1616 papers
This study establishes an auditable multi-model verification framework for a pushrod-actuated double-wishbone suspension that integrates kinematic, dynamic, and geometric analyses to demonstrate how rocker geometry significantly alters wheel rates and frequency estimates, ultimately identifying steering hardpoints as the priority for future refinement.
This paper presents a unified mathematical framework that integrates multi-scale multiphysics modeling, stochastic uncertainty propagation, and reliability assessment to optimize maintenance strategies for sustainable, climate-resilient infrastructure under uncertain environmental conditions.
This paper demonstrates that while machine learning models can accurately predict ground effect on irregular terrain offline, their deployment in closed-loop control often fails to improve performance over analytical baselines due to unaccounted actuator-level dynamics and asymmetric sensitivity to overprediction biases, necessitating specific correction strategies like asymmetric clamping rather than relying solely on predictive accuracy.
This paper reviews global vacuum test facilities for spacecraft propulsion, specifically evaluating the operational capabilities and mass-flow limits of a medium-scale university chamber at Sivas Bilim ve Teknoloji Üniversitesi through a mass conservation model to establish a framework for planning propulsion experiments.
This study evaluates and compares conventional, terrestrial laser scanning, and underground drone surveying methods at an Elazığ chrome mine, demonstrating that drone-based technologies offer significant advantages for mapping hazardous sub-level caving areas while enhancing occupational health and safety.
This paper proposes a robust hybrid neural network framework for photovoltaic power forecasting that integrates comprehensive data imputation techniques with an improved Golden Jackal optimizer to enhance accuracy and reliability in the presence of missing monitoring data.
This paper presents a low-cost, non-invasive solution for diesel engines where a biodegradable, non-toxic crankcase additive effectively cleans piston rings in situ to eliminate blow-by and oil smoke without requiring costly engine disassembly.
This study demonstrates that machine learning models, particularly XGBoost for regression and Random Forest for classification, can effectively predict electrical submersible pump discharge pressure and flow rate while identifying system failures, thereby enabling proactive maintenance to reduce downtime and enhance operational efficiency.
This study demonstrates that an Artificial Neural Network (ANN) model significantly outperforms other machine learning algorithms and traditional correlations in accurately predicting pump intake pressure for wells lacking downhole sensors, achieving low error rates using only readily available field measurements.
This paper proposes a novel structural health monitoring framework that integrates Variational Autoencoders with Bayesian risk estimation and optimization to achieve high-accuracy anomaly detection, precise localization, and intelligent sensor placement while effectively quantifying uncertainty and ensuring scalability for real-time applications.