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DOE OSTI2022

Data-driven linear time advance operators for the acceleration of plasma physics simulation

In this study, we demonstrate the application of data-driven linear operator construction for time advance with a goal of accelerating plasma physics simulation. We apply dynamic mode decomposition (DMD) to data produced by the nonlinear SOLPS-ITER (Scrape-off Layer Plasma Simulator - International Thermonuclear Experimental Reactor) plasma boundary code suite in order to estimate a series of linear operators and monitor their predictive accuracy via online error analysis. We find that this approach defines when these dynamics can be represented by a sequence of approximate linear operators and is essential for providing consistent projections when compared to an unconstrained application. For linear diffusion and advection–diffusion fluid test problems, we construct and apply operators within explicit and implicit time advance schemes, demonstrating that stability can be robustly guaranteed in each case. We further investigate the use of the linear time advance operators within several integration methods including forward Euler, backward Euler, and the matrix exponential. The application of this method to simulation data from SOLPS-ITER, with varying levels of Markov chain Monte Carlo numerical noise, shows that constrained DMD operators yield a capability to identify, extract, and integrate a (slow) subset of the present timescales. Example applications show that for projected speedup factors of [Formula: see text], and [Formula: see text], a mean relative error of 3%, 5%, and 8% and maximum relative error less than 20% are achievable, which appears acceptable for typical SOLPS-ITER steady-state simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Temperature evolution of interferometer visibility in a small radius fibre coil current sensor

We have experimentally demonstrated periodic oscillations of interferometer visibility in a current sensor in response to changes in the temperature of a small-radius spun fibre sensing coil. The observed effect has been shown to depend on the magnitude of the bend-induced linear birefringence in the spun fibre in the magnetically sensitive coil and the temperature behaviour of the built-in linear birefringence in the fibre. Using a model that considers a helical structure of the built-in linear birefringence axes in spun fibre, we have shown that the above factors result in periodic oscillations of the ellipticity of polarised light at the ends of the sensing coil and that this underlies the temperature oscillations of interferometer visibility in the current sensor. The effect may cause errors in the response of a fibre-optic current sensor with a small sensing coil. (fibre-optic sensors)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

A new stabilizing regime of tearing mode entrainment in the presence of a static error field

Uncorrected static error fields in axisymmetric fusion devices are one of few remaining serious obstacles for advancing the present tokamak-based approach to practical reactors. The disruption due to magnetohydrodynamic tearing mode locking is one of them. Recently, a hypothesis has been proposed that there may exist a self-healing stabilized regime in which the static resonant error field is effectively shielded by forcing the mode slowly to rotate inductively by the applied non-axisymmetric field, that has been developed based on non-linear, resistive, reduced MHD simulation with cylindrical single helicity model. Proof-of-principle experiments in the DIII-D device showed that the magnetic mode structure on the plasma surface is qualitatively consistent with the simulation prediction. However, radial mode profiles revealed qualitatively different behavior. This finding led to a revised hypothesis that in actual non-circular toroidal devices, a tearing layer in forced rotation induces the shielding-out process at other rational surfaces when we take into account of multi-resonant Fourier components. The time evolution of the radial penetration was supportive of this hypothesis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System

Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.

Dahale, Shweta↗

DOE OSTI2022

User Manual for MC 2 -3 Gamma Library Generation

This report provides the documentation for the revised procedure to generate the MC 2 -3 gamma library with the latest versions of PreGAMMA and GenGAMMA which have recently been updated to fix the program errors and inconsistent data processing for some isotopes identified in a recent verification and validation study of the MC 2 -3 gamma library. A few associated errors associated with gamma processing were also identified and fixed in the MC 2 -3 code. Addressing the changes in MC 2 -3, the procedure to generate the PMATRX and GAMISO datasets is discussed as well. The input data of PreGAMMA and GenGAMMA are discussed in detail using sample input data. In addition, the structure and formats of the MC 2 -3 gamma library are provided in detail.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Precision Measurements of the Neutron Magnetic Form Factor to High Momentum Transfer using Durand's Method

