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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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Multi-Stage FNO
This is the code developed for paper - Reducing Frequency Bias of Fourier Neural Operators in 3D Seismic Wavefield Simulations Through Multi-Stage Training (https://arxiv.org/abs/2503.02023), it contains training the multi-stage FNO model to generate the seismic forward simulations in 3D medium.
OODG (OnlineOptDynaGrid) [SWR-24-07]
This is a repository for the code developed by NREL under the DynaGrid project. It is a Julia package for running a model-free real-time OPF algorithm to operate dynamic networked microgrids given network configuration results from the Maximal Load Delivery (MLD) problem in the PowerModelsONM.jl package. See Example.jl for a working example on how to use the package. Note: Julia package PowerModelsONM.jl is needed.
sparse_bias
This is a python package used to fit an unknown function from data that potential contains systematic biases related to metadata. The model fits the unknown function and uses a Bayesian horseshoe prior model to impose sparsity on the bias terms. This code has been generalized from research code developed for AIACHNE into a package that should have more general application in a wider class of statistical models.
Hurtado summer 2025 REDWOODS PWFS code
This release includes code developed by Benjamin Gerard and Joel Hurtado in summer 2025 to model the performance of different modes of the reflective three-sided pyramid wavefront sensor instrument within the REDWOODS module of the Shane Adaptive Optics system at Lick observatory.
Plant Reload Optimization (prlo)
The PRLO framework is built on a modular and extensible architecture that tightly couples advanced evolutionary optimization algorithms with nuclear fuel depletion solvers (i.e., nuclear physics neutronics code). It supports exploring complex, high-dimensional design spaces constrained by user-specified operational, safety, and economic constraints. Objectives such as minimizing fresh fuel enrichment, flattening radial and axial power distributions, and maximizing discharge burnup are evaluated. PRLO’s equilibrium cycle optimization capability enables the identification of core configurations that maintain fuel cycle sustainability over extended planning horizons. Its integration with the RAVEN platform facilitates optimization of loading patterns or fuel shuffling schemes across multiple cycles. The interface with SIMULATE, a licensed industry-standard nodal code developed by Studsvik, ensures accurate neutronic and thermal-hydraulic feedback for reactor core design. PRLO’s automated workflow engine supports iterative design refinement, enabling utilities to streamline core design processes and meet evolving performance and regulatory targets.
Modeling a Hypothetical Fusion Blanket Design Using MELCOR-TMAP
MELCOR (not an acronym) is a nuclear safety code developed by Sandia National Laboratories for the Nuclear Regulatory Commission. Idaho National Laboratory collaborates with Sandia to maintain and develop a version of MELCOR specifically for fusion device applications. Most recently, the tritium migration analysis program (TMAP) has been incorporated into MELCOR for fusion, in a version called MELCOR-TMAP. We show the results applying MELCOR-TMAP to model steady-state inventory and tritium retention in a liquid immersion blanket design for a hypothetical fusion device under steady-state operation.
TASK3 Laboratory Experiments Data
Laboratory Experiments Data of TASK3 of DOE project DE- FE0031686. Each signal has its own excel file. 150 signals are sent through the rock sample per each load configuration. There were approximately 50 to 150 load combinations tested during the experiments. Experiments were repeated after changing the polarity direction of the transducers. Signals of these repeats are in separate folders called "Reverse". There are three formations tested from the Michigan Core and four formations tested from the FutureGen site. The "Code" folder contains the Python code developed to analyze the signals. Large Dataset, please contact EDXSupport@netl.doe.gov
Machine learning at the Spallation Neutron Source accelerator and target
We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.
Comparison of Generation of Higher-Order Neutron Scattering Cross Sections
The generation of high-order neutron scattering cross sections consistent with high-fidelity simulations remains an area of active research. Popular options include generating cross sections from continuous energy Monte Carlo calculations or generating cross sections from a deterministic neutron transport calculation with high-fidelity tabulated cross sections. Both options present challenges. Monte Carlo simulations can naturally process continuous energy cross section data and allow for the general description of anisotropic neutron scattering given a scattering law. However, Monte Carlo simulations are inherently related to particle weighting and it has been suggested that this may be unacceptable for generating high-order neutron scattering cross sections. Deterministic neutron transport calculations can easily calculate high-order moments of the flux to appropriately calculate higher-order neutron scattering cross sections but are generally limited by discretization of space, energy, and angle. In this work, the trade-offs between generation of high-order neutron scattering cross sections via Monte Carlo and deterministic neutron transport methods are investigated. The methods implemented in the Monte Carlo computer program Serpent and the deterministic fast reactor neutron cross section generator MC2-3 are compared. Cross sections resulting from these methods are used in Rattlesnake, a deterministic neutron transport code developed by Idaho National Laboratory (INL), and results are compared to a reference continuous energy Monte Carlo calculation. Whereas previous work investigating the effects of anisotropic neutron scattering has focused on light water reactor (LWR) simulations, this work focuses on high-order neutron scattering cross sections as they relate to fast reactor simulations. To investigate the consequences of the Serpent and MC2-3 methodologies, a test problem is developed. The test problem is a one-dimensional geometry with fast reactor materials designed to demonstrate deep penetration and exacerbate the effects of high-order neutron scattering. Based on the results of the deep penetration test problem, it is concluded that P3 neutron scattering cross sections are sufficient to describe anisotropic scattering in fast reactor materials. Any neutron scattering of order higher than P3 offers negligible change in the eigenvalue of the test problem. Additionally, it is determined that the methodology as implemented in Serpent is applicable for generating high-order neutron scattering cross sections through at least P3 in fast reactor materials.
