Search NASA⌕ Search

SEARCH · Search NASA

Results for “Composite Fatigue analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

20 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Performance Evaluation of AG-1 FC HEPA Filters and Medium in Nuclear Complex Facilities - 20434

High Efficiency Particulate Air (HEPA) filters are credited as the final barrier against the release of radioactive aerosol contamination in nearly every operating U. S. Department of Energy (DOE) and National Nuclear Security Agency (NNSA) nuclear facility. The Institute for Clean Energy Technology (ICET) at Mississippi State University maintains a research program that studies various aspects of these components of containment systems and seeks to answer key questions from across the industry. This programmatic overview will include results from recent and ongoing studies, including a study on filter design and performance envelope, degradation due to aging or fatigue, fire event impact on HEPA filters, and accelerated aging of medium and component parts. ICET has completed a study helping to define the threshold for combined elevated temperature and relative humidity resistance of separator style and separator-less style HEPA filters that are used in DOE complexes. The study provides experimental data that considers elevated temperature, elevated relative humidity (RH), and target differential pressure (dP) in an attempt to gain a better understanding or more comprehensive insight into the operating envelope of different configurations of ASME (American Society for Mechanical Engineers) AG-1 Section FC filters. Test variables including air temperature ranges of 48.88 deg. C, 54.44 deg. C, or 60 deg. C (120, 130, or 140 deg. F); air RH ranges including 60-70%, 80%, or 90+% with initial filter dP of either 497.68 Pa, 746.52 Pa, or 995.36 Pa (2 inches water column (in. w. c.), 3 in. w. c., or 4 in. w. c.) included for full bracketing of each set of conditions. A study examining the effects of aging and fatigue on nuclear grade HEPA filters and medium elucidates the physical properties of media along with testing of new and aged ASME AG-1 HEPA filters. In addition to performing autopsies of the tested, aged filters to help better understand the service life of their performance, newly designed accelerated aging chambers allow new medium to undergo accelerated aging and exposure treatments. Evaluation of degradation in physical properties and functionality requires a high population of aged filters of different ages, designs, manufacturers, and operational histories. The limited availability of this population and the impropriety of looking solely at formulations and components from past years requires experimental design that will provide foresight into future filter performance. This undertaking therefore involves not only properties analysis of aged medium from different operational histories, ages, designs, and manufacturers, but also prescience in analytical goals via accelerated aging studies of newly manufactured media. A more complete understanding of the mechanisms of degradation from past formulations as well as current and future formulations is possible. Accelerated Aging of newly manufactured media utilizes exposure treatments based on the Arrhenius equation to artificially age medium and therefore glimpse into the future. Evaluation of current media properties This testing allows for the comparison of performance and durability of new filters under upset or design basis conditions with aged filters that were in service under ambient conditions and other aged filters retained in storage. Susceptibility of HEPA filters to the effects of combustion byproducts, heat, and water is also being studied in an attempt to gain understanding on the prevention of filter failures during fire events in nuclear facilities. ICET seeks to determine the effect of filter performance due to smoke loading using characterized smoke from variable fuel compositions, geometries, and loads; the smoke capacity of various filters with different burning conditions, heat release rates and transport, as well as mass transport from the air stream will be studied in low flow containment systems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
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↗