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DOE OSTI2022

Exploring Functional Materials by Understanding Beam‐Sample Interactions

Abstract Ultra‐low‐dose electron diffraction is performed with a double metal cyanide catalyst (DMC) to understand how electron irradiation stimulates structural alterations in functional materials. The commonly fading diffraction patterns with dose accumulation depend on the irradiated area and the beam current even when below 50 femto Amperes. Heat generation is observed and modeled by statistical, inelastic scattering events to describe how phonon excitations modulate radiation hardness. Specifically, the characteristic 1/e‐decay of Bragg intensities from DMC is delayed from 6 to 30 eÅ −2 at room temperature, which is comparable to the effect of embedding radiation soft matter in ice. DMC's radiation hardness is enhanced by a latency dose that forms during a phase transformation. This unifying model predicts that a critical dose rate exists for any material that varies between 0.1 and 10 4 eÅ −2 s −1 because of a material dependent competition of heat generation and spread. It shows that Brillouin scattering causes time dependent perturbations in electron irradiated solids that trigger time‐temperature‐transformations on a time scale of nanoseconds to microseconds at room temperature, which is not included in traditional models describing the decay of Bragg intensities by radiolysis.

Kisielowski, Christian↗

DOE OSTI2021

Gravitational waves from first-order phase transitions in Majoron models of neutrino mass

We show how the generation of right-handed neutrino masses in Majoron models may be associated with a first-order phase transition and accompanied by the production of a stochastic background of gravitational waves (GWs). We explore different energy scales with only renormalizable operators in the effective potential. If the phase transition occurs above the electroweak scale, the signal can be tested by future interferometers. We consider two possible energy scales for phase transitions below the electroweak scale. If the phase transition occurs at a GeV, the signal can be tested at LISA and provide a complementary cosmological probe to right-handed neutrino searches at the FASER detector. If the phase transition occurs below 100 keV, we find that the peak of the GW spectrum is two or more orders of magnitude below the putative NANOGrav GW signal at low frequencies, but well within reach of the SKA and THEIA experiments. We show how searches of very low frequency GWs are motivated by solutions to the Hubble tension in which ordinary neutrinos interact with the dark sector. We also present general calculations of the phase transition temperature and Euclidean action that apply beyond Majoron models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Creation of Synthetic Electric Grids (SPP/MISO) Supporting PERFORM (Final Report)

Over the course of the project, two “realistic but not real” synthetic transmission-level grid models over the SPP-MISO and ERCOT footprints were created to provide more realistic data and increase the reliability and resiliency of the grids under a variety of scenarios. The synthetic ERCOT transmission grid is compatible with the distribution grid developed in collaboration with NREL. All generators are based on the EIA 860 data and a column with EIA plant code and Gen ID is added to generators of both grids so that they can be easily mapped. The improvements are also made to electric grids including N-1 contingencies with some remedial actions, improving the transmission lines to avoid lines in lakes, including an HVDC line to the SPP-MISO case, providing several generator parameters and their temporal constraints that were not included in EIA 860 form, generators’ cost curves, load offer curves, adding phase shifters and tap changers with impedance correction tables, adding reactive power control and partitioning the grids into active and reactive reserve zones and determine different types of the required reserve for each zone. Hourly load time series at the bus level were generated to create scenarios for solving power flow in different loading conditions. Weather measurement information and the models of renewable generators are used to directly include the impact of weather on the grids. Based on a variety of load and weather conditions the grids are improved to accommodate different conditions. The ERCOT 7k-bus grids were also modeled for the year 2030 with predicted improvements in renewable resources. The renewable generation model was also improved with historic weather data included. The impact of electric vehicles on the ERCOT grid is also modeled.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

