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At least 253 records · Page 14

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DOE OSTI2024

Evaluation of material accountancy techniques for 233 Pa from thorium nuclear fuels

Thorium is a promising alternative to uranium as nuclear fuel with advantages such as higher abundance, lower production of long-lived transuranic elements, and potentially better proliferation resistance. However, thorium presents a potential pathway for proliferation where produced 233 Pa can be diverted for the clandestine production of safeguarded 233 U. To prevent this, the ability to detect and measure 233 Pa must be assessed. This paper reviews several nuclear material accountancy techniques to determine their suitability for detecting 233 Pa extracted from irradiated thorium fuel. Hybrid K-edge densitometry and passive gamma spectroscopy have been found to be the best options based on technology maturity, cost, accuracy, and acquisition time. Thorium can be used in various reactor designs such as pressurized water reactors (PWRs), Canada deuterium uranium (CANDU) reactors, and molten salt reactors (MSRs). Therefore, thorium-uranium oxide fueling was modeled for three representative reactors (PWR, CANDU, MSR), burning the fuel to 47 GWd/MTHM for PWR, 19 GWd/MTHM for CANDU, and at a steady power of 52.711 MW/MTHM for MSR. Within each model, the protactinium element in the used fuel was extracted and its isotopic content analyzed. Simulated results indicated that 233 Pa can be detected using passive gamma spectroscopy in each fuel type at all decay times (0–300 days) following separation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

First-principles modeling of chemistry in mixed solvents: Where to go from here?

Mixed solvents (i.e., binary or higher order mixtures of ionic or nonionic liquids) play crucial roles in chemical syntheses, separations, and electrochemical devices because they can be tuned for specific reactions and applications. Apart from fully explicit solvation treatments that can be difficult to parameterize or computationally expensive, there is currently no well-established first-principles regimen for reliably modeling atomic-scale chemistry in mixed solvent environments. We offer our perspective on how this process could be achieved in the near future as mixed solvent systems become more explored using theoretical and computational chemistry. We first outline what makes mixed solvent systems far more complex compared to single-component solvents. An overview of current and promising techniques for modeling mixed solvent environments is provided. We focus on so-called hybrid solvation treatments such as the conductor-like screening model for real solvents and the reference interaction site model, which are far less computationally demanding than explicit simulations. We also propose that cluster-continuum approaches rooted in physically rigorous quasi-chemical theory provide a robust, yet practical, route for studying chemical processes in mixed solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Towards fast, accurate predictions of RF simulations via data-driven modeling: Forward and lateral models

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. More accurate simulations with fast electron diffusion are even slower, requiring multiple hours of run time with parallel processing. The machine learning models use a database of 16,000+ GEN-RAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods implemented in πScope ensure that the database covers the range of 9 input parameters (n e0 , T e0 , I p , B t , R 0 , n ∥︀ , Z e f f , V loop , P LHCD ) with sufficient density in all regions of parameter space. The surrogate models reduce the computation time from minutes-hours to ms with high accuracy across the input parameter space. Data-driven surrogate models also allow for solving inverse and “lateral” problems. A surrogate model for the inverse problem maps from a desired current drive or power deposition profile to a set of input parameters that would result in such a profile, while a surrogate model for the lateral problem maps from a measured experimental quantity such as hard x-ray emission to a current drive or power deposition profile. In conclusion, the πScope database creation workflow is flexible and applicable to other RF simulation codes such as TORIC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

A Practical Approach to Inertia Assessment: Analyzing New York's Grid Response to Real Events

The non-uniform distribution of inverter-based resources (IBRs) such as solar and wind energy leads to the formation of weakly coupled regions within the broader interconnection. This highlights the urgency of investigating regional dynamics. This paper proposes a practical framework for estimating regional inertia using actual event measurements from the New York (NY) region. The regional frequency is determined using measurement data from frequency disturbance recorders (FDRs) deployed across NY. The rate-of-change of frequency (RoCoF) is estimated using a hybrid methodology that combines the benefits of both moving and non-moving window techniques. By utilizing the power mismatch value and the estimated RoCoF, various inertia metrics such as regional RoCoF, inertia arrival time, and inertia percentage relative to the interconnection inertia are calculated. The identified confirmed-events that took place between 2017–2022 in NY are used in the study. This assessment of inertia using real event data reveals the true behavior of the grid under stress. The results indicate that as the generation mix in NY evolves with the rise of IBRs, both the RoCoF levels and the regional inertia undergo significant changes.