Resiliency of Degraded Built Infrastructure
Infrastructure resiliency depends on the ability of infrastructure systems to withstand, adapt, and recover from chronic and extreme stresses. In this white paper, we address the resiliency of infrastructure assets and discuss improving infrastructure stability through development of our understanding of cement and concrete degradation. The resiliency of infrastructure during extreme events relies on the condition, adaptability, and recoverability of built infrastructure (roads, bridges, dams), which serves as the backbone of existing infrastructure systems. Much of the built infrastructure in the US has consistently been rated D+ by the American Society of Civil Engineers (ASCE). Aged infrastructure introduces risk to the system, since unreliable infrastructure increases the likelihood of failures under chronic and extreme stress and are particularly concerning when extreme events occur. To understand and account for this added risk from poor infrastructure quality, more research is needed on (i) how the changing environment alters the aging of new and existing built infrastructure and (ii) how degradation causes unique failure mechanisms. The aging of built infrastructure is based on degradation of the structural materials, such as concrete and steel supports, which causes failure. Current work in cement/concrete degradation is based on (i) the development of high strength and degradation resistance concrete mixtures, (ii) methods of assessing the age and reliability of existing structures, and (3) modeling of structural stability and the microstructural evolution of concrete/cement from degradation mechanisms (sulfide attack, carbonation, decalcification). Sandia National Laboratories (SNL) has made several investments in studying the durability and degradation of cement based materials, including using SNL-developed codes and methodologies (peridynamics, PFLOTRAN) to focus on chemo-mechanical fracture of cement for energy applications. Additionally, a recent collaboration with the University of Colorado Boulder has included fracture of concrete gravity dams, scaling the existing work to applications in full sized infrastructure problems. Ultimately, SNL has the experience in degradation of cementitious materials to extend the current research portfolio and answer concerns about the resilience of aging built infrastructure.
Stockpile Assessment (Quad Chart)
Our goal was to characterize certain aspects of shaped charges. In order to determine the pressure field created by the jet and the jet velocity, I worked with and modified simulations using CTH, a code developed at Sandia. I was able to manipulate and modify certain variables in order to observe their effects and measure the pressure and velocities at different points in the simulation.
Institutional Computing: Annual Progress Report, Precision early universe simulations to constrain nuclear reactions and beyond standard model physics
The current one-year allocation (w20 qburst) is the first year in a two year allocation of the same name. The project has supported two publications in peer-review journals, another manuscript in preparation for publication, a white paper for the Astro2020 Decadal Survey, and a number of talks (see below). Code development continues on implementing a neutrino quantum kinetic equation solver into BURST, now called QBURST. While implementing coherent and forward-scattering processes into the dynamical neutrino evolution equations, multiple numerical issues have appeared. We have remedied a number of these issues through extensive debugging and testing, which include streamlining interpolation and implementing new ODE integration techniques. Further testing is required to solve the problem at a precision of better than one part in 10 6 . We expect that we will understand the numerical difficulties and implement solutions by end of year 2021.
Increasing the Usability of Sockeye: Implementing Customer-Driven Modeling Improvements, Features, and Documentation of Sockeye
A number of tasks for the heat pipe simulation tool Sockeye were identified based on customer needs and included both user documentation and code development: (a) Implement saturation-dependence in the capillary pressure model; (b) Provide an analytic utility to analyze the frozen startup limit; (c) Demonstrate custom friction closures; (d) Develop examples for different types of heat transfer sources and boundary conditions; (e) Demonstrate the capillary and sonic limits; and (f) Provide discretization details in the theory manual. These tasks were all completed, and results are given where applicable.
