Search Results (27,933)

To improve energy savings and emission reduction in industrial heating furnaces, this study investigated the impact of various molar fractions of hydrogen on natural gas combustion and compared the results of the Non-Premixed Combustion Model with the Eddy Dissipation Combustion Model. Initially, natural gas combustion in an industrial heating furnace was investigated experimentally, and these results were used as boundary conditions for CFD simulations. The diffusion flame and combustion characteristics of natural gas were simulated using both the non-premixed combustion model and the Eddy Dissipation Combustion Model. The results indicated that the Non-Premixed Combustion Model provided simulations more consistent with experimental data, within acceptable error margins, thus validating the accuracy of the numerical simulations. Additionally, to analyze the impact of hydrogen doping on the performance of an industrial gas heater, four gas mixtures with varying hydrogen contents (15% H₂, 30% H₂, 45% H₂, and 60% H₂) were studied while maintaining constant fuel inlet temperature and flow rate. The results demonstrate that the Non-Premixed Combustion Model more accurately simulates complex flue gas flow and chemical reactions during combustion. Moreover, hydrogen-doped natural gas significantly reduces CO and CO₂ emissions compared to pure natural gas combustion. Specifically, at 60% hydrogen content, CO and CO₂ levels decrease by 70% and 37.5%, respectively, while NO emissions increase proportionally; at this hydrogen content, NO concentration in the furnace chamber rises by 155%. Full article

Directed Energy Deposition (DED) processes necessitate a consistent material flow to the melt pool, typically achieved through pneumatic conveying of metal powder via thin pipes. This study aims to record and analyze the multiphase fluid–solid flow. An experimental setup utilizing a high-speed camera and specialized optics was constructed, and the flow through thin transparent pipes was recorded. The resulting information was analyzed and compared with coupled Computational Fluid Dynamics-Discrete Element Modeling (CFD-DEM) simulations, with special attention to the solids flow fluctuations. The proposed methodology shows a significant improvement in accuracy and reliability over existing approaches, particularly in capturing flow rate fluctuations and particle velocity distributions in small-scale systems. Moreover, it allows for accurately analyzing Particle Size Distribution (PSD) in the same setup. This paper details the experimental design, video analysis using particle tracking, and a novel method for deriving volumetric concentrations and flow rate from flat images. The findings confirm the accuracy of the CFD-DEM simulations and provide insights into the dynamics of pneumatic conveying and individual particle movement, with the potential to improve DED efficiency by reducing variability in material deposition rates. Full article

Background/Objectives: The prevalence of metabolic syndrome (MetS) is increasing worldwide, and an increasing number of cases are diagnosed in younger age groups. This study aimed to propose predictive models based on demographic, anthropometric, and non-invasive clinical variables to predict MetS in adolescents. Methods: A total of 2064 adolescents aged 18–19 from São Luís-Maranhão, Brazil were enrolled. Demographic, anthropometric, and clinical variables were considered, and three criteria for diagnosing MetS were employed: Cook et al., De Ferranti et al. and the International Diabetes Federation (IDF). A feed-forward artificial neural network (ANN) was trained to predict MetS. Accuracy, sensitivity, and specificity were calculated to assess the ANN’s performance. The ROC curve was constructed, and the area under the curve was analyzed to assess the discriminatory power of the networks. Results: The prevalence of MetS in adolescents ranged from 5.7% to 12.3%. The ANN that used the Cook et al. criterion performed best in predicting MetS. ANN 5, which included age, sex, waist circumference, weight, and systolic and diastolic blood pressure, showed the best performance and discriminatory power (sensitivity, 89.8%; accuracy, 86.8%). ANN 3 considered the same variables, except for weight, and exhibited good sensitivity (89.0%) and accuracy (87.0%). Conclusions: Using non-invasive measures allows for predicting MetS in adolescents, thereby guiding the flow of care in primary healthcare and optimizing the management of public resources. Full article

This study presents an improved data-centric approach to short-term water demand forecasting using univariate time series from water reservoir levels. The dataset comprises water level recordings from 21 reservoirs in Eastern Thessaloniki collected over 15 months via a SCADA system provided by the water company EYATH S.A. The methodology involves data preprocessing, anomaly detection, data imputation, and the application of predictive models. Techniques such as the Interquartile Range method and moving standard deviation are employed to identify and handle anomalies. Missing values are imputed using LSTM networks optimized through the Optuna framework. This study emphasizes a data-centric approach in deep learning, focusing on improving data quality before model application, which has proven to enhance prediction accuracy. This strategy is crucial, especially in regions where reservoirs are the primary water source, and demand distribution cannot be solely determined by flow meter readings. LSTM, Random Forest Regressor, ARIMA, and SARIMA models are utilized to extract and analyze water level trends, enabling more accurate future water demand predictions. Results indicate that combining deep learning techniques with traditional statistical models significantly improves the accuracy and reliability of water demand predictions, providing a robust framework for optimizing water resource management. Full article

