Search Results (10,748)

Some countries grapple with data scarcity for calibration purposes when establishing current hydrodynamic models, which often require many parameters. In this context, this research presents a practical simulation methodology for hydrodynamic modelling suitable for application in bay and estuarine systems based on mass and momentum equations and requiring only one parameter for calibration—bed friction. The proposed simulation methodology is applied to a linear open channel measuring 200,000 m long. A sensitivity analysis of the bed friction is conducted to assess the proposed methodology’s response to the maximum water levels achieved. The results are compared to linear theory, indicating that the proposed simulation methodology effectively represents the water phase. In all simulations, the maximum root mean square error is less than 2.1% when neglecting bed friction and 4.69% when a bed friction of 0.005 is considered. The proposed simulation methodology can be a practical tool for hydrodynamic modelling in shallow waters. Full article

In this work, floating-gate organic field-effect transistor memory using the n-type semiconductor poly-{[N,N′-bis(2-octyldodecyl) naphthalene-1,4,5,8-bis (dicarbo- ximide)-2,6-dili]-alt-5,5′-(2,2′-bithiophene)} (N2200) as a charge-trapping layer is presented. With the assistance of a technology computer-aided design (TCAD) tool (Silvaco-Atlas), the storage characteristics of the device are numerically simulated by using the carrier injection and Fower–Nordheim (FN) tunneling models. The shift in the transfer characteristic curves and the charge-trapping mechanism after programming/erasing (P/E) operations under different P/E voltages and different pulse operation times are discussed. The impacts of different thicknesses of the tunneling layer on storage characteristics are also analyzed. The results show that the memory window with a tunneling layer thickness of 8 nm is 16.1 V under the P/E voltage of ±45 V, 5 s. After 1000 cycle tests, the memory shows good fatigue resistance, and the read current on/off ratio reaches 10³. Full article

This work introduces the design, implementation, and validation of a virtual reality (VR) experience aimed at promoting the inclusion of individuals with dyslexia in university settings. Unlike traditional awareness methods, this immersive approach offers a novel way to foster empathy by allowing participants to experience firsthand the challenges faced by students with dyslexia. Specifically, the experience raises awareness by exposing non-dyslexic individuals to the difficulties commonly encountered by dyslexic students. In the virtual environment, participants explore a virtual campus with multiple buildings, navigating between them while completing tasks and simultaneously encountering barriers that simulate some of the challenges faced by individuals with dyslexia. These barriers include reading signs with shifting letters, following directional arrows that may point incorrectly, and dealing with a lack of assistance. The campus is a comprehensive model featuring both indoor and outdoor spaces and supporting various modes of locomotion. To validate the experience, more than 30 non-dyslexic participants from the university environment, mainly professors and students, evaluated it through ad hoc satisfaction surveys. The results indicated heightened awareness of the barriers encountered by students with dyslexia, with participants deeming the experience a valuable tool for increasing visibility and fostering understanding of dyslexic students. Full article

This paper presents a novel adaptive crack-tip extended isogeometric analysis (adaptive CT XIGA) framework based on locally refined B-splines (LR B-splines) for efficient and accurate fracture modeling in two-dimensional solids. The XIGA method facilitates crack modeling without requiring the specific locations of crack faces and enables crack propagation simulation without remeshing by employing localized enrichment functions. LR B-splines, as an advanced extension of B-splines and NURBS, offer high-order continuity, precise geometric representation, and local refinement capabilities, thereby enhancing computational accuracy and efficiency. Various local mesh refinement strategies, designed based on crack and crack-tip locations, are investigated. Among these strategies, the crack-tip topological refinement strategy is adopted for local refinement in the adaptive CT XIGA framework. Stress intensity factors (SIFs) are evaluated using the contour interaction integral technique, while the maximum circumferential stress criterion is adopted to predict the crack growth direction. Numerical examples demonstrate the accuracy, efficiency, and robustness of adaptive CT XIGA. The results confirm that the proposed framework achieves superior error convergence rates and significantly reduces computational costs compared to a-posteriori-error-based adaptive XIGA methods, particularly in crack propagation simulations. These advantages establish adaptive CT XIGA as a powerful and efficient tool for addressing complex fracture problems in solid mechanics. Full article

