Search Results (1,898)

This study experimentally investigates the boiling heat transfer characteristics of R32 and R410A refrigerants in heat exchangers, systematically analyzing the effects of tube thickness, saturation temperature, latent heat, liquid-phase density, and viscosity. The average boiling heat transfer coefficients (HTCs) of R32 and R410A were compared across varying mass flow rates and saturation temperatures. The results reveal that, independent of tube thickness, the boiling HTC of R32 exhibits a non-monotonic increase followed by a decrease with rising mass flow rate. Additionally, elevated saturation temperatures reduced vaporization latent heat, liquid-phase density, and gas-phase viscosity, while the flow pattern may also change. Meanwhile, R32 demonstrated superior boiling heat transfer performance compared to R410A under equivalent conditions. Furthermore, the correlation is proposed to predict the HTCs, indicating ±10% prediction error. This study provides critical insights for optimizing refrigeration systems and advancing heat exchanger modeling frameworks. Full article

Electromagnetic Railguns Face Severe Ablation and Melting Risks Due to Extremely High Transient Thermal Loads During High-Speed Launching, Directly Impacting Launch Reliability and Service Life. To address this thermal management challenge, this study proposes and validates the effectiveness of spray cooling technology. Leveraging its high heat transfer coefficient, exceptional critical heat flux (CHF) carrying capacity, and strong transient cooling characteristics, it is particularly suitable for the unsteady thermal control during the initial launch phase. An experimental platform was established, and a three-dimensional numerical model was developed to systematically analyze the dynamic influence mechanisms of nozzle inlet pressure, flow rate, spray angle, and spray distance on cooling performance. Experimental results indicate that the system achieves maximum critical heat flux (CHF) and rail temperature drop at an inlet pressure of 0.5 MPa and a spray angle of 0°. Numerical simulations further reveal that a 45° spray cone angle simultaneously achieves the maximum temperature drop and optimal wall temperature uniformity. Key parameter sensitivity analysis demonstrates that while increasing spray distance leads to larger droplet diameters, the minimal droplet velocity decay combined with a significant increase in overall momentum markedly enhances convective heat transfer efficiency. Concurrently, increasing spray distance effectively improves rail surface temperature uniformity by optimizing the spatial distribution of droplet size and velocity. Full article

Self-lubricating coatings are an effective solution for achieving stable and reliable lubrication in mechanical equipment; however, most self-lubricating coatings currently available still have certain shortcomings in terms of lubricity. In this paper, by regulating the argon and nitrogen flow ratio, a CrN-Ag composite self-lubricating coating with excellent lubrication performance was prepared, with a minimum wear rate and friction coefficient of only 2.3 mm³·10⁻⁵/N·m and 0.15, respectively, and a stable performance during long-term service. Furthermore, through systematic characterization of the coating composition, structure, and performance, the laws of the coating’s evolution were revealed based on the argon–nitrogen ratio. The results confirmed that the argon-to-nitrogen ratio had no significant effect on the coating composition and structure, while the addition of Ag dominated the high-temperature oxidation process of the coating and improved its tribological properties. In addition, while increasing the nitrogen flow ratio to a certain extent is beneficial for preparing coatings with high bonding strength and low wear rates and friction coefficients, at the same time, an excessively high nitrogen flow ratio can reduce the density of the coating, increase its hydrophilicity, and deteriorate its corrosion resistance. Full article

This study experimentally investigates the dynamic behavior and energy conversion characteristics of a single contaminated bubble (d_eq = 2.49–3.54 mm, Re_b = 470–830) rising near a vertical wall (S* = 1.41–2.02) in a linear shear flow (the conditions of average flow rate 0.1 m/s and shear rate 0.5 s⁻¹) using a vertical water tunnel and varying sodium dodecyl sulfate (SDS) concentrations (0–50 ppm) and bubble sizes (via needle nozzles). High-speed imaging with orthogonal shadowgraphy captures bubble trajectories, rotation, deformation, and oscillation modes (2, 0) and (2, 2), revealing that an increasing SDS concentration suppresses deformation and the inclination amplitude while enhancing the oscillation frequency, particularly for smaller bubbles. Velocity analysis shows that vertical components remain steady, whereas wall-normal and spanwise fluctuations diminish with surfactant concentration, indicating stabilized trajectories. Additional mass force coefficients are larger for bigger bubbles and decrease with contamination level. Energy analysis demonstrates that surface energy dominates the total energy budget, with vertical kinetic energy comprising over 70% of the total kinetic energy under high SDS concentrations. The results highlight strong scale dependence and Marangoni effects in controlling near-wall bubble motion and energy transfer, providing insights for optimizing gas–liquid two-phase flow processes in chemical and environmental engineering applications. Full article

