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Results for “accuracy”
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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Improving the accuracy of spin-lattice calculations with machine-learned interatomic potentials.
Abstract not provided.
Gamma ray Imaging Based Methods to Improve the Accuracy of Uranium Holdup Quantification
Deposits in commercial uranium processing facilities of interest to nuclear safeguards are predominantly low enriched uranium (LEU). The shapes and sizes of holdup deposits can vary significantly, thus making it difficult to model the deposits accurately for calibration purposes. This presents a challenge to currently employed methods such as the Generalized Geometry Holdup (GGH) that rely on quantifying 235U mass by simplifying deposit shapes as a point, a line, or an area. In this work, gamma ray imaging using high energy resolution Germanium Gamma Imagers (GeGI™) is employed to determine the distribution of uranium inside the source containment. Two types of imaging methods are employed; coded-aperture imaging and Compton imaging. Gamma-ray emissions seen in different regions of the image are quantified using an inverse gamma-ray transport solver being developed at ORNL. Using measured data, the inverse method will be used to solve for unknown source parameters such as source matrix thickness, density, and attenuation due to container wall and shielding. The intrinsic efficiency of the detector is determined based on a library of built-in response functions and key dimensions of the detector (e.g., thickness and radius of the detector crystal). Measurements are performed in both imaging modalities using uranium sources of well-known masses and enrichments configured inside mocked-up holdup fixtures. Results from the two imaging modalities are intercompared. The paper presents preliminary results of the imaging measurements and the progress made in the computational methods to quantify uranium mass.
Evaluation of Time-step Frequency on Prediction Accuracy Applied to Deep Learning Neural Network Surrogate Models for CO2 Storage
This PowerPoint presentation was presented virtually at the American Geophysical Union (AGU) Fall Meeting 2020.
Update on the DVC Challenge: Role of XCT equipment and scan parameters on DVC accuracy.
Abstract not provided.
Improving the accuracy of spin-lattice simulations with machine-learning interatomic potentials.
Abstract not provided.
A Path to the Exascale for Atomistic Simulations with Improved Accuracy Length and Time Scales.
Abstract not provided.
X-ray Diffraction Precision Accuracy and Confidence Investigation for Determining Crystallite Size in Nanopowders.
Abstract not provided.
Assessing the Accuracy of ML Explanations for Model Credibility.
Abstract not provided.
Effects of the Accuracy and Visual Representation of Machine Learning Outputs on Human Decision Making.
Abstract not provided.
Enabling solution verification and efficient, high-accuracy simulations through spline-based adaptivity in MOOSE
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Different Visualizations of Machine Learning Outputs Influence the Speed and Accuracy of User Evaluations.
Abstract not provided.
Different Visualizations of Machine Learning Outputs Influence the Speed and Accuracy of User Evaluations.
Abstract not provided.
Empirical Results Suggest Quasi-Monte Carlo Sampling Increases Accuracy in the Estimation of Annual Energy Production from Operational Data
Quasi-Monte Carlo sampling was implemented in the OpenOA annual energy production analysis method. Preliminary results show that this method improves the order of the asymptotic convergence of OpenOA’s AEP estimates.
Improving detection accuracy of microseismic activity during geologic carbon storage using a machine learning method.
Abstract not provided.
Tensor Sensitivity and long-range Coulomb interactions improve the accuracy and extensibility of Machine Learning Potential
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Modernization and Scalability Enhancements for Sub‐Nanosecond Accuracy Diagnostic Cross Timing for Use at Current and Future NNSS Testbeds
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