Search NASA⌕ Search

SEARCH · Search NASA

Results for “Variance to Mean”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Pyrolysis Molecular Beam Mass Spectrometry_Analysis_of_Natural_Variants_of_Poplulus_Trichocarpa_Leaves

Select leaves from natural variants of Poplar (Populus Trichocarpa) grown in a greenhouse at Oak Ridge National Laboratory were analyzed by Pyrolysis-Molecular Beam Mass Spectrometry (Py-MBMS). Leaves were harvested, cryomilled and kept frozen until analysis. Py-MBMS analysis was conducted using approximately 4 mg of biomass and each sample was analyzed in duplicate. A Frontier PY2020 unit pyrolyzed samples at 500°C for 30 s in 80 µL deactivated stainless steel cups. An Extrel Super-Sonic MBMS Model Max 1000 was used to collect mass spectral data fromm/z30 to 450 at 17 eV and processed using Merlin Automation software (V3). Spectral ion intensities were normalized to the total ion chromatogram signal for each sample for analysis of spectral variance. Lignin content (wt %) was estimated based on relative responses from standards of known Klason lignin content using mean-normalized ion intensities ofm/z120, 124 (G), 137 (G), 138 (G), 150 (G), 152, 154 (S), 164 (G), 167 (S), 168 (S), 178 (G), 180, 181, 182 (S), 194 (S), 208 (S) and 210 (S) where G indicates guaiacyl-derived ions, S indicates syringyl-derived ions, and other ions either derive from other lignin monomers or multiple sources. Ratios of S and G lignin monomer units (S/G) were obtained by dividing the sum of S-based ions by the sum of G-based ions using mean-normalized ion intensities.

CBI↗
DOE OSTI2026

Pyrolysis_Molecular_Beam_Mass_Spectrometry_Analysis_of_Specific_Switchgrass_Genotypes

Select natural variant switchgrass genotypes grown in Tifton, GA were analyzed by Pyrolysis-Molecular Beam Mass Spectrometry (Py-MBMS). Biomass was harvested, milled, several genotypes were analyzed with and without being destarched and extracted with ethanol prior to analysis (indicated with -DE if destarched and extracted). Py-MBMS analysis was conducted using approximately 4 mg of biomass and each sample was analyzed in duplicate. A Frontier PY2020 unit pyrolyzed samples at 500°C for 30 s in 80 µL deactivated stainless steel cups. An Extrel Super-Sonic MBMS Model Max 1000 was used to collect mass spectral data fromm/z30 to 450 at 17 eV and processed using Merlin Automation software (V3). Spectral ion intensities were normalized to the total ion chromatogram signal for each sample for analysis of spectral variance. Lignin content (wt %) was estimated based on relative responses from standards of known Klason lignin content using mean-normalized ion intensities ofm/z120, 124 (G), 137 (G), 138 (G), 150 (G), 152, 154 (S), 164 (G), 167 (S), 168 (S), 178 (G), 180, 181, 182 (S), 194 (S), 208 (S) and 210 (S) where G indicates guaiacyl-derived ions, S indicates syringyl-derived ions, and other ions either derive from other lignin monomers or multiple sources. Ratios of S and G lignin monomer units (S/G) were obtained by dividing the sum of S-based ions by the sum of G-based ions using mean-normalized ion intensities.

CBI↗
DOE OSTI2026

Pyrolysis_Molecular_Beam_Mass_Spectrometry_Analysis_of_hybrid_cross_of_Populus_tremula_x_P_alba_717-1B4_and_overexpression_of_a_lectin_receptor-like_kinase_(PtLecRLK1)

