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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon

Modeling tools can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations, but subject to uncertainties associated with parameters in model physical parameterizations. Different sensitivity analysis (SA) approaches may lead to different conclusions about the underlying sensitivities. In this study, we quantify simulation uncertainties related to parameter perturbations, and use different approaches to conduct parameter SA on the WRF model pertaining to L-A coupling strength for simulations over the Amazon region. A total of twenty parameters from the Yonsei University (YSU) planetary boundary layer (PBL) and the revised MM5 surface layer (SL) schemes were selected in this analysis. Three different SA methods, the Morris One-at-A-Time (MOAT) method, the Multivariate Adaptive Regression Splines (MARS) method, and the Sobol’ method, were employed to analyze seven WRF-simulated variables and five L-A coupling metrics. Results show that 1) parameter perturbations cause large simulation uncertainties which are comparable to those in the observations; 2) three different SA methods give consistent L-A coupling strength outcomes; 3) six out of the twenty parameters contribute 80%–95% of the total variance in the metrics analyzed, and first-order effects dominate over interaction effects; 4) the twelve variables/metrics of interest show similar sensitivity patterns to the selected parameters, which is consistent across all the methods used. Physical mechanisms for how the sensitive parameters act in determining the L-A coupling strength and associated variables also are illustrated. Our results will help quantifying L-A coupling strength and establishing a basis for parameter calibration over the Amazon region.

Wang, Chen↗
DOE OSTI2021

Melting rate correlation with batch properties and melter operating conditions during conversion of nuclear waste melter feeds to glasses

The rate of conversion of nuclear waste melter feed to glass is affected by the selection of melter feed materials and by melter design and operation. The melting rate correlation (MRC) is an equation that relates the glass production rate with two types of variables: (1) feed and melt properties: conversion heat, cold-cap bottom temperature, and glass melt viscosity; and (2) melter design and operation parameters: melter geometry, melter operating temperature, and gas bubbling rate. The MRC shows good agreement for an extended melting-rate data set of high-level waste (HLW) melter feeds and a data set generated for low-activity waste (LAW) melter feeds. Laboratory observation of heated melter feed samples is often used to assess the cold-cap bottom temperature of HLW melter feeds (moderately foaming feeds), but this technique appears inadequate for LAW melter feeds (vigorously foaming feeds). For LAW feeds, an adequate assessment of the cold-cap bottom temperature was achieved using evolved gas analysis, which allows identification of the collapse of primary foam for oxidized feeds. This assessment shows that the cold-cap bottom temperature for vigorously foaming LAW feeds is higher than that for moderately foaming HLW feeds. When the results of MRC are compared, LAW feeds are generally less sensitive to the bubbling rate and melt viscosity, and more sensitive to the cold-cap bottom temperature than HLW feeds. The MRC qualifies as a promising tool to support the selection of melter feed materials and melter operating conditions, which is determined from expensive independent scaled melter experiments, and sophisticated mathematical models.

Lee, Seung Min↗
DOE OSTI2024

Grain structure and texture selection regimes in metal powder bed fusion

Additive manufacturing (AM) offers opportunities to produce complex part geometries not possible with conventional processing and in some cases even improve part performance. However, adoption has been slowed by difficulties assessing microstructure variability and there is no straightforward approach to relate processing to grain structure characteristics. In this study, datasets from AdditiveFOAM heat transport simulations of laser powder bed fusion (LPBF) are used to drive ExaCA simulations of grain structure. The GPU utilization of ExaCA and an algorithmic update for modeling melt pool overlap region solidification enabled rapid and parallel simulation across a wider range of process conditions than previously explored with cellular automata-based solidification models. A texture selection angle $θ_s$ is defined based on melt pool overlap geometry, and the range of $θ_s$ over which a commonly observed texture transition occurs in characterized AM builds was well-reproduced by ExaCA simulations over a wide range of melt pool shape, hatch spacing, and layer height. ExaCA simulations with 90 degree rotation of the scan direction on every other layer reproduced a number of trends from the AM literature including grain refinement, the dominance of layers with larger melt pools on the final grain structure, and the weakening or strengthening of texture depending on odd and even layer melt pool overlap geometry. EBSD data from a benchmark AM part is used to validate the simulated mechanism of a layer rotation-induced texture strengthening effect. Importantly, these results expand the understanding of the mechanisms for texture selection in alloys with cubic crystal symmetry and offer an approach to easily evaluate processing conditions. With this new understanding, these modeling tools will enable anticipation of previously unexpected variations in grain structure and target specific microstructures and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

