2594 papers
This paper presents a Type-1 linear controller for a 6-DOF Stewart platform designed to generate repeatable dynamic motion for EEG phantom experiments, demonstrating effective tracking and zero steady-state error within a 24-state linear model that incorporates actuator dynamics and integral error states.
This paper proposes and validates a technical solution involving a cooling jacket on submersible pump motors to mitigate overheating in aggressive ISL environments, demonstrating through mathematical modeling, CFD simulation, and laboratory testing that the device significantly increases fluid velocity and reduces motor surface temperatures at a negligible capital cost.
This paper presents a predictive modeling framework that integrates first-principles physics—specifically thermal history, solidification kinetics, and phase transformations—to accurately forecast the yield strength of Wire Arc Additive Manufactured (WAAM) metal parts, demonstrating superior generalization and accuracy over purely empirical methods for enabling real-time quality control and digital twin applications.
This paper demonstrates a practical framework for predicting droplet landing positions in nozzle-free, angle-controlled surface acoustic wave jetting by linking ejection angles to landing coordinates through a geometric model, achieving high placement precision and cross-day predictability while defining operational limits for single-droplet ejection.
This paper addresses the problem of fortifying acyclic distribution networks against node-based disruptions by formulating it as a nonlinear multiplicative programming problem, proving its NP-Hardness on general trees, and providing polynomial-time solutions for two-level systems with uniform disruptions alongside a near-optimal heuristic for general trees with S-shaped disruption distributions.
This study examines the Dietenbach district in Freiburg to demonstrate how community shared energy storage systems can effectively utilize second-life electric vehicle batteries as a sustainable, affordable solution, while emphasizing the critical need for standardized regulations to ensure safe and reliable implementation.
This study presents a physics-informed, uncertainty-quantified AI surrogate model that utilizes a novel Rheological Heat-Loss Index and ensemble learning to rapidly and accurately predict thermal and hydraulic losses in Omani crude oil pipelines, offering a fast, interpretable alternative to traditional CFD for real-time operational optimization.
This study develops a Bayesian–deterministic stochastic framework to generate physically traceable seismic-demand scenarios for the Baish Dam in Saudi Arabia, utilizing the 2014 Jazan earthquake source to establish conditional engineering screening envelopes while identifying rupture-length constraints and uncertainty drivers for future finite-fault evaluations.
This study demonstrates the feasibility of directly bonding power chips to non-metallized cyanate ester composite substrates via low-temperature nano-silver sintering, achieving high initial shear strength and good thermal-aging reliability, with long-term performance ultimately limited by substrate thermal degradation rather than interface failure.
This study combines a curated literature database with machine learning and process carbon accounting to demonstrate that while CO₂ curing of cement can yield net carbon benefits, the positive balance is highly sensitive to electricity carbon intensity and supply chain assumptions.