Engineering reports : open access最新文献

The rapid growth in the electric vehicle (EV) population necessitates the widespread deployment of charging infrastructure. However, establishing fully functional EV chargers at every required location is impractical due to resource and planning constraints. To address this, adaptive charging facilities offer a flexible alternative, particularly suited for space and power constrained environments such as urban roadsides, hotels, and parking lots. This study proposes the development of a DC Adaptive Charging Facility (DCACF), designed to meet three critical objectives: cost-effectiveness, energy efficiency, and user accessibility. The system operates in three intelligent charging modes: fixed price mode (FPM), fixed SOC mode (FSM), and advanced distance mode (ADM). In FPM, the charger delivers energy based on a prepaid monetary value; in FSM and ADM, it supplies the amount of energy required to achieve a target state of charge (SOC) or driving range, respectively. Smart charging strategies are implemented for each mode, and an intelligent controller manages system dynamics to ensure safe and reliable operation. A comprehensive SIL-based validation using the OPAL-RT simulator demonstrates that the proposed adaptive charging system achieves 98% accuracy in cut-off control. Mode-wise analysis highlights the cost-saving potential of partial, need-based charging under dynamic tariff conditions, thereby demonstrating the system's suitability for real-world urban deployment.

With the expansion of cloud computing and Internet of Things (IoT), Distributed Denial of Service (DDoS) attacks have become a serious threat to cybersecurity. Accurate and fast detection of these attacks is of great importance. In this study, a two-stage detection method based on group feature fusion is presented for detecting DDoS attacks in cloud computing environment. In the first stage, the optimal feature selection was performed using a combination of several meta-heuristic algorithms including genetic algorithm, gray wolf, particle swarm optimization, Harris hawk, and whale. Then, three feature fusion methods including voting-based fusion, weight-based fusion, and learning-based fusion were used to combine the selected features. In the second step, a hybrid deep learning model was designed, consisting of a convolutional neural network (CNN) and a long-short term memory network (LSTM). CNN extracts spatial features of network traffic, and LSTM models temporal dependencies. This combination has improved the model's performance in accurately detecting DDoS attacks. Experimental results on two datasets, NSL-KDD and BoT-IoT, show that the proposed method achieves 99.1% and 99.2% accuracy, respectively, which is a significant improvement over previous methods. In addition to increasing detection accuracy, the proposed method also reduces the false positive rate and has high generalizability against various types of cyber-attacks. In the future, the efficiency of this method in real environments can be improved by optimizing the model structure, utilizing pre-trained networks, and reducing computational complexity.

With the expansion of cloud computing and Internet of Things (IoT), Distributed Denial of Service (DDoS) attacks have become a serious threat to cybersecurity. Accurate and fast detection of these attacks is of great importance. In this study, a two-stage detection method based on group feature fusion is presented for detecting DDoS attacks in cloud computing environment. In the first stage, the optimal feature selection was performed using a combination of several meta-heuristic algorithms including genetic algorithm, gray wolf, particle swarm optimization, Harris hawk, and whale. Then, three feature fusion methods including voting-based fusion, weight-based fusion, and learning-based fusion were used to combine the selected features. In the second step, a hybrid deep learning model was designed, consisting of a convolutional neural network (CNN) and a long-short term memory network (LSTM). CNN extracts spatial features of network traffic, and LSTM models temporal dependencies. This combination has improved the model's performance in accurately detecting DDoS attacks. Experimental results on two datasets, NSL-KDD and BoT-IoT, show that the proposed method achieves 99.1% and 99.2% accuracy, respectively, which is a significant improvement over previous methods. In addition to increasing detection accuracy, the proposed method also reduces the false positive rate and has high generalizability against various types of cyber-attacks. In the future, the efficiency of this method in real environments can be improved by optimizing the model structure, utilizing pre-trained networks, and reducing computational complexity.