Protons and neutrons, collectively known as nucleons, along with electrons, constitute the funda- mental building blocks of the visible universe. Understanding their internal structure is crucial for addressing key scientific questions about our origin and existence. Elastic electron-nucleon scatter- ing provides insights into the spatial distributions of charge and current within nucleons through their electromagnetic form factors. Accurate knowledge of these form factors over a broad range of Q2, the squared four-momentum transfer in the scattering process, reveals details about the nucleon’s internal structure. However, high-Q2 data of the nucleon electromagnetic form factor is scarce due to the challenges associated with such measurements. This thesis reports preliminary results from high-precision measurements of the neutron magnetic form factor (Gn M ) to unprecedented Q2 using Durand’s method, also known as the “ratio” method. Systematic errors are greatly reduced by extracting Gn M from the ratio of neutron-coincident (D(e, e'n)) to proton-coincident (D(e, e'p)) quasi-elastic electron scattering from deuteron. The scattered electrons were detected in the BigBite spectrometer, which features multiple Gas Elec- tron Multiplier (GEM) layers with large active area for high-precision tracking at very high rates. Simultaneous nucleon detection was performed by the Super BigBite spectrometer, which utilizes a dipole magnet with large solid angle acceptance at forward angles and a novel hadron calorimeter with very high and comparable detection efficiencies for both protons and neutrons. This setup could handle very high luminosity, making high-Q2 measurements feasible. Data were collected at five Q2 points: 3, 4.5, 7.4, 9.9, and 13.6 (GeV/c)2. Preliminary results are reported for all, with the lowest two Q2 points in good agreement with existing world data, while the higher points significantly extend the Q2 range in which Gn M is known accurately. The precision of the highest Q2 point is expected to remain unmatched for years to come.

Datta, Provakar↗

DOE OSTI2026

Probabilistic Inference of Low-Surface-Brightness Galaxy Morphological Parameters Using Simulation-Based Inference

Low-surface-brightness galaxies (LSBGs) are diffuse, often dark-matter-dominated systems whose faintness makes their structural parameters difficult to measure reliably in wide-field imaging surveys. Robust parameter inference, including uncertainty quantification, is important for population studies and for comparisons with models of galaxy formation, as future surveys are expected to produce increasingly large samples of diffuse galaxies. In practice, LSBG profile modeling is sensitive to sky- background errors, masking choices, contaminating background sources, and the computational cost of obtaining posterior-level uncertainties for large samples. Motivated by these questions, we develop a simulation-based inference (SBI) framework for estimating posterior distributions of LSBG morphological parameters from simulated galaxy images. Using PyImfit, we generate DES-like single-Sersic profile LSBG images with known position angle, ellipticity, Sersic index, effective surface brightness, and effective radius. We then train a normalizing-flow-based neural posterior estimator using the sbi package to infer these parameters from the simulated images. For isolated simulated galaxies, the SBI posterior recovers the true input parameters, produces posterior predictive residuals consistent with the assumed noise model, and shows good empirical calibration in a DES-motivated test regime. We also compare SBI with PyImfit-based MCMC inference and find broadly comparable posterior constraints, while SBI enables substantially faster posterior sampling after training. Finally, we test robustness to compact background contaminants. A model trained only on isolated galaxies produces undercovered posteriors on contaminated images, whereas training on simulations with variable contaminant positions and fluxes improves calibration across contaminated test sets. These results demonstrate the promise of SBI for scalable, uncertainty-aware LSBG morphology inference, while emphasizing that posterior reliability strongly depends on whether training simulations include relevant observational complications.