HTRI xchanger Suite 6.0 software was employed to analyze the thermodynamic performance and thermal resistance distribution of the fuel/lubricating oil heat exchanger A for an aeroengine. Calculated results demonstrated good agreement with experimental results for both heat transfer and flow resistance characteristics. The thermal resistance analysis revealed that the tube-side contribution dominated, accounting for 84.6% of the total resistance. The whole aeroengine test revealed that insufficient tube-side velocity resulted in prolonged fuel filling time, subsequently delaying fuel ignition and affecting aeroengine starting. To address these issues while maintaining lubricating oil cooling requirements, a structural optimization incorporating twisted tape inserts was proposed. It was calculated by HTRI software that when the twist ratio and the thickness of twisted tape inserts was 4 and 0.5 mm, respectively, the optimized fuel/lubricating oil heat exchanger B demonstrated remarkable performance improvements, with an 82.6% reduction in total thermal resistance, a 213% increase in overall heat transfer coefficient, and an 18.0% reduction in total mass. A subsequent whole aeroengine test at the performance evaluation point confirmed that heat exchanger B successfully met all technical requirements of total mass, flow resistance, heat transfer rate, and aeroengine starting, simultaneously. The demonstrated methodology presents significant potential for broader aerospace thermal management applications, such as performance prediction of enhanced heat exchangers, multi-objective optimization of thermal systems, and integrated thermal management solutions. Full article

Addressing the problems of wind power’s anti-peak regulation characteristics, increasing system peak regulation difficulty, and wind power uncertainty causing frequency deviation leading to power imbalance, this paper considers the peak shaving and valley filling function and frequency regulation characteristics of energy storage, establishing a day-ahead and intraday coordinated two-stage optimization scheduling model for research. Stage 1 establishes a deterministic wind power prediction model based on time series Autoregressive Integrated Moving Average (ARIMA), adopts dynamic peak-valley identification method to divide energy storage operation periods, designs energy storage peak regulation working interval and reserves frequency regulation capacity, and establishes a day-ahead 24 h optimization model with minimum cost as the objective to determine the basic output of each power source and the charging and discharging plan of energy storage participating in peak regulation. Stage 2 still takes the minimum cost as the objective, based on the output of each power source determined in Stage 1, adopts Monte Carlo scenario generation and improved scenario reduction technology to model wind power uncertainty. On one hand, it considers how energy storage improves wind power system inertia support to ensure the initial rate of change of frequency meets requirements. On the other hand, considering energy storage reserve capacity responding to frequency deviation, it introduces dynamic power flow theory, where wind, thermal, load, and storage resources share unbalanced power proportionally based on their frequency characteristic coefficients, establishing an intraday real-time scheduling scheme that satisfies the initial rate of change of frequency and steady-state frequency deviation constraints. The study employs improved chaotic mapping and an adaptive weight Particle Swarm Optimization (PSO) algorithm to solve the two-stage optimization model and finally takes the improved IEEE 14-node system as an example to verify the proposed scheme through simulation. Results demonstrate that the proposed method improves the system net load peak-valley difference by 35.9%, controls frequency deviation within ±0.2 Hz range, and reduces generation cost by 7.2%. The proposed optimization scheduling model has high engineering application value. Full article