The wear performance of additively manufactured (AM) AlSi10Mg components is critical for their deployment in tribological applications, yet a comparative analysis of the wear behavior under as-built, T6, and stress-relief (SR) conditions remains insufficiently explored. To broaden the industrial adoption of AM components, it is crucial to evaluate their wear behavior, as this underpins reliability and safety while promoting creativity in both design and material choices. This research examines the wear characteristics of the AlSi10Mg alloy created using Selective Laser Melting (SLM), evaluated in the as-built condition and following T6 and SR heat treatments. The microstructural variations under these three states were analyzed using scanning electron microscopy (SEM). The0020as-built specimens exhibited the highest hardness (137.3 HV), due to the presence of a refined α-Al cellular framework embedded with Si particles generated by rapid solidification. Heat treatment altered this structure, leading to Si phase coarsening and a corresponding reduction in hardness to 103.35 HV in the T6 condition and further down to 73.75 HV in the SR condition. Wear experiments were carried out under applied loads ranging from 5 to 15 N (max load 15 N) for a duration of 300 s, along with assessments of the coefficient of friction (COF), the surface morphology following wear, and the loss of material. The findings indicated that the as-built specimens consistently demonstrated lower wear volume loss across all load levels in comparison to the samples that underwent heat treatment. Additionally, the heat-treated specimens developed compressive residual stresses, while the as-built SLM parts primarily exhibited tensile stresses.

The wear performance of additively manufactured (AM) AlSi10Mg components is critical for their deployment in tribological applications, yet a comparative analysis of the wear behavior under as-built, T6, and stress-relief (SR) conditions remains insufficiently explored. To broaden the industrial adoption of AM components, it is crucial to evaluate their wear behavior, as this underpins reliability and safety while promoting creativity in both design and material choices. This research examines the wear characteristics of the AlSi10Mg alloy created using Selective Laser Melting (SLM), evaluated in the as-built condition and following T6 and SR heat treatments. The microstructural variations under these three states were analyzed using scanning electron microscopy (SEM). The0020as-built specimens exhibited the highest hardness (137.3 HV), due to the presence of a refined α-Al cellular framework embedded with Si particles generated by rapid solidification. Heat treatment altered this structure, leading to Si phase coarsening and a corresponding reduction in hardness to 103.35 HV in the T6 condition and further down to 73.75 HV in the SR condition. Wear experiments were carried out under applied loads ranging from 5 to 15 N (max load 15 N) for a duration of 300 s, along with assessments of the coefficient of friction (COF), the surface morphology following wear, and the loss of material. The findings indicated that the as-built specimens consistently demonstrated lower wear volume loss across all load levels in comparison to the samples that underwent heat treatment. Additionally, the heat-treated specimens developed compressive residual stresses, while the as-built SLM parts primarily exhibited tensile stresses.

A scaled apparatus was designed for conducting Nakazima stretch-forming tests with a rotating tool. A 3D finite element model was developed and validated to simulate the modified Nakazima experiments. To reduce the device weight, the 5182-O aluminum alloy was used as a base material for the blank, die, blank-holder, and die-support, while the forming tool and tightening system components were made of high-hardness armor steel (500 HB). Unlike existing numerical models, all the device components are replicated in the finite element code and are considered deformable. Numerical simulations were conducted to ascertain the equivalent strain and nodal displacement distributions over the tooling components and the principal strain distributions over the sheet. The results showed that the equivalent strains and nodal displacement variations were negligible, thereby demonstrating the resistance and stability of the entire device during the tests. The forming limit curve, major and minor strain variations with dome height to tool diameter ratio, and major strain variation with minor strain were ascertained and compared to the experiments. Good agreement was obtained between the numerical and experimental results, demonstrating the good ability of the developed device to reproduce the modified Nakazima tests.

A scaled apparatus was designed for conducting Nakazima stretch-forming tests with a rotating tool. A 3D finite element model was developed and validated to simulate the modified Nakazima experiments. To reduce the device weight, the 5182-O aluminum alloy was used as a base material for the blank, die, blank-holder, and die-support, while the forming tool and tightening system components were made of high-hardness armor steel (500 HB). Unlike existing numerical models, all the device components are replicated in the finite element code and are considered deformable. Numerical simulations were conducted to ascertain the equivalent strain and nodal displacement distributions over the tooling components and the principal strain distributions over the sheet. The results showed that the equivalent strains and nodal displacement variations were negligible, thereby demonstrating the resistance and stability of the entire device during the tests. The forming limit curve, major and minor strain variations with dome height to tool diameter ratio, and major strain variation with minor strain were ascertained and compared to the experiments. Good agreement was obtained between the numerical and experimental results, demonstrating the good ability of the developed device to reproduce the modified Nakazima tests.

