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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Uncertainty quantification of microstructural properties due to variability in measured pole figures

Experimental pole figures are an important input for microstructure homogenization models. In this paper, we derive an exact analytical formulation to quantify the uncertainties in homogenized properties due to uncertainty in the experimentally measured pole figures. The pole figures are acquired from a set of Ti-7Al alloy samples. These samples were obtained from the same process: by compressing a beta forged ingot at room temperature followed by annealing. The samples were taken from different regions of the original ingot, and this created variability in the resulting pole figures. The joint multivariate probability distributions of the computed orientation distribution function (ODF) is then found using the method of characteristic functions based on a Gaussian model of the pole figures. Engineering properties such as elastic modulus can be obtained by volume averaging over the ODF. We also show that uncertainty in elastic properties can be analytically obtained using direct transformation of random variables in the homogenization approach.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2019

Statistics for Testing Under Adverse Conditions

Here we develop a statistical basis for limited adverse testing. This type of testing simultaneously evaluates system performances against minimum requirements and minimizes costs, particularly for large scale engineering projects. Because testing is often expensive and narrow in scope, the data obtained is relatively limited—precisely the opposite of the recent “large data” movement but no less compelling. Although a remarkably common approach for industrial and large-scale government projects, a statistical basis for adverse testing remains poorly explored. Here we prove mathematically, under specific conditions, that setting each independent variable to an adverse condition leads to a similar level of adversity in the dependent variable. For example, setting all normally distributed independent variables to at least their 95th percentile values leads to a result at the 95th percentile. The analysis considers sample size estimates to clarify the value of replicates in this type of testing, determines how many of the independent variables must be set to adverse condition values, and highlights the essential assumptions, so that engineers, statisticians, and subject matter experts know when this statistical framework may be applied successfully.

42 ENGINEERING↗
DOE OSTI2021

Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index

Abstract The second UN Sustainable Development Goal establishes food security as a priority for governments, multilateral organizations, and NGOs. These institutions track national-level food security performance with an array of metrics and weigh intervention options considering the leverage of many possible drivers. We studied the relationships between several candidate drivers and two response variables based on prominent measures of national food security: the 2019 Global Food Security Index (GFSI) and the Food Insecurity Experience Scale’s (FIES) estimate of the percentage of a nation’s population experiencing food security or mild food insecurity (FI ). We compared the contributions of explanatory variables in regressions predicting both response variables, and we further tested the stability of our results to changes in explanatory variable selection and in the countries included in regression model training and testing. At the cross-national level, the quantity and quality of a nation’s agricultural land were not predictive of either food security metric. We found mixed evidence that per-capita cereal production, per-hectare cereal yield, an aggregate governance metric, logistics performance, and extent of paid employment work were predictive of national food security. Household spending as measured by per-capita final consumption expenditure (HFCE) was consistently the strongest driver among those studied, alone explaining a median of 92% and 70% of variation (based on out-of-sample R 2 ) in GFSI and FI , respectively. The relative strength of HFCE as a predictor was observed for both response variables and was independent of the countries used for model training, the transformations applied to the explanatory variables prior to model training, and the variable selection technique used to specify multivariate regressions. The results of this cross-national analysis reinforce previous research supportive of a causal mechanism where, in the absence of exceptional local factors, an increase in income drives increase in food security. However, the strength of this effect varies depending on the countries included in regression model fitting. We demonstrate that using multiple response metrics, repeated random sampling of input data, and iterative variable selection facilitates a convergence of evidence approach to analyzing food security drivers.