With the rapid increase in the human population and urbanization worldwide, the demand for food production has played a significant role in driving the integration of technology into agriculture. Various Cloud-based systems, such as livestock tracking systems, have been proposed. In those systems, data were collected by the sensors and sent to the Cloud for processing. However, significant issues with those systems were noted, such as high bandwidth utilization and security concerns, such as a high volume of row data traveling from the data collection devices (such as sensors) to the Cloud through the Internet. Additionally, the long distance between the Cloud and the data collection devices makes it unsuitable for latency-sensitive livestock disease monitoring and tracking systems. Therefore, this paper proposes a Fog–Cloud-based approach, where the processing is conducted at the Fog layer, closer to the data collection devices, and only the result is sent to the Cloud for remote viewing. The proposed method aims to reduce power consumption and latency in communication. To validate the proposed method, both the Cloud-based and Fog–Cloud-based scenarios are simulated using iFogSim (a novel simulation tool for IoT and Cloud computing), and the result shows that there is less than twice the power consumption in some scenarios and that the time consumed in the proposed Fog–Cloud-based system, depending on the number of sensors, is five to ten times lower. This study further supports the point that the Fog–Cloud-based is suitable for latency-dependent farming systems such as livestock tracking systems. Full article

Background: Remanufacturing products for sustainability involves layout and production planning, tools and equipment, material arrangement and handling, inventory management, technology integration, and more. This study presents an empirical vision through a discrete event simulation (DES) model integrating lean manufacturing (LM) and supply chain (SC) strategies with industry 4.0 (I4.0) technologies, applied to a case in a railway company. Methods: The work presents scenarios following a methodology with an incremental approach to implement strategies of lean manufacturing (LM) and supply chain (SC) in the context of I4.0 and their effects represented in DES models with applicability in remanufacturing and production line management. Five simulation scenarios were analyzed according to strategies layered incrementally. Results: Behaviors and outcomes were compared across the scenarios considering the remanufactured engines, percentage of process time, human labor occupation, and the statistical analysis of the process capability. Scenario five achieved the objective of remanufacturing 40 engines in one year with a cycle time of 214.45 h. Conclusions: The purpose was to design an engine remanufacturing line incorporating LM and SC strategies via a DES model, highlighting the importance of their gradual adoption toward I4.0 implementation. The integration of previous strategies improves flexibility and productivity in manufacturing processes. Full article

An ocean target electric field signal is an effective approach for analyzing the ocean environment and is widely used for detecting ocean targets, extracting their features, and tracking them. Low-frequency analysis and recording (LOFAR) is a commonly used time–frequency analysis tool that provides the time–frequency spectrum of a signal; however, its reliance on the Fourier transform (FT) results in a low frequency resolution and signal-to-noise ratio (SNR), which limits its target detection capabilities. To address this problem, we propose a method called low-frequency analysis and recording based on basis pursuit (LOFAR-BP) for analyzing and detecting ocean target electric field signals. LOFAR-BP uses basis pursuit (BP) with the L1 norm for frequency analysis, whereas LOFAR utilizes the FT. We demonstrate that the FT is the L2 norm mathematically. LOFAR-BP generates the time–frequency spectrum in the same way that LOFAR does. By extracting characteristic values from the time–frequency spectrum, targets can be detected using an appropriate threshold. Both simulation and ocean experiments showed that LOFAR-BP effectively enhances target signals and suppresses noise. Compared with LOFAR, LOFAR-BP improved the frequency resolution by 60% in both experiments and increased the SNR by 54.82 dB in the simulation experiment and by 39.59 dB in the ocean experiment. When applied to target detection, LOFAR-BP can detect targets 6 s earlier than LOFAR can. Full article

This article presents a novel four-port DC–DC converter designed to integrate photovoltaics, fuel cells, and supercapacitors with one DC charging single-output port with a reduced component count. The proposed converter ensures an efficient power management strategy to manage the load power demand and optimize the power flow from the sources. The power management controller helps enhance the performance of the system by dynamically prioritizing the sources based on their availability and the demand of the load. A comprehensive reliability analysis is conducted to measure the converter’s robustness under varying load conditions, proving its suitability for real-world applications. The proposed topology’s performance was validated in three different scenarios for 1 kW using a simulation tool, and experiments in the laboratory were conducted. The failure rate and efficiency of the system are analyzed, and the converter promises a 96.5% efficiency for 1 kW and a failure rate of 4.6216 × 10⁶ failures per hour. The simulation and experimental results validate the converter’s performance, highlighting its superior efficiency, reliability, and scalability. Full article