Batbayar, Bilguun [U. Chicago (main)]↗

DOE OSTI2022

Controlling Structural, Electronic, and Energy Flow Dynamics of Catalytic Processes through Tailored Nanostructures

MoS 2 (molybdenum disulfide) is a highly-versatile catalyst material for support of numerous reactions from hydrodesulfurization and denitrogenation to the focus of this renewal proposal: hydrogenation of CO/CO 2 towards (higher) alcohols. At the same time, MoS 2 is a non-toxic, environmentally-benign and rather inert material- which under ambient conditions for some time has served as a lubricant and, more recently, as next-generation electronic material. The apparent contrast between inertness and stability in ambient, and catalytic activity under reactive conditions is puzzling and calls for a synergistic theoretical and experimental investigation with the long-term objective of enabling the rational design of MoS 2 -based catalysts for alcohol-formation reactions by providing a microscopic understanding of the environmental factors that determines site activity and selectivity. Our research project seeks answers to the questions (a) what conformation does MoS 2 adopt under reaction conditions (as opposed to that under ultrahigh vacuum and low temperatures)?; (b) what reaction pathways exist on such a material?; (c) how can the local environment of the active sites be manipulated so as to make MoS 2 an efficient catalyst for production of higher alcohol from syngas? In particular, research strategies will explore how the basal plane composed of sulfur atoms can be activated so as to exhibit a reactivity of its own, by addressing three research targets and building on extensive preliminary and enabling work: (1) vacancies and vacancy aggregates on the basal plane; (2) non-local catalyst transformation through alkali doping, hydrogenation and phase transition; (3) fabrication of a metal-nanoparticle-activated MoS 2 system, in which particle anchoring, reactive sites and pathways as well as selectivity are controlled by design, as an example of predictive development of a catalyst material,. All strategies are directed to improve the efficacy of the key reactive sites and selectivity of chemical pathways by design, to replace the inefficient methodology of trial and error in catalyst development. This research project represents a synergistic combination of computational guidance, foundational surface-science-based experiments and validation under reactive conditions that aims at transformative new insights into the working of MoS 2 -based hydrogenation-catalysts, a topic squarely at the center of the interest of DOE BES. Guided and led by Talat Rahman, a computational physicist, this project will apply density functional theory to understand structure, reaction pathways and chemical potentials associated with MoS 2 -based CO/CO 2 hydrogenation, augmented by kinetic Monte Carlo methods for reaction rates and prefactors as well as ab-initio molecular dynamics for evaluation of thermal stability. Complementary experimental input and validation will originate from co-PI Ludwig Bartels, a physical chemist and materials scientist, whose group focuses on local imaging and preparation of MoS 2 materials, from co-PI Peter Dowben, an experimental physicist, whose group is expert in the spectroscopy of occupied and unoccupied electronic states, and from senior collaborator Michael White of Brookhaven National Laboratory, whose group generates high-resolution electronic and activity information on size-selected well-defined metal chalcogenide clusters. This collaborative effort will enable a comprehensive understanding of the correlation of structural integrity and catalytic activity of MoS 2 in forms ranging from extended films to individual particles with known geometries and binding sites. Alcohol formation from syngas is a rapidly emerging application that has great potential through facile, economic, and decentralized biomass gasification. CO 2 activation is one of the most pressing concerns of our time: increasing CO 2 levels in the atmosphere change the climate and expose the globe to environmental transformations with the potential for enormous economic and societal impact. We will investigate CO/CO 2 hydrogenation via an interdisciplinary research collaboration with established synergy – one that, in accordance with the mission of the DOE, involves accredited Hispanic-Serving Institutions and that, through student exchange with international collaborators and National Labs, will directly benefit a broad spectrum of communities and generate human resources in sciences essential to their future.

2D materials↗

DOE OSTI2022

Inadequacy of current approaches for characterizing membrane transport properties at high salinities

Cost optimal design of osmotic membrane processes requires an accurate estimate of membrane transport parameters across their full operational range. However, standard approaches for estimating these parameters rely on empirical methods, the accuracy of which remains unquantified as a function of temperature, salinity, and measurement error. Herein, we present a systematic accuracy analysis of previously developed methods for estimation of membrane transport properties in reverse osmosis, high-pressure reverse osmosis, forward osmosis, pressure retarded reverse osmosis, and osmotically assisted reverse osmosis. We use a Monte Carlo approach to sample the full range of feasible membrane water permeabilities, salt permeabilities, structural parameters, and operating conditions for these processes. These material and process parameters are then incorporated into a physical transport model for each process. Our analysis shows that the statistical uncertainty of current empirical methods for estimating membrane parameters increases by 5 times from low-salinity to high-salinity conditions. Here, the result of this work demonstrates that empirical methods are inadequate for precisely estimating membrane transport properties at high salinity and highlight a critical need for the development of statistically validated higher accuracy methods.