A polyurethane slurry was developed to simultaneously enhance the strength and permeability of geological formations, differing from the conventional fracture grouting used for soft-soil reinforcement. Injected via splitting grouting, the slurry cures to form high-strength, highly permeable channels that increase reservoir permeability while improving mechanical stability (dual-enhanced stimulation). To quantify its diffusion behavior and guide field application, we built a splitting-grouting model using the finite–discrete element method (FDEM), parameterized with the reservoir properties of coalbed methane (CBM) formations in the Ordos Basin and the slurry’s measured rheology and filtration characteristics. Considering the stratified structures within coal rock formed by geological deposition, this study utilizes Python code interacting with Abaqus to divide the coal seam into coal rock and natural bedding. We analyzed the effects of engineering parameters, geological factors, and bedding characteristics on slurry–vein propagation patterns, the stimulation extent, and fracturing pressure. The findings reveal that increasing the grouting rate from 1.2 to 3.6 m³/min enlarges the stimulated volume and the maximum fracture width and raises the fracturing pressure from 26.28 to 31.44 MPa. A lower slurry viscosity of 100 mPa·s promotes the propagation of slurry veins, making it easier to develop multiple veins. The bedding-to-coal rock strength ratio controls crossing versus layer-parallel growth: at 0.3, veins more readily penetrate bedding planes, whereas at 0.1 they preferentially spread along them. Raising the lateral pressure coefficient from 0.6 to 0.8 increases the likelihood of the slurry expanding along the beddings. Natural bedding structures guide directional flow; a higher bedding density (225 lines per 10,000 m³) yields greater directional deflection and a more intricate fracture network. As the angle of bedding increases from 10° to 60°, the slurry veins are more susceptible to directional changes. Throughout the grouting process, the slurry veins can undergo varying degrees of directional alteration. Under the studied conditions, both fracturing and compaction grouting modes are present, with fracturing grouting dominating in the initial stages, while compaction grouting becomes more prominent later on. These results provide quantitative guidance for designing dual-enhanced stimulation to jointly improve permeability and mechanical stability. Full article

Compared to shallow geothermal systems, coaxial medium-deep and deep borehole heat exchangers (MDBHE and DBHE) offer higher temperatures and heat extraction rates while requiring less surface area, making them attractive options for sustainable heat supply in combination with ground-source heat pumps (GSHP). However, existing simulation tools for such systems are often limited in computational efficiency or open-source availability. To address this gap, we propose a rapid modeling approach using the open-source Python package “pygfunction” (v2.3.0). Its workflow was adjusted to accept the fluid inlet temperature as input. The effective undisturbed ground temperature and ground thermophysical properties were weight-averaged considering stratified ground layers. Validation of the approach was conducted by comparing simulation results with 12 references, including established models and experimental data. The proposed method enables fast estimation of fluid temperatures and heat extraction rates for single boreholes and small-scale bore fields in both homogeneous and heterogeneous geological conditions at depths of 700–3000 m, thus supporting rapid assessments of the coefficient of performance (COP) of GSHP. The approach systematically underestimates fluid outlet temperatures by up to 2–3 °C, resulting in a maximum underestimation of COP of 4%. Under significant groundwater flow or extreme geothermal gradients, these errors may increase to 4 °C and 6%, respectively. Based on the available data, these discrepancies may result in errors in GSHP electric power estimation of approximately ±10%. The method offers practical value for GSHP performance evaluation, geothermal potential mapping, and district heating network planning, supporting geologists, engineers, planners, and decision-makers. Full article

This paper considers the possibility of using Physics-Informed Neural Networks (PINNs) to study the hydrological processes of model river sections. A fully connected neural network is used for the approximation of the Saint-Venant equations in both 1D and 2D formulations. This study addresses the problem of determining the velocities, water level, discharge, and area of water sections in 1D cases, as well as the inverse problem of calculating the roughness coefficient. To evaluate the applicability of PINNs for modeling flows in channels, it seems reasonable to start with cases where exact reference solutions are available. For the 1D case, we examined a rectangular channel with a given length, width, and constant roughness coefficient. An analytical solution is obtained to calculate the discharge and area of the water section. Two-dimensional model examples were also examined. The synthetic data were generated in Delft3D code, which included velocity field and water level, for the purpose of PINN training. The calculation in Delft3D code took about 2 min. The influence of PINN hyperparameters on the prediction quality was studied. Finally, the absolute error value was assessed. The prediction error of the roughness coefficient n value in the 2D case for the inverse problem did not exceed 10%. A typical training process took from 2.5 to 3.5 h and the prediction process took 5–10 s using developed PINN models on a server with Nvidia A100 40GB GPU. Full article