A Reduced-Order Model of a Nuclear Power Plant with Thermal Power Dispatch

This paper presents reduced-order modeling of thermal power dispatch (TPD) from a pressurized water reactor (PWR) for providing heat to nearby heat consuming industrial processes that seek to take advantage of nuclear heat to reduce carbon emissions. The reactor model includes the neutronics of the reactor core, thermal–hydraulics of the primary coolant cycle, and a three-lump model of the steam generator (SG). The secondary coolant cycle is represented with quasi-steady state mass and energy balance equations. The secondary cycle consists of a steam extraction system, high-pressure and low-pressure turbines, moisture separator and reheater, high-pressure and low-pressure feedwater heaters, deaerator, feedwater and condensate pumps, and a condenser. The steam produced by the SG is distributed between the turbines and the extraction steam line (XSL) that delivers steam to nearby industrial processes, such as production of clean hydrogen. The reduced-order simulator is verified by comparing predictions with results from separate validated steady-state and transient full-scope PWR simulators for TPD levels between 0% and 70% of the rated reactor power. All simulators indicate that the flow rate of steam in the main steam line and turbine systems decrease with increasing TPD, which causes a reduction in PWR electric power generation. The results are analyzed to assess the impact of TPD on system efficiency and feedwater flow control. Due to the simplicity of the proposed reduced-order model, it can be scaled to represent a PWR of any size with a few parametric changes. In the future, the proposed reduced-order model will be integrated into a power system model in a digital real-time simulator (DRTS) and physical hardware-in-the-loop simulations.

08 HYDROGEN↗

DOE OSTI2020

Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model

Recently, there has been substantial interest in the study of various random networks as mathematical models of complex systems. As real-life complex systems grow larger, the ability to generate progressively large random networks becomes all the more important. This motivates the need for efficient parallel algorithms for generating such networks. Naïve parallelization of sequential algorithms for generating random networks is inefficient due to inherent dependencies among the edges and the possibility of creating duplicate (parallel) edges. In this article, we present message passing interface-based distributed memory parallel algorithms for generating random scale-free networks using the preferential-attachment model. Our algorithms are experimentally verified to scale very well to a large number of processing elements (PEs), providing near-linear speedups. The algorithms have been exercised with regard to scale and speed to generate scale-free networks with one trillion edges in 6 minutes using 1,000 PEs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Computational study of tungsten and depleted uranium photoneutron targets for a 20 MeV electron linear accelerator

Neutron production can be realized with a high energy electron linear accelerator by using Bremsstrahlung and photoneutron converters. In this study, Monte Carlo N-Particle Code (MCNP) was used to evaluate potential photonuclear target designs for a high energy electron linear accelerator for applications such as neutron radiography and neutron resonance spectroscopy. A computational model was developed to inform a target design that would yield a high number of neutrons. It consists of a 20 MeV electron beam incident on a Bremsstrahlung target and a photonuclear target to generate neutrons. This computational model showed that a thickness of 0.75 inches for both tungsten and depleted uranium yields the most neutrons from photoneutron reactions. Saturation in the total number of generated neutrons was observed at over 0.75-inch thickness for both evaluated materials. Depleted uranium yielded approximately twice the number of neutrons overall compared to tungsten. The highest neutron surface flux for Depleted Uranium was 1.06 × 10-4 neutrons/cm2/source electron, and for Tungsten it was 5.12 × 10-5 neutrons/cm2/source electron. The optimal target design for this study’s application would consist of a 0.75 inch-thick block of depleted uranium with the length, width, and/or diameter varying dependent on application.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Evolution of Scenario Generation Capabilities in the ExaSGD Project

High-fidelity renewable energy scenarios and rare-event high-impact contingencies are essential for modeling operations and infrastructure expansion of the next generation power grids using exascale computing resources. As the ExaSGD project progressed, our capabilities for generating scenarios for modeling wind power output at multiple wind farms significantly improved, evolving from the use of independent random perturbations to importance sampling-based techniques capturing representative spatial-temporal relations. Contingency generation also improved, evolving from standard N-1 techniques to probabilistic models of infrastructure damage resulting from extreme weather events. We review the developments in the realistic intermittent-energy scenario forecasting and extreme-event contingency creation as the ExaSGD project progressed and discuss the future work in these areas.

economic dispatch↗

DOE OSTI2020

Effects of surface charge and cluster size on the electrochemical dissolution of platinum nanoparticles using COMB3 and continuum electrolyte models