42 ENGINEERING↗

DOE OSTI2026

Generative modeling enables molecular structure retrieval from Coulomb explosion imaging

Capturing the structural changes that molecules undergo during chemical reactions in real space and time is a long-standing dream and an essential prerequisite for understanding and ultimately controlling femtochemistry. A key approach to tackle this challenging task is Coulomb explosion imaging, which benefited decisively from recently emerging high-repetition-rate X-ray free-electron laser sources. With this technique, information on the molecular structure is inferred from the momentum distributions of the ions produced by the rapid Coulomb explosion of molecules. Retrieving molecular structures from these distributions poses a highly non-linear inverse problem that remains unsolved for molecules consisting of more than a few atoms. Here, we address this challenge using a diffusion-based Transformer neural network. We show that the network reconstructs unknown molecular geometries from ion-momentum distributions with a mean absolute error below one Bohr radius, which is half the length of a typical chemical bond.

Artificial Intelligence (cs.AI)↗

DOE OSTI2025

Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradation

Abstract Understanding subsurface temperature variations is crucial for assessing material degradation in underground structures. This study maps subsurface temperatures across the contiguous United States for depths from 50 to 3500 m, comparing linear interpolation, gradient boosting (LightGBM), neural networks, and a novel hybrid approach combining linear interpolation with LightGBM. Results reveal heterogeneous temperature patterns both horizontally and vertically. The hybrid model performed best achieving a root mean square error of 2.61 °C at shallow depths (50–350 m). Model performance generally decreased with depth, highlighting challenges in deep temperature prediction. State-level analyses emphasized the importance of considering local geological factors. This study provides valuable insights for designing efficient underground facilities and infrastructure, underscoring the need for depth-specific and region-specific modeling approaches in subsurface temperature assessment.

Science & Technology - Other Topics↗

DOE OSTI2019

Level-set Modeling Simulations of Chemical Vapor Infiltration for Ceramic Matrix Composites Manufacturing

Silicon-carbide (SiC) reinforced ceramic matrix composites (CMCs) are a key enabling technology to reduce fuel consumption and emissions of gas turbine engines. In one manufacturing approach, chemical vapor infiltration (CVI) is limited to only coating SiC fibers. The preform is then fabricated using a lay-up of basic plys or 2-D woven sheets composed of the precoated fibers. At the other extreme, CVI is used to completely densify a 3-D woven preform shaped almost like the gas turbine component itself. The latter approach is more suitable for highly engineered components which sit directly in the gas path of the engine, for example, a high pressure turbine blade. In this case, the geometry is necessarily complex for aerodynamic, stress, and lifing (multi-physics) requirements. Presently, optimizing the CVI-dominated manufacturing approach is largely by trial-and-error. In this work, a first-principles modeling of CVI is performed to realize optimization of SiC/SiC CMC manufacturing. The modeling is based on a level-set framework to describe the interface between the vapor and solid phases. A finite-difference numerical scheme using an immersed boundary method is developed for fixed, structured meshes. Massively parallel direct numerical simulations (DNS) of CVI through fiber-woven geometries are performed using one-step chemistry, and over a range of Thiele moduli. Illustrative applications of the resulting large DNS data sets are given, including the development of fiber-weave specific infiltration models and structure functions for mean-field (porous media) Computational Fluid Dynamics (CFD) simulations of CVI.

