ProCAMS: Probabilistic Corrosion Analysis of Multi-material Structures
Abstract
Dissimilar material joining—the process of uniting two or more distinct materials—plays a crucial role in naval applications. The creation of multi-material systems through dissimilar material joining significantly enhances the durability, functionality, and safety of various naval structures and components that operate in demanding maritime environments. Various types of joining technologies such as resistance spot welding (RSW), self-pierce riveting (SPR), and rivet-weld (R-W), are extensively used in Navy systems for joining dissimilar materials such as aluminum-titanium (Al-Ti) and graphite/aluminum metal matrix composite (MMC). While these materials joining methods can successfully create joint structures with superior mechanical properties, the joints produced are susceptible to corrosion failure, particularly galvanic and crevice corrosion. Such corrosion typically occurs in crevices and gaps between materials. The formation of corrosion modifies the contacting surfaces of the metals, thereby inducing unexpected changes in internal joint loading, stress concentrations, and other forms of damage. Consequently, corrosion-induced material loss and geometry changes in the joints lead to performance degradation, significantly impacting their fatigue and failure performance under a corrosive environment. These factors undermine the reliability and longevity of these joints considerably, posing substantial challenges to maintaining the robustness, quality, performance, and safety standards in Navy systems. Existing efforts have been focused on corrosion modeling that investigates the influence of varying materials, geometrical patterns, and environmental factors on the corrosion initiation and progression, and quantitatively characterizes the corrosion phenomena. Nevertheless, critical gaps still exist in scalable, probabilistic analysis of corrosion pertaining to dissimilar material joints. A notable deficiency is the lack of high-fidelity multiphysics multiscale models that can accurately predict corrosion onset and evolution in multi-material systems consisting of a wide range of joint configurations and materials. Furthermore, the probabilistic and statistical evaluation of corrosion is presently hindered by the prohibitively high computational costs. Similar challenges are mirrored in the uncertainty quantification (UQ) for corrosion modeling. These technological gaps inhibit the successful analysis of corrosion phenomena in dissimilar material joints in the presence of diverse material combinations, joint varieties, and corrosive environments encountered. This research will create an effective modeling and UQ platform for corrosion analysis of dissimilar material joints, referred to as Probabilistic Corrosion Analysis of Multi-material Systems (ProCAMS). ProCAMS integrates three interconnected computational components. This project will develop high-fidelity, multiscale, multiphysics-based computational models to accurately simulate corrosion initiation and progression processes in dissimilar material joints. A novel physics-informed machine learning (PIML) framework will be created to facilitate fast, cost-effective surrogate modeling through hybrid multi-task learning of multi-fidelity data. The research will also develop an effective UQ technique with adaptive sampling to quantify the uncertainties associated with the corrosion processes for the ProCAMS platform. Ultimately, ProCAMS will provide effective and efficient modeling tools for corrosion analysis, thereby facilitating informed and optimal maintenance decision-making within Navy Systems.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 22, 2024
- Source ID
- N004212410004
Entities
People
- Yumeng Li
Organizations
- United States Navy
- University of Illinois Urbana–Champaign