1641 papers
This study presents and experimentally validates a high-power, integrated downhole motor-drive system utilizing a triple-closed-loop PID control architecture and dsPIC-based hardware, which successfully demonstrated stable, continuous operation for over 200 hours at 175°C with minimal current fluctuations and rapid response times under extreme logging-while-drilling conditions.
This study numerically evaluates the fatigue performance of PMMA/ZrO₂/nHA nanocomposites under biaxial loading, establishing a novel fatigue criterion correlated with monotonic response and demonstrating that increasing nanoparticle volume fractions significantly enhances durability.
This study utilizes FEMA P695 methodology and Incremental Dynamic Analysis on 3D reinforced-concrete building models to demonstrate that bidirectional seismic excitation significantly reduces collapse capacity, necessitating a reduction in the response modification factor (R) from 8 to 4–5, while validating an efficient 2DOF framework for accurate bidirectional collapse assessment.
This paper presents a multi-objective optimization framework combining finite element analysis, surrogate modeling, and an improved whale algorithm to determine the optimal geometric parameters for an adjustable orifice valve's insulation lining, successfully balancing thermal insulation performance with structural reliability and reducing maximum equivalent stress by approximately 36.3%.
This paper evaluates a hybrid AI framework for wildfire relief logistics using the 2026 Thunder Bay 36 event and finds that while predictive models accurately forecast road disruptions, they only improve operational outcomes when network closures coincide with active service periods, as schedule slack often renders advanced routing redundant.
This paper proposes a multiscale graph generative adversarial network that leverages two-level CLJP coarsening and graph attention to accurately predict the spatiotemporal temperature field and degree of cure in CFRP autoclave processes, achieving superior accuracy and a 99% reduction in computation time compared to traditional CFD simulations.
This study optimizes the binder jetting 3D printing process for GH2132 superalloy green parts by combining orthogonal experiments with an Artificial Neural Network and Bayesian optimization to identify key parameter interactions, ultimately achieving a validated relative density of 58.3% and dimensional deviations within 2%.
This study utilizes validated CFD simulations and ballistic modeling to demonstrate that the exhaust plume of a 155 mm Rocket-Assisted Projectile significantly alters base flow aerodynamics, increasing drag by up to 90% and reducing maximum range by approximately 720 meters, thereby providing a framework for optimizing propulsion systems to enhance artillery performance.
This study demonstrates that a GNSS-RTK and dual-IMU wearable system offers acceptable inter-system agreement and good-to-excellent interunit reliability for monitoring key soccer external-load metrics like total distance and peak speed, though metrics for very high-speed running exhibit greater variability and require cautious interpretation.
This paper proposes a feature-driven optimization framework for free-form bulge-formed joints that decomposes surface geometry into key parameters, constructs a high-accuracy Kriging surrogate model to capture nonlinear performance relationships, and validates the method's effectiveness in optimizing joint design compared to alternative modeling approaches.