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DOE OSTI2022

Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network

Spin glasses, generally defined as disordered systems with randomized competing interactions, are a widely investigated complex system. Theoretical models describing spin glasses are broadly used in other complex systems, such as those describing brain function, error-correcting codes5 or stock-market dynamics. This wide interest in spin glasses provides strong motivation to generate an artificial spin glass within the framework of artificial spin ice systems. Here we present the experimental realization of an artificial spin glass consisting of dipolar coupled single-domain Ising-type nanomagnets arranged onto an interaction network that replicates the aspects of a Hopfield neural network. Using cryogenic X-ray photoemission electron microscopy (XPEEM), we performed temperature-dependent imaging of thermally driven moment fluctuations within these networks and observed characteristic features of a two-dimensional Ising spin glass. Specifically, the temperature dependence of the spin glass correlation function follows a power-law trend predicted from theoretical models on two-dimensional spin glasses. As a result, we observe clear signatures of the hard-to-observe rugged spin glass free energy in the form of sub-aging, out-of-equilibrium autocorrelations and a transition from stable to unstable dynamics.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Modeling of particle transport, neutrals and radiation in magnetically-confined plasmas with Aurora

In this work, we present Aurora, an open-source package for particle transport, neutrals and radiation modeling in magnetic confinement fusion plasmas. Aurora's modern multi-language interface enables simulations of 1.5D impurity transport within high-performance computing frameworks, particularly for the inference of particle transport coefficients. A user-friendly Python library allows simple interaction with atomic rates from the Atomic Data and Atomic Structure database as well as other sources. This enables a range of radiation predictions, both for power balance and spectroscopic analysis. We discuss here the superstaging approximation for complex ions, as a way to group charge states and reduce computational cost, demonstrating its wide applicability within the Aurora forward model and beyond. Aurora also facilitates neutral particle analysis, both from experimental spectroscopic data and other simulation codes. Leveraging Aurora's capabilities to interface SOLPS-ITER results, we demonstrate that charge exchange is unlikely to affect the total radiated power from the ITER core during high performance operation. Finally, we describe the ImpRad module in the one modeling framework for integrated task framework, developed to enable experimental analysis and transport inferences on multiple devices using Aurora.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

A scaling law of the neutral penetration length and Balmer- α wing shape in high-temperature plasmas

Hydrogen atoms penetrating deep inside high-temperature magnetically confined plasmas by repetitive charge-exchange collisions result in a particle source of the plasma, which affects the plasma transport significantly. In this paper, we present an approximate solution of the fluid equations for neutral transport and an analytical representation of the neutral penetration length, in a simplified plasma geometry. This analysis predicts a power-law decay in the Balmer-α line wings which reflects the velocity distribution of the neutral atoms, with the power-law exponent analytically represented as well. These scaling laws are compared with a simple Monte–Carlo simulation and spectroscopic observations of Large Helical Device plasmas. Since the Balmer-α line wings are experimentally accessible, our formulation opens the possibility to quickly estimate the neutral penetration length from spectroscopic observations.

neutral opacity↗

DOE OSTI2025

Toward shell model interactions with credible uncertainties

Background: The nuclear shell model is a powerful framework for predicting nuclear structure observables, but relies on interaction matrix elements fit to experimental data as its inputs. Extending the shell model's applicability, particularly toward dripline nuclei, requires efficient fitting methods and credible uncertainty quantification. Traditional approaches face computational challenges and may underestimate uncertainties. Purpose: We develop and test a framework combining eigenvector continuation and Markov chain Monte Carlo to efficiently fit shell model interaction matrix elements and quantify their uncertainties. Methods: Eigenvector continuation is used to emulate shell model calculations, reducing computational costs. The emulator enables Markov chain Monte Carlo sampling to optimize interaction matrix elements and rigorously assess parametric uncertainties. Here, the framework is benchmarked using the USDB interaction in the 𝑠⁢𝑑 shell. Results: The emulator reproduces the USDB interaction with negligible error, validating its use in shell model fitting applications. However, we find that to obtain credible predictive intervals, the model defect of the shell model itself, rather than experimental or emulator error, must be taken into account in order to obtain credible uncertainties. Conclusions: The proposed framework provides an efficient and rigorous approach for fitting shell model interactions and quantifying uncertainties. Further, the normality assumption used in the past appears sufficient to describe the distribution of interaction matrix elements. However, it is crucial to account for model correlations to avoid underestimating uncertainties.

