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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Multi-Scale Computational Platform for Predictive Modeling of Corrosion in Al-Steel Joints (Final Report)

The research team proposed to develop innovative multi-scale models to predict corrosion and the resulting mechanical performances in aluminum-steel joints. The methods of joining considered are resistance spot welding, self-piercing riveting, and rivet-welding, all suitable for mass production applications. The multi-scale models integrate high throughput first-principle calculations based on density functional theory (DFT), high throughput calculation of phase diagrams (CALPHAD) modeling, and finite element method (FEM) simulations. These models are to be validated through laboratory experiments. Furthermore, the models are available as open source so as to enable scientists and engineers in the community to adapt and contribute to the development and application. The approaches rely on the research team’s extensive experience on the prediction of properties of individual phases at finite temperatures and variable compositions through DFT calculations, and our broad expertise on dissimilar material joining and their corrosion. The proposed computational framework enables high throughput computations for improved predictions of corrosion and the associated mechanical performance in dissimilar material joints, resulting in significant reduction in computational time needed by the current state-of-the-art methods. With the participation of researchers from three universities, an auto manufacturer, two manufacturing technology/equipment suppliers, and a software developer/vendor, the interdisciplinary research team applies the technical development on both phase-based modeling and laboratory experiments into the automobile body joining processes for validation and technology demonstration. The global cost of corrosion was estimated at about 3.4% of the global GDP in 2013. By using available corrosion control practices, it is estimated a saving between 15-35% of the cost of corrosion. In the U.S., more than $276 billion is spent repairing corrosion damage. Prediction of the corrosion and its impact on performance of the dissimilar material joints is critical for reducing the massive number of the current corrosion-based recalls for automobiles. Thus, the project goal is to develop models to enable predictive maintenance and end-of-life planning of multi-metal joints with risk of corrosion under different conditions such as exposure to high temperatures in summer and salt solutions in winter, quantified through its pH. An academia-industry consortium led by the University of Michigan and including Pennsylvania State University, University of Illinois Urbana-Champaign, University of Georgia, General Motors Company, Livermore Software Technology Corporation, and Optimal Process Technologies, LLC. created multi-scale models for prediction of corrosion in aluminum-steel joint structures such of them used in vehicle subassemblies – chassis and transmission systems. Starting from the first principle calculations, the team developed mathematical and data-driven models to predict the metallic components, which are formed during joining of two metals, for example aluminum and steel - a lightweight multilateral system which is currently used in more than 60% car bodies. These models were used for simulating chemical reactions that are happening when the joining metallic components are exposed to high temperatures and different pH values. The team was able to predict how the corrosion installs on the metallic components and how they lead to a sudden failure of components in cars. Newly developed machine learning algorithms combining Science, Technology, Engineering and Math disciplines, advanced finite element simulation and experimental validations have been integrated in a platform for prediction of the corrosion evolution and prediction the failure of joints under mechanical loadings and fatigue. Moreover, based on machine learning and inverse analysis, the team proposed solutions for designing new metallic alloys less susceptible to corrosion when joining multi-material assembles. An average of 4% error compared with experiments was achieved for the most common joints that are used in vehicle subassemblies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Predictive design of novel nickel-based superalloys beyond Haynes 282. Final Report FWP-AL-19-510-097

Nickel-based superalloys are in great demand for harsh-service conditions involving high temperatures and oxidative environments. Haynes 282 stands out due to its excellent high-temperature properties and easy fabricability. However, the upper usage temperature of Haynes 282 is limited due to its relatively low liquidus temperature. Through high-fidelity density functional theory calculations and high-throughput experiments, new compositions that show higher liquidus temperature and higher strength are explored. While maintaining processability, the newly designed alloy shows improved strength and ductility at room temperature and better oxidation resistance up to 800°C. The new compositions showcase a minor change in the refractory and metalloid content can have a significant impact on the mechanical and oxidation performance of superalloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Surface Tagging of Al 6061-T6 using Photoluminescent Oxides

The goal of tagging nuclear fuels (or related materials, such as fuel cladding) is to provide a persistent marker, traceable to the fuel manufacturer, that can be detected after some length of time, likely after the fuel has been lost, rerouted, or partially destroyed. Ideally, the tag is complicated enough to encode data pertaining to the provenance (manufacturer, date or time, constituents, etc.) and robust enough to survive in-pile exposure and/or further destruction. The taggant should not change the behavior or performance of the fuel in any meaningful way. Photoluminescent tagging was an attractive solution to many of these challenges, as it is undetectable under normal lighting conditions, visible to the naked eye under specific lighting conditions, does not require a significant quantity of material to be photoluminescent, observable with simple commercial detection tools, and is versatile and robust in application. In FY23, the team focused on embedding photoluminescent taggants onto the replicate fuel cladding surface using welding. From a welding standpoint, photoluminescent taggants, in the form of oxides, are a viable option for tagging because the taggants have high melting points, and thus likely to survive welding temperatures (i.e., remain as a secondary phase particle and maintain luminescent properties). Additionally, these taggants have the potential to act as a metal matrix composite which may increase the strength-to-weight ratio, stiffness, and ductility of the base material, all beneficial to cladding and fuel performance.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