We study the site-dependent dissolution of platinum nanoparticles under electrochemical conditions to assess their thermodynamic stability as a function of shape and size using empirical molecular dynamics and electronic-structure models. The third-generation charge optimized many-body potential is employed to determine the validity of uniform spherical representations of the nanoparticles in predicting dissolution potentials (the Kelvin model). To understand the early stages of catalyst dissolution, implicit solvation techniques based on the self-consistent continuum solvation method are applied. It is demonstrated that interfacial charge and polarization can shift the dissolution energies by amounts on the order of 0.74 eV depending on the surface site and nanoparticle shape, leading to the unexpected preferential removal of platinum cations from highly coordinated sites in some cases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Reduced Order Model for Guided Wave Propagation on Gas Pipelines to Enable Real-Time Simulation

Reduced order model for simulation of Guided wave propagation is presented here. The utilization of reduced order models ensures efficient data generation for a variety of parameters where it takes huge computational effort to simulate, crucial for timely monitoring and decision-making. Autoencoder based reduced order models are proposed here, which are trained on simulated data from open-source finite element framework, Firedrake.

Bukka, Sandeep Reddy↗

DOE OSTI2022

1D Heat Loss Models Validation Experiment

Contains data from the model validation in the 1D Heat Loss Models to Predict the Aquifer Temperature Profile during Hot/Cold Water Injection Project. The data include two COMSOL models (2D axisymmetric benchmark model and 2D Vinsome model), one python code (1D Vinsome based FEM numerical simulation), one matlab main code (1D Newton analytical solution and all results comparison visualization), and output files generated from the above models.

1D↗

DOE OSTI2025

Dataset, Code, and Models for Training Deep Learning Potentials for Low Temperature Plasma-Surface Interactions

This repository contains datasets, training scripts, and finished models, and test simulations used in the development of DeepREBO— a machine-learned interatomic potential trained to emulate the REBO2 empirical potential. The data was generated to study deep potential development for simulations of plasma-surface interactions. It uses an active learning framework, starting from a minimal dataset and iteratively expanding it. Included are those generated datasets, the trained models, and simulations used to evaluate the performance of the training process. This resource supports reproducibility and provides a reference framework for training deep potentials in plasma-surface interaction studies.

active learning↗

DOE OSTI2024

Reduced Order Model for Guided Wave Propagation on Gas Pipelines to Enable Real-Time Simulation

Bukka, Sandeep Reddy↗

DOE OSTI2023

Topology, criticality, and dynamically generated qubits in a stochastic measurement-only Kitaev model

Here we consider a paradigmatic solvable model of topological order in two dimensions, Kitaev's honeycomb Hamiltonian, and turn it into a measurement-only dynamics consisting of stochastic measurements of two-qubit bond operators. We find an entanglement phase diagram that resembles that of the Hamiltonian problem in some ways, while being qualitatively different in others. When one type of bond is dominantly measured, we find area-law entangled phases that protect two topological qubits (on a torus) for a time exponential in system size. This generalizes the recently proposed idea of Floquet codes, where logical qubits are dynamically generated by a time-periodic measurement schedule, to a stochastic setting. When all types of bonds are measured with comparable frequency, we find a critical phase with a logarithmic violation of the area law, which sharply distinguishes it from its Hamiltonian counterpart. The critical phase has the same set of topological qubits, as diagnosed by the tripartite mutual information, but protects them only for a time polynomial in system size. Furthermore, we observe an unusual behavior for the dynamical purification of mixed states, characterized at late times by the dynamical exponent z = $\frac{1}{2}$ a superballistic dynamics made possible by measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Data Validation Experiments with a Computer-Generated Imagery Dataset for International Nuclear Safeguards

Computer vision models have great potential as tools for international nuclear safeguards verification activities, but off-the-shelf models require fine-tuning through transfer learning to detect relevant objects. Because open-source examples of safeguards-relevant objects are rare, and to evaluate the potential of synthetic training data for computer vision, we present the Limbo dataset. Limbo includes both real and computer-generated images of uranium hexafluoride containers for training computer vision models. Here, we generated these images iteratively based on results from data validation experiments that are detailed here. The findings from these experiments are applicable both for the safeguards community and the broader community of computer vision research using synthetic data.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2025

Topography, surface water distribution and subsurface structure in 2023 across an Arctic coastal tundra site near Utqiagvik, Alaska