42 ENGINEERING↗
DOE OSTI2020

Assessment of the Utility of the Oral Fluid and Plasma Proteome for Hydrocodone Usage

Non-medical use and abuse of prescription opioids is a growing problem in both the civilian and military communities, with minimal technologies for detecting hydrocodone use. This study explored the proteomic changes that occur in the oral fluid and blood plasma following controlled hydrocodone administration in twenty subjects. The global proteomic profile was determined for samples taken at four time points per subject of pre-exposure and 4, 6, or 168 hours post-exposure. The oral fluid samples analyzed herein provided greater differentiation between “baseline” and “response” time points than was observed with blood plasma, at least partially due to significant person-to-person variability in the plasma proteome. A total of 399 proteins were identified from oral fluid samples, and the abundance of 102 of those proteins was determined to be significantly different upon metabolism of hydrocodone (4 and 6 hour time points) as compared to baseline levels in the oral fluid (pre-dose and 168 hour). This is the first, to our knowledge, assessment of the oral fluid and blood plasma proteome following hydrocodone administration, which demonstrates the potential of oral fluid as a non-invasive sample that may reveal features of hydrocodone or other opioid use and/or misuse.

Kaiser, Brooke LD↗
DOE OSTI2018

No 182 W excess in the Ontong Java Plateau source

We report that small-scale W isotope variations in ancient and modern terrestrial rocks provide insights into Earth's accretion and early differentiation history as well as the long-term evolution of the Earth's mantle. Tungsten isotope studies on such rocks have exploited advances in mass spectrometry, both NTIMS and MC-ICPMS, which now permit the determination of W isotope ratios at unprecedented precision. While W isotope studies performed in different labs by MC-ICPMS and NTIMS generally exhibit excellent agreement, obtaining accurate W isotope data at this level of precision remains analytically challenging. For example, a recent NTIMS study reported a relatively large, +24 ppm excess in 182 W/ 184 W for a Phanerozoic sample from the Ontong Java Plateau (OJP), but no such 182 W/ 184 W anomaly was found in another study by MC-ICPMS. The present study aims to resolve the discrepancy between these two previous studies, and more generally to evaluate the agreement between different recent W isotope studies by NTIMS and MC-ICPMS. To this end, we report new W isotope data for OJP drill core samples obtained by MC-ICPMS. Additionally, the OJP samples analyzed here exhibit no resolvable 182 W/ 184 W excess relative to the standards and most terrestrial rocks. Moreover, the OJP samples as well as the terrestrial rock standards analyzed here exhibit small but variable W isotope variations for ratios involving 183 W, producing coupled variations in both ‘radiogenic’ (i.e., 182 W/ 183 W) and ‘non-radiogenic’ (i.e., 183 W/ 184 W) ratios. These W isotope variations are analytical in origin, induced during sample preparation, and very likely caused by a nuclear field shift isotope fractionation affecting primarily the odd isotope ( 183 W). The recently reported 182 W excess for an OJP sample may result from this nuclear field shift effect, as the NTIMS analyses had to rely on a double normalization involving the 183 W/ 184 W ratio. More generally, these results demonstrate that using 183 W data from any MC-ICPMS or NTIMS study requires a careful quantification of any potential analytical 183 W effect. Nevertheless, once such effects are taken into account, then both 182 W/ 184 W and 183 W/ 184 W can accurately be determined to a very high level of precision.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Operator Relaxation and the Optimal Depth of Classical Shadows

Classical shadows are a powerful method for learning many properties of quantum states in a sample-efficient manner, by making use of randomized measurements. Here we study the sample complexity of learning the expectation value of Pauli operators via “shallow shadows,” a recently proposed version of classical shadows in which the randomization step is effected by a local unitary circuit of variable depth t. Here we show that the shadow norm (the quantity controlling the sample complexity) is expressed in terms of properties of the Heisenberg time evolution of operators under the randomizing (“twirling”) circuit—namely the evolution of the weight distribution characterizing the number of sites on which an operator acts nontrivially. For spatially contiguous Pauli operators of weight k, this entails a competition between two processes: operator spreading (whereby the support of an operator grows over time, increasing its weight) and operator relaxation (whereby the bulk of the operator develops an equilibrium density of identity operators, decreasing its weight). From this simple picture we derive (i) an upper bound on the shadow norm which, for depth t~log⁡(k), guarantees an exponential gain in sample complexity over the t=0 protocol in any spatial dimension, and (ii) quantitative results in one dimension within a mean-field approximation, including a universal subleading correction to the optimal depth, found to be in excellent agreement with infinite matrix product state numerical simulations. Our Letter connects fundamental ideas in quantum many-body dynamics to applications in quantum information science, and paves the way to highly optimized protocols for learning different properties of quantum states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Molybdenum isotope compositions of uranium ore concentrates by double spike MC-ICP-MS

