1616 papers
This study demonstrates that optimizing key process parameters in Cold Metal Transfer (CMT) welding of thin aluminum joints is essential to balance reduced heat input with adequate fusion, thereby minimizing defects and distortion while maximizing mechanical performance.
This study utilizes finite element simulations to reveal that under horizontal impact loads, concrete-filled steel tubular rock-socketed piles exhibit damped free vibrations with complex, often opposing, internal force distributions between the steel tube and concrete core, ultimately demonstrating that the steel tube fails to share shear and bending moments, thereby increasing the load on the concrete core.
This paper presents a machine learning framework using convolutional neural networks and U-Nets to reconstruct the longitudinal phase-space distribution of the LANSCE accelerator beam from limited phase-scan measurements, demonstrating that these models can significantly reduce data collection requirements while achieving reconstruction accuracy comparable to manual calibration and validating results through forward-propagated simulations.
This study demonstrates that while pipeline operating pressure is the dominant factor governing the dispersion and flammability of under-expanded jets, blending natural gas with up to 20% hydrogen has a negligible impact on flow behavior, suggesting that existing safety guidelines for natural gas releases remain applicable.
This paper proposes a readiness-gated management system for manufacturing ramp-up that replaces fixed calendar milestones with evidence-based cross-functional reviews of six critical areas to optimize the timing of transitioning from stabilization to broader improvement, thereby balancing the risks of premature scaling against the costs of unnecessary delays.
This study demonstrates that low-cost waste-derived antibacterial materials, particularly copper-based waste, eggshell powder, and zinc oxide, effectively inhibit bacterial growth in construction applications when incorporated into either cementitious composites or epoxy coatings, thereby enhancing the durability and sustainability of infrastructure.
This study demonstrates that neural network models trained exclusively on idealized finite-element simulations can effectively reconstruct damage in functional conductive sheet materials using electrical resistance tomography, achieving robust performance across different materials despite experimental challenges like contact effects and coating non-uniformity.
This study experimentally characterizes the hydro-mechanical evolution of sand–clay mixtures across a 0–100% clay content range and evaluates various data-driven models to demonstrate that while clay content governs the transition from granular to cohesive behavior, model suitability is property-specific and most effective when interpreted alongside experimental evidence within the measured domain.
This study characterizes the non-monotonic bearing capacity of sand–clay mixtures by revealing a "resistance-deficit window" near 80% clay content where the decay of granular friction outpaces the mobilization of cohesive strength, a phenomenon validated through a comprehensive multiphysics framework combining analytical, limit-analysis, and finite-element methods.
This paper establishes a unified information-theoretic framework for Earth–Moon optical links that integrates stochastic lunar dust attenuation and photon-starved regimes to derive composite channel models, characterize asymptotic capacity limits for various modulation schemes, and optimize power allocation strategies for future lunar communication systems.