Stem tissues from the hybrid poplarPopulus tremula × P. albaclone 717-1B4 and from lectin receptor-like kinase overexpression lines PP7 and PP19 were individually colonized with the ectomycorrhizal fungiLaccaria bicolorstrain S238N,Hyaloscypha finlandicastrain PMI746, orUmbelopsis vinaceastrain PMI3018, as well as with a mixed fungal inoculum; non-inoculated plants served as controls. Plants were grown in a greenhouse at Oak Ridge National Laboratory and harvested in January 2025. Stem samples were analyzed using Pyrolysis–Molecular Beam Mass Spectrometry (Py-MBMS). Stems were harvested, debarked, dried, milled, destarched and ethanol extracted prior to analysis. Py-MBMS analysis was conducted using approximately 4 mg of wood from biomass and each sample was analyzed in duplicate. A Frontier PY2020 unit pyrolyzed samples at 500°C for 30 s in 80 µL deactivated stainless steel cups. An Extrel Super-Sonic MBMS Model Max 1000 was used to collect mass spectral data fromm/z30 to 450 at 17 eV and processed using Merlin Automation software (V3). Spectral ion intensities were normalized to the total ion chromatogram signal for each sample for analysis of spectral variance. Lignin content (wt %) was estimated based on relative responses from standards of known Klason lignin content using mean-normalized ion intensities ofm/z120, 124 (G), 137 (G), 138 (G), 150 (G), 152, 154 (S), 164 (G), 167 (S), 168 (S), 178 (G), 180, 181, 182 (S), 194 (S), 208 (S) and 210 (S) where G indicates guaiacyl-derived ions, S indicates syringyl-derived ions, and other ions either derive from other lignin monomers or multiple sources. Ratios of S and G lignin monomer units (S/G) were obtained by dividing the sum of S-based ions by the sum of G-based ions using mean-normalized ion intensities.

CBI↗
DOE OSTI2025

Air temperature and precipitation constraining the modelled wetland methane emissions in a boreal region in northern Europe

Wetland methane responses to temperature and precipitation are studied in a boreal wetland-rich region in northern Europe using ecosystem process models. Six ecosystem models (JSBACH-HIMMELI, LPX-Bern, LPJ-GUESS, JULES, CLM4.5, and CLM5) are compared to multi-model means of ecosystem models and atmospheric inversions from the Global Carbon Project and upscaled eddy covariance flux results for their temperature and precipitation responses and seasonal cycles of the regional fluxes. Two models with contrasting response patterns, LPX-Bern and JSBACH-HIMMELI, are used as priors in atmospheric inversions with Carbon Tracker Europe–CH4 (CTE-CH4) in order to find out how the assimilation of atmospheric concentration data changes the flux estimates and how this alters the interpretation of the flux responses to temperature and precipitation. Inversion moves wetland emissions of both models towards co-limitation by temperature and precipitation. Between 2000 and 2018, periods of high temperature and/or high precipitation often resulted in increased emissions. However, the dry summer of 2018 did not result in increased emissions despite the high temperatures. The process models show strong temperature and strong precipitation responses for the region (51 %–91 % of the variance explained by both). The month with the highest emissions varies from May to September among the models. However, multi-model means, inversions, and upscaled eddy covariance flux observations agree on the month of maximum emissions and are co-limited by temperature and precipitation. The setup of different emission components (peatland emissions, mineral land fluxes) has an important role in building up the response patterns. Considering the significant differences among the models, it is essential to pay more attention to the regional representation of wet and dry mineral soils and periodic flooding which contribute to the seasonality and magnitude of methane fluxes. The realistic representation of temperature dependence of the peat soil fluxes is also important. Furthermore, it is important to use process-based descriptions for both mineral and peat soil fluxes to simulate the flux responses to climate drivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis

We show that "single-atom” catalysts (SACs) have demonstrated excellent activity and selectivity in challenging chemical transformations such as photocatalytic CO 2 reduction. For heterogeneous photocatalytic SAC systems, it is essential to obtain sufficient information of their structure at the atomic level in order to understand reaction mechanisms. In this work, a SAC was prepared by grafting a molecular cobalt catalyst on a light-absorbing carbon nitride surface. Due to the sensitivity of the X-ray absorption near edge structure (XANES) spectra to subtle variances in the Co SAC structure in reaction conditions, different machine learning (ML) methods, including principal component analysis, K-means clustering, and neural network (NN), were utilized for in situ Co XANES data analysis. As a result, we obtained quantitative structural information of the SAC nearest atomic environment thereby extending the NN-XANES approach previously demonstrated for nanoparticles and size-selective clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The DECADE cosmic shear project II: photometric redshift calibration of the source galaxy sample

We present the photometric redshift characterization and calibration for the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The redshifts are estimated from a combination of wide-field photometry, deep-field photometry with associated redshift estimates, and a transfer function between the wide field and deep field that is estimated using a source injection catalog. We construct four tomographic bins for the galaxy catalog, and estimate the redshift distribution, $n(z)$, within each one using the Self-organizing Map Photo-Z (SOMPZ) methodology. Our estimates include the contributions from sample variance, zeropoint calibration uncertainties, and redshift biases, as quantified for the deep-field dataset. The total uncertainties on the mean redshifts are $σ_{\langle}$$_z$$_{\rangle} ≈ 0.01$. The SOMPZ estimates are then compared to those from the clustering redshift method, obtained by cross-correlating our source galaxies with galaxies in spectroscopic surveys, and are shown to be consistent with each other.

