Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Modeling The Ballistic Limit Of Fragment Simulating Projectiles Impacting A36 Mild Steel Spaced Armor Configurations, Daniel H. Rios-Estremera
Modeling The Ballistic Limit Of Fragment Simulating Projectiles Impacting A36 Mild Steel Spaced Armor Configurations, Daniel H. Rios-Estremera
Theses and Dissertations
Terminal ballistics study multivariate behavior and aftermath of projectile and target interactions. Tests and models are often based on monolithic armors, however, layered and spaced armors are common in real world applications. Such configurations add complexities that require research to understand their effects on terminal ballistics. The ballistic limit velocity (V50) represents the speed where armor perforation probability is 50%. It is used for quantitative comparison of protection capabilities for different armors. This research studied the V50 of spaced and layered A36 steel armors against fragment simulating projectiles (FSPs). Four methods for estimating armor V50 were evaluated and compared to …
Uncertainty-Aware Deep Learning For Prediction Of Remaining Useful Life Of Mechanical Systems, Samuel J. Cornelius
Uncertainty-Aware Deep Learning For Prediction Of Remaining Useful Life Of Mechanical Systems, Samuel J. Cornelius
Theses and Dissertations
Remaining useful life (RUL) prediction is a problem that researchers in the prognostics and health management (PHM) community have been studying for decades. Both physics-based and data-driven methods have been investigated, and in recent years, deep learning has gained significant attention. When sufficiently large and diverse datasets are available, deep neural networks can achieve state-of-the-art performance in RUL prediction for a variety of systems. However, for end users to trust the results of these models, especially as they are integrated into safety-critical systems, RUL prediction uncertainty must be captured. This work explores an approach for estimating both epistemic and heteroscedastic …