36 MATERIALS SCIENCE↗

DOE OSTI2022

2.2.2.405 - Verdant/NREL Research Measurement Campaign

The marine renewable energy industry primarily uses thermoset composite materials for blades and other hydrodynamic components, which can have up to 50% reduction in strength when exposed to seawater and are not recyclable. Thermoplastic composite materials have been shown at a coupon-scale to have improved seawater saturated properties but have not been validated at full scale. The primary goal of the project is to demonstrate the structural properties of thermoplastic-fiberglass composite blades compared to epoxy-fiberglass composite blades in seawater at a tidal energy site on an operational turbine. Through this work, NREL manufactured, tested and deployed thermoplastic blades and a data acquisition system on Verdant Power's turbine TriFrame in the East River, NYC. The thermoplastic blades and DAQ were retrieved after a 6-month deployment with no signs of degradation and all cable connections, structural supports and strain gages had a 100% survival rate. The blades are currently undergoing post-deployment structural validation to compare their performance to traditional epoxy blades that were deployed for the same amount of time. This will increase confidence in thermoplastic materials and move it closer to commercial adoption, as well as increase confidence in the design of a DAQ and instrumentation method for a tidal turbine. However, due to an error in the DAQ software that was introduced after the DAQ and software were validated, there was no loads data collected during the deployment.

blade testing↗

DOE OSTI2017

Heterogeneous Neutron Leakage Model for PWR Pin-by-pin Calculation

In the last few decades, the nodal diffusion calculation accompanied with the assembly homogenization has been widely employed. The single assembly calculation with the reflective boundary condition (BC) would be carried out to determine the assembly-homogenized parameters. Because of the reflective BC, no axial and/or radial leakage rates have been taken into accounted for the lattice calculation. To represent the global influence of the reactor core on an assembly calculation, one mostly frequently uses a procedure called leakage model to approximately take into account the absorption due to the macroscopic structure of the physical flux in a heterogeneous situation. For the assembly homogenization, this absorption is introduced as the product of the leakage coefficient and the buckling obtained for an equivalent homogeneous medium using the well-known homogeneous B1 theory. Clearly, this is an imperfect representation of leakages in a heterogeneous medium. However, the errors introduced by the foregoing procedure are small for the whole? -assembly homogenization in a PWR. When it comes to the pin-by-pin calculation, it is not the case. The leakage rate? will be affected by the neutron streaming effect caused by the heterogeneity of the assembly. Especially when poison pins or empty channels are present, the streaming effect becomes important. The errors introduced appear particularly visible. The heterogeneous leakage model? which assumes that the neutron flux is variational and the leakage rate is not uniform over the lattice pitch is a more appropriate choice. When the heterogeneous leakage model is applied in the lattice calculation for the pin-cell homogenization, the pin-homogenized problem would be changed compared with the original assembly problem without any homogenization technique. So, the Cooperation of the heterogeneous leakage model with the pin-cell homogenization theory is unsettled. In this paper, both homogenous and heterogeneous leakage models are evaluated in pin-by-pin calculation. Their implements used in pin-cell homogenization of the lattice calculation are studied. The diffusion coefficient obtained from transport cross-section and the space-dependent leakage coefficient together with the two leakage models are analyzed for the practical reactor-core applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Turbulent Processes that Influence Boundary-Layer Cloud Structure (Final Technical Report)

Many models of the atmosphere simulate vertical profiles of observable fields, such as temperature, moisture, and vertical velocity. To assess the realism of such model output, it is important to evaluate the modeled profiles against observations. However, many extant evaluations consist of comparing time averaged profiles from the model and the observations. Such time averages can smooth out unrealistic features that appear in instantaneous profiles. In this way, the time averages may obscure certain model errors from detection. For instance, many models misrepresent turbulent processes, leading to profiles that are too smoothed out or too noisy. E.g., if a model has coarse vertical resolution and overly strong diffusion, then the model may be unable to produce observed sharp peaks. On the other hand, a model may have artificial approximations that produce spurious jumps in the vertical. Much can be learned by browsing through instantaneous snapshots of model output. But the volume of output produced by a global model is far too great to rely purely on visual inspection. In addition, we need a way to statistically characterize the profiles, so that anomalies can be detected more conveniently.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model

