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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

MULTIPHYSICS-MODELING OF FIRE-INDUCED URANIUM AEROSOL FORMATION – A-POSTERIORI BENCHMARKING OF EXPERIMENTS

The formulation and solution of a model that properly described the temperature profiles of uranium in fire conditions was prepared, verified and validated, to support a-posteriori benchmarking of historical experiments. Data from the multi-physics model combined with data regression from the experiments provides a useful tool for exploring sources of potential bias in historical experiments. The approaches used are augmented by visual observations and photographic evidence allowing for correlations between various phenomena, independent of whether or not that specific phenomena are part of the validated model. In this manner, the validated model is a tool for a-posteriori benchmarking, and not a model designed to replicate the experimental output exactly for any specific experiment. For experiments like the Clark (2015) experiments, this integrated model (including chemistry) demonstrated that the largest bias was associated in the metallurgy of the coupons. The model developed does serve as a basis for defending which of the historical alloys tested are the most appropriate for any other alloy being considered (e.g., one that has not yet been tested). The model identifies key reactions that impact uranium chemistry and can significantly bias experimental results. Through use of the model and energy balances, data from historical experiments by Elder and Tinkle (1980) were regressed and benchmarked. This study identifies that all uranium alloys are not equal and that there are distinct differences between the dominant alloys representing distinct metallurgical phases.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Multiphysics Simulation of Supercritical CO2 Gasification for Hydrogen Production

A bench-scale supercritical CO2 gasifier is being considered by the U.S. Department of Energy’s National Energy Technology Laboratory (NETL). A reacting computational fluid dynamics (CFD) model was developed using ANSYS/Fluent in order to investigate the impact of various operating parameters on syngas composition. The model was validated using available data in the literature. Simulations were conducted over a range of coal-slurry loadings, reaction temperatures, system pressures, and oxygen mass flowrates with the goal of optimizing hydrogen production for the considered reactor. The results from the simulations identified a positive correlation between slurry loading and hydrogen production, while reactor temperature and pressure had a limited impact on H2 production at the conditions of interest. Additionally, the simulations indicated that oxygen mass flowrate has less of an impact on hydrogen production at higher slurry loadings.

Prlina, Alexander↗
DOE OSTI2022

Multiphysics Modeling in Support of NASA Nuclear Thermal Propulsion Designs

This presentation provides and overview of INL and some of the work we do, but focuses on analysis and methods at INL being performed in support of NASA nuclear thermal propulsion. This presentation will be given at department seminars at Texas A&M University and at Georgia Tech.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Neural Network-Enhanced Reproducing Kernel Particle Method for Image-Based Multiphysics Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant stresses during charge/discharge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), image-based modeling techniques are used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electrochemical-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in capturing crack propagation due to mesh dependency. Additionally, commonly used damage models, such as the continuous damage model and the cohesive zone model, often have steep tradeoffs between discontinuous field accuracy and computational expense. In this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022.

damage modeling↗