Dulal, Saurav [University of Tennessee, Knoxville ↗

DOE OSTI2025

A Radiation-Hard 8-Channel 15-Bit 40-MSPS ADC for the ATLAS Liquid Argon Calorimeter Readout

The custom design of a radiation-hardened, 8-channel, 40-MSPS, 15-bit resolution, 14.2-bit dynamic range, 11.4-ENOB ADC data acquisition ASIC fabricated in a commercial 65-nm triple-well CMOS technology is presented. The ADC is developed for and integrates seamlessly into the readout system for the ATLAS liquid argon (LAr) calorimeter in the high-luminosity large hadron collider (HLLHC) upgrade at CERN, which will require a total of 364 936 ADC channels. A three-stage MDAC+SAR pipelined ADC architecture was designed to meet the physics requirements and scientific goals of the ATLAS experiment. The ADC is a fully self-contained data acquisition system that includes foreground calibration, digital data processing, digital control, and supporting circuitry. The measured performance shows the ADC achieves a competitive dynamic range and SNDR, and it meets or exceeds the ATLAS analog requirements. Radiation tolerance and scalability design considerations were implemented at the device-, circuit-, and system-level. Radiation-hardening-by-design techniques used include redundancy for digital circuits, the use of MiM capacitors, and a hybrid RC-DAC for the ADC core. The ADC ASIC was demonstrated to be robust against the effects of the intense radiation expected in the HL-LHC experimental environment.

DAQ↗

DOE OSTI2021

Source Authentication of Distribution Synchrophasors for Cybersecurity of Microgrids

This letter proposes a hybrid approach combining Self-Adaptive Mathematical Morphology (SAMM) and Time-Frequency (TF) techniques to authenticate the source information of Distribution Synchrophasors (DS) within near-range locations. The SAMM can adaptively regulate the synchrophasors variations which are representatives of local environmental characteristics. Subsequently, TF mapping is employed to extract informative signatures from the regulated synchrophasors variation. Finally, Random Forest Classification (RFC) is used to correlate the extracted signatures with the source information based on the derived TF mapping. Experiment results using DS collected at multiple small geographical scales validated the proposed methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Fusion RF Modeling Machine Learning (FusionML_RF) v1.0

FusionML_RF consists of multiple codes and trained machine learning (ML) models that perform low-cost output modeling from the Genray-CQL3D. Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. For example, completing a single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time. On the other hand, these ML models achieve ~ms of inference time with high accuracy across the input parameter space. This software collection consists of multiple components. (1) codes that use ML methods and precomputed Genray-CQL3D simulation output to build regression models that enable approximate computations of Genray-CLQ3D outputs from arbitrary but physically meaningful input parameters (surrogate modeling); (2) three trained models created by the team, using a database of 16,000+ GENRAY/CQL3D simulations, to study the performance of ML models for surrogate modeling; (3) codes that load the trained models and simulation data, and then compute mean squared error between the models' predictions and the ground truth of simulation output data. This collection is being made available in conjunction with a scientific publication about the work to promote reusability and provide an artifact of the scientific work.

Bai, Zhe↗

DOE OSTI2022

Towards Fast and Accurate Predictions of Radio Frequency Power Deposition and Current Profile via Data-driven Modeling

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Autonomous phototaxis of hydrogel swimmers

The design of synthetic soft matter capable of emulating the complex behaviors of living organisms, such as sensing and adapting to their environment, remains an important challenge in developing biomimetic materials. Functionalized hydrogels are ideal candidates for such materials since they are highly responsive to their environment and can be operated in water. In this work, we investigate a hybrid bonding hydrogel composed of peptide amphiphile supramolecular nanofibers covalently attached to a photoresponsive network, in which high-aspect-ratio ferromagnetic nanowires are aligned along the length of the sample, designed to swim under oscillating magnetic fields. This hybrid hydrogel swimmer can autonomously swim toward a light source by utilizing photoinduced interactions between supramolecular and covalent networks reminiscent of phototactic swimming in living systems. Using a combination of experimental techniques and a continuum model incorporating photochemistry, magnetoelasticity, and hydrodynamics, we explain the swimming mechanism and predict phototactic behavior. Our work highlights the potential role of hybrid bonding polymers, which leverage the interplay between supramolecular assemblies and covalent networks. We demonstrate how these polymers can be tailored to react dynamically to their environment, paving the way for developing intelligent and autonomous robotic systems.