The molybdenum (Mo) isotope composition of uranium ore concentrate (UOC) has been proposed as a nuclear forensic signature due to the ubiquitous presence of Mo in the nuclear fuel cycle and to the possibility that different UOC production pathways lead to characteristic Mo isotope fractionation. Furthermore, Mo forms a volatile hexafluoride which leads to the retainment of Mo in the nuclear fuel cycle through U enrichment and reactor operation processes. As a result, it is possible that the nuclear fuel cycle generates a rich array of Mo isotope signatures. The first step in interpreting Mo isotope signatures generated during the nuclear fuel cycle is to characterize the Mo isotope composition of starting materials. In this study, a suite of UOC samples were analyzed for their Mo isotope composition in order to constrain the range of Mo isotope compositions entering the front end of the nuclear fuel cycle. Analytical protocols were developed to purify Mo from a U rich matrix prior to Mo isotope analysis by double spike MC-ICP-MS. A >3‰ variation in the 98 Mo/ 95 Mo ratio was observed in the suite of UOC samples with δ 98 Mo ranging from -1.15 to +1.96‰ relative to the NIST 3134 Mo standard reference material. The δ98Mo range observed in the UOC samples is very similar to the δ 98 Mo range observed in crustal igneous rocks and Mo-bearing minerals, suggesting that Mo isotope variability in UOC is driven by variability in the parent U ores. However, Mo isotope fractionation during UOC production is evident with one of the UOC samples being isotopically heavy relative to its parent U ore by 0.75‰, suggesting that different UOC production processes also contribute to the total Mo isotope variability observed. Overall, it is expected that the Mo isotope signatures generated during enrichment and reactor process will be clearly discernible from those generated during UOC production.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Multicolor Variability of Young Stars in the Lagoon Nebula: Driving Causes and Intrinsic Timescales

Space observatories have provided unprecedented depictions of the many variability behaviors typical of low-mass, young stars. However, those studies have so far largely omitted more massive objects (~2 M {sub ⊙} to 4–5 M {sub ⊙}) and were limited by the absence of simultaneous, multiwavelength information. We present a new study of young star variability in the ~1–2 Myr old, massive Lagoon Nebula region. Our sample encompasses 278 young, late B to K-type stars, monitored with Kepler/K2. Auxiliary u, g, r, i, Hα time-series photometry, simultaneous with K2, was acquired at the Paranal Observatory. We employed this comprehensive data set and archival infrared photometry to determine individual stellar parameters, assess the presence of circumstellar disks, and tie the variability behaviors to inner disk dynamics. We found significant mass-dependent trends in variability properties, with B/A stars displaying substantially reduced levels of variability compared to G/K stars for any light-curve morphology. These properties suggest different magnetic field structures at the surface of early-type and later-type stars. We also detected a dearth of some disk-driven variability behaviors, particularly dippers, among stars earlier than G. This indicates that their higher surface temperatures and more chaotic magnetic fields prevent the formation and survival of inner disk dust structures corotating with the star. Finally, we examined the characteristic variability timescales within each light curve and determined that the day-to-week timescales are predominant over the K2 time series. These reflect distinct processes and locations in the inner disk environment, from intense accretion triggered by instabilities in the innermost disk regions to variable accretion efficiency in the outer magnetosphere.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Bayesian modeling of inconsistent plastic response due to material variability

The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. To account for material response variability through variations in physical parameters, we adapt a recent Bayesian embedded modeling error calibration technique. We use Bayesian model selection to determine the most plausible of a variety of plasticity models and the optimal embedding of parameter variability. To expedite model selection, we develop an adaptive importance-sampling-based numerical integration scheme to compute the Bayesian model evidence. In conclusion, we demonstrate that the new framework provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2026