This paper presents a study aiming to provide insight into the complex flow and heat transfer processes in the exhaust manifold of a four-stroke, compression ignition engine. An experimental system has been constructed capable of capturing temperature and heat flux high-frequency signals as they develop in the exhaust pipe wall during the engine cycle, under its steady-state operation. The values of the Heat Transfer Coefficient obtained by applying the classic convection relations have been correlated in the form of a Nusselt–Reynolds number relationship for local and spatially averaged steady-state heat transfer and compared with available experimental data obtained at the same position of the exhaust manifold. It has been shown that the use of conventional steady-state heat transfer relationships for fully developed steady-state turbulent flow in pipes underpredicts heat transfer rates when compared with those experimentally observed. Periodic flow of high frequency and geometrical effects at the exhaust entrance are expected to affect the validity of the application of the classic steady-state correlations for the exhaust manifold. To overcome this problem it is developed and presented a new correlation for the time-averaged heat transfer rates. To verify the heat transfer mechanism, the thermal field of the whole engine cylinder head, including the intake and exhaust manifolds, was analyzed using FEA (Finite Element Analysis), and the results are compared and verified with available experimental data. Full article

The Centre for Affordable Water and Sanitation Technology (CAWST) (2012) recommends size standards for the effective size (ES) and uniformity coefficient (UC) of filtration media in biosand filters (BSFs) to ensure optimal effluent flow rates and contaminant removal. The recommended, laboratory-determined ES and UC ranges are also advised for field use despite differences in mass- versus volume-based protocols and no studies comparing laboratory versus field protocols to date. ES and UC values of five sand samples were compared using mass- versus volume-based measurements and lab versus field protocols. Results suggest that the use of mass- or volume-based measurements generally does not affect the ES or UC of a given method (except in cases where the density of sand grains varies significantly across the size distribution range). Differences between laboratory and field protocols, however, were found to affect ES and UC values by up to 24%. Overall, differences in laboratory and field sand size determination protocols should be further evaluated to ensure standardized field construction of BSFs and to limit potential impacts to filter efficacy and sustained, household filter usage. Full article

This study presents an integrated experimental and computational investigation into the thermal and hydraulic performance of three oxide-based nanofluids: aluminum oxide (Al₂O₃), titanium dioxide (TiO₂), and copper oxide (CuO) for advanced engine cooling applications. A custom-built test rig was used to assess nanofluid behavior under varying flow rates, nanoparticle volume fractions, and temperature gradients, replicating realistic engine conditions. According to the results, at ideal concentrations, CuO nanofluids continuously demonstrate better heat transfer properties, outperforming TiO₂ by up to 15% and AlO₃ by 7%. However, performance plateaus beyond 1.5% volume fraction due to increased viscosity and pressure drop. A multilayer feedforward artificial neural network (ANN) model was developed to predict convective heat transfer coefficients and friction factors based on experimental inputs, achieving a mean absolute percentage error below 5% and a coefficient of determination (R²) exceeding 0.98. The ANN demonstrated robust generalization across varying operating conditions and nanoparticle types, confirming its utility for surrogate modeling and optimization. This work is distinguished by its dual focus on thermal efficiency and hydraulic stability, as well as its use of data-driven modeling validated by empirical results. The findings provide actionable insights for thermal management system design in internal combustion, hybrid, and electric vehicles, where efficient, compact, and reliable cooling solutions are increasingly vital. The study advances the practical application of nanofluids by offering a comparative, ANN-validated framework that bridges the gap between lab-scale performance and real-world automotive cooling demands. Full article