Abstract Hot rolling and annealing are critical intermediate steps for controlling microstructures and thickness variations when fabricating uranium alloyed with 10% molybdenum (U-10Mo), which is highly relevant to worldwide nuclear non-proliferation efforts. This work proposes a machine-learning surrogate model combined with sensitivity analysis to identify and predict U-10Mo microstructure development during thermomechanical processing. Over 200 simulations were collected using physics-based microstructure models covering a wide range of thermomechanical processing routes and initial alloy grain features. Based on the sensitivity analysis, we determined that an increase in rolling reduction percentage at each processing pass has the strongest effect in reducing the grain size. Multi-pass rolling and annealing can significantly improve recrystallization regardless of the reduction percentage. With a volume fraction below 2%, uranium carbide particles were found to have marginal effects on the average grain size and distribution. The proposed stratified stacking ensemble surrogate predicts the U-10Mo grain size with a mean square error four times smaller than a standard single deep neural network. At the same time, with a significant speedup (1000×) compared to the physics-based model, the machine learning surrogate shows good potential for U-10Mo fabrication process optimization.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Self-consistent implementation of locally scaled self-interaction-correction method

Recently proposed local self-interaction correction (LSIC) method is a one-electron self-interaction-correction (SIC) method that uses an iso-orbital indicator to apply the SIC at each point in space by scaling the exchange–correlation and Coulomb energy densities. The LSIC method is exact for the one-electron densities, also recovers the uniform electron gas limit of the uncorrected density functional approximation, and reduces to the well-known Perdew–Zunger SIC (PZSIC) method as a special case. This article presents the self-consistent implementation of the LSIC method using the ratio of Weizsäcker and Kohn–Sham kinetic energy densities as an iso-orbital indicator. The atomic forces as well as the forces on the Fermi-Löwdin orbitals are also implemented for the LSIC energy functional. Results show that LSIC with the simplest local spin density functional predicts atomization energies of the AE6 dataset better than some of the most widely used generalized-gradient-approximation (GGA) functional and barrier heights of the BH6 database better than some of the most widely used hybrid functionals. The LSIC method [a mean absolute error (MAE) of 0.008 Å] predicts bond lengths of a small set of molecules better than the PZSIC-LSDA (MAE 0.042 Å) and LSDA (0.011 Å). Furthermore, this work shows that accurate results can be obtained from the simplest density functional by removing the self-interaction-errors using an appropriately designed SIC method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Probing gravity with a joint analysis of galaxy and CMB lensing and SDSS spectroscopy

We present measurements of EG, a probe of gravity from large-scale structure, using BOSS LOWZ and CMASS spectroscopic samples, with lensing measurements from SDSS (galaxy lensing) and Planck (CMB lensing). Using SDSS lensing and the BOSS LOWZ sample, we measure ⟨ E G ⟩ = 0.40 − 0.04 + 0.05 (stat), ±0.026 (systematic), consistent with the predicted value from the Planck ΛCDM model, EG=0.46. Using CMB lensing, we measure ⟨ E G ⟩ = 0.46 − 0.09 + 0.08 (stat) for LOWZ (statistically consistent with galaxy lensing and Planck predictions) and ⟨ E G ⟩ = 0.39 − 0.05 + 0.05 (stat) for the CMASS sample, consistent with the Planck prediction of E G =0.40 given the higher redshift of the sample. We also study the redshift evolution of E G by splitting the LOWZ sample into two samples based on redshift, with results being consistent with model predictions. We estimate systematic uncertainties on the above $\langle$E G $\rangle$ numbers to be ~6% (when using galaxy-galaxy lensing) or ~3% (when using CMB lensing), subdominant to the quoted statistical errors. These systematic error budgets are dominated by observational systematics in galaxy-galaxy lensing and by theoretical modeling uncertainties, respectively. We do not estimate observational systematics in galaxy-CMB lensing cros-correlations.

79 ASTRONOMY AND ASTROPHYSICS↗