An air-rock bed thermal storage system was designed for small-scale powered generation and analyzed with computational fluid dynamics (CFD) using ANSYS-Fluent simulation. An experimental system was constructed to compare and validate the simulation model results. The storage unit is a cylindrical steel container with granite rock pebbles as a storage medium. The CFD simulation used a porous flow model. Transient-state simulations were performed on a 2D axisymmetric model using a pressure-based solver. During charging, heat input that keeps the bottom temperature at 550 °C was applied to raise the storage temperature. Performance analysis was conducted under various porosities, considering natural and forced convection. The natural convection analysis showed insignificant convection contribution after 10 h of charging, as observed in both average air velocity and the temperature profile plots. The temperature distribution profiles at various positions for both convection modes showed good agreement between the simulation and experimental results. Additionally, both cases exhibited similar temperature growth trends, further validating the models. Forced convection reduced the charging time from 60 h to 5 h to store 70 MJ of energy at a porosity of 0.4, compared to natural convection, which stored only 50 MJ in the same time. This extended charging period was attributed to poor natural convective heat transfer, indicating that relying solely on natural convection for thermal energy storage under the given conditions is not practical. Using a small fan to enhance heat transfer, forced convection is a more practical method for charging the system, making it suitable for power generation applications. Full article

It has been more than five decades since optimum power flow (OPF) emerged as one of the most famous and frequently used nonlinear optimization problems in power systems. Despite its long-standing existence, the OPF problem continues to be widely researched due to its critical role in electrical network planning and operations. The general formulation of OPF is complex, representing a large-scale optimization model with nonlinear and nonconvex characteristics, incorporating both discrete and continuous control variables. The inclusion of control factors such as transformer taps and shunt capacitors, and the integration of renewable energy sources like wind power further complicates the system’s design and solution. To address these challenges, a variety of classical, evolutionary, and improved optimization techniques have been developed. These techniques not only provide new solution pathways but also enhance the quality of existing solutions, contributing to reductions in computational cost and operational efficiency. Multi-objective approaches are frequently employed in modern OPF problems to balance trade-offs between competing objectives like cost minimization, loss reduction, and environmental impact. This article presents an in-depth review of various OPF problems and the wide array of algorithms, both traditional and evolutionary, applied to solve these problems, paying special attention to wind power integration and multi-objective optimization strategies. Full article

This study investigates the dynamics of mass transfer between gas and liquid during the fuel scrubbing inerting process, utilizing a mixed inert gas (MIG) composed of CO₂, N₂, and trace amounts of O₂. The goal is to lower oxygen concentrations in aircraft fuel tanks, thereby reducing the risk of explosions. The experiments were conducted on a fuel scrubbing inerting platform, where an MIG was utilized to deoxygenate aviation fuel. Changes in the oxygen concentration in the ullage (OCU) and the dissolved oxygen concentration in the fuel (DOCF) were measured during the scrubbing process. Validated by these experimental data, Computational Fluid Dynamics (CFD) simulations demonstrated the reliability of the model. The discrepancies between CFD predictions and experimental measurements were 4.11% for OCU and 5.23% for DOCF. The influence of the MIG bubble diameter, MIG flow rate, and fuel loading rate on DOCF, gas holdup (GH), and the oxygen volumetric mass transfer coefficient (OVMTC) was comprehensively examined. The results reveal that larger MIG bubble diameters lead to an increased DOCF but reduced GH and OVMTC. In contrast, a higher MIG flow rate decreases DOCF while boosting GH and OVMTC. Additionally, a greater fuel loading rate increases DOCF but decreases GH and OVMTC. These findings offer important insights for optimizing fuel scrubbing inerting systems, underscoring the necessity of selecting suitable operating parameters to enhance oxygen displacement and ensure aircraft safety. Full article