Anbajagane, D. [Univ. of Chicago, IL (United State↗
DOE OSTI2019

Performance Analysis and Tank Test Validation of a Hybrid Wave-Current Energy Converter with a Single Power Takeoff

Marine and hydrokinetic (MHK) energy, including ocean waves, tidal current, ocean current and river current, has been recognized as a promising power source due to its full-day availability and high energy potential. At this stage, ocean current energy, tidal energy and ocean wave energy are currently the most competitive sourves among all the categories of MHK. The state of art MHK energy harvesting technology mainly focus on harvesting either ocean wave energy or current energy, but not both. However, a significant amount of ocean waves and tidal/ ocean current coexist in many sites and traditional devices that harvest from a single form of MHK energy, cannot make full use of the coexisting ocean energy. Furthermore, MHK energy harvesting devices need to advance to be cost-effective and competitive with other energy sources. This is difficult to achieve. Ocean wave excitation is irregular, which means that ocean wave height and wave periods are unpredictable and excitation forces on energy harvesting devices can have large variance in amplitude and frequency. Such problems/ restrictions can be possibly addressed by the concept of a hybrid energy converter. In this sense, a hybrid wave-current ocean energy conveter (HWCEC) that simutaneously harvests energy from current and wave with one single power takeoff (PTO) is designed.The wave energy is extracted through relative heaving motion between a floating buoy and a submerged second body, while the current energy is extracted using a marine current turbine (MCT). Energy from both sources are integrated by a hybrid PTO whose concept is based on a mechanical motion rectifier (MMR). In this study, different working modes are investigated together with switching criteria.Simulations were conducted with hydrodynamic coefficients obtained from computational fluid dynamics analysis and boundary element method. Tank tests were conducted for a HWCEC under coexisting wave and current inputs. For comparison, separate baseline tests of a turbine and a two-body point absorber, each acting in isolation, are conducted. Experimental results validate the dynamic modeling and show that a HWCEC can increase the output power with a range between 29-87 percent over either current turbine and wave energy converter acting individually, and it can reduce by up to 70 percent the peak-to-average power ratio compared with the wave energy converter on the tested conditions.Such results demonstrate the potential of the HWCEC as an efficient and cost-effective design.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Modes and Mechanisms of Pacific Decadal-Scale Variability

The modes of Pacific decadal-scale variability (PDV), traditionally defined as statistical patterns of variance, reflect to first order the ocean's integration (i.e., reddening) of atmospheric forcing that arises from both a shift and a change in strength of the climatological (time-mean) atmospheric circulation. While these patterns concisely describe PDV, they do not distinguish among the key dynamical processes driving the evolution of PDV anomalies, including atmospheric and ocean teleconnections and coupled feedbacks with similar spatial structures that operate on different timescales. In this review, we synthesize past analysis using an empirical dynamical model constructed from monthly ocean surface anomalies drawn from several reanalysis products, showing that the PDV modes of variance result from two fundamental low-frequency dynamical eigenmodes: the North Pacific–central Pacific (NP-CP) and Kuroshio–Oyashio Extension (KOE) modes. Both eigenmodes highlight how two-way tropical–extratropical teleconnection dynamics are the primary mechanisms energizing and synchronizing the basin-scale footprint of PDV. While the NP-CP mode captures interannual- to decadal-scale variability, the KOE mode is linked to the basin-scale expression of PDV on decadal to multidecadal timescales, including contributions from the South Pacific.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Description of the multinucleon transfer mechanism for Ca 48 + Pu 244 and Kr 86 + Pt 198 reactions in a quantal transport approach