The electro-hydraulic composite intelligent completion technology is one of the most effective ways to solve the efficient development of oil and gas. The development of an electro-hydraulic composite wet joint tool that is compatible with the electro-hydraulic composite intelligent completion system can achieve intelligent control between the upper and lower pipe columns of deepwater oil and gas wells and the pluggable transmission of monitoring signals. This article proposes a new type of electromagnetic coupling electro-hydraulic composite wet joint designed to address the defects of friction damage and poor contact in current wet joint direct contact power transmission. The joint uses claw docking and wireless energy transmission to achieve the composite transmission of hydraulic and electric power. Firstly, we independently designed a DC power supply inverter circuit, rectification circuit, and wireless power transmission coil assembly to form a wireless power transmission system. We also conducted testing and analysis on the wireless power transmission efficiency, which exceeded 60%. When the input voltage was above 80 V, the output power was greater than 60 W, meeting the design requirements. Secondly, the mechanical structure of the new electro-hydraulic signal wet joint tool was optimized and its strength was verified. The simulation results showed that the maximum stress was 891.8 MPa, and the maximum deformation of the wet joint docking overall structure was 0.123 mm. The strength and deformation met the design requirements. The hydraulic and electrical connectivity indoor tests were conducted on the electromagnetic coupling wet joint, and all aspects of transmission were normal, thus forming a design scheme for the underground electromagnetic coupling electro-hydraulic signal wet joint. The wireless transmission type electro-hydraulic signal wet joint designed in this article is of great significance for accelerating the promotion and application process of deepwater intelligent completion systems. Full article

The integration of artificial intelligence (AI) with additive manufacturing (AM) is driving breakthroughs in personalized rehabilitation and physical therapy solutions, enabling precise customization to individual patient needs. This article presents the current state of knowledge and perspectives of using personalized solutions for rehabilitation and physiotherapy thanks to the introduction of AI to AM. Advanced AI algorithms analyze patient-specific data such as body scans, movement patterns, and medical history to design customized assistive devices, orthoses, and prosthetics. This synergy enables the rapid prototyping and production of highly optimized solutions, improving comfort, functionality, and therapeutic outcomes. Machine learning (ML) models further streamline the process by anticipating biomechanical needs and adapting designs based on feedback, providing iterative refinement. Cutting-edge techniques leverage generative design and topology optimization to create lightweight yet durable structures that are ideally suited to the patient’s anatomy and rehabilitation goals .AI-based AM also facilitates the production of multi-material devices that combine flexibility, strength, and sensory capabilities, enabling improved monitoring and support during physical therapy. New perspectives include integrating smart sensors with printed devices, enabling real-time data collection and feedback loops for adaptive therapy. Additionally, these solutions are becoming increasingly accessible as AM technology lowers costs and improves, democratizing personalized healthcare. Future advances could lead to the widespread use of digital twins for the real-time simulation and customization of rehabilitation devices before production. AI-based virtual reality (VR) and augmented reality (AR) tools are also expected to combine with AM to provide immersive, patient-specific training environments along with physical aids. Collaborative platforms based on federated learning can enable healthcare providers and researchers to securely share AI insights, accelerating innovation. However, challenges such as regulatory approval, data security, and ensuring equity in access to these technologies must be addressed to fully realize their potential. One of the major gaps is the lack of large, diverse datasets to train AI models, which limits their ability to design solutions that span different demographics and conditions. Integration of AI–AM systems into personalized rehabilitation and physical therapy should focus on improving data collection and processing techniques. Full article

Aquifer Thermal Energy Storage (ATES) systems are a promising solution for sustainable energy storage, leveraging underground aquifers to store and retrieve thermal energy for heating and cooling. As the global energy sector faces rising energy demands, climate change, and the depletion of fossil fuels, transitioning to renewable energy sources is imperative. ATES systems contribute to these efforts by reducing greenhouse gas (GHG) emissions and improving energy efficiency. This review uses the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology as a systematic approach to collect and analyze relevant literature. It highlights trends, gaps, and advancements in ATES systems, focusing on simulation methods, environmental impacts, and economic feasibility. Tools like MODFLOW, FEFLOW, and COMSOL Multiphysics are emphasized for optimizing design and system performance. Europe is identified as a continent with the most favorable predispositions for ATES implementation due to its diverse and abundant aquifer systems, strong policy frameworks supporting renewable energy, and advancements in subsurface energy technologies. Full article

