Search Results (744)

Ship structures are subjected to cyclic loading from waves and currents during operation, which can lead to fatigue failure, particularly at locations with structural discontinuities such as welds. Although various fatigue assessment methods have been developed, there is a lack of experimental data and comparative studies for actual ship structure details. This study addresses this limitation by evaluating the fatigue strength of longi-web connections in hull structures using local stress approaches, including hot spot stress, effective notch stress, notch stress intensity factor, and structural stress methods. Finite element analyses were conducted, and the predicted fatigue lives and failure locations were compared with experimental results. Although there are some differences between each method, all methods are valid and reasonable for predicting the primary failure locations and evaluating fatigue life. These findings provide a basis for considering suitable fatigue assessment methods for welded ship structures with respect to joint geometry and failure mechanisms. Full article

Due to the limited endurance of UAVs, especially in scenarios involving large areas and dense target nodes, it is challenging for multiple UAVs to complete diverse tasks while ensuring timely execution. Toward this, we propose a cross-platform system consisting of an aircraft carrier (AC) and multiple UAVs, which makes unified task planning for included heterogeneous platforms to maximize the efficiency of the entire combat system. The carrier-based UAV swarm mission planning problem is formulated to minimize completion time and resource utilization, taking into account large-scale targets, multi-type tasks, and multi-obstacle environments. Since the problem is complex, we design a decoupled framework to simplify the solution by decomposing it into two levels: upper-level AC path planning and bottom-level multi-UAV cooperative mission planning. At the upper level, a drop point determination method and a discrete genetic algorithm incorporating improved A* (DGAIIA) are proposed to plan the AC’s path in the presence of no-fly zones and radar threats. At the bottom level, an improved differential evolution algorithm with a market mechanism (IDEMM) is proposed to minimize task completion time and maximize UAV utilization. Specifically, a dual-switching search strategy and a neighborhood-first buying-and-selling mechanism are developed to improve the search efficiency of the IDEMM. Simulation results validate the effectiveness of both the DGAIIA and IDEMM. An animation of the simulation results is available at simulation section. Full article

This study explores the sustainable valorization of Annurca apple by-products by examining the polyphenolic content and antioxidant activity of peel, flesh, and core at two ripening stages. Ripening significantly enhanced the concentration of bioactive compounds, particularly in the peel, where total polyphenols increased from 124.4 to 423.3 mg of CAE/100 g FW, flavonoids from 18.2 to 51.3 mg of quercetin equivalents, and ortho-diphenols from 11.9 to 36.1 mg of CAE. The flesh and core showed more moderate increases. Antioxidant activity, assessed using five in vitro assays (DPPH, ABTS, FRAP, TAC, and H₂O₂), was consistently highest in the peel, especially in the ABTS assay. Although the flesh had fewer phenolics, it showed a 1.5-fold increase during ripening, accompanied by improved antioxidant performance. The core also proved notable antioxidant potential, particularly in ripe samples. UHPLC-MS/MS analysis identified 11 phenolic compounds, showing tissue- and ripening-specific distribution. SDS-PAGE revealed a ripening-related increase in Thaumatin-like Protein 1a (TLP1a), especially in the core and flesh. Its association with tissues showing high antioxidant ability suggests a possible role in enhancing the bioactivity of polyphenol-rich extracts. From an agri-food waste valorization perspective, the peel and core represent promising sources of bioactive compounds for developing functional foods and nutraceuticals. Full article

Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically expected. These manipulations can disrupt the inherent symmetry of document layouts, making the detection of such inconsistencies crucial for forgery identification. Conventional CNN-based models face limitations in capturing such edge-level asymmetric features, as edge-related information tends to weaken through repeated convolution and pooling operations. To address this issue, this study proposes an edge-focused method composed of two components: the Edge Attention (EA) layer and the Edge Concatenation (EC) layer. The EA layer dynamically identifies channels that are highly responsive to edge features in the input feature map and applies learnable weights to emphasize them, enhancing the representation of boundary-related information, thereby emphasizing structurally significant boundaries. Subsequently, the EC layer extracts edge maps from the input image using the Sobel filter and concatenates them with the original feature maps along the channel dimension, allowing the model to explicitly incorporate edge information. To evaluate the effectiveness and compatibility of the proposed method, it was initially applied to a simple CNN architecture to isolate its impact. Subsequently, it was integrated into various widely used models, including DenseNet121, ResNet50, Vision Transformer (ViT), and a CAE-SVM-based document forgery detection model. Experiments were conducted on the DocTamper, Receipt, and MIDV-2020 datasets to assess classification accuracy and F1-score using both original and forged text images. Across all model architectures and datasets, the proposed EA–EC method consistently improved model performance, particularly by increasing sensitivity to asymmetric manipulations around text boundaries. These results demonstrate that the proposed edge-focused approach is not only effective but also highly adaptable, serving as a lightweight and modular extension that can be easily incorporated into existing deep learning-based document forgery detection frameworks. By reinforcing attention to structural inconsistencies often missed by standard convolutional networks, the proposed method provides a practical solution for enhancing the robustness and generalizability of forgery detection systems. Full article