Nuclear forces↗

DOE OSTI2020

DMFT Reveals the Non-Hermitian Topology and Fermi Arcs in Heavy-Fermion Systems

When a strongly correlated system supports well-defined quasiparticles, it allows for an elegant one-body effective description within the non-Hermitian topological theory. While the microscopic many-body Hamiltonian of a closed system remains Hermitian, the one-body quasiparticle Hamiltonian is non-Hermitian due to the finite quasiparticle lifetime. We use such a non-Hermitian description in the heavy-fermion two-dimensional systems with the momentum-dependent hybridization to reveal a fascinating phenomenon which can be directly probed by the spectroscopic measurements, the bulk “Fermi arcs.” Starting from a simple two-band model, we first combine the phenomenological approach with the perturbation theory to show the existence of the Fermi arcs and reveal their connection to the topological exceptional points, special points in the Brillouin zone where the Hamiltonian is nondiagonalizable. The appearance of such points necessarily requires that the electrons belonging to different orbitals have different lifetimes. This requirement is naturally satisfied in the heavy-fermion systems, where the itinerant c electrons experience much weaker interaction than the localized f electrons. We then utilize the dynamical mean field theory to numerically calculate the spectral function and confirm our findings. We show here that the concept of the exceptional points in the non-Hermitian quasiparticle Hamiltonians is a powerful tool for predicting new phenomena in strongly correlated electron systems.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Long range energy-energy correlator at the LHC

We study the forward-backward azimuthal angular correlations of hadrons in association with multiparticle production in the central rapidity region in proton-proton collisions at the LHC. We apply the nucleon energy-energy correlator framework, where the spinning gluon distribution introduces a nontrivial cos⁡(2⁢𝜙) asymmetries. We will demonstrate that the fundamental helicity structure of quantym chromodynamics (QCD) amplitudes predicts a unique power counting rule: cos⁡(2⁢𝜙) asymmetry starts at 𝒪⁡(𝛼$^{2}_{s}$) order for dijet, 𝒪⁡(𝛼 𝑠 ) for three jet and 𝒪⁡(1) for four (and more) jet productions. Our results will help us to understand the longstanding puzzle of nearside ridge behavior observed in high multiplicity events of 𝑝⁢𝑝 collisions at the LHC.

Quantum chromodynamics↗

DOE OSTI2026

nnopf

SAND2026-19433O The nnopf tool addresses the optimal power flow (OPF) problem by optimizing electricity delivery from generating plants to consumers. At the same time, it minimizes costs and adheres to power grid constraints. While traditional methods for solving OPF can be computationally intensive, the tool mitigates this challenge by training neural networks on power grids to predict optimal solutions for the OPF problem. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Hart, William [Sandia National Lab. (SNL-CA), Live↗

DOE OSTI2022

Evaluation of the Fitch Wind-Farm Wake Parameterization with Large-Eddy Simulations of Wakes Using the Weather Research and Forecasting Model

Abstract Wind-farm parameterizations in weather models can be used to predict both the power output and farm effects on the flow; however, their correctness has not been thoroughly assessed. We evaluate the wind-farm parameterization of the Weather Research and Forecasting Model with large-eddy simulations (LES) of the wake performed with the same model. We study the impact on the velocity and turbulence kinetic energy (TKE) of inflow velocity, roughness, resolution, number of turbines (one or two), and inversion height and strength. We compare the mesoscale with the LES by spatially averaging the LES within areas correspondent to the mesoscale horizontal spacing: one covering the turbine area and two downwind. We find an excellent agreement of the velocity within the turbine area between the two types of simulations. However, within the same area, we find the largest TKE discrepancies because in mesoscale simulations, the turbine-added TKE has to be highest at the turbine position to be advected downwind. Within the downwind areas, differences between velocities increase as the wake recovers faster in the LES, whereas for the TKE both types of simulations show similar levels. From the various configurations, the impact of inversion height and strength is small for these heights and inversion levels. The highest impact for the one-turbine simulations appears under the low-speed case due to the higher thrust, whereas the impact of resolution is low for the large-eddy simulations but high for the mesoscale simulations. Our findings demonstrate that higher-fidelity simulations are needed to validate wind-farm parameterizations.

