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DOE OSTI2022

Riverscape heterogeneity in estimated Chinook Salmon emergence phenology and implications for size and growth

Abstract Many salmonid‐bearing rivers exhibit thermal and hydrologic heterogeneity at multiple spatial and temporal scales, but how this translates into spatiotemporal patterns of fry emergence is poorly understood. Understanding this variability is important because emergence timing determines the biophysical conditions fish first experience (e.g., temperature, flow, and food supply), thereby influencing growth opportunities and survival during this critical life stage. We predicted spring Chinook Salmon ( Oncorhynchus tshawytscha ) emergence phenology across four northeastern Oregon subbasins over 5–9 years using empirical spawning and temperature data. We then related interannual emergence timing estimates to juvenile salmon size and growth rates at consistent sampling locations. There were clear longitudinal patterns of predicted emergence timing in each subbasin: The shape of these patterns was consistent among years, but not among subbasins. In two subbasins, emergence occurred progressively later with distance upstream, whereas in the other two subbasins emergence was earliest at upstream sites. Within each year, median emergence dates among sites within each subbasin ranged between 44 and 58 days. This spatial variation was comparable to interannual variation, with median emergence dates for a given location in each subbasin ranging between 47 and 74 days among years. Contrary to our expectations, juvenile salmon were not larger in years with earlier emergence, owing to slower estimated spring and summer growth rates compared to years with later emergence. Despite large interannual variation in estimated emergence dates, these results suggest that other factors (e.g., stream flow, temperature, and density‐dependence) were more important than growth duration in determining juvenile salmon growth rates and size among years. We demonstrated considerable spatial and interannual variation in emergence phenology within these subbasins. Understanding how this variation translates to spatiotemporal patterns of juvenile salmon habitat use, growth, and survival has important implications for guiding restoration efforts and understanding how climate change may impact these populations.

Kaylor, Matthew J.↗

DOE OSTI2021

A generalized machine learning workflow to visualize mechanical discontinuity

Accurate detection and mapping of mechanical discontinuity in materials has widespread industrial and research applications. Herein, we developed a generalized machine-learning framework for visualizing single mechanical discontinuity embedded in material of any composition, velocity, density, porosity, and size with limited data. The proposed visualization of discontinuity requires accurate estimations of the length, location, and orientation of the embedded discontinuity by processing multipoint wave-transmission measurements. k-Wave simulator is used to create a large dataset of elastic waveforms recorded during multi-point wave-transmission measurements through materials containing single mechanical discontinuity. k-Wave simulator considers the wave attenuation, dispersion, and mode conversion in wave motion. Discrete wavelet transform (DWT) and statistical feature extraction are essential for data preprocessing prior to the data-driven model development. DWT also minimizes the effect of noise. Using hyper-parameter tuning and cross validation, gradient boosting regression can visualize the mechanical discontinuity with an accuracy of 0.85, in terms of coefficient of determination. A double-layered neural network-based regression has better performance with an accuracy of 0.95. Use of convolutional neural network converts the predictive task from a waveform processing to an image processing problem. Convolutional neural network achieved a generalization performance of 0.91. The proposed generalized workflow requires robust simulation of wave propagation, signal processing, feature engineering, and model evaluation. Sensors closest to the source and those located opposite the source are the most significant for the desired visualization. Notably, the sensors closest to the source capture the non-linear associations, whereas the sensor on the border opposite to the source capture the linear associations between the measured waveforms and the properties of the mechanical discontinuity.

42 ENGINEERING↗

DOE OSTI2020

Validation of gyrokinetic simulations in NSTX and projections for high-k turbulence measurements in NSTX-U

An extensive validation effort performed for a modest-beta NSTX NBI-heated H-mode discharge predicts that electron thermal transport can be entirely explained by electron-scale turbulence fluctuations driven by the electron temperature gradient mode (ETG), both in conditions of strong and weak ETG turbulence drive. Thermal power-balance estimates computed by TRANSP as well as the shape of the high-k density fluctuation wavenumber spectrum and the fluctuation level ratio between strongly driven and weakly driven ETG-turbulence conditions can be matched by nonlinear gyrokinetic simulations and a synthetic diagnostic for high-k scattering. Linear gyrokinetic simulations suggest that the ion-scale instability in the weak ETG condition is close to the critical threshold for the kinetic ballooning mode instability, and nonlinear ion-scale gyrokinetic simulations show that turbulence might be in a state reminiscent of a Dimits' shift regime, opening speculation on the role that ion-scale turbulence might play for the weak ETG condition. A simulation that matched all experimental constraints is chosen to project high-k turbulence spectra in NSTX-U, revealing that the new high-k system [R. Barchfeld et al., Rev. Sci. Instrum. 89, 10C114 (2018)] should be sensitive to density fluctuations from radially elongated streamer structures. In this study, two schemes are designed to characterize the radial and poloidal wavenumber dependence of the density fluctuation wavenumber power spectrum around the streamer peak, suggesting future high-k fluctuation measurements could be sensitive to an asymmetry in the k r spectrum introduced due to the presence of strong background flow shear.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Magnetic states of atomic vacancies in graphite probed by scanning tunneling microscopy

