Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Surrogate Models”

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.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

AL-ASMR: Active Learning of Atomistic Surrogate Models for Rare Events

Atomistic simulation with artificial intelligence (AI) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials with optimized/targeted properties. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside of their training domain and typically fail catastrophically in the prediction of rare events in molecular dynamics (MD) simulations. For effective AL loops to distinguish the informative data from enhanced sampled configurations, we developed a decision engine by configurational similarity and uncertainty quantification (UQ) with data augmentation.

Jung, Gang Seob↗
DOE OSTI2025

mphys-surrogate-model

This repository contains python scripts for building and studying reduced-order-modeling representations of droplet coalescence for eventual use in atmospheric models. The included data are generated from high-fidelity superdroplet methods and are utilized by machine learning pipelines to build data-driven models of droplet size distributions that evolve under coalescence. This repository further includes scripts to determine prediction (uncertainty) intervals on the data-driven model products based on conformal prediction.

Katona, JonasE [Lawrence Livermore National Labora↗
DOE OSTI2020

Surrogate Model Development of Spent Fuel Degradation for Repository Performance Assessment

In model simulations of deep geologic repositories, UO 2 fuel matrix degradation typically begins as soon as the waste package breaches and groundwater contacts the fuel surface. The initial degradation rate depends on the timing of these events, burnup of the fuel, temperature, and concentrations of dissolved reactants. Estimating the initial rate of degradation is fairly straightforward, but as UO 2 corrosion products precipitate on the fuel surface and the movement of dissolved species between the fuel surface and environment is impeded by the precipitated solids, the rate is more difficult to quantify. At that point, calculating the degradation rate becomes a reactive-transport problem in which a large number of equations must be solved by iteration for a large number of grid cells at each time step. The consequence is that repository simulations, which are already expensive, become much more expensive, especially when hundreds or thousands of waste packages breach. The Fuel Matrix Degradation (FMD) model is the process model of the Spent Fuel and Waste Science and Technology (SFWST) campaign of the US Department of Energy (DOE). It calculates spent fuel degradation rates as a function of radiolysis, redox reactions, electrochemical reactions, alteration layer growth, and diffusion of reactants through the alteration layer. Like other similar fuel degradation process models, it is a complicated model requiring a large number of calculations and iterations at each time step.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