Science & Technology - Other Topics↗

DOE OSTI2021

Hybrid Machine Learning for Scanning Near-Field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Surface-Attached Molecular Catalysts on Visible-Light-Absorbing Semiconductors: Opportunities and Challenges for a Stable Hybrid Water-Splitting Photoanode

Hybrid photoanodes for water-splitting photoelectrochemical cells (WS-PECs) consist of a visible-light-absorbing semiconductor (VLASC) with a molecular water-oxidation catalyst (mWOC) attached, and show great promise for solar fuel generation. In this perspective, we will focus on the less emphasized but crucial subject of stability in these systems. The stability of each component, including the VLASC, the mWOC, and the connection of the two, must individually be considered, as well as how the system functions as a whole. We offer design strategies for each of these parts, as well as some characterization techniques helpful in the study of degradation. Lastly, we propose some future directions for the development of hybrid WS-PEC photoanodes with long-term stability that could be suitable for practical application.

14 SOLAR ENERGY↗

DOE OSTI2021

A Constrained Data Assimilation Algorithm Based on GSI Hybrid 3D-EnVar and Its Application

Abstract Data assimilation (DA) at mesoscales is important for severe weather forecasts, yet the techniques of data assimilation at this scale remain a challenge. This study introduces dynamical constraints in the Gridpoint Statistical Interpolation (GSI) three-dimensional ensemble variational (3D-EnVar) data assimilation algorithm to enable the use of high-resolution surface observations of precipitation to improve atmospheric analysis at mesoscales. The constraints use the conservations of mass and moisture. Mass constraint suppresses the unphysical high-frequency oscillation, while moisture conservation constrains the atmospheric states to conform with the observed high-resolution precipitation. We show that the constrained data assimilation (CDA) algorithm significantly reduced the spurious residuals of the mass and moisture budgets compared to the original data assimilation (ODA). A case study is presented for a squall line over the Southern Great Plains on 20 May 2011 during Midlatitude Continental Convective Clouds Experiment (MC3E) of the Atmospheric Radiation Measurement (ARM) program by using ODA or CDA analysis as initial condition of forecasts. The state variables, and the location and intensity of the squall line are better simulated in the CDA experiment. Results show how surface observation of precipitation can be used to improve atmospheric analysis through data assimilation by using the dynamical constraints of mass and moisture conservations.

Wang, Jia↗

DOE OSTI2022

Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity

In carbon capture and sequestration (CCS), developing rapid and effective imaging techniques is crucial for real-time monitoring of the spatial and temporal dynamics of CO 2 propagation during/after injection. With continuing improvements in computational power and data storage, data-driven techniques based on machine learning (ML) have been effectively applied to seismic inverse problems. In particular, ML helps alleviate the ill-posedness and high computational cost of full-waveform inversion (FWI). However, such data-driven inversion techniques require massive high-quality training data sets to ensure prediction accuracy, which hinders their application to time-lapse monitoring of CO 2 sequestration. We propose an efficient “hybrid” time-lapse workflow that combines physics-based FWI and data-driven ML inversion. The scarcity of the available training data is addressed by developing a new data-generation technique with physics constraints. The method is vali dated on a synthetic CO 2 -sequestration model based on the Kimberlina storage reservoir in California. The proposed approach is shown to synthesize a large volume of high-quality, physically realistic training data, which is critically important in accurately characterizing the CO 2 movement in the reservoir. In conclusion, the developed hybrid methodology can also simultaneously predict the variations in velocity and saturation and achieve high spatial resolution in the presence of realistic noise in the data.

58 GEOSCIENCES↗

DOE OSTI2025

Signal-preserving CMB component separation with machine learning

Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation using multifrequency methods, whereby different signals are isolated according to their different frequency behaviors. Many so-called blind methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or contaminants, and only assume knowledge of the frequency dependence of the signal. The ILC produces a minimum-variance linear combination of the measured frequency maps. In the case of Gaussian, statistically isotropic fields, this is the optimal linear combination, as the variance is the only statistic of interest. However, in many cases the signal we wish to isolate, or the foregrounds we wish to remove, are non-Gaussian and/or statistically anisotropic (in particular for the case of Galactic foregrounds). In such cases, it is possible that machine learning (ML) techniques can be used to exploit the non-Gaussian features of the foregrounds and thereby improve component separation. However, many ML techniques require the use of complex, difficult-to-interpret operations on the data. We propose a hybrid method whereby we train an ML model using only combinations of the data that , and combine the resulting ML-predicted foreground estimate with the ILC solution to reduce the error from the ILC. We demonstrate our methods on simulations of extragalactic temperature and Galactic polarization foregrounds and show that our ML model can exploit non-Gaussian features, such as point sources and spatially varying spectral indices, to produce lower-variance maps than ILC—e.g., reducing the variance of the B-mode residual by factors of up to 5—while preserving the signal of interest in an unbiased manner. Moreover, we often find improved performance even when applying our ML technique to foreground models on which it was not trained. Published by the American Physical Society 2025

