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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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Estimation of stagnation performance metrics in magnetized liner inertial fusion experiments using Bayesian data assimilation
Here we present a new analysis methodology that allows for the self-consistent integration of multiple diagnostics including nuclear measurements, x-ray imaging, and x-ray power detectors to determine the primary stagnation parameters, such as temperature, pressure, stagnation volume, and mix fraction in magnetized liner inertial fusion (MagLIF) experiments. The analysis uses a simplified model of the stagnation plasma in conjunction with a Bayesian inference framework to determine the most probable configuration that describes the experimental observations while simultaneously revealing the principal uncertainties in the analysis. We validate the approach by using a range of tests including analytic and three-dimensional MHD models. An ensemble of MagLIF experiments is analyzed, and the generalized Lawson criterion χ is estimated for all experiments.
Robust experimental data assimilation for the Spalart-Allmaras turbulence model
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Resource-Constrained Model Selection for Uncertainty Propagation and Data Assimilation
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Deep learning-based data assimilation in the latent space for real-time forecasting of geologic carbon storage.
Abstract not provided.
Wave Data Assimilation in Support of Wave Energy Converter Power Prediction.
Abstract not provided.
Simultaneous inference of multiple stagnation performance metrics in MagLIF Experiments using Bayesian Data Assimilation .
Abstract not provided.
Physics-based Deep Learning Driven CO2 Flow Modeling and Data Assimilation for Real-Time Forecasting.
Abstract not provided.
Physics-based Reduction with Monitoring Data Assimilation for Adaptive Representations in Structural Systems.
Abstract not provided.
Physics-based Deep Learning Driven CO2 Flow Modeling and Data Assimilation for Real-Time Forecasting.
Abstract not provided.
Towards data assimilation of ship induced aerosol-cloud interactions .
Abstract not provided.
Turbulent Inflow Data Assimilation with a 2.8 MW Wind Turbine to Validate Mid-Fidelity Aeroservoelastic Modeling Techniques .
Abstract not provided.
Deep learning-based data assimilation in the latent space for real-time forecasting of geologic carbon storage.
Abstract not provided.
Data assimilation for microstructure evolution: Grain growth in kinetic Monte Carlo.
Abstract not provided.
Machine Learning-Driven Poroelasticity Modeling and Real-time Data Assimilation.
Abstract not provided.
Data assimilation for microstructure evolution: Grain growth in kinetic Monte Carlo
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Field scale application of deep learning-based data assimilation for parameter estimation and uncertainty quantification at a geologic carbon storage site
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Addressing Spurious Correlations in Data Assimilation
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