WHONDRS 2016 Sediment Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon

This dataset supports a broader synoptic effort to map morphological, hydrological, chemical, and biological conditions across a fifth-order mountain stream network. Samples were generated through a collaborative synoptic sampling effort in 2016. The dataset provides sediment Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) from 60 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Related data were collected as part of the event and were published separately in collaboration with other team members. The data are available at http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1567-2019 (Ward et al., 2019). The hydroshare data package contains processed FTICR-MS data from the samples included in this data package. The data were processed via Formultitude (previously called Formularity; https://github.com/PNNL-Comp-Mass-Spec/Formultitude). However, we have re-processed the data using Core-MS and included it in this data package. Additional related data collected in 2025 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3023310 and http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of sample data; (2) data dictionary; (3) file-level metadata; (4); (5) coordinates; and (6) readme. The sample data subfolder contains 12 Tesla (12T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, or .json.

Biogeochemistry↗
DOE OSTI2020

Applications of Hyporheic Zone Sampling for Assessing VOC Plume Engineered Remediation and Natural Attenuation Effectiveness - 20414

Determining the efficacy of engineered remedial actions and/or natural attenuation as an acceptable EPA Record of Decision (ROD) must address long-term temporal and spatial variability. Since final plume downslope destinations are aquifers and related surface streams and rivers, hyporheic zone sampling is essential. Comprehensive hyporheic sampling documents plume groundwater remediation prior to upwelling into stream benthic strata and natural attenuation within this dynamic zone itself. Our intensive sampling of the Savannah River Site (SRS) Chemical, Metals, and Pesticides (CMP) Pits VOC plume-fringe from 2005 to 2018 documented PCE natural attenuation and effectiveness of In Situ Thermal Treatment (ISTT). Most plume hyporheic upwelling was delimited to a 20 m inflow reach along Pen Branch. Our study utilized corroborative sampling methodologies: ambient hole-water grab samples: passive diffusion bags (PDB); and hole-core sediment samples to confirm station to station spatial and temporal trends. VOC hot spot detections were confirmed from both groundwater and soil core samples. VOC pathways through the Pen Branch hyporheic zone were assessed at hole-installation using sequential soil samples from augered cores and grab-samples of hole-bottom water. Longer term, composite water samples were collected from PDBs deployed for a minimum two-week equilibration period. Soil samples were collected from the 15 and 65 cm core levels in December 2017 and June 2018 at six stations. Additional core depths of 40 and 85 cm were sampled in June 2018 at the highest VOC station (5DB80). All samples were analyzed in an EPA certified lab by purge and trap GC-MS. Effectiveness of previous ISTT and soil vapor extraction (SVE) for this CMP Pits PCE plume, was clearly documented by significant downward trends in VOC at hyporheic zone stations prior to December 2015. Achieving final stage conversions of PCE daughter products to ethene and CO{sub 2} may be limited depending upon suitable interactions between hyporheic zone sediment morphology, hydrology, and redox conditions. Because declines in VOC daughter products at our hyporheic stations may also be caused by physical actions of dilution and dispersion rather than true biochemical dechlorination, full quantification of natural attenuation requires the use of compound-specific isotope ratio analysis (CSIA) and microbial 16S rRNA with qPCR. To assist these next-phase efforts, a natural attenuation index (NAI) was used to help produce quicker, more cost-effective decision making for locating CSIA and microbial sampling efforts. The NAI used natural logarithm transformed GC-MS results for station VOC degradation compounds versus VOC parent compounds. Wide differences were observed between stations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

In Situ Diffraction and Ex Situ Transmission X‐Ray Microscopy Studies of Solid‐State Upcycling for NMC Cathodes

Upcycling of recycled LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) cathodes offers an economical route to produce cathode materials with increased energy density (i.e., LiNi 0.8 Mn 0.1 Co 0.1 O 2 , NMC811) that meet the performance needs of present-day electric vehicles. In this work, solid-state upcycling of NMC622 via calcination with Ni(OH) 2 and LiOH was monitored using in situ synchrotron powder X-ray diffraction measurements. Sequential Rietveld refinements indicate that the calcination proceeds by initially converting Ni(OH) 2 to a rocksalt NiO phase followed by lithiation of NiO to form LiNiO 2 (LNO), with both NMC and LNO phases present in nearly equal proportions at the calcination endpoint. Variable-energy transmission X-ray microscopy tomograms of upcycled samples reveal that the NMC and LNO domains are intermixed at sub-micron length scales. Depth-dependent analysis of multi-elemental fitting maps matches the expected NMC811 composition at the secondary particle level and indicates that transition metal diffusion is not limited by the secondary particle size.