McCarthy, Fiona (ORCID:0000000253893565)↗

DOE OSTI2025

Simulation Models for Exploring Magnetic Reconnection

Simulations have played a critical role in the advancement of our knowledge of magnetic reconnection. However, due to the inherently multiscale nature of reconnection, it is impossible to simulate all physics at all scales. For this reason, a wide range of simulation methods have been crafted to study particular aspects and consequences of magnetic reconnection. This article reviews many of these methods, laying out critical assumptions, numerical techniques, and giving examples of scientific results. Plasma models described include magnetohydrodynamics (MHD), Hall MHD, Hybrid, kinetic particle-in-cell (PIC), kinetic Vlasov, Fluid models with embedded PIC, Fluid models with direct feedback from energetic populations, and the Rice Convection Model (RCM).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Enabling selective zinc-ion intercalation by a eutectic electrolyte for practical anodeless zinc batteries

Abstract Two major challenges hinder the advance of aqueous zinc metal batteries for sustainable stationary storage: (1) achieving predominant Zn-ion (de)intercalation at the oxide cathode by suppressing adventitious proton co-intercalation and dissolution, and (2) simultaneously overcoming Zn dendrite growth at the anode that triggers parasitic electrolyte reactions. Here, we reveal the competition between Zn 2+ vs proton intercalation chemistry of a typical oxide cathode using ex-situ/ operando techniques, and alleviate side reactions by developing a cost-effective and non-flammable hybrid eutectic electrolyte. A fully hydrated Zn 2+ solvation structure facilitates fast charge transfer at the solid/electrolyte interface, enabling dendrite-free Zn plating/stripping with a remarkably high average coulombic efficiency of 99.8% at commercially relevant areal capacities of 4 mAh cm −2 and function up to 1600 h at 8 mAh cm −2 . By concurrently stabilizing Zn redox at both electrodes, we achieve a new benchmark in Zn-ion battery performance of 4 mAh cm −2 anode-free cells that retain 85% capacity over 100 cycles at 25 °C. Using this eutectic-design electrolyte, Zn | |Iodine full cells are further realized with 86% capacity retention over 2500 cycles. The approach represents a new avenue for long-duration energy storage.

25 ENERGY STORAGE↗

DOE OSTI2020

Optimizing hybrid rotational femtosecond/picosecond coherent anti-Stokes Raman spectroscopy in nitrogen at high pressures and temperatures

We demonstrate the use of hybrid rotational femtosecond/picosecond (fs/ps) coherent anti-Stokes Raman spectroscopy (HR-CARS) as a technique for temperature measurements in nitrogen gas at high pressures and temperatures. A broadband pulse shaper-adjusted 42 fs pulse interacts with a narrow-bandwidth, frequency-upconverted 5.5 ps pulse in a cell containing N 2 at pressures of 1–70 atm and temperatures of 300–1000 K. A computational code is used to model spectra and fit experimental results to obtain best-fit temperatures. We demonstrate good qualitative fits as well as good accuracy and precision between thermocouple measured and best-fit temperatures over the explored pressure and temperature regimes. The overall average percentage temperature difference between thermocouple measurements and best-fit temperatures is − <#comment/> 0 . 3 % <#comment/> with a standard deviation of 7.1%, showing the suitability of HR-CARS for characterizing high-pressure and -temperature environments.

Torge Mecker, Nils (ORCID:0000000238977170)↗

DOE OSTI2025

Mechanical and Tribological Performance of Additively Manufactured Nanocrystalline Aluminum via Cryomilling and Cold Spray

In this study, nanocrystalline (NC) aluminum (Al) and magnesium (Mg)-doped Al bulk components were fabricated using a hybrid manufacturing process that combines cryomilling and high-pressure cold spray (HPCS) additive deposition techniques. Yttria-stabilized zirconia (YSZ) was also added during the HPCS process to improve deposition efficiency and build-up thickness via peening. The evolution of morphology, crystallite size, and elemental composition of both cryomilled powders and cold-sprayed (CS’ed) components was systematically characterized using X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Transmission Electron Microscopy (TEM). Mechanical characterization was performed using Vickers microhardness and uniaxial tensile testing, while the tribological behavior was assessed using sliding wear tests under dry/lubricated conditions. XRD analysis revealed that increased cryomilling duration led to significant crystallite refinement, which directly correlated with enhanced hardness and strength. This mechanical strengthening was accompanied by an increase in coefficient of friction (COF) and lower wear rates. The results also showed that the Mg-doped Al exhibited superior hardness, tensile strength, and tribological performance compared to pure Al. The study further explores the underlying mechanisms responsible for these enhancements, highlighting the potential of solute-assisted grain boundary stabilization in tailoring high-performance NC Al alloys.

36 MATERIALS SCIENCE↗