Fibre deformation (or shearing of yarns) can develop during the liquid moulding of composites due to injection pressures or polymerisation (cross-linking) reactions (e.g., chemical shrinkage). On that premise, this may also induce potential residual stress–strain, warpage, and design defects in the composite part. In this paper, a developed numerical framework is customised to analyse deformations and the residual stress–strain of fibre (at a micro-scale) and yarns (at a meso-scale) during a liquid composite moulding (LCM) process cycle (fill and cure stages). This is achieved by linking flow simulations (coupled filling–curing simulation) to a transient structural model using ANSYS software. This work develops advanced User-Defined Functions (UDFs) and User-Defined Scalers (UDSs) to enhance the commercial CFD code with extra models for chemorheology, cure kinetics, heat generation, and permeability. Such models will be hooked within the conservation equations in the thermo-chemo-flow model and hence reflected by the structural model. In doing so, the knowledge of permeability, polymerisation, rheology, and mechanical response can be digitally obtained for more coherent and optimised manufacturing processes of advanced composites. Full article

In order to predict cerebrovascular event occurrences, this work introduces a novel method that combines computational fluid dynamics (CFD), structural analysis, and machine learning (ML). The study presents a multidisciplinary approach to evaluate the risk of carotid atherosclerosis and cerebrovascular event prediction by utilizing both imaging and non-imaging data. The study uses blood-flow simulations and 3D reconstruction techniques to identify important properties of plaque that may indicate cerebrovascular events. The analysis shows high accuracy of the model in predicting these events and is validated on a dataset of 134 asymptomatic individuals with carotid artery disease. The goal of this work is to improve clinical decision-making by providing a tool that blends machine learning algorithms, structural analysis, and CFD. The dataset imbalance was treated with two approaches in order to select the optimal one for the training of the Gradient Boosting Tree (GBT) classifier. The best GBT model yielded a balanced accuracy of 88%, having a ROC AUC of 0.92, a sensitivity of 0.88, and a specificity of 0.91. Full article

A theoretical model for the calculation of thermal elastohydrodynamic lubrication performance of the flow distribution pair of piston pumps is established, which is composed of the oil film pressure governing equation and energy equation, and solved by means of numerical solution and simulation. We carry out quantitative analysis of the influence of various parameters on the thermal elastohydrodynamic lubrication characteristics of the flow distribution pair. The results indicate that both the oil film thickness and the cylinder tilt angle of the flow distribution pair vary in a periodic manner. The increase in the rotational speed of the cylinder block will increase the film thickness of the oil film and reduce the fluctuation, and the inclination angle of the cylinder block and its fluctuation amplitude will decrease. An increase in working pressure will lead to a decrease in the average oil film thickness, an increase in fluctuations, and an elevation in both the tilt angle of the cylinder block and its fluctuation amplitude. The increase in the rotational speed of the cylinder block and the increase in the working pressure will lead to the increase in the viscous friction dissipation of the flow distribution pair, the increase in the oil film temperature and the increase in the leakage. The reduction in the sealing belt will lead to the reduction in oil film friction torque and leakage. Full article

The quality of metal powder is essential in additive manufacturing (AM). The defects and mechanical properties of alloy parts manufactured through AM are significantly influenced by the particle size, sphericity, and flowability of the metal powder. Gas atomization (GA) technology is a widely used method for producing metal powders due to its high efficiency and cost-effectiveness. In this work, a multi-phase numerical model is developed to compute the alloy liquid breaking in the GA process by capturing the gas–liquid interface using the Coupled Level Set and Volume-of-Fluid (CLSVOF) method and the realizable k-ε turbulence model. A GA experiment is carried out, and a statistical comparison between the particle-size distributions obtained from the simulation and GA experiment shows that the relative errors of the cumulative frequency for the particle sizes sampled in two regions of the GA chamber are 5.28% and 5.39%, respectively. The mechanism of powder formation is discussed based on the numerical results. In addition, a discrete element model (DEM) is developed to compute the powder flowability by simulating a Hall flow experiment using the particle-size distribution obtained from the GA experiment. The relative error of the time that finishes the Hall flow in the simulation and experiment is obtained to be 1.9%. Full article

Physical side-channel attacks utilize power, electromagnetic (EM), or timing signatures from cryptographic implementations during operation to retrieve sensitive information from security-critical devices. This paper provides a comprehensive review of these potent attacks against cryptographic hardware implementations, with a particular emphasis on pre-silicon leakage assessment methodologies. We explore the intricacies of cryptographic algorithms, various side-channel attacks, and the latest mitigation techniques. Although leakage assessment techniques are widely adopted in the post-silicon phase, pre-silicon leakage assessment is an emerging field that addresses the inherent limitations of its post-silicon counterpart. We scrutinize established post-silicon techniques and provide a detailed comparative analysis of pre-silicon leakage assessment across different abstraction levels in the hardware design and verification flow. Furthermore, we categorize and discuss existing pre-silicon power and electromagnetic modeling techniques for leakage detection and mitigation that can be integrated with electronic design automation (EDA) tools to automate security assessments. Lastly, we offer insights into the future trajectory of physical side-channel leakage assessment techniques in the pre-silicon stages, highlighting the need for further research and development in this critical area of cybersecurity. Full article