The operation of reservoirs has prompted rivers to transition from natural ecosystems to “natural–artificial” composite ecosystems, which has not only altered the water–sediment processes but has also affected river ecology in the downstream river channels. To reveal the impact of the Xiaolangdi Reservoir (China) on sediment transport and aquatic organisms in the Lower Yellow River (LYR), this article analyzes the changes in the water–sediment processes and sediment transport characteristics prior to and following the reservoir construction, based on measured water–sediment data of 688 floods from 1960 to 2023. It derives a theoretical formulation for the sediment delivery ratio (SDR) of flood events based on the sediment transport rate equation and evaluates the living environment of aquatic organisms in the LYR. The results indicate that after the construction of Xiaolangdi Reservoir, the frequency of floods with an average flow discharge below 1000 m³/s increased from 26.08% to 37.42%, and the frequency of floods with an average sediment concentration below 20 kg/m³ increased from 46.34% to 89.03%. The SDR of flood events significantly correlates positively with the average flow discharge and the water load variation coefficient. Conversely, it negatively correlates with the average sediment concentration and the incoming sediment coefficient. The sediment transport capacity of various river reaches in the LYR gradually increases along the direction of the river channel. The use of Xiaolangdi Reservoir has enhanced sediment transport in the upper LYR reach while decreasing it in the lower reach, aligning the overall sediment transport capacity of the downstream river channel. Additionally, the water–sediment process of the flood events following the completion of the Xiaolangdi Reservoir construction has improved the living environment for aquatic organisms, which is conducive to restoring biodiversity and improving the ecological environment of the river. The research results have enriched the understanding of the impact of reservoir construction on downstream water–sediment transport and aquatic organisms in sandy rivers, providing technical support for the health and sustainable development of rivers. Full article

This study presents the development and implementation of an AI-driven control system for dynamic regulation of hydrogen blending in natural gas networks. Leveraging supervised machine learning techniques, a Random Forest Classifier was trained to accurately identify the origin of gas blends based on compositional fingerprints, achieving rapid inference suitable for real-time applications. Concurrently, a Random Forest Regression model was developed to estimate the optimal hydrogen flow rate required to meet a user-defined higher calorific value target, demonstrating exceptional predictive accuracy with a mean absolute error of 0.0091 Nm³ and a coefficient of determination (R²) of 0.9992 on test data. The integrated system, deployed via a Streamlit-based graphical interface, provides continuous real-time adjustments of gas composition, alongside detailed physicochemical property estimation and emission metrics. Validation through comparative analysis of predicted versus actual hydrogen flow rates confirms the robustness and generalizability of the approach under both simulated and operational conditions. The proposed framework enhances operational transparency and economic efficiency by enabling adaptive blending control and automatic source identification, thereby facilitating optimized fuel quality management and compliance with industrial standards. This work contributes to advancing smart combustion technologies and supports the sustainable integration of renewable hydrogen in existing gas infrastructures. Full article

This study developed an optimized cooling die configuration to improve the fibrous structure and texture of protein extrudates. Six designs, combining three cross-sectional shapes and two flow channel layouts, were evaluated through numerical simulations based on the physical properties of pea protein isolate (PPI) and extrusion parameters. The results show that PPI exhibits pronounced shear-thinning behavior, with viscosity decreasing by more than 85% as the temperature increases from 35 °C to 135 °C. Among all designs, the rectangular outlet with a serpentine cooling channel performed best, showing a center-to-wall temperature difference of 12.4 °C compared with 7.8 °C for the circular die, a 35% higher heat transfer coefficient, a wall-to-center viscosity ratio of 7.4 compared with 4.9 for the square die and 3.7 for the circular die, and a maximum wall shear rate of 3.42 s⁻¹ compared with 2.15 s⁻¹ for the circular die. The rectangular outlet increases the center-to-wall temperature gradient, while the serpentine channel extends the flow path to raise shear and velocity gradients, together promoting fiber alignment and improving the structure of plant-based meat. These findings provide a theoretical foundation for cooling die optimization and offer a practical approach to control fiber formation in plant-based meat. Full article