High temperature air oxidation behavior of Hastelloy X processed by Electron Beam Melting (EBM) and Selective Laser Melting (SLM)

The microstructure and oxidation behavior of Laser Beam Melted, Electron Beam Melted and wrought Hastelloy X were studied at 950 °C in dry air, 50 h cycles, up to 1000 h. The variability of processing parameters and powder compositions strongly impacted the oxidation and spallation behavior. Finally, the alloys with high Mn and Si were associated with higher oxidation rates, finer Cr 2 O 3 grain microstructure and increased spallation was observed for the alloys with large elongated grain microstructures (aspect ratio of 10 ± 3).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optimization of the moderators in the STS preliminary design

This report details the results for an optimization of the dimensions of the moderators in the preliminary design of the Spallation Neutron Source Second Target Station (STS). This study uses the optimization algorithms of Dakota and an unstructured mesh model for the moderators in MCNP. More details on the unstructured mesh model and the automated mesh generation can be found in [3]. Parallel to this effort, the same moderator geometries have been optimized using a constructive solid geometry (CSG) MCNP model. More details on this model and its results can be found in [4]. Three optimal designs are selected for each moderator: one that is optimized for maximum peak brightness, one for maximum time-integrated brightness, and one for a combination of peak and time-integrated brightness. The backbone of the optimization work flow is provided by Dakota. For each set of design parameters requested by Dakota, a new solid geometry is automatically built in Creo and SpaceClaim, and subsequently exported to Attila4MC to generate an unstructured mesh geometry for MCNP. After the MCNP calculation is finished, the objective function (e.g., brightness metric) is returned to Dakota. After the new design has been evaluated, a result-file is written, and Dakota proposes the next set of design parameters to be evaluated. The loop continues until a specified convergence criterion has been met. The design parameters of the cylindrical (upper) moderator include the hydrogen radius, the premoderator thickness (top, bottom, radial), the beryllium radius and the horizontal position of the moderator. The crucial design choice is the hydrogen radius. A radius of 62 mm is shown to provide the maximum time-integrated brightness. The maximum peak brightness occurs with a radius of 40 mm. A combined (middle) design, which balances peak and time-integrated brightnesses, is obtained with a hydrogen radius of 50 mm. The premoderator thicknesses and the beryllium radius are slightly larger in the design optimized for time-integrated brightness than in the design optimized for peak brightness. The sensitivity to these two parameters is relatively small close to the optimal configurations. The hydrogen vessel and vacuum vessel wall thicknesses are dependent on the radius of the liquid hydrogen due to structural integrity requirements. The increased wall thicknesses for larger vessels significantly penalize the time-integrated brightness, with the maximum obtainable value reduced by more than 10% relative to earlier studies which used fixed vessel wall thicknesses. The impact of the variable wall thicknesses is much less for the peak brightness and combined brightness designs. The design parameters of the tube (lower) moderator selected for the optimization are the tube length, the annular premoderator thickness, the beryllium radius and the horizontal position of the moderator. The tube length is the crucial parameter and is chosen large (210 mm) and small (125 mm) in the designs optimized for time-integrated and peak brightness respectively. A combined optimal design has a tube length of 170 mm. The premoderator thickness and the beryllium radius are chosen larger in the design optimized for time-integrated brightness.

42 ENGINEERING↗
DOE OSTI2025

Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots

The cultivation of sterile giant miscanthus (Miscanthus × giganteus, M × g) for bioenergy and bioproducts has expanded into grain-cropped land in the United States (US) as local markets developed for this high-yielding perennial grass (10–30 Mg DM ha −1 ). However, the magnitude of spatial and temporal variability in yield within US Corn Belt fields, along with impacts on economic return and sustainable land management, is poorly understood. This study established a diagnostic model relating remote sensing-derived vegetation indices to ground truth data from 105 hand-harvested stem biomass samples, which were strategically selected to represent the full range of vegetation index observations. The high-resolution satellite-sensed vegetation indices captured > 90% of the yield variation measured within fields. This model was then used to predict yield variability and assess economic performance across four of the first commercial M × g fields in the Corn Belt state of Iowa, US. Significant spatial variability in biomass dry matter (DM) yields (9.3–18.1 Mg DM ha −1 ) and net profits ($\$$83 to $\$$1211.5 ha −1 ) was observed. All fields were profitable in all site-years. When low profit occurred, it was explained by limited management experience of the crop in Iowa. The breakeven yield at a selling price of $\$$130 Mg −1 varied from 9.0–12.1 Mg ha −1 at 15% moisture content (7.6–10.3 Mg DM ha −1 ). Breakeven prices ranged from $\$$73 to $\$$122.4 Mg −1 , matching ranges used in the Department of Energy Billion Ton Report (US Department of Energy, 2023). Notably, M × g yield and profits were commensurate with grain crops particularly with favorable precipitation. This study provides insight on the M × g management “learning curve”, performance on marginal land and in drought conditions, and demonstrates that addressing yield gaps, reducing costs, and implementing precision agriculture strategies can enhance profitability. These findings emphasize the value of remote sensing technologies in guiding sustainable and competitive commercial-scale M × g production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The role of radio loudness in Eigenvector 1 and the Baldwin Effect of [O iii ] λ5007