Intrinsic defects in graphitic materials, like vacancies and edges, have been expected to possess magnetic states from the many-body interaction of localized electrons. However, charge screening from graphite bulk carriers significantly reduces the localization effect and hinders the observation of those magnetic states. Here, we use an ultra-low-temperature scanning tunneling microscope with a high magnetic field to observe the magnetic states of atomic vacancies in graphite generated by ion sputtering. Scanning tunneling spectroscopy reveals localized states at the vacancies, which exhibit splitting at a certain magnetic field whose separation increases with the field strength. The transition is well described by the “Anderson model,” which describes the emergence of localized magnetic states inside the metallic reservoir through electron–electron interaction. The interaction strength is estimated to be between 1 meV and 3 meV, which is supported by the density functional theory calculation. The observation provides an important foundation for application of intrinsic defects to carbon-based spintronic devices.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Transfer learning for analysis of collective and non-collective Thomson scattering spectra

Thomson scattering (TS) diagnostics provide reliable, minimally perturbative measurements of fundamental plasma parameters, such as electron density (⁠n e ) and electron temperature (⁠T e ⁠). Deep neural networks can provide accurate estimates of ⁠n e and T e when conventional fitting algorithms may fail, such as when TS spectra are dominated by noise, or when fast analysis is required for real-time operation. Although deep neural networks typically require large training sets, transfer learning can improve model performance on a target task with limited data by leveraging pre-trained models from related source tasks, where select hidden layers are further trained using target data. We present five architecturally diverse deep neural networks, pre-trained on synthetic TS data and adapted for experimentally measured TS data, to evaluate the efficacy of transfer learning in estimating n e and T e in both the collective and non-collective scattering regimes. We evaluate errors in n e and T e estimates as a function of training set size for models trained with and without transfer learning, and we observe decreases in model error from transfer learning when the training set contains ≲ 200 experimentally measured spectra.

Artificial neural networks↗

DOE OSTI2020

Observation of non-thermal electron formation during the thermal quench of shattered pellet injection shutdowns in DIII-D

Formation of non-thermal (hot) electrons is studied during the thermal quench of shattered pellet injection (SPI) discharge shutdowns in the DIII-D tokamak. Both pure neon and mixed neon/deuterium shutdowns are studied. High-energy radiation (SXR and EUV) brightness levels show indications of non-thermal electron formation during the TQ. The non-thermal densities are of order 10 18 m –3 and non-thermal temperatures are estimated to be of order 10 keV. The non-thermal electrons appear to be mostly lost during the subsequent current quench, as no signal of relativistic runaway electrons is typically seen at the end of the current quench. In conclusion, the non-thermals appear to have an effect on the deposition profile of the SPI pellets, especially at the end of the TQ, due to increased pellet ablation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Diffusion coefficients predicting facet-dependent crystallization in amorphous silicon nitride

Amorphous silicon nitride is a common material in microelectronics devices, which acts as an insulating barrier. Extended annealing times at elevated temperature can initiate crystallization of α-Si 3 ⁢ N 4 , which does not possess the same barrier properties. Molecular dynamics can resolve the fundamental mechanism for α-Si 3 ⁢N 4 crystallization and the influence of local environments. Here, we compare two interatomic potentials and conclude that these models predict structural features (e.g., angular distributions and densities) which span the range of experimental measurements. We confirmed these models reproduce experimental estimates of activation energy and leveraged these models to identify crystallization drivers. We conclude that near-T g , facet-dependent silicon nitride crystal growth rates can be predicted directly by either bulk or interfacial diffusion properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Glassy quantum nuclear pasta in neutron star crusts