Multinucleon transfer (MNT) reactions involving heavy projectile and target combinations stand as a promising method for synthesizing new neutron-rich exotic nuclei, which may not be possible using hot or cold fusion reactions or fragmentation. Exploring the mechanisms behind MNT reactions is essential and it requires a comprehensive theoretical framework that can explain the physical observables in these reactions. This work aims to show that the quantal diffusion approach based on the stochastic mean-field (SMF) theory is capable of explaining the reaction dynamics observed in MNT reactions. Primary product mass distributions in 48 Ca + 244 Pu reaction at E c.m. = 203.2 MeV and 86 Kr + 198 Pt reaction at E c.m. = 324.2 MeV are calculated and compared with the available experimental data. In this work, we utilize the time-dependent Hartree-Fock (TDHF) calculations to analyze the mean-field reaction dynamics computationally in the reactions 48 Ca + 244 Pu and 86 Kr + 198 Pt for a broad range of initial angular momenta. Quantal transport description based on the SMF approach is used to calculate quantal diffusion coefficients and mass variances in 48 Ca + 244 Pu and 86 Kr + 198 Pt systems. The primary products arising from quasifission reactions are described by joint probability distribution in the SMF approach and those arising from fusion-fission are estimated by using the statistical deexcitation code gemini + +. Mean values of charge and mass numbers, scattering angles of the primary reaction products, and the total kinetic energies after the collision are calculated within the TDHF framework for a broad range of initial angular momenta. Throughout all the collisions, drift toward the mass symmetry and large mass dispersion associated with this drift are observed. Here, the calculated primary fragment and mass distributions using the SMF approach successfully explain experimental observations for the 48 Ca + 244 Pu and 86 Kr + 198 Pt systems. The primary mass distributions, mean values of binary products, and mass dispersions are determined and results are compared with the available experimental data. The observed agreement between the experimental data and SMF results highlights the effectiveness of the quantal diffusion mechanism based on the SMF approach, which does not include any adjustable parameters other than standard parameters of Skyrme energy density functional.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

The Effect of Surface Heating Heterogeneity on Boundary Layer Height and Its Dependence on Background Wind Speed

The planetary boundary layer height (PBLH) is a fundamental variable of the planetary boundary layer (PBL). The effect of surface heterogeneity is challenging in the PBL parameterization for numerical weather and climate models. Here we use large eddy simulation data to investigate the impact of surface heating heterogeneity on the PBLH spatial variations and mean values over a domain of typical mesoscale model grid size. It is found that this impact depends on background wind condition. Variance decomposition and Fourier spectra both reveal that for the fully developed sheared convective PBL, surface heterogeneity contributes little to the spatial variation of PBLH, except for the case with heterogeneity scale of 14.4 km. This suggests that surface heterogeneity with length scales less than ~10 km, which is typical for mesoscale modeling, does not induce significant subgrid spatial variation of PBLH. The domain-averaged PBLH is found to generally increase as surface heterogeneity scale decreases, until reaching the scale on the order of PBLH. The findings can be used to inform the parameterization development of PBL processes over heterogeneous surface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Global Sensitivity Analysis of Bulk Properties of an Atomic Nucleus

We perform a global sensitivity analysis of the binding energy and the charge radius of the nucleus 16 O to identify the most influential low-energy constants in the next-to-next-to-leading order chiral Hamiltonian with two- and three-nucleon forces. For this purpose, we develop a subspace-projected coupled-cluster method using eigenvector continuation [Frame D. et al., Phys. Rev. Lett. 121, 032501 (2018)]. With this method, we compute the binding energy and charge radius of 16 O at more than 106 different values of the 16 low-energy constants in one hour on a standard laptop computer. For relatively small subspace projections, the root-mean-square error is about 1% compared to full-space coupled-cluster results. We find that 58(1)% of the variance in energy can be apportioned to a single contact term in the 3 S 1 wave, whereas the radius depends sensitively on several low-energy constants and their higher-order correlations. The results identify the most important parameters for describing nuclear saturation and help prioritize efforts for uncertainty reduction of theoretical predictions. The achieved acceleration opens up an array of computational statistics analyses of the underlying description of the strong nuclear interaction in nuclei across the Segrè chart.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Choosing Transport Events for Initiating Splitting and Rouletting

A study was performed to determine which transport events should be used to initiate a weight window lookup to achieve the best variance reduction performance. A weight window lookup potentially triggers particle splitting (in important regions of phase space) or rouletting (in unimportant regions), thereby optimizing computational effort. Potential initiating transport events include collisions (both pre- and post-collision), geometry surface crossings, traversing a mean-free path, and streaming across a weight window boundary. Permutations of these initiating events were tested on an urban model with background radiation sources and a spent fuel cask with a neutron dose mesh tally. Generally, all methods perform better with finer weight window meshes. Tracking on weight windows performs well for coarse weight window meshes, while a combination of splitting each mean-free path, geometric surface crossing, and before collisions performs well for fine weight window meshes.