ABSTRACT The radio emission is an important observable of quasars, but its relationship to Eigenvector 1 and the [O iii] λ5007 Baldwin Effect is not fully understood. We explore these issues based on a sample of 1800 quasars taken from the Sloan Digital Sky Survey Data Release 7. We employ a new approach of selecting subsamples in the plane of fundamental physical parameters of the black hole mass and Eddington ratio, so as to reduce variables and complexity in analyses. Based on these subsamples, we investigate the relationship between radio loudness R and Eigenvector 1 and find that radio loudness is correlated with [O iii] λ5007 emission, but has no clear relationship with optical Fe ii emission, which indicates that the radio power is probably not a driver of Eigenvector 1 but merely a secondary process. In addition, we also investigate the impact of radio loudness on the Baldwin Effect of [O iii] λ5007. We find that when the radio loudness is not strong (log R < 2), the Baldwin Effect of [O iii] λ5007 is clear, while in the samples of extreme radio loudness (log R ≥ 2), the Baldwin Effect of [O iii] λ5007 becomes weaker or even disappears. We suggest that both radio loudness and luminosity influence the relative strength of [O iii].

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

An Assessment of Non-Powered Dam Hydropower Development Opportunities in the United States

Retrofitting non-powered dams (NPDs) to add hydropower offers multiple benefits over traditional, new hydropower development. Since much of the civil works infrastructure already exists, many retrofits require minimal new construction and can leverage existing water conveyances and discharge for generation. Although NPDs vary considerably in terms of their defining characteristics, identifying similarities helps support targeted investment and find opportunities to develop solutions for common challenges. With the publication of NPD-related data in 2022 (Hansen et al. 2022b), the US Department of Energy and relevant stakeholders are equipped with key information across roughly 89,000 US NPDs. These data represent the best-available information to date and extend beyond previous assessments of potential power capacity by describing design, operational, socioeconomic, environmental aspects of NPDs (Hadjerioua et al. 2012). To capitalize on these efforts to improve breadth and depth of NPD data access, this study uses a data-driven approach to assess NPD hydropower development opportunities in the United States. To help describe project feasibility drivers for NPDs, recent NPD retrofits were reviewed to examine variability with respect to a variety of characteristics. Based on the assessment of recent retrofits and data availability, several attributes were selected to characterize the remaining NPD population: (1) owner type, (2) 30% exceedance flow (a common design flow that describes the flow that is exceeded by 30% of the flow in the record), (3) hydraulic head, and (4) maximum reservoir storage. These four characteristics were used as inputs to a statistical clustering analysis (a common technique for grouping individuals of a population based on similarities or how closely associated individuals are to one another) of a set of 2,709 NPDs with at least 100 kW potential capacity and recently retrofit dams with available data. With the large dataset of NPDs, the clusters help describe how the population breaks down into different types (i.e., how many types of dams there are and what portion of the population belongs to each cluster).

13 HYDRO ENERGY↗
DOE OSTI2026

Inter-year Variability in EDS Standards

This report provides a deep dive into how the stability of energy dispersive spectroscopy (EDS) standards yielded insight into the shortfalls of using software to randomly select points for spectral acquisition when using heterogeneous standards. Some procedural recommendations are included which are meant to minimize the impact of anomalous reference standard spectra by detecting them before the standard is implemented into data processing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