We conduct a comprehensive survey of the shape parameter space of the nuclear pasta phases in neutron star crusts by conducting three-dimensional Hartree-Fock + BCS calculations. Spaghetti, waffles, lasagna, bicontinuous phases and cylindrical holes occupy local minima in the resulting constant-pressure Gibbs energy surfaces, implying multiple geometries coexist at a given depth. Notably, the bicontinuous phase, in which both the neutron gas and nuclear matter extend continuously in all dimensions appears over a large depth range. Our results support the idea that nuclear pasta is a glassy system. At a characteristic temperature, of order 10 8 – 10 9 K, different phases may become frozen into domains whose sizes we estimate to be 1–50 times the lattice spacing and over which the local density and electron fraction can vary. Above this temperature, very little long-range order exists and matter is an amorphous solid. Electron scattering off domain boundaries may contribute to the disorder resistivity of the pasta phases. Annealing of the domains may occur during cooling; repopulating of local minima during crustal heating might lead to temperature-dependent transport properties in the deep crust layers. We identify four regions distinguished by whether pasta is the true ground state, and whether the pasta structure allows delocalization of protons. The whole pasta region can occupy up to 70% of the crust by mass and 25% by thickness, and the layer in which protons are delocalized could occupy 45% of the crust mass and 15% of its thickness.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Modeling of Supercritical CO2 Shell-and-Tube Heat Exchangers Under Extreme Conditions: Part II: Heat Exchanger Model

Abstract Heat exchangers play a critical role in supercritical CO2 Brayton cycles by providing necessary waste heat recovery. Supercritical CO2 thermal cycles potentially achieve higher energy density and thermal efficiency operating at elevated temperatures and pressures. Accurate and computationally efficient estimation of heat exchanger performance metrics at these conditions is important for the design and optimization of sCO2 systems and thermal cycles. In this paper (Part II), a computationally efficient and accurate numerical model is developed to predict the performance of shell-and-tube heat exchangers (STHXs). Highly accurate correlations reported in Part I of this study are utilized to improve the accuracy of performance predictions, and the concept of volume averaging is used to abstract the geometry and reduce computation time. The numerical model is validated by comparison with computational fluid dynamics (CFD) simulations and provides high accuracy and significantly lower computation time compared to existing numerical models. A preliminary optimization study is conducted and the advantage of using supercritical CO2 as a working fluid for energy systems is demonstrated.

Engineering↗

DOE OSTI2025

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration

This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. Furthermore, to facilitate Bayesian inference with intractable likelihoods, we propose to utilize optimization iterates for maximum a posteriori estimation to build a Gaussian process surrogate model for the unnormalized log-posterior density. We provide bounds for the Hellinger distance between the true and the approximate posterior distributions in terms of the number of design points. We demonstrate the effectiveness of our Bayesian optimization algorithms in nonconvex benchmark objective functions, in a machine learning hyperparameter tuning problem, and in a black-box engineering design problem. The effectiveness of our posterior approximation approach is demonstrated in two Bayesian inference problems for parameters of dynamical systems.

Bayesian inference↗

DOE OSTI2025

Benchmark Exercise Report for Experimental Study of Bubble Scrubbing in Sodium Pool

Mechanistic source term (MST) analyses are likely to be an important part of advanced reactor licensing applications. For the purpose, an MST analysis code applicable to newly introduced advanced reactors, called SRT (Simplified Radionuclide Transport) code, has been developed by Argonne National Laboratory. SRT can track overall behaviors of radionuclides especially in metal fuel-based sodium fast reactors (SFRs) and microreactors. Throughout the simulation, migration inside fuel pins before failure, interaction with coolant (for SFR), removal/leakage in cover gas and containment (or confinement), and environmental dose impacts are considered alongside radioactive decay for short-lived nuclides. Among the postulated process, pool scrubbing phenomenon, especially under sodium pool condition, has been identified as high importance with limited supportive data. The phenomenon plays a crucial role in assessing the degree of radiological impacts as radioactive aerosols or vapors are efficiently and effectively removed during the process. To provide validation basis for SRT in assessing pool scrubbing performance inside sodium pools, the University of Wisconsin-Madison performed tests including extensive parametric effects. Separate effect tests were conducted to directly evaluate the SRT models and to estimate degree of contribution by each contributing factor. Specifically, bubble size, aerosol size, aerosol density, aerosol concentration, pool depth, system temperature, and bubble swarm effects were considered. According to the parametric effects, decontamination performance enhances with decreasing bubble size, large density, and deeper pool height. Aerosol concentration provides no effect for the whole range of interest, and pool temperature variation shows minor effects under the considered temperature condition. When multiple bubbles are injected generating a bubble swarm condition, DF performance further enhances by bubble interactions and turbulence characteristics. The measurement shows the exceptional importance of aerosol size range considered, with the lowest decontamination, where most radionuclides are expected to escape.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

In-Plane Liftout and Push-to-Pull for In Situ Mechanical Testing of Irradiated Inconel X-750