By embedding optical fiber sensors into fiber preforms and utilizing liquid molding processes such as resin transfer molding (RTM), intelligent composite materials with self-sensing capabilities can be fabricated. In the liquid molding process of these intelligent composites, the quality of the final product is highly dependent on the resin flow and impregnation effects. The embedding of optical fibers can affect the microscopic flow and impregnation behavior of the resin; therefore, it is necessary to investigate the specific impact of optical fiber embedding on the resin flow and impregnation of fiber bundles. Due to the difficulty of directly observing this process at the microscopic scale through experiments, numerical simulation has become a key method for studying this issue. This paper focuses on the resin micro-flow in RTM processes for intelligent composites with embedded optical fibers. Firstly, a steady-state analysis of the resin flow and impregnation process was conducted using COMSOL 6.0 obtaining the velocity and pressure field distribution characteristics under different optical fiber embedding conditions. Secondly, the dynamic process of resin flow and impregnation of fiber bundles at the microscopic scale was simulated using Fluent 2022R2. This study comprehensively analyzes the impact of different optical fiber embedding configurations on resin flow and impregnation characteristics, determining the impregnation time and porosity after impregnation under different optical fiber embedding scenarios. Additionally, this study reveals the mechanisms of pore formation and their distribution patterns. The research findings provide important theoretical guidance for optimizing the RTM molding process parameters for intelligent composite materials. Full article

This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN’s exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt temperature and holding pressure) and product quality is amplified by PSO’s intelligent search capability, which efficiently navigates the high-dimensional parameter space. Together, this hybrid approach achieves what neither method could accomplish alone: the BPNN accurately models the intricate process-quality relationships, while PSO rapidly converges on optimal parameter sets that simultaneously meet strict quality targets (66–70 g weight, 3–5 mm thickness) and minimize energy consumption. The significance of this integration is demonstrated through three key outcomes: First, the BPNN-PSO combination reduced optimization time by 40% compared to traditional trial-and-error methods. Second, it achieved remarkable prediction accuracy (RMSE 0.8229 for thickness, 1.5123 for weight) that surpassed standalone BPNN implementations. Third, the method’s efficiency enabled SMEs to achieve CAE-level precision without expensive software, reducing setup costs by approximately 25%. Experimental validation confirmed that the optimized parameters decreased energy use by 28% and material waste by 35% while consistently producing parts within specifications. This research provides manufacturers with a practical, scalable solution that transforms injection molding from an experience-dependent craft to a data-driven science. The BPNN-PSO framework not only delivers superior technical results but does so in a way that is accessible to resource-constrained manufacturers, marking a significant step toward sustainable, intelligent production systems. For SMEs, this framework offers a practical pathway to achieve both economic and environmental sustainability, reducing reliance on resource-intensive CAE tools while cutting production costs by an estimated 22% through waste and energy savings. The study provides a replicable blueprint for implementing data-driven sustainability in injection molding operations without compromising product quality or operational efficiency. Full article

Background/Objectives: This case study presents the first documented use of a low-cost, simulated, patient-specific three-dimensional (3D) printed model to support presurgical planning for an infant with Apert syndrome in a resource-limited setting. The primary objectives are to (1) demonstrate the value of 3D printing as a simulation tool for preoperative planning in low-resource environments and (2) identify opportunities for future AI-enhanced simulation models in craniofacial surgical planning. Methods: High-resolution CT data were segmented using InVesalius 3, with mesh refinement performed in ANSYS SpaceClaim (version 2021). The cranial model was fabricated using fused deposition modeling (FDM) on a Creality Ender-3 printer with Acrylonitrile Butadiene Styrene (ABS) filament. Results: The resulting 3D-printed simulated model enabled the surgical team to assess cranial anatomy, simulate incision placement, and rehearse osteotomies. These steps contributed to a reduction in operative time and fewer complications during surgery. Conclusions: This case demonstrates the value of accessible 3D printing as a simulation tool in surgical planning within low-resource settings. Building on this success, the study highlights potential points for AI integration, such as automated image segmentation and model reconstruction, to increase efficiency and scalability in future 3D-printed simulation models. Full article