MEASUREMENT OF Fn 2 /Fp 2 FROM DEEP INELASTIC ELECTRON SCATTERING OFF A=3 MIRROR NUCLEI AT JEFFERSON LAB

This research is part of the Je?erson Lab MARATHON experiment (MeAsurement of the Fn 2 /Fp 2 , d/u RAtios and A = 3 EMC E?ect in Deep Inelastic Electron Scattering Off the Tritium and Helium MirrOr Nuclei). MARATHON is an experiment on electron deep inelastic scattering off the 3H, 3He mirror nuclei. It has extracted, from the measured deep inelastic scattering cross section ratio o? 3H and 3He, the ratio of the neutron to proton inelastic structure functions Fn 2 /Fp 2 . The extraction has taken advantage of the isospin symmetry of the A = 3 mirror nuclei within a novel technique, which avoids the theoretical uncertainties that are present in the SLAC experiments of the 1970s, which used hydrogen and deuterium targets. The experiment was performed using the upgraded 11 GeV beam of the Jefferson Lab electron accelerator, the Hall A Experimental Facility, and a newly designed cryogenic, high-pressure gas target system containing 3H and 3He cells. Electrons scattered o? the gas targets were detected in the two superconducting, high-resolution magnetic spectrometers of Hall A. The cross section data cover the four-momentum transfer squared range 2.0 < Q2 < 14.0 (GeV/c)2, and the Bjorken x scaling variable range 0.19 < x < 0.83. The experiment also measured, for calibration purposes, electron deep inelastic scattering off hydrogen and deuterium at selected kinematics.

Su, Tong↗
DOE OSTI2023

Techno-Economic Case Study: Fermentation Cost Impacts from Selected Critical Material Attributes

This report summarizes analysis conducted to support a case study under the Feedstock-Conversion Interface Consortium (FCIC) focused on techno-economic analysis (TEA) modeling to quantify the economic implications of biomass hydrolysate substrate variability on fermentation performance and resultant biorefinery fuel yields. It is known that fermentation inhibitors or byproducts, as may either come from constituents in the biomass feedstock or imparted through biorefinery processing operations, can detrimentally impact fermentation rates and yields. However, it is often difficult to isolate fermentation impacts to individual components given the complex nature of biomass varying simultaneously in multiple attributes from one lot to another. For this study, we worked with FCIC researchers to obtain data on key fermentation performance metrics, namely productivity rates and conversion yields, across a number of material attribute species previously selected by the researchers as "critical" attributes which may be found in hydrolysate used as microbial carbon sources during fermentation. These data were run through TEA models to estimate resultant impacts on biorefinery economics, reported as minimum fuel selling price (MFSP). Overall, the trends in MFSP closely followed fuel yields, in turn tied to fermentation process yields and selectivity, with minimal economic impact from variations in fermentation productivity.

09 BIOMASS FUELS↗
DOE OSTI2026

A network of soil moisture, soil temperature, air temperature, net radiation, ground heat flux and ground water for Chicago, Illinois

This dataset contains environmental monitoring data collected using solar-powered Multi-Function Research (MFR) Long Range Wide Area (LoRaWAN)-enabled nodes at 11 sites in Chicago, Illinois, as part of the DOE Urban Integrated Field Lab CROCUS project. The MFR node system consists of an Input/Output Digital Input Module (IB8) interface box (ICT International) providing wired connections for environmental sensors and an MFR-Node-L data logger that manages power, data processing, and LoRaWAN communication. The wireless data are ingested via Sage network (https://sagecontinuum.org/) nodes that contain LoRaWAN antennae. Measurements were collected from 11 MFR nodes deployed across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), West Woodlawn "Blacks in Green" (BIG), and Indian Boundary Prairies (IBP). Each MFR node supports a consistent suite of sensors measuring atmospheric, soil, and hydrological variables. Atmospheric measurements include 2m air temperature (°C), 2m vapor pressure deficit (kPa), and 2m shortwave/longwave radiation (incoming and outgoing, W/m²) measured using ATH-VPD and Apogee SN500 sensors. Soil measurements include volumetric water content (VWC, %) and temperature (°C) at four depths (15, 30, 45, and 60 cm below surface) using Meter Teros54 sensors, and heat flux (W/m²) at 10 cm depth using Huske HFP01-05 sensors. At selected locations, Meter Hydros21 sensors measure groundwater depth (mm), specific conductivity (dS/m), and temperature (°C). The dataset includes timestamps, site identifiers with location names, device IDs, Global Positioning System (GPS) coordinates, variable names with units, measurement depths, values, sensor names, and Sage node identifiers. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 6-bit binary system indicating physical range violations, step spikes, 24-hour flat-line conditions, 6-hour jitter, 7-day ultra-low variance, and persistent high offset. Data is provided in CSV and CF-compliant NetCDF formats. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Sap Flow Meter (SFM1x) sensors.