Subsurface electrical resistivity tomography (ERT), active layer thickness measurements, photogrammetry, and topographic data were collected in September 2023 along a 475 m long, 20 m wide corridor that traverses various polygon types within the Barrow Environmental Observatory (BEO) on the Alaskan Arctic Coastal Plain, approximately 4 miles from the Beaufort Sea near Utqiaġvik, Alaska. These measurements were designed to assess decadal changes in surface water distribution, topography, and subsurface structure across this dynamic landscape. This archive contains the datasets acquired in 2023 and references to the datasets acquired previously at the same location. The ERT survey was conducted along the 475 m transect using 0.5 m electrode spacing and a roll-along acquisition strategy. Thaw layer thicknesses were measured with a tile probe along the same transect. Photogrammetry data were acquired using an unoccupied aerial vehicle (UAV) and were used to generate a digital elevation model and an RGB mosaic. A real-time kinematic (RTK) GPS was used to survey the ERT electrodes and the ground control points for the aerial imagery. The dataset contains 5 *.csv data files, 6 *.csv metadata files, and 6 *.tif files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Apprentice for Event Generator Tuning

Apprentice is a tool developed for event generator tuning. It contains a range of conceptual improvements and extensions over the tuning tool Professor. Its core functionality remains the construction of a multivariate analytic surrogate model to computationally expensive Monte-Carlo event generator predictions. The surrogate model is used for numerical optimization in chi-square minimization and likelihood evaluation. Apprentice also introduces algorithms to automate the selection of observable weights to minimize the effect of mis-modeling in the event generators. We illustrate our improvements for the task of MC-generator tuning and limit setting.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Scalable balanced training of conditional generative adversarial neural networks on image data

Here, we propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models. Our method reduces the imbalance between generator and discriminator by partitioning the training data according to data labels, and enhances scalability by performing a parallel training where multiple generators are concurrently trained, each one of them focusing on a single data label. Performance is assessed in terms of inception score, Fréchet inception distance, and image quality on MNIST, CIFAR10, CIFAR100, and ImageNet1k datasets, showing a significant improvement in comparison to state-of-the-art techniques to training DC-CGANs. Weak scaling is attained on all the four datasets using up to 1000 processes and 2000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Evaluation of Ocean Biogeochemistry and Carbon Cycling in CMIP Earth System Models With the International Ocean Model Benchmarking (IOMB) Software System

Abstract The International Ocean Model Benchmarking (IOMB) software package is a new community resource that we use here to evaluate surface and upper ocean biogeochemical variables and integrated anthropogenic carbon uptake from earth system models (ESMs) contributing to the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). IOMB generates graphics and tables for systematically comparing model predictions against multiple datasets. Our analysis reveals some improvement in the multi‐model mean from CMIP5 to CMIP6 for most of the variables we examined. Compared to data‐constrained estimates of ocean anthropogenic carbon uptake for the 1994–2007 period, negative biases exist for many models between 30 and 50°S. Global model estimates of anthropogenic carbon uptake for the same period do not change significantly from CMIP5 to CMIP6, with the combined ensemble mean estimate of 27.8 ± 0.5 Pg C lower than a data‐constrained estimate of 33.0 ± 4.0 Pg C. At the same time, the change in the natural carbon inventory from CMIP is estimated to be a source of 0.7 ± 0.3 Pg C, which is considerably smaller in magnitude than a data‐constrained estimate of 5.0 ± 3.0 Pg C. With chlorofluorocarbon (CFC) predictions available for several models, we demonstrate that negative anthropogenic dissolved inorganic carbon biases coincide with negative biases in CFC concentration, highlighting the importance of weak exchange between the surface and interior ocean in regulating rates of anthropogenic carbon uptake. To examine the robustness of this attribution across the CMIP models, we calculate the global vertical temperature gradient between 200 and 1,000 m as a metric for global stratification and exchange between the surface and deeper waters. We find a linear relationship between the bias of the vertical temperature gradients and the bias in global anthropogenic carbon uptake, consistent with the hypothesis that model biases in anthropogenic carbon uptake are related to biases in surface‐to‐interior exchange by physical processes.

58 GEOSCIENCES↗