A streamlined sample preparation method for nanomechanical testing is needed to improve the quality of specimens, reduce the cost, and increase the versatility of specimen fabrication. This work outlines an in-plane liftout focused ion beam (FIB) fabrication procedure to prepare electron-transparent specimens for in situ transmission electron microscopy (TEM) nanomechanical testing. Ion etching and electron backscatter diffraction (EBSD) techniques were used to lift out a [110] oriented grain from a neutron-irradiated bulk X-750 alloy. Careful control of voltages and currents ensured precision. Top surface thinning sweeps prevented resurfacing and redeposition while dog-bone geometries were shaped with a 1:4 gauge width-to-milling pattern diameter ratio. Nanotensile testing in the TEM with a picoindenter allowed for the estimation of an ultimate tensile strength of 2.41 GPa, and inspection revealed a high density of bubbles in the X-750 matrix. The proposed fabrication procedure is significant for preparing samples from radioactive materials, studying complex structures that are orientation-dependent, and analyzing desired planar areas.

36 MATERIALS SCIENCE↗

DOE OSTI2020

The HOSTS Survey for Exozodiacal Dust: Observational Results from the Complete Survey

The Large Binocular Telescope Interferometer (LBTI) enables nulling interferometric observations across the N band (8 to 13 μm) to suppress a star’s bright light and probe for faint circumstellar emission. We present and statistically analyze the results from the LBTI/Hunt for Observable Signatures of Terrestrial Systems survey for exozodiacal dust. By comparing our measurements to model predictions based on the solar zodiacal dust in the N band, we estimate a 1σ median sensitivity of 23 zodis times the solar system dust surface density in its habitable zone (HZ; 23 zodis) for early-type stars and 48 zodis for Sun-like stars, where 1 zodi is the surface density of HZ dust in the solar system. Of the 38 stars observed, 10 show significant excess. A clear correlation of our detections with the presence of cold dust in the systems was found, but none with the stellar spectral type or age. The majority of Sun-like stars have relatively low HZ dust levels (best-fit median: 3 zodis, 1σ upper limit: 9 zodis, 95% confidence: 27 zodis based on our N band measurements), while ~20% are significantly more dusty. The solar system’s HZ dust content is consistent with being typical. Our median HZ dust level would not be a major limitation to the direct imaging search for Earth-like exoplanets, but more precise constraints are still required, in particular to evaluate the impact of exozodiacal dust for the spectroscopic characterization of imaged exo-Earth candidates.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Clarissa Family Age from the Yarkovsky Effect Chronology

The Clarissa family is a small collisional family composed of primitive C-type asteroids. It is located in a dynamically stable zone of the inner asteroid belt. In this work we determine the formation age of the Clarissa family by modeling planetary perturbations as well as thermal drift of family members due to the Yarkovsky effect. Simulations were carried out using the SWIFT-RMVS4 integrator modified to account for the Yarkovsky and Yarkovsky–O’Keefe–Radzievskii–Paddack (YORP) effects. We ran multiple simulations starting with different ejection velocity fields of fragments, varying proportion of initially retrograde spins, and also tested different Yarkovsky/YORP models. Our goal was to match the observed orbital structure of the Clarissa family which is notably asymmetrical in the proper semimajor axis, a {sub p}. The best fits were obtained with the initial ejection velocities ≲20 m s{sup −1} of diameter D ≃ 2 km fragments, ∼4:1 preference for spin-up by YORP, and assuming that ≃80% of small family members initially had retrograde rotation. The age of the Clarissa family was found to be t {sub age} = 56 ± 6 Myr for the assumed asteroid density ρ = 1.5 g cm{sup −3}. Small variation of density to smaller or larger value would lead to slightly younger or older age estimates. This is the first case where the Yarkovsky effect chronology has been successfully applied to an asteroid family younger than 100 Myr.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

High-resolution Giant Metrewave Radio Telescope H I 21 cm Imaging of the Host Galaxy of FRB 20250316A