cathode upcycling↗
DOE OSTI2020

Machine learning to predict biomass sorghum yields under future climate scenarios

Crop yield modeling is critical in the design of national strategies for agricultural production, particularly in the context of a changing climate. Forecasting yields of bioenergy crops at fine spatial resolutions can help to evaluate near-term and long-term pathways for scaling up bio-based fuel and chemical production, and for understanding the impacts of abiotic stressors such as severe droughts and temperature extremes on potential biomass supply. In this work we used a large dataset of 28,364 Sorghum bicolor yield samples (uniquely identified by county and year of observation), environmental variables, and multiple approaches to analyze historical trends in sorghum productivity across the USA. We selected the most accurate machine learning approach (a variation of the random forest approach) to predict future trends in sorghum yields under four greenhouse gas (GHG) emission scenarios and two irrigation regimes. We identified irrigation practices, vapor pressure deficit, and time (a proxy for technological improvement) as the most important predictors of sorghum productivity. Our results showed a decreasing trend of sorghum yields over future years (on average 2.7% from 2018 to 2099), with greater decline under a high GHG emissions scenario (3.8%) and in the absence of irrigation (4.6%). Geographically, we observed the steepest predicted declines in the Great Lakes (8.2%), Upper Midwest (7.5%), and Heartland (6.7%) regions. Our study demonstrates the use of machine learning to identify environmental controllers of sorghum biomass yield and predict yields with reasonable accuracy. These results can inform the development of more realistic biomass supply projections for bioenergy if sorghum production is scaled up. (c) 2020 Society of Chemical Industry and John Wiley & Sons, Ltd

09 BIOMASS FUELS↗
DOE OSTI2020

Optimal sensor network design for multi-scale, time-varying differential algebraic equation systems: Application to an entrained-flow gasifier refractory brick

An algorithm for optimal sensor network design for multi-scale, time-varying differential algebraic equation systems with non-separable dynamics is presented. As the process is time-varying, an integral normalized posterior error covariance of a multi-scale filter is minimized to obtain the optimal sensor locations. For reducing the computational cost, an adaptive sampling rate approach is considered for the slowly-varying variables. The algorithm is applied to a smart refractory brick with embedded sensors as part of an entrained-flow gasifier. Thermistors and interdigital capacitors are considered as candidate measurement technologies for estimating temperature and slag penetration profile along the gasifier wall. Finally, when the optimal set of sensors obtained from the algorithm is used for estimating temperature and slag penetration profiles in a multi-scale Kalman filter framework, satisfactory estimates are obtained despite high measurement noise and model mismatch.

42 ENGINEERING↗
DOE OSTI2025

Determination of a suitable molar absorption coefficient (ε) for lignin analysis of fibrous plants using the CASA method

In this study, the benchmarking and applicability of the CASA method for the analysis of fibrous plants, such as flax, hemp, and jute, is evaluated. Lignin is a phenolic biopolymer present in plant cell walls and is composed of three primary monomeric units, designated G, S, and H. Various factors, such as the genetic variability, influence the relative proportions of these units in plant samples. Recently, the cysteine-assisted sulfuric acid (CASA) method has been introduced as a rapid method for the quantification of lignin in wood samples. The aim of this study is to establish a suitable molar absorption coefficient (ε, L·g –1 ·cm –1 ) to adapt the CASA method for use with annual plant fibers. This investigation was motivated by the technical advantages of the CASA method, including higher throughput, lower reaction temperatures, and ecological benefits due to the absence of carcinogenic, mutagenic, or reprotoxic (CMR) substances and the need for minimal sample quantities. In this method, lignin solubilization is facilitated by cysteine, