed short glass fibre-reinforced PLA adherents, while also using artificial neural networks to predict failure modes, providing a novel approach to optimise the performance of adhesively joined 3D printed components.

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.

This study introduces a refined techno-economic, demand-oriented methodology to assessment and optimization of solar water pumping systems in line with the needs of sustainable agriculture in arid, water-limited regions. For Baghdad, Iraq, winter wheat, we balanced technical performance versus economic viability. System performance for three fixed-tilt angle strategies was analyzed using PVsyst software: summer-tuned (17°), winter-tuned (47°), and annual-tuned (28°). The analysis revealed the most efficient to be the summer-optimized tilt angle of 17°. This approach not only boasts the highest high-performance ratio but also yields the highest average volume of pumped water (298.8 m³/day) precisely when it is most critical: during the plant’s crucial growth stage. Moreover, the 17° tilt angle has the highest overall system and pump performance coupled with the lowest percentage of missing water. The approach is also the most cost-effective, with the lowest water cost at 0.4 $/m³. These findings unequivocally prove that a peak-demand-optimized, demand-based design is technically superior and less expensive than conventional annually optimized solutions. This research offers an adaptable and reliable framework for the formulation and implementation of effective solar water pumping systems; hence, it greatly enhances food and water security in desert areas.

Practical applications such as solar power energy systems, electronic cooling, and the convective drying of vented enclosures require continuous developments to enhance fluid and heat flow. Numerous studies have investigated the enhancement of heat transfer in L-formed vented cavities by inserting heat-generating components, filling the cavity with nanofluids, providing an inner rotating cylinder and a phase-change packed system, etc. Contemporary work has examined the thermal performance of L-shaped porous vented enclosures, which can be augmented by using metal foam, using nanofluids as a saturated fluid, and increasing the wall surface area by corrugating the cavity’s heating wall. These features are not discussed in published articles, and their exploration can be considered a novelty point in this work. In this study, a vented cavity was occupied by a copper metal foam with PPI=10 and saturated with a copper–water nanofluid. The cavity walls were well insulated except for the left wall, which was kept at a hot isothermal temperature and was either non-corrugated or corrugated with rectangular waves. The Darcy–Brinkman–Forchheimer model and local thermal non-equilibrium models were adopted in momentum and energy-governing equations and solved numerically by utilizing commercial software. The influences of various effective parameters, including the Reynolds number (20≤Re≤1000), the nanoparticle volume fraction (0%≤φ≤20%), the inflow and outflow vent aspect ratios (0.1≤D/H≤0.4), the rectangular wave corrugation number (N=5 and N=10), and the corrugation dimension ratio (CR=1 and CR=0.5) were determined. The results indicate that the flow field and heat transfer were affected mainly by variations in Re, D/H, and φ for a non-corrugated left wall; they were additionally influenced by N and CR when the wall was corrugated. The fluid- and solid-phase temperatures of the metal foam increased with an increase in Re and D/H. The fluid-phase Nusselt number near the hot left sidewall increased with an increase in φ by 25–60%, while the solid-phase Nusselt number decreased by 10–30%, and these numbers rose by around 3.5 times when the Reynolds number increased from 20 to 1000. For the corrugated hot wall, the Nusselt numbers of the two metal foam phases increased with an increase in Re and decreased with an increase in D/H, CR, or N by 10%, 19%, and 37%. The original aspect of this study is its use of a thermal, non-equilibrium, nanofluid-saturated metal foam in a corrugated L-shaped vented cavity. We aimed to investigate the thermal performance of this system in order to reinforce the viability of applying this material in thermal engineering systems.

PCMs store thermal energy during phase transitions without temperature changes, making them valuable for various thermal applications. When direct PCM use isn't practical, researchers have developed encapsulation methods as an alternative approach. Computational models can simulate various aspects including temperature patterns, species movement, fluid behavior, phase change regions, transport coefficients, energy utilization, and thermal performance metrics. This study explores the thermodynamic and flow characteristics of double‐diffusive convection in systems where nano‐encapsulated phase change materials are suspended in elliptical tube configurations, with additional consideration of exothermic chemical reactions. The investigation considers parameters including Rayleigh values (103–105), Lewis number (0.1–10), Hartmann number (0–50), buoyancy proportions (1–5), NEPCM densities (0.01–0.035), relative melting points (0.1–0.9), Stefan number (0.1–0.9), magnetic field alignments (0°–90°), and Frank‐Kamenetskii number (0–2.5). Analysis shows that NEPCM concentration and magnetic field properties significantly affect both thermal‐hydraulic efficiency and entropy development. The complex relationships between parameters (Ra, FK, Le, Nz, ϕ, Ha) reveal their significant roles in determining heat transfer effectiveness and irreversibility formation.

This study investigates double‐diffusive transport and entropy generation in a wavy cylindrical enclosure containing Cu─H2O Casson nanofluid under magnetic field and thermal radiation effects. The governing equations were solved numerically using the finite element method with Galerkin formulation. The investigation covered parametric ranges including Rayleigh number (10³ ≤ Ra ≤ 10⁶), Hartmann number (0 ≤ Ha ≤ 40), magnetic field inclination (0° ≤ γ ≤ 90°), nanoparticle volume fraction (0 ≤ φ ≤ 0.15), Casson parameter (0.1 ≤ η ≤ 1), radiation parameter (0 ≤ Rd ≤ 4), thermal conductivity parameter (0 ≤ λ ≤ 4), Lewis number (0.5 ≤ Le ≤ 5), and buoyancy ratio (0.25 ≤ Nz ≤ 1.5). Results demonstrated that increasing Ra from 10³ to 10⁶ enhanced heat transfer by 60%, while increasing Ha to 40 reduced fluid circulation by 75%. The Casson parameter significantly influenced flow characteristics, with stream function values increasing by 75% as η approached Newtonian behavior. Thermal radiation parameters jointly moderated temperature gradients, with Rd causing a 15%–20% reduction in thermal stratification. The Lewis number and buoyancy ratio showed strong coupled effects, with the Sherwood number increasing by 150% as Le increased from 0.5 to 5. These findings have practical applications in advanced heat exchanger design, thermal energy storage systems, electronic cooling technologies, and biomedical devices, where controlled heat and mass transfer of non‐Newtonian fluids is crucial.