17 WIND ENERGY↗

DOE OSTI2025

Overview of leakage scenarios in supervised machine learning

Machine learning (ML) provides powerful tools for predictive modeling. ML’s popularity stems from the promise of sample-level prediction with applications across a variety of fields from physics and marketing to healthcare. However, if not properly implemented and evaluated, ML pipelines may contain leakage typically resulting in overoptimistic performance estimates and failure to generalize to new data. This can have severe negative financial and societal implications. Our aim is to expand understanding associated with causes leading to leakage when designing, implementing, and evaluating ML pipelines. Illustrated by concrete examples, we provide a comprehensive overview and discussion of various types of leakage that may arise in ML pipelines.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

PARET/ANL (V.7.7) Verification and Validation Report

This report documents the software testing which has been performed for the PARET/ANL version 7.7 software. The software testing is based on code capabilities identified by research reactor analysts as frequently used in their safety analyses. The verification and validation procedures have been performed and documented to address the steady-state capabilities of the software, as described in Chapter 2, and the transient capabilities, as described in Chapter 3. Testing based on the comparison between PARET/ANL calculations and analytical solutions, hand calculations, or other code calculations of the test cases confirms that all the identified capabilities of the software were implemented correctly. In addition, results from code comparisons against SPERT-I and SPERT-IV experiments for various flow rates are reported for the peak power, energy release and cladding surface temperature. The comparisons showed overall good agreement for the peak power and conservative predictions of the cladding surface temperature

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

PARET/ANL v7.7 Verification and Validation Report

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Evaluating Cell Temperature Models and the Effect of Wind Speed in PV System Capacity Testing: Preprint

Capacity testing is a routine procedure for assessing a photovoltaic system's performance relative to expectations. The most common test method involves fitting a regression model that predicts system output power using operating weather conditions including wind speed. Structural modifications to the regression model to incorporate wind in different ways improved the model's ability to fit measured system performance, but the observed improvements were small and unlikely to change the result of a capacity test. However, the results showed that the choice of reporting wind speed and inclusion or exclusion of wind speed in the performance model used as the test benchmark can significantly change the test result.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2024

Studies of e+e- Pair Photo-Production on Proton Target at 8 GeV in the GlueX Experiment

Lepton pair production has played an important role in both nuclear and particle physics, being among the earliest calculations utilizing QED, and seen most famously in the discovery of the J= at BNL. A technique is presented for measuring the linear polarization of GeV scale photon beams through the detection of e+e? pairs photo-produced in the target. This technique is applied to the analysis of GlueX data on proton target. Simulation predicts the analyzing power for pair production to be :5725 ? 0:0025 for the GlueX data, and the analysis of experimental data gives a linear polarization of approximately 35%, in good agreement with other measurements of beam polarization. The pair production technique is complementary to other electromagnetic and hadronic measurements of beam polarization, and is generally applicable in experiments that allow for forward angle electron and positron identi?fication and tracking. To facilitate this study, a neural net was trained for e=? separation to eliminate the pion background. Further, it is demonstrated that these e+e? pairs are sensitive to the proton charge form factor, which opens up the possibility for a new method to measure the proton RMS charge radius.

Schick, Andrew↗

DOE OSTI2025

Physics-Informed Recurrent Neural Networks to Predict Reactor Operations of the AGN-201 Nuclear Reactor

4 page paper submitted to ANS Student conference. Summary of paper similar to the following abstract: The ability to predict how a reactor will operate, understand when anomalous conditions arise, and ensure a reactor is being operated as expected is crucial for deploying new nuclear facilities. Digital twins serve as a unique solution to recognizing reactor behavior; however, they require data to be useful. For next-generation reactors, this data may not currently be available. To explore how synthetic physics-informed reactor data can be used to predict reactor operations, a recurrent neural network was implemented for the Idaho State University AGN-201 digital twin. The goal of this work is to determine how synthetic data can be used to train a recurrent neural network model for predicting the reactor power of the AGN-201. The recurrent neural network was validated using both synthetic and real operational data. We envision this approach will help bridge the gap between the virtual and physical sides of a digital twin, where reactor physics models based on as-built data can be corrected for actual operating parameters to ensure the virtual model mirrors reality.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2022

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Planck 2018 results. V. CMB power spectra and likelihoods