Chicago↗
DOE OSTI2023

Soil management legacy interacts with wheat genotype to determine access to organic N in a dryland system

Organic nutrient management through the application of compost and/or cover crops provides mineralizable sources of nutrients for plants while often building soil organic matter (SOM) and various aspects of soil health. Variability in nutrient acquisition strategies between crop genotypes may confer advantages under different soil health contexts and could be important for crop selection and breeding, but crop response under field conditions remains unexplored. We investigated the ability of different genotypes of winter wheat (Triticum aestivum L.) to access nitrogen (N) from newly added cover crop residues in two soils with contrasting levels of SOM and biological activity. We planted three previously characterized wheat genotypes in a long-term dryland compost amendment field trial: 1) Byrd (modern, deep roots, low exudation), 2) Cheyenne (historic, drought susceptible, intermediate exudation), and 3) Snowmass (modern, drought-susceptible, high exudation). 15 N-labelled cover crop residue was added to each plot and traced into wheat tissue. In the low SOM soil, the high exudate genotype Snowmass and historic genotype Cheyenne took up the most residue-derived N (6.4–8.1 kg N ha −1 ) compared to the low-exudate genotype Byrd (4.4 kg N ha −1 ), suggesting a strong exudate effect in the more carbon-limited soil. However, in the high SOM soil, the low-exudate, deep rooted genotype, Byrd, took up the most residue N (4.6 kg N ha −1 vs. 2.8 and 3.3 hg N ha −1 for Cheyenne and Snowmass, respectively), which indicated higher native N cycling activities and greater importance of drought resistance. Enzyme activity, inorganic N, and microbial communities were not influenced by genotype, though did show strong effects of compost application legacy. Furthermore, our results show that belowground allocation strategies that favor microbial stimulation may be less successful under water limitation, especially when high native SOM and biological activity can support mineralization of residue N without added investment in root inputs. Increased soil health through SOM-building management likely enhances nutrient cycling, and may better support root strategies that invest less in microbial stimulation in favor of other limiting resources.

Compost↗
DOE OSTI2020

Evidence for top quark production in nucleus-nucleus collisions

We report droplets of quark-gluon plasma (QGP), an exotic state of strongly interacting quantum chromodynamics (QCD) matter, are routinely produced in heavy nuclei high-energy collisions. Although the experimental signatures marked a paradigm shift away from expectations of a weakly coupled QGP, a challenge remains as to how the locally deconfined state with a lifetime of a few fm can be resolved. The only colored particle that decays mostly within the QGP is the top quark. Here we demonstrate, for the first time, that top quark decay products are identified, irrespective of whether interacting with the medium (bottom quarks) or not (leptonically decaying W bosons). Using 1.7±0.1 nb -1 of lead-lead (A = 208) collision data recorded by the CMS experiment at a nucleon-nucleon center-of-mass energy of 5.02 TeV, we report evidence of top quark pair ($t\bar{t}$) production. Dilepton final states are selected, and the cross section ($σ_{t\bar{t}}$) is measured from a likelihood fit to a multivariate discriminator using lepton kinematic variables. The $σ_{t\bar{t}}$ measurement is additionally performed considering the jets originating from the hadronization of bottom quarks, which improve the sensitivity to the $t\bar{t}$ signal process. After background subtraction and analysis corrections, the measured $σ_{t\bar{t}}$ is 2.56 ± 0.82(tot) and 2.02 ± 0.69(tot)μb in the two cases, respectively, consistent with predictions from perturbative QCD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Intersubband Relaxation in CdSe Colloidal Quantum Wells

The dynamics of intersubband relaxation are critical to quantum well technologies such as quantum cascade lasers and quantum well infrared photodetectors. In this work, intersubband relaxation in CdSe colloidal quantum wells, or nanoplatelets, is studied via pump-push-probe transient spectroscopy. An initial interband pump pulse is followed by a secondary infrared push excitation, resonant with intersubband absorption, which promotes electrons from the first conduction band of the quantum well to the second conduction band. A probe pulse monitors subsequent electron cooling to the band edge of the quantum well. Using this technique, intersubband relaxation is studied as a function of critical variables such as colloidal quantum well size and thickness, surface ligand chemistry, temperature, and excitation pulse intensity. Larger quantum well sizes, judicious selection of surface ligand chemistry (e.g., thiolates), low temperatures, and elevated push pulse fluences slow intersubband relaxation. However, compared to resonant intraband relaxation in colloidal quantum dots (up to hundreds of picoseconds), intersubband relaxation in colloidal quantum wells is rapid (<1 ps) under all examined conditions. These experiments indicate that rapid relaxation is driven by both LO phonon and surface scattering. The short time scale of relaxation observed in these materials may hinder intersubband technologies such as mid-infrared detectors, although such rapid relaxation may prove valuable in optical switching.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Choice of 16S ribosomal RNA primers affects the microbiome analysis in chicken ceca