42 ENGINEERING↗
DOE OSTI2021

Lower Tropospheric Processes: A Control on the Global Mean Precipitation Rate

The spread in global mean precipitation among climate models is explored in two ensembles using the complementary perspectives of surface evaporation and energy budgets. Models with higher global mean precipitation have stronger oceanic evaporation, driven by drier near-surface air. The drier surface conditions occur alongside increases in near-surface temperature and moisture at 925 hPa, which point to stronger boundary layer mixing. Correlations suggest that the degree of lower tropospheric mixing explains 18%–49% of the intermodel precipitation variance. To test this hypothesis, the degree of mixing is indirectly varied in a single-model experiment by adjusting the relative humidity threshold that controls low-cloud fraction. Indeed, increasing lower tropospheric mixing results in more global mean precipitation. Energetically, increased precipitation rates are associated with more downwelling longwave radiation to the surface and weaker sensible heat fluxes. These results highlight how lower-tropospheric processes must be better constrained to reduce the precipitation discrepancy among climate models.

58 GEOSCIENCES↗
DOE OSTI2022

Influence of living grass Roots and endophytic fungal hyphae on soil hydraulic properties

Soil hydraulic properties are often estimated based on laboratory data or pedotransfer functions dependent on soil physical properties, which often do not consider potential impacts of soil roots or fungal hyphae. Here, we first review current knowledge of how these soil biotic components affect hydraulic properties, then we conducted laboratory experiments to specifically test if the presence of roots and mycorrhizal fungi had a significant effect on the hydraulic properties of two soils with contrasting textures: Flint sand and Hamblen silt loam. Soil cores were seeded with (Panicum virgatum) and grown in a greenhouse over three separate growth periods. The endophytic fungus Serendipita indica was injected as liquid inoculant into designated mycorrhizal cores. Saturated hydraulic conductivity (K sat ) measurements were made with a constant head permeameter, and soil water retention curves were obtained by the evaporation method, supplemented at the dry end for Hamblen silt loam with water activity meter data. Retention curve parameters were obtained by fitting the van Genuchten equation to the resulting measurements. Mean root volume ratios were higher in the mycorrhizal inoculated treatment than in the uninoculated treatment for both soils. For Flint sand, analysis of variance revealed that K sat was reduced by the presence of roots as compared to bare soil. This was likely due to roots clogging soil pores. Results also indicated the presence of roots changed the shape of the water retention curve for Flint sand by increasing water content at saturation and by reducing the slope of the curve. These changes suggested roots created additional porosity and broadened the pore-size distribution. The presence of mycorrhizal fungi accentuated the root effects. The influence of roots and mycorrhizal fungi on hydraulic properties was less obvious for the Hamblen silt loam, as none of the treatments differed from each other at p < 0.05. The results highlight the necessity to consider the impact of root and fungal structures on models of soil hydraulic properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Empirical Validation of UBEM: An Assessment of Bias in Urban Building Energy Modeling for Chicago

Residential and commercial buildings currently account for 30% of total global final energy consumption. Urban-scale building energy modeling (UBEM) can enable scalable investments and unlock building improvements by quantifying energy, demand, emissions, and cost reductions of specific measures or packages for building-specific technologies in large geographic regions. While the sophistication of UBEM data sources and technologies have increased dramatically in the past decade, there remains a knowledge gap for empirical validation and sources of bias between building-specific energy models and measured data at varying geographic scales.As UBEM continues to develop, systemic analysis of accuracy, bias, and limitations of the resulting models is necessary to inform best practices and move toward standardization. These are characterized for the Automatic Building Energy Modeling (AutoBEM) software suite with an initial case study involving metered electricity consumption data from 247,188 buildings in Chicago, Illinois, USA - averaged across years 2019-2021 - compared to the following datasets: (1) the AutoBEM-generated nation-scale Model America version 2 (MAv2) data for 596,064 buildings, (2) tax assessor data for 579,829 buildings, (3) tax assessor data filled with MAv2, and (4) 102 representative dynamic archetypes. The accuracy is reported for every building type and vintage combination, along with multiple sources of bias for unique building descriptors. The AutoBEM simulation workflow produced energy consumption estimates that closely match aggregated metered electricity consumption data for different types of buildings constructed during various time periods at the city scale - with initial normalized mean bias error of 10.9%, and 1.1% after removing outliers. Contribution of statistically significant factors including building type, land use, age, and size to variance in UBEM bias is quantified.