We describe the legacy Planck cosmic microwave background (CMB) likelihoods derived from the 2018 data release. The overall approach is similar in spirit to the one retained for the 2013 and 2015 data release, with a hybrid method using different approximations at low ( ℓ < 30) and high ( ℓ ≥ 30) multipoles, implementing several methodological and data-analysis refinements compared to previous releases. With more realistic simulations, and better correction and modelling of systematic effects, we can now make full use of the CMB polarization observed in the High Frequency Instrument (HFI) channels. The low-multipole EE cross-spectra from the 100 GHz and 143 GHz data give a constraint on the ΛCDM reionization optical-depth parameter τ to better than 15% (in combination with the TT low- ℓ data and the high- ℓ temperature and polarization data), tightening constraints on all parameters with posterior distributions correlated with τ . We also update the weaker constraint on τ from the joint TEB likelihood using the Low Frequency Instrument (LFI) channels, which was used in 2015 as part of our baseline analysis. At higher multipoles, the CMB temperature spectrum and likelihood are very similar to previous releases. A better model of the temperature-to-polarization leakage and corrections for the effective calibrations of the polarization channels (i.e., the polarization efficiencies) allow us to make full use of polarization spectra, improving the ΛCDM constraints on the parameters θ MC , ω c , ω b , and H 0 by more than 30%, and n s by more than 20% compared to TT-only constraints. Extensive tests on the robustness of the modelling of the polarization data demonstrate good consistency, with some residual modelling uncertainties. At high multipoles, we are now limited mainly by the accuracy of the polarization efficiency modelling. Using our various tests, simulations, and comparison between different high-multipole likelihood implementations, we estimate the consistency of the results to be better than the 0.5 σ level on the ΛCDM parameters, as well as classical single-parameter extensions for the joint likelihood (to be compared to the 0.3 σ levels we achieved in 2015 for the temperature data alone on ΛCDM only). Minor curiosities already present in the previous releases remain, such as the differences between the best-fit ΛCDM parameters for the ℓ < 800 and ℓ > 800 ranges of the power spectrum, or the preference for more smoothing of the power-spectrum peaks than predicted in ΛCDM fits. These are shown to be driven by the temperature power spectrum and are not significantly modified by the inclusion of the polarization data. Overall, the legacy Planck CMB likelihoods provide a robust tool for constraining the cosmological model and represent a reference for future CMB observations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Implementation of an Orificing Optimization Algorithm in the DASSH Subchannel Analysis Code

The Ducted Assembly Steady-State Heat transfer code (DASSH) performs full-core subchannel thermal hydraulics calculations in liquid metal fast reactors. One of the applications of subchannel codes is to optimize coolant flow orificing. As a design activity, the primary task is to determine the best way to divide assemblies into groups and distribute coolant flow rates among them. This report documents an algorithm implemented in DASSH to automatically optimize coolant orificing. Over the course of multiple iterations, DASSH determines the orifice grouping and flow distribution that minimizes peak coolant, clad, or fuel temperatures across all timesteps for a user-specified number of assembly groups. The total coolant flow rate in the reactor is constrained to achieve the specified core-average outlet temperature. The flow rate to each orifice group may also be constrained by the allowable pressure drop. The distribution of coolant flow among groups is accelerated using a predictor-corrector algorithm based on interpolated results from single-assembly parametric calculations. The assembly orificing grouping is initially predicted based on assembly power but can be refined if results demonstrate that an assembly would fit better in another group. The algorithm is demonstrated with two case studies. The first is a simple model for a reactor core consisting of just fuel assemblies; the pin power distributions are specified to create a situation where the initial assembly grouping prediction is suboptimal. This example is used to describe the initial grouping, demonstrate convergence over multiple iterations, and highlight the impact of regrouping. Then, the algorithm is applied to minimize peak clad and fuel temperatures in an example sodium-cooled fast reactor, the Versatile Test Reactor. The multicycle optimization confirms prior calculations for the reference core design. The example highlights how optimizing for different peak temperatures affects the results and demonstrates the use of the pressure drop constraint to limit the maximum flow rate.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Methods and systems for coordinating predictive cruise control, engine-off coasting, and hybrid power split

The enclosed disclosure relates to hybrid vehicles and systems with an engine, a drivetrain with a clutch and a transmission, an electric machine, and a controller. The controller receives lookahead information within a lookahead window and present state information of the hybrid vehicle. The controller determines a predicted coasting opportunity exceeding a predetermined threshold within the lookahead window and determines a cruise control reference speed, a power split between the engine and the electric machine, and a timing of enabling engine-off coasting during the coasting opportunity. The controller deactivates the engine and disengages the clutch at a start of the coasting opportunity when the engine-off coasting is enabled.

Gupta, Rohinish↗