Dynamic process modeling is essential for simulating time-evolving biochemical systems, particularly those with multistate interactions and combinatorial complexity. Traditional Ordinary Differential Equation (ODE) models offer mechanistic clarity but struggle with scalability and context-sensitive encoding. Rule-Based Modeling (RBM) frameworks address these limitations through modular rule abstraction, yet require manual specification and lack adaptive learning. This study introduces algorithmic innovations within the Neural Ordinary Differential Equation (Neural ODE) paradigm to bridge the gap between mechanistic interpretability and scalable expressivity. Neural ODEs can be considered as a revolutionary approach in the field of modeling dynamic biochemical interactions. They have made it possible to create models of such interactions that are flexible enough to adapt to different scenarios and do so without requiring any manual intervention in terms of rule encoding or predefined reaction schemes. This is achieved by employing differential solvers within the framework of neural networks, thus enabling a learning process that is in accordance with the behavior of the system. Using the DARPP-32 signaling network—a benchmark system characterized by multivalent phosphorylation and dynamic perturbations—the proposed Neural ODE framework demonstrates the ability to replicate key dynamic behaviors observed in ODE and RBM models. Comparative simulations under baseline and perturbed conditions reveal that Neural ODEs maintain trajectory fidelity while offering enhanced modularity and computational efficiency. Feature importance analysis and latent space visualizations further validate the model's interpretability and robustness. Unlike ODEs and RBMs, Neural ODEs adapt to structural mutations and binding schemes through latent trajectory learning, enabling flexible simulation of biochemical variability without manual rule encoding. This work establishes Neural ODEs as a viable and scalable alternative for modeling complex biochemical systems, combining the strengths of data-driven learning with the interpretability of differential equations.

In this work, the influence of strut thickness on the deformation and failure mechanisms of new vascular bundle–inspired structures, which exhibit comparable or better mechanical properties than honeycomb and star-shaped lattices, is presented. The novelty of the work lies on the design of the structure; this is a new structure, and its behavior has not been reported elsewhere. Structures consisting of 0.2, 0.5, 1.0-, and 1.15-mm strut thicknesses were designed, modeled, fabricated, and tested. A finite element model of a quasi-static compression test is developed in ANSYS Explicit Dynamics to evaluate the deformation and failure mechanisms of the various structures. It is demonstrated that 0.2- and 0.5-mm structures exhibit stretch-dominated stress–strain behavior, whereas 1.0- and 1.15-mm structures show bend-dominated stress–strain characteristics. As the strut thickness increases, there is an increase in peak stresses (with reported peak stresses of 1.3, 1.4, 5, and 5.1 MPa for 0.2, 0.5, 1.0, and 1.15 mm, respectively) and energy absorption (reported values of 33.84, 31.48, 159.28, and 179.07 J for thicknesses of 0.2, 0.5, 1.0, and 1.15 mm, respectively) characteristics. Poisson's ratio values of the samples ranged between 0.6 and 1.2. Additionally, the deformation mechanisms transform from perpendicular collapse of the structure to 45° bending (shearing) of the structure from low to higher strut thickness. As the strut thickness increases, the failure mechanisms transform from ductile fracture to near-brittle failure of the structures. The findings in this paper provide key insights into the design and fabrication of next-generation vascular bundle–inspired multifunctional materials for lightweight structural applications. As a contribution, the energy absorption and peak stress values for the vascular bundle structures presented in this paper are comparable to published data on similar PLA lattice structures.

Wear performance of the bearing couple in total hip prostheses is a critical determinant of implant longevity, patient outcomes, and the likelihood of revision surgeries. Among the various methods developed to evaluate wear behavior, computational approaches using finite element analysis have emerged as powerful tools due to their flexibility, cost-effectiveness, and ability to simulate complex biomechanical interactions. This literature review focuses specifically on the application of Archard's wear law within finite element frameworks to predict wear in single mobility bearing of total hip prosthesis under walking conditions. Emphasis is placed on modeling methodologies, the incorporation of physiological gait cycles, boundary condition considerations, and validation through experimental data. The review also explores recent advancements aimed at improving simulation accuracy, including the use of multi-directional loading, sliding trajectory mapping, and realistic material properties. Finally, future directions are discussed, such as duration of computational wear prediction, sliding trajectory, surface roughness and lubrication modeling in computational wear prediction, textured surfaces for wear reduction, surface coatings for enhanced wear resistance, dual mobility total hip prosthesis, and experimental validation and integration with computational modeling, all collectively aim to enhance predictive reliability and support the development of more durable, personalized orthopedic implants.