Garg, Ankur↗
DOE OSTI2020

Diffusional growth of cloud droplets in homogeneous isotropic turbulence: DNS, scaled-up DNS, and stochastic model

This paper presents a novel methodology to use direct numerical simulation (DNS) to study the impact of isotropic homogeneous turbulence on the condensational growth of cloud droplets. As shown by previous DNS studies, the impact of turbulence increases with the computational domain size, that is, with the Reynolds number, because larger eddies generate higher and longer-lasting supersaturation fluctuations that affect growth of individual cloud droplets. The traditional DNS can only simulate a limited range of scales because of the excessive computational cost that comes from resolving all scales involved, that is, from large scales at which the turbulent kinetic energy (TKE) is introduced down to the Kolmogorov microscale, and from following every single droplet. The novel approach is referred to as the “scaled-up DNS”. The scaling up is done in two parts, first by increasing both the computational domain and the Kolmogorov microscale and second by using super-droplets instead of real droplets. To ensure proper dissipation of TKE and scalar variance at small scales, molecular transport coefficients are appropriately scaled up with the grid length. For the scaled-up domains, say, meters and tens of meters, one needs to follow billions of real droplets. This is not computationally feasible, and so-called super-droplets are applied in scaled-up DNS simulations. Each super-droplet represents an ensemble of identical real droplets, and the number of real droplets represented by a super-droplet is referred to as the multiplicity attribute. After simple tests showing the validity of the methodology, scaled-up DNS simulations are conducted for five domains, the largest of 64 3 m 3 volume using a DNS of 256 3 grid points and various multiplicities. All simulations are carried out with vanishing mean vertical velocity and with no mean supersaturation, similarly to past DNS studies. As expected, the supersaturation fluctuations as well as the spread in droplet size distribution increase with the domain size, with the droplet radius variance increasing in time t as t 1∕2 as identified in previous DNS studies. Scaled-up simulations with different multiplicities document numerical convergence of the scaled-up solutions. Finally, we compare the scaled-up DNS results with a simple stochastic model that calculates supersaturation fluctuations based on the vertical velocity fluctuations updated using the Langevin equation. Overall, the results document similar scaling to previous small-domain DNS simulations and support the notion that the stochastic subgrid-scale model is a valuable tool for the multi-scale simulation of droplet spectral evolution applying a large-eddy simulation model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Modeling Systematic Discrepancies in Nuclear-Data Measurements with Machine Learning

Nuclear data and their associated co-variances are constantly being reevaluated as techniques improve and as new experimental data, as well as nuclearmodel developments, emerge. A standard technique used to evaluate mean values in nuclear data, and their associated covariances, is the generalized linear least squares (GLLS) method. Aligning with recent efforts to incorporate measurement features into nuclear data evaluation, we augment GLLS by including a linear term which attempts to predict potential systematic discrepancies in experimental data as related to the measurement features. Due to the general nature of this augmentation, we are able to apply this evaluation to three key observables: neutroninduced fission cross sections, the average prompt neutron multiplicity, and the prompt-fission neutron spectrum of ²³⁹Pu.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

The Spatial and Temporal Variability of the Clear Convective Boundary Layer at the ARM SGP Supersite

The convective boundary layer (CBL), also known as the mixing layer, constitutes the critical lower segment of the atmosphere that significantly influences daily human activities. The growing demand for precise weather forecasts is driven by the requirements of agriculture, transportation, and routine societal functions. Here, to enhance understanding of the CBL, this study investigates the spatiotemporal variability in the CBL and its controlling factors using four-year Doppler lidar, surface flux, and profiling measurements at five ARM Southern Great Plains sites within a 100 km radius. This investigation utilizes data collected exclusively under clear-sky conditions or scattered low-cloud conditions. Results reveal significant spatial differences in CBL evolutions. Daily mixing layer heights (MLHs) vary up to 1 km (30% of the mean) in late afternoon. There is a clear east–west contrast: western sites (C1, E32, E37) exhibit higher summer MLH (1.9–2.1 km) and vertical velocity variances (1.0–1.2 m 2 s −2 ) than eastern sites (1.6–1.8 km), reversing in winter. Temporally, the MLH peaks at 70% of the sunrise–sunset interval, the lagging heat flux (HF) peaks at 50%; and the seasonal MLH maxima lag the HF by approximately one month, influenced by nighttime PBL (planetary boundary layer) properties. The HF and lower tropospheric stability are the main factors of influence for the CBL, but site-specific dependencies highlight the critical roles of local factors, underscoring the need for including them in CBL modeling.

ARM SGP site↗