The thermal management of cylindrical battery packs, widely used in electric vehicles and energy storage systems, is a critical aspect of ensuring their safety, performance, and longevity. As energy densities increase, effective cooling solutions become essential to address the challenges posed by excessive heat generation and uneven temperature distribution. This review has highlighted the promising potential of hybrid nanofluids and phase change materials (PCMs) in advancing thermal management systems for battery packs. Hybrid nanofluids, offering enhanced heat transfer properties, and PCMs, capable of storing and dissipating latent heat, represent a promising synergy for improving thermal management systems. This review provides a comprehensive analysis of the role of hybrid nanofluids and PCM in addressing the thermal challenges of cylindrical battery packs. The paper discusses heat generation mechanisms, the drawbacks of existing cooling methods, and the advantages of integrating these advanced materials into thermal management systems. By identifying research gaps and opportunities, this review offers a pathway for optimizing battery performance and highlights future research directions necessary for scalable and sustainable solutions. According to this review, future research should concentrate on creating hybrid cooling systems that effectively combine active, passive, and hybrid cooling techniques. Additional advancements in computer modeling, nanotechnology, and material science will be crucial to achieving the full potential of these innovative materials and overcoming existing limitations.

This study numerically investigates thermal transport and fluid dynamics in a triangular cavity filled with a MgO–Ag–H2O hybrid nanofluid containing an undulating porous fin under electromagnetic field and thermal radiation influences. The governing equations are solved numerically using the Galerkin finite element methodology with Darcy–Forchheimer formulation for porous media representation. A comprehensive parametric study examines the effects of Rayleigh number (Ra, 10³–10⁶), Darcy number (Da, 10⁻⁵–10⁻²), Hartmann number (Ha, 0–80), magnetic field orientation angle (γ, 0°–90°), nanoparticle concentration (φ, 0.005–0.02), heat generation coefficient (λ, 1–5), fin waviness parameter (nw, 0–6), and radiation intensity factor (Rd, 1–5). The numerical model is validated against established benchmark solutions, demonstrating excellent agreement. Findings demonstrate that increasing Ra substantially improves thermal transport and flow intensity, with the average Nusselt number rising by up to 65% and maximum velocity magnitudes increasing by over 500 times. Electromagnetic field application inhibits thermal transport, with (Nuav) decreasing by 55.6% as Ha increases from 0 to 80. Magnetic field angle optimization shows that γ = 60° provides better heat transfer than γ = 0° at high Ha values. Nanoparticle addition provides moderate thermal enhancement, with an 11.1% increase in Nuav as φ increases from 0.005 to 0.02, particularly in low‐Ra regimes. Radiation effects become most significant at elevated Ra values, with (Nuav) nearly tripling as Rd increases from 1 to 5 at Ra = 10⁶. Entropy generation analysis reveals that the Bejan number decreases by 98.7% as Ra increases, indicating fluid friction dominance at higher Ra values. These results offer essential guidance for optimizing thermal management systems involving porous structures, nanofluids, and electromagnetic fields.

The properties of a semi‐closed combined cycle power system make it a better option for this study than an open system, since it turns an open‐cycle gas turbine into a pollutant‐free power system. Also in the selected cycle, the exhaust is channeled toward a divider rather than being released into the atmosphere, and the exhaust is divided into a separation duct and a return duct by the divider. Part of the exhaust is directed back toward the compressor via the return duct. This study investigates the effect of thermodynamic parameters analysis (turbine inlet temperature, ambient air temperature, pressure ratio, and regenerator effectiveness) on thermal efficiency and specific fuel consumption (S.F.C.) for a semi‐closed system. The properties of a semi‐closed combined cycle power system make it a better option for this study than an open system, since they turn an open‐cycle gas turbine into a pollutant‐free power system. Also in the selected cycle, the exhaust is channeled toward a divider rather than being released into the atmosphere. The exhaust is divided into a separation duct and a return duct by the divider. Part of the exhaust is directed back toward the compressor via the return duct. This study investigates the effect of thermodynamic parameters analysis (turbine inlet temperature, ambient air temperature, pressure ratio, and regenerator effectiveness) on thermal efficiency and S.F.C. for a semi‐closed gas turbine cycle. The operating conditions are taken into account when determining the analytical formulas for assessing thermal efficiency and S.F.C., which are calculated by using thermodynamic equations. The model is constructed using MATLAB®. The results show that the thermal efficiency is increased due to increased turbine inlet temperature, increased regenerator effectiveness, and decreased ambient air temperature. Conversely, S.F.C. decreases. It was also found that when the pressure ratio was roughly 2, the thermal efficiency rose, while the S.F.C. started to decrease. After this value, the thermal efficiency began to decline gradually, and the S.F.C. increased. Also, as the regenerator's effectiveness increased to roughly 0.95, the data indicate that the thermal efficiency achieved its maximum value of 0.60. and at a turbine inlet temperature of about 1600 K, while the S.F.C recorded a minimum value of 0.1394.