662 papers
This study combines numerical simulations using LsDyna and experimental verification to investigate how die gaps and friction-induced heating affect the edge quality and stress state of 16MnCr5 steel during ultra-high-speed blanking, ultimately developing a finite element model capable of predicting surface defects.
This study utilizes particle-based numerical simulations to demonstrate that ramp geometry and wave conditions significantly influence overtopping inflow velocities, revealing that linear ramps generate higher velocities while convex ramps enhance energy dissipation, thereby providing critical velocity-based insights for optimizing turbine design in overtopping breakwater energy conversion systems.
This paper proposes a hybrid deep learning framework that integrates Gaussian mixture model-based unsupervised clustering with a BiLSTM-CRF network to achieve accurate and geologically consistent lithology identification from well logs, even in the presence of data imbalance, nonlinearity, and incomplete logging curves.
This paper proposes a software–hardware integrated system featuring a non-planar slicing and hybrid path generation method that utilizes curved-layer conformal slicing and coordinate transformation to achieve high-quality, defect-free remanufacturing of large sprockets by overcoming geometric mismatches and motion interference inherent in conventional planar slicing.
This study established and validated a calibrated Discrete Element Method (DEM) model for high-oleic peanut seeds by optimizing contact friction parameters through experimental design, thereby providing a reliable foundation for the precision design of peanut seed metering devices.
This study analyzes the dynamic response of 2024-T3 aluminum aircraft plates to projectile impact, revealing that damage severity and failure mechanisms are governed by the interplay of projectile geometry, mass, material, and velocity, with conical tips causing perforation while blunt noses promote elastic deformation.
This paper proposes a novel multi-criteria decision-making framework utilizing complex intuitionistic fuzzy rough Frank aggregation operators to effectively select optimal cybersecurity measures for power systems by addressing data uncertainty and imprecision, as validated through a case study and comparative analysis.
This study identifies significant dissatisfaction with maintenance practices in Kuwait's governmental schools and proposes a validated, centralized digital system leveraging mobile apps, cloud platforms, and ArcGIS technologies to enhance efficiency, safety, and responsiveness in the public education sector.
This paper proposes a distributed electricity–heat energy sharing framework for multi-microgrids that integrates spatio-temporally correlated distributionally robust optimization to handle source–load uncertainties and an asymmetric Nash bargaining mechanism to ensure fair benefit allocation, ultimately achieving low-cost, low-carbon, and robust coordinated operation.
This study demonstrates that an XGBoost machine learning model, trained on 810 experimental mixtures and interpreted via SHAP analysis, achieves high predictive accuracy for Ultra-High-Performance Concrete (UHPC) compressive strength while identifying fiber content and curing age as the most influential governing parameters.