The simulation analysis of a novel stay-in-place formwork (SIPF) beam reinforced with perforated steel pipe skeleton was conducted. The SIPF beam consists of a modified magnesium oxysulfide mortar (MMOM) formwork, a square steel pipe skeleton with holes dug on the sides and top, and cast-in-place concrete. The finite element (FE) analysis model of the SIPF beam was established by using the ABAQUS CAE 2021 software, and simulation analysis was conducted with the shear span ratio (SSR), the distance between the remaining steel strips, and the strength of concrete as the variation parameters. The results show that the stiffness and shear capacity of the SIPF beam decrease with the increase in SSR and increase with the decrease in strip spacing. Under the same conditions, when the concrete strength grade is increased from C30 to C50, the shear bearing capacity of the SIPF beam increases by 11.8% to 16.2%. When the spacing of the steel strips is reduced from 200 mm to 150 mm, the shear bearing capacity can be increased by 12.7% to 31.5%. When the SSR increases from 1.5 to 3.0, the shear bearing capacity decreases by 26.9% to 37.3%. Moreover, with the increase in the SSR, the influence of the steel strip spacing on the shear bearing capacity of the SIPF beam improves, while the influence of the concrete strength on the shear bearing capacity decreases. Taking parameters such as SSR, steel strip spacing, and concrete strength as variables, the influence of steel pipe constraining the core concrete on the shear bearing capacity was considered. The calculation formula for the shear bearing capacity of the SIPF beam with perforated steel pipe skeleton was established. The calculation results fit well with the laboratory test and simulation test results and can be used for the design and calculation of engineering structures. Full article

To investigate the crack extension mechanism in CFRP-reinforced concrete, this paper derives analytical expressions for the external load and crack opening displacement in the fracture process of CFRP concrete beams based on the crack emergence toughness criterion and the Paris displacement formula as the theoretical basis. A numerical iterative method was used to computationally simulate the fracture process of CFRP-reinforced concrete beams and to analyze the effect of different initial crack lengths on the fracture process. The research results indicate that the numerical simulation results of the crack initiation load are in good agreement with the test results, and the crack propagation curves and the test results are basically consistent before the CFRP-concrete interface peels off. The numerical results of ultimate load are lower than the test results, but it is safe for fracture prediction in actual engineering. With the increase in the initial crack length, the effect of the initial crack length on the critical effective crack propagation length is more obvious. Full article

The potential damage to aviation products caused by vibration and shock during road transportation has long been overlooked, despite structural failure under dynamic loading emerging as a critical technical challenge affecting product reliability. For aviation components, both stress and vibration analysis are essential prerequisites prior to formal assembly. This study investigates a symmetric vertical tail, a common aviation structure, employing an innovative model group analysis method to characterize its dynamic stress and strain distributions under real transportation conditions. Experimental measurements of vibration acceleration and impact loads during transport served as input data for constructing a numerical model based on stress and vibration theory. The model elucidates the mechanical responses of the tail in both modal and vibrational states, enabling effectively evaluation of dynamic vibrations on the tail and its critical subcomponents during road transport. The findings provide actionable insights for optimizing aviation component packaging design, mitigating vibration-induced damage, and enhancing transportation safety. Full article

Thin-walled hemispherical shell structures are mainly used in the aerospace industry as energy absorbers. However, their thin walls frequently lead to stability problems. To create a stable structure, double-layered thin-walled hemispherical shell structures were developed. In this study, we investigated the deformation behaviors of these structures through both experimental and numerical methods. The shell span diameter is taken as 200 mm. Monolithic layers have thicknesses of 1.0 mm compared with double-layered shells which have thicknesses of 0.5 mm (inner)/0.5 mm (outer). We developed numerical models to simulate the structural responses of monolithic and double-layered spherical shell structures using ABAQUS/CAE^® V6.14 software. These models were validated against experimental results. Our results show that double-layered shells absorb more energy compared to monolithic shells. These insights provide a foundation for improved designs of hemispherical structures, ultimately enhancing their energy absorption performance. Full article

