Computational Tools for Corrosion-Related Ship Maintenance and Design

Corrosion-related maintenance encompasses a fifth of all maintenance costs expended by the U.S. Navy.  In marine environments, such as those experienced by Naval vessels, corrosion can be aggravated via the continuous exposure and high salt content.  Understanding, predicting, and forecasting the level of expected corrosion on a given ship can help inform long-term maintenance scheduling that is based on corrosion severity in contrast to periodic scheduled intervals.  Therefore, the proposed work will address these challenges by creating a multi-tiered computational toolset for corrosion-resistant design and maintenance of Navy ships.  Geometry-independent machine learning (ML) models will provide a rapid first approximation of degradation severity.  More computationally expensive physics-based models then will be used for higher-fidelity estimates of component lifetime and risk mapping over a component.  Corrosion protection systems will be integrated, to determine the efficacy and increase in component lifetime.  Predicted maintenance and component lifetimes will be related to maintenance costs and savings, to help inform designers and maintenance managers.  All developed models will be incorporated into user-friendly software tools written in open-source code for integration with existing Navy maintenance systems, such as the Navy Data Environment, Planned Maintenance System, and SKED Planned Maintenance Scheduling Software.  The proposed team includes a wide variety of expertise, computational modeling and analysis (University of Virginia, CMI BEASY), combined effects degradation (Luna Labs, University of Pittsburgh), and ship maintenance (Vision Point Systems, Lockheed Martin).  In addition, the proposed work has received letters of support from possible end-users and Phase II collaborators, including Naval Surface Warfare Center, Carderock Division (NSWCCD) and the Naval Air Warfare Center, Aircraft Division (NAWCAD).