We evaluated the effect of applying different sets of 16S rRNA primers on bacterial composition, diversity, and predicted function in chicken ceca. Cecal contents from Ross 708 birds at 1, 3, and 5 weeks of age were collected for DNA isolation. Eight different primer pairs targeting different variable regions of the 16S rRNA gene were employed. DNA sequences were analyzed using open-source platform QIIME2 and the Greengenes database. PICRUSt2 was used to determine the predicted function of bacterial communities. Changes in bacterial relative abundance due to 16S primers were determined by GLMs. The average PCR amplicon size ranged from 315 bp (V3) to 769 bp (V4–V6). Alpha- and beta-diversity, taxonomic composition, and predicted functions were significantly affected by the primer choice. Beta diversity analysis based on Unweighted UniFrac distance matrix showed separation of microbiota with four different clusters of bacterial communities. Based on the alpha- and beta-diversity and taxonomic composition, variable regions V1–V3(1) and (2), and V3–V4 and V3–V5 were in most consensus. Our data strongly suggest that selection of particular sets of the 16S rRNA primers can impact microbiota analysis and interpretation of results in chicken as was shown previously for humans and other animal species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Modeling of the metal–insulator transition temperature in alio-valently doped VO 2 through symbolic regression

The correlated semiconductor vanadium dioxide (VO 2 ) exhibits an insulator–metal transition (IMT) near room temperature, which is of interest in various device applications. Precise IMT temperature control is crucial to determine the use cases across technologies such as thermochromic windows, actuators for robots or neuronal oscillators. Doping the cation or anion sites can modulate the IMT by several tens of degrees and control hysteresis. However, modeling the effects of control parameters (e.g., doping concentration, type of dopants) is challenging due to complex experimental procedures and limited data, hindering the use of traditional data-driven machine learning approaches. Symbolic regression (SR) can bridge this gap by identifying nonlinear expressions connecting key input parameters to target properties, even with small data sets. In this work, we develop SR models to capture the IMT trends in VO 2 influenced by different dopant parameters. Using experimental data from the literature, our study reveals a dual nature of the IMT temperature with varying tungsten (W) doping concentrations. The symbolic model captures data trends and accounts for experimental variability, providing a complementary approach to first-principles calculations. Our feature-driven analysis across a broader class of dopants informs selectivity and provides qualitative insights into tuning phase transition properties valuable for neuromorphic computing and thermochromic windows.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Comparative Evaluation of Spectral Methods for Robust Reactor Noise Estimation

Reactor noise analysis provides a noninvasive means to determine neutron kinetic parameters from stochastic fluctuations in detector signals. However, standard cross-power spectral density (CPSD) analyses can be sensitive to numerical processing choices, which may introduce processing-dependent systematic shifts in estimates of the prompt neutron decay constant (α) and limit reproducibility. This study uses a hybrid multitaper–Welch spectral estimator to analyze subcritical noise measurements from a fast-spectrum critical assembly. The decay constant α was extracted using three frequency-domain methods: the CPSD, the magnitude-squared coherence (MSC), and the generalized magnitude-squared coherence (GMSC). These coherence-based estimators normalize detector auto-spectral structure and are expected to reduce the sensitivity of fitted α values to processing parameters. A Sobol global sensitivity analysis identified which numerical inputs most strongly influence the fitted values of α. All estimators produced a linear dependence of α on inverse count rate, with delayed-critical extrapolations near 1.7 × 10 4 s −1 , in agreement within 8% of MCNP6.3 KOPTS benchmark calculations. Sensitivity results show that while the CPSD depends on both time-bin width and taper selection, the MSC and GMSC are dominated by time-bin width alone, indicating reduced parameter coupling and greater robustness to processing variability. These findings demonstrate the feasibility and practical value of coherence-based spectral estimators for extracting α from reactor noise and support their broader application to multi-detector and irregular datasets in subcritical system characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