2594 papers
This study demonstrates that for orbital AI data centers, minimizing chip leakage is as critical as increasing temperature ratings for mass reduction, because current leakage levels largely negate the mass savings expected from operating at higher temperatures, though improved thermal management can still yield significant gains.
This study proposes a novel intelligent framework that integrates Finite Element Modeling with spatiotemporal deep learning, specifically leveraging a high-performance ConvLSTM model, to achieve rapid, non-destructive, and high-precision prediction of protective coating service life for copper alloy cultural relics.
This paper experimentally validates common analytical vortex models against measurements from four hovering rotor configurations, revealing that while minor modifications improve predictions of core parameters, significant challenges remain in accurately modeling blade tip vortices, particularly for configurations with unknown parameters.
This paper proposes a consistent lifecycle framework for optimizing AI systems that explicitly separates physical resource consumption from scarcity and attributional factors, revealing through synthetic analysis that carbon-only routing strategies can drastically increase water usage and that including fixed hardware burdens significantly alters the net environmental impact of such optimization policies.
This paper proposes an adaptive boundary control strategy, utilizing a Galerkin approximation and Lyapunov stability theory, to effectively suppress vibrations and estimate uncertain boundary stiffness in a Euler-Bernoulli overhead bridge subjected to moving vehicle loads.
This study utilizes IDDES numerical simulations to demonstrate that coordinating collective pitch and rotational speed can enhance the net propulsive efficiency of hybrid aquatic-aerial vehicles by up to 5.2% through optimized rotor-wing interactions, despite a trade-off where increased pitch improves system efficiency but may reduce rotor efficiency at excessively high settings.
This study proposes a multi-sensor monitoring framework utilizing an Adjustable Ring Mode laser, photodiodes, and infrared thermal cameras to effectively detect and characterize defects such as porosity, undercut, and non-penetration during the laser welding of SUS304L stainless steel for electric vehicle applications.
This study demonstrates that extreme temperature gradients in additive manufacturing generate substantial, anisotropic thermoelectric currents in AlSi10Mg that rival those in dedicated thermoelectric materials, significantly influencing melt-pool dynamics and solidification through Joule and Thomson heating, thereby challenging the conventional assumption that thermoelectric effects are negligible in engineering alloys.
This paper proposes a distributed stability-constrained operation method for receiving-end grids with multiple operators and HVDC infeeds that utilizes a reduced-order dynamic model, a Lyapunov-based stability index embedded via an adaptive cutting-plane algorithm, and a selection-matrix-based ADMM consensus approach to coordinate regional operations while minimizing stability violations with negligible cost increase.
This study presents an interpretable machine learning framework for assessing rainfall-related slope instability in Aba Prefecture, China, which employs a dual-source sampling strategy to mitigate bias, achieves high predictive accuracy through ensemble models, and utilizes spatial validation and SHAP analysis to confirm that terrain and rainfall are the dominant drivers of susceptibility while minimizing reliance on exposure-induced shortcut variables.