The current work includes experimental and numerical investigations into the effects of biodiesel (Eichhornia Crassipes) blends with different aluminum oxide nanoparticle concentrations on the combustion process in diesel engines. The experiments included measuring performance parameters and emissions tests while changing the engine speed and increasing loads. IC Engine Fluent, a specialist computational tool included in the ANSYS software (R19.0 version), was used to simulate internal combustion engine dynamics and combustion processes. All investigations were carried out using biodiesel blends with three concentrations of Al₂O₃ nanoparticles: 50, 100, and 150 ppm. The tested samples are called D100, D80B20, D80B20N50, D80B20N100, and D80B20N150, accordingly. The combustion characteristics are improved due to the catalytic effect and higher surface area of nano additives. The results showed improvements in the combustion process as the result of the nanoparticles’ addition, which led to the higher peak cylinder pressure. The increases in the peak cylinder pressures for D80B20N50, D80B20N100, and D80B20N150 about D80B20 were 3%, 5%, and 8%, respectively, at a load of 200 Nm, while the simulation found that the maximum temperature for biodiesel blends diesel was higher than that for pure diesel; this was due to the higher hydrocarbon values of D80B20. Also, nano additives caused a decrease in temperatures in the combustion of biofuels. Finally, nano additives caused an enhancement of the emissions test results for all parameters when compared to pure diesel fuel and biofuel. Full article

The current trend in machining with robotic arms involves leveraging Industry 4.0 technologies to propose solutions that reduce path deviation errors. This approach presents significant challenges alongside promising advancements, as well as a substantial increase in the cost of future industrial robotic cells, which is not always amortizable. As an alternative or complementary approach to this trend, methods encouraging the occasional use of Industry 4.0 devices for characterizing the behavior of the actual physical cell, calibration, or adjustment are proposed. One such method, called FlePFaM, predicts flatness errors in face milling operations using robotic arms. This is achieved by estimating tool path deviation errors through the integration of a simple model of the robot arm’s mechanics with the cutting forces vector of the process, thereby optimizing machining conditions. These conditions are determined through prior empirical estimations of mass, stiffness, and damping. The conducted tests enabled the selection of the most favorable combination of variables, such as the robot wrist configuration, the position and orientation of the workpiece, and the predominant milling orientation. This led to the identification of the configuration with the lowest absolute flatness error according to the model’s predictions. The results demonstrated a high degree of similarity—between 97% for the closest case and 57% for the farthest case—between simulated and experimental flatness error values. FlePFaM represents a significant step forward in adopting innovative robotic arm solutions for reliable and efficient production. FlePFaM includes dimensional flatness indicators that provide practical support for decision making. Full article

The study uses surrogate modeling techniques to evaluate cost optimization methodologies for post-tensioned concrete slab bridges. These structures are key components in transportation infrastructure, where design efficiency can yield significant economic benefits. The research focuses on a three-span slab bridge, with spans of 24, 34, and 28 m, optimized through the Kriging surrogate model combined with heuristic algorithms such as simulated annealing. Input variables included deck depth, base geometry, and concrete grade, with Latin Hypercube Sampling ensuring diverse design exploration. Results reveal that the optimized design achieves a 6.54% cost reduction compared to conventional approaches, primarily by minimizing material usage—concrete by 14.8% and active steel by 11.25%. Among the predictive models analyzed, the neural network demonstrated the lowest prediction error but required multiple runs for stability, while the Kriging model offered accurate local optimum identification. This work highlights surrogate modeling as a practical and efficient tool for bridge design, reducing costs while adhering to structural and serviceability criteria. The methodology facilitates better-informed decision-making in structural engineering, supporting more economical bridge designs. Full article

This review describes the process of metal additive manufacturing and focuses on the possibility of correlated input parameters that are important for this process. The correlation of individual parameters in the metal additive manufacturing process is considered using simulation tools that allow the prediction of various defects, thus making the real production process more efficient, especially in terms of time and costs. Special attention is paid to multiple applications using these simulation tools as an initial analysis to determine the material’s behavior when defining various input factors, including the results obtained. Based on this, further procedures were implemented, including real production parts. This review also points out the range of possible variations that simulation tools have, which helps to effectively predict material defects and determine the volume of consumed material, supports construction risk, and other information necessary to obtain a quality part in the production process. From the overview of the application of simulation tools in this process, it was found that the correlation between theoretical knowledge and the definition of individual process parameters and other variables are related and are of fundamental importance for achieving the final part with the required properties. In terms of some specific findings, it can be noted that simulation tools identify adverse phenomena occurring in the production processes and allow manufacturers to test the validity of the proposed conceptual and model solutions without making actual changes in the production system, and they have the measurable impact on the design and production of quality parts. Full article