Isotopic and Fuel Lattice Parameter Trends in Extended Enrichment and Higher Burnup LWR Fuel, Volume II: BWR
Commercial light water reactor (LWR) operators and fuel vendors in the United States are pursuing changes to nuclear fuel that include extended enrichment (EE) and accident-tolerant fuel (ATF) designs to further improve reactor safety and plant economics. Extended fuel enrichments above 5% 235 U pin enrichment and up to 10% 235 U are a subset of high assay low-enriched uranium (HALEU) that may be deployed in commercial US LWRs in the near term. ATF features, such as cladding coatings or alternative cladding materials, are designed to improve fuel system performance under accident conditions. One goal of EE is to improve fuel cycle economy by enabling fuel to be depleted to higher burnup than the typical current limits (62 gigawatt-days per metric ton of uranium [GWd/MTU]). Adoption of EE, ATF, and high burnup (HBU) fuels in the US commercial fleet requires a clear understanding of the effects on core physics parameters and used fuel isotopic content, as well as confidence in the accuracy of computer code predictions over an expanded range of materials, enrichment, and burnup. A thorough understanding of the applicability and adequacy of benchmark data (e.g., criticality, decay heat, isotopic content) for computer code validation is necessary to ensure that appropriate safety margins are maintained. As part of the US Nuclear Regulatory Commission (NRC) agreement number 31310019N0008, “SCALE Code Development, Assessment and Maintenance,” the effects of EE, ATF, and HBU are being assessed for selected representative LWR fuel designs. The project is divided into phases, and this report summarizes the findings of the Phase 1 work, which focuses on lattice physics parameter and used fuel isotopic changes for a conventional GE14 10 x 10 boiling water reactor (BWR) design with GNF-2 part length rod patterns to model a modern BWR assembly design.
Large Eddy Simulations of Turbulence below Antarctic Ice Shelves
Turbulence below ice shelves is of key importance for predicting ice-shelf melt rates and consequently the contribution of ice sheets to sea level rise. In this project we conducted Large-Eddy Simulations (LES) to improve our understanding of sub-ice-shelf ocean turbulence and the relationship between ice-shelf melt rates and ocean conditions. Over the course of the second and final year of this project (FY2020), we accomplished both major code developments for the PArallel Large-eddy simulation Model (PALM; Maronga et al., 2015) and conducted a suite of simulations which form the basis for a publication in preparation.
Statistical Distributions for Mesh Independent Solutions in ALEGRA
The representation of material heterogeneity (also referred to as "spatial variation") plays a key role in the material failure simulation method used in ALEGRA. ALEGRA is an arbitrary Lagrangian-Eulerian shock and multiphysics code developed at Sandia National Laboratories and contains several methods for incorporating spatial variation into simulations. A desirable property of a spatial variation method is that it should produce consistent stochastic behavior regardless of the mesh used (a property referred to as "mesh independence"). However, mesh dependence has been reported using the Weibull distribution with ALEGRA's spatial variation method. This report describes efforts towards providing additional insight into both the theory and numerical experiments investigating such mesh dependence. In particular, we have implemented a discrete minimum order statistic model with properties that are theoretically mesh independent.
2D Magnetohydrodynamic Simulations of the Electrothermal Instability in Metallic Liners
The Virginia Tech (VT) Plasma Dynamics Laboratory Computational (PDCL) and Lawrence Livermore National Laboratory (LLNL) are performing two dimensional (2D) simulations of the electrothermal instability (ETI) using the LLNL multi-physics code Ares. Ares is a multi-physics arbitrary–Lagrangian-Eulerian (ALE) code developed by LLNL and is of particular use in studying magnetohydrodynamic (MHD) instabilities like the ETI due to its resistive MHD, magnetic diffusion, and radiative-hydrodynamics packages. Among its capabilities, it has the ability to model material strength, perform adaptive mesh refinement (AMR), and incorporate a wide variety of equations of state models and conductivity models. The 2D Ares simulation model created by VT-LLNL for studying the development and growth of the electrothermal instability has been configured with initial conditions based on the Mykonos Electrothermal Instability II (METI-II) experiments described by this grant and conducted by team members at the University of Nevada (UNR), the University of New Mexico (UNM), and Sandia National Laboratories. Previously, preliminary 2D Ares simulations of the ETI had been run to approximately 80ns. The rods in these simulations were initiated with sinusoidal perturbations at a similar order of magnitude to those measured on the aluminum rods used for the Mykonos experiment. This model has been improved by increasing the spatial resolution of the simulations and running the simulations further in time. In addition to the simulation run-times extending, the preliminary sinusoidal perturbation has been replaced with a perturbation derived from amplitude measurements by the experimental team, thereby correlating the simulation inputs better to the experimental runs. These new runs are capable of reaching 120ns of simulated time for the uncoated cases and to 200ns the 41 μm coated cases.