Abstract: From a value-added trade perspective, this study utilizes the world input–output tables and the water footprint data of each industry in each country in the Eora database to explore the virtual water resources of 19 countries (the G20 countries excluding the European Union) in 2016. We calculated nine value chain decompositions and the pattern of virtual water flows and then explored the implied virtual water use due to the trade of intermediate goods and final goods, and we also analyzed the unequal trade of virtual water and added value among countries. The results indicate the following. Firstly, in most countries, the largest portion of virtual water is attributed to exports of intermediate inputs that are produced in the source country and fully utilized by the direct import countries, followed by the foreign value-added component of intermediate goods, while the smallest share of virtual water is returned to the country. Secondly, in value-added trade, excluding the rest of the world (ROW), China, France, Italy, Japan, Mexico, South Korea, South Africa, Saudi Arabia, and Germany are net importers in the virtual water trade between G20 countries, and the USA is the largest net exporter of virtual water. Thirdly, intermediate product trade is the dominant form of implied virtual water trade among countries, which leads to a net flow ratio of implied virtual water of about 80% to 90%. Lastly, the Virtual Water Inequality Index shows that thirteen country combinations, including Brazil and Argentina, exhibit significant inequality, and most countries are in a relatively equal state. In addition, the virtual water and added value of the relatively economically developed regions benefit more from the virtual water trade. Therefore, it is crucial for countries to reduce their consumption of virtual water when trading intermediate products to develop high-value-added and low-water-consumption industries and to choose appropriate virtual water trade targets. Full article

A novel conductive heating method using solar energy for soil remediation was introduced in this work. Contaminated industrial heritage sites will affect the sustainable development of the local ecological environment and the surrounding air environment, and frequent exposure will have a negative impact on human health. Soil thermal desorption is an effective means to repair contaminated soil, but thermal desorption is accompanied by a large amount of energy consumption and secondary pollution. Therefore, a trough solar heat collection desorption system (TSHCDS) is proposed, which is applied to soil thermal desorption technology. The effects of different water inlet temperature, water inlet velocity and soil porosity on the evolution of soil temperature field were discussed. The temperature field of contaminated soil can be numerically simulated, and a small experimental platform is built to verify the accuracy of the numerical model for simulation research. It is concluded that the heating effect is the best when the water entry temperature is the highest, at 70 °C, and the temperature of test point 4 is increased by 50.71% and 1.42%, respectively. When the inlet water flow rate is increased from 0.1 m/s to 0.2 m/s, the heating effect is significantly improved; when the inlet water flow rate is increased from 0.5 m/s to 1.5 m/s, the heating effect is not significantly improved. Therefore, when the flow rate is greater than a certain value, the heating effect is not significantly improved. The simulation analysis of soil with different porosity shows that larger porosity will affect the thermal diffusivity, which will make the heat transfer effect worse and reduce the heating effect. The effects of soil temperature distribution on the removal of petroleum hydrocarbon C₆–C₉ and trichloroethylene (TCE) were studied. The results showed that in the thermal desorption process of petroleum hydrocarbon C₆–C₉-contaminated soil, the removal rate of pollutants increased significantly when the average soil temperature reached 80 °C. In the thermal desorption of trichloroethylene-contaminated soil, when the thermal desorption begins, the soil temperature rises rapidly and reaches the target temperature, and a large number of pollutants are removed. At the end of thermal desorption, the removal of both types of pollutants reached the target repair value. This study provides a new feasible method for soil thermal desorption. Full article

Oil temperature plays a crucial role in hydraulic closed-circuit systems (HCS), and conventional thermal equilibrium models and coupled simulation models face challenges in terms of accuracy, efficiency, and cost when calculating oil temperature. This study introduces an innovative HCS oil temperature precise prediction model and oil refilling parameter optimization method. The initial sample space was determined through a Sobol sensitivity analysis and improved Latin hypercube sampling, leading to the development of a combinatorial agent model (CAM) suitable for oil temperature prediction with superior accuracy and stability compared to other methods. Based on CAM, the optimal oil refilling flow rates under various operational conditions are computed. To validate the efficacy of the theoretical analysis, an HCS experiment platform was established. The data indicates that the temperature prediction error range of the CAM model falls between 0.30 °C and 1.05 °C, and optimizing the oil refilling flow rate can effectively enhance system efficiency while ensuring that the oil temperature remains within permissible limits. The research methodology and findings are applicable in engineering practice and can be extended to optimize the design of other hydraulic systems. Full article