This study presents the design and development of a structurally optimized mobile robotic system with a machine learning-based energy-efficient path planning framework. Topology optimization (TO) and finite element analysis (FEA) were applied to reduce structural weight while maintaining mechanical integrity. The optimized components were manufactured using Fused Deposition Modeling (FDM) with ABS (Acrylonitrile Butadiene Styrene) material. A custom power analysis tool was developed to compare energy consumption between the optimized and initial designs. Real-world current consumption data were collected under various terrain conditions, including inclined surfaces, vibration-inducing obstacles, gravel, and direction-altering barriers. Based on this dataset, a path planning model was developed using machine learning algorithms, capable of simultaneously optimizing both energy efficiency and path length to reach a predefined target. Unlike prior works that focus separately on structural optimization or learning-based navigation, this study integrates both domains within a single real-world robotic platform. Performance evaluations demonstrated superior results compared to traditional planning methods, which typically optimize distance or energy independently and lack real-time consumption feedback. The proposed framework reduces total energy consumption by 5.8%, cuts prototyping time by 56%, and extends mission duration by ~20%, highlighting the benefits of jointly applying TO and ML for sustainable and energy-aware robotic design. This integrated approach addresses a critical gap in the literature by demonstrating that mechanical light-weighting and intelligent path planning can be co-optimized in a deployable robotic system using empirical energy data. Full article

The formation of hot tearing during direct chill casting of aluminum alloys, specifically AA6111, is a significant challenge in the production of ingots for industrial applications. This study investigates the role of hydrostatic pressure and tensile stress in the formation of hot tearing during direct chill casting of rectangular ingots. Combining experimental results and finite element modeling with ABAQUS/CAE 2022, the mechanical behavior of the semi-solid AA6111 alloy was analyzed under different cooling conditions. “Hot” (low water flow) and “Cold” (high water flow) conditions were the two types of cooling conditions that produced cracked and sound ingots, respectively. The outcomes indicate that high tensile stress and localized negative hydrostatic pressure in the hot condition are the main factors promoting the initiation and propagation of cracks in the mushy zone, whereas the improvement of the cooling conditions reduces these defects. Full article

As a pivotal technology and infrastructure component for modern power systems, energy storage has experienced significant advancement in recent years. A fundamental prerequisite for designing future energy storage facilities lies in the systematic evaluation of energy conversion capabilities across diverse storage technologies. This study conducted a comparative analysis between pumped hydroelectric storage (PHS) and compressed air energy storage (CAES), defining the concepts of height exergy and temperature exergy. Height exergy is the maximum work capacity of a liquid due to height differences, while temperature exergy is the maximum work capacity of a gas due to temperature differences. The temperature exergy represents innovation in thermodynamic analysis; it is derived from internal exergy and proven through the Maxwell relation and the decoupling method of internal exergy, offering a more efficient method for calculating energy storage capacity in CAES systems. Mathematical models of height exergy and temperature exergy were established based on their respective forms. A unified calculation formula was derived, and their respective characteristics were analyzed. In order to show the meaning of temperature exergy more clearly and intuitively, a height exergy model of temperature exergy was established through analogy analysis, and it was concluded that the shape of the reservoir was a cone when comparing water volume to heat quantity, intuitively showing that the cold source had a higher energy storage density than the heat source. Finally, a typical hybrid PHS–CAES system was proposed, and a mathematical model was established and verified in specific cases based on height exergy and temperature exergy. It was demonstrated that when the polytropic exponent n = 1.2, the theoretical loss accounted for the largest proportion, which was 2.06%. Full article

The air injection for brine drainage affects the thermodynamic characteristics of salt caverns in the operation of compressed air energy storage (CAES). This study develops a thermodynamic model to predict temperature and pressure variations during brine drainage and operational cycles, validated against Huntorf plant data. Results demonstrate that increasing the air injection flow rate from 80 to 120 kg/s reduces the brine drainage initiation time by up to 47.3% and lowers the terminal brine drainage pressure by 0.62 MPa, while raising the maximum air temperature by 4.9 K. Similarly, expanding the brine drainage pipeline cross-sectional area from 2.99 m² to 9.57 m² reduces the total drainage time by 33.7%. Crucially, these parameters determine the initial pressure and temperature at the completion of brine drainage, which subsequently shape the pressure bounds of the operational cycles, with variations reaching 691.5 kPa, and the peak temperature fluctuations, with differences of up to 4.9 K during the first cycle. This research offers insights into optimizing the design and operation of the CAES system with salt cavern air storage. Full article