We report Giant Metrewave Radio Telescope (GMRT) H I 21 cm imaging of NGC 4141, the host galaxy of FRB 20250316A at z = 0.0063. Our GMRT H I 21 cm images have spatial resolutions, at z ≈ 0.0063, of ≈0.48–8.0 kpc, and provide evidence for (i) a companion galaxy, LEDA 2582852, to the southwest, (ii) a nearby (27 kpc distant) H i cloud to the southwest, (iii) disturbances in the H I distributions of both NGC 4141 and LEDA 2582852, and (iv) high H I column densities in the southwestern outskirts of NGC 4141. A Sloan Digital Sky Survey spectrum yields a low metallicity and a high star formation rate (SFR) surface density in the southwestern disk of NGC 4141, and an Hα-based SFR estimate that is significantly higher than that at the same location from the Galaxy Evolution Explorer near-ultraviolet image, indicating a recent burst of star formation. The total SFR of NGC 4141 is also found to be higher via the Hα line than from the 1.4 GHz radio continuum. The above evidence indicates that NGC 4141 has recently (within the last ≈3 Myr) acquired metal-poor gas, via either a merger or accretion, that resulted in the southwestern starburst and that may also have triggered large-scale star-formation activity in NGC 4141, resulting in the formation of the stellar progenitor of FRB 20250316A and the other transients. Our highest-resolution (480 pc) GMRT H I 21 cm image finds no H I 21 cm emission from the location of FRB 20250316A or the nearby star-forming region, suggesting that most of the H I here has been either ionized or converted into the molecular phase. Our nondetection of continuum emission at the location of FRB 20250316A yields the 3σ upper limit νL 1.38 GHz < 4.4 × 10 34 erg s −1 on the 1.38 GHz radio luminosity of a putative persistent radio source associated with FRB 20250316A, one of the strongest constraints on the radio luminosity of such an associated persistent radio source.

Kaur, Balpreet [Pune University (India)] (ORCID:00↗

DOE OSTI2022

Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z = 118), it is estimated that as many as about 8,000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but they play an essential role in astrophysical events such as supernova explosions or neutron star mergers that are presumed to be at the origin of most heavy elements in the Universe. Understanding the structure, reactions, and decays of nuclei across the entire chart of nuclides is an enormous challenge because of the experimental difficulties in measuring properties of interest in such fleeting objects and the theoretical and computational issues of simulating strongly-interacting quantum many-body systems. Nuclear density functional theory (DFT) is a fully microscopic theoretical framework which has the potential of providing such a quantitatively accurate description of nuclear properties for every nucleus in the chart of nuclides. Thanks to high-performance computing facilities, it has already been successfully applied to predict nuclear masses, global patterns of radioactive decay like β or γ decay, and several aspects of the nuclear fission process such as, e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear spectroscopy—the low-lying excited states and transitions between them—or of nuclear fission, or the quantification of theoretical uncertainties and their propagation to basic or applied nuclear science applications, would require several orders of magnitude more calculations than currently possible. However, most of this computational effort would be spent into generating a suitable basis of DFT wavefunctions. Such a task could potentially be considerably accelerated by borrowing tools from the field of machine learning and artificial intelligence. In this paper, we review different approaches to applying supervised and unsupervised learning techniques to nuclear DFT.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

A Rate Theory Model of Radiation-Induced Swelling in an Austenitic Stainless Steel

Many rate theory models of cavity (void) swelling have been published over the past 50 years, all having the same, or similar, structures. A rigorous validation of the models has not been possible because of the dearth of information concerning the microstructures that correspond with the swelling data. Whereas the lack of microstructure information is still an issue for historical swelling data, in the past 10–20 years data have been published on the evolution of the microstructure (point defect yields from collision cascades, cavity number densities, and dislocation densities/yield strengths) allowing certain gaps in information to be filled when considering historic swelling data. With reasonable estimates of key microstructure parameters, a standard rate theory model can be applied, and the model parameter space explored, in connection with historical swelling data. By using published data on: (i) yield strength as a function of dose and temperature (to establish an empirical expression for dislocation density evolution); (ii) cavity number densities as a function of temperature; and (iii) freely migrating defect (FMD) production as a function of primary knock-on atom (PKA) spectrum, the necessary parameter and microstructure inputs that were previously unknown can be used in model development. This paper describes a rate-theory model for void swelling of 316 stainless steel irradiated in the EBR-2 reactor as a function of irradiation temperature and neutron dose.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Mathematical modeling and parameter estimation of MFI membranes for para/ortho‐xylene separation

Abstract A mathematical model of a membrane with a thin, oriented, and selective MFI layer, which also includes contributions from defects, pore blockages, support layer, and external mass transfer, was formulated based on the Maxwell‐Stefan equations. It was validated using reported (Kim et al., Angew Chemie Int Ed, 2018, 57:480–485; Jeon et al., Nature, 2017, 543:690–694) para/ortho‐xylene separation data from five MFI membranes. The diffusivities of the xylenes were considered to be the same for all membranes, while the thickness and the density of defects and pore blockage were treated as unique properties of each membrane, and their contributions were estimated by fitting the model to the corresponding separation data. The effects of these properties and the role of permeate pressure on the separation performance were subsequently analyzed. The proposed modeling, parameter estimation, and analysis framework allow one to quantitatively interpret the variation of separation performance, to understand separation bottlenecks, and to provide guidance for designing membranes with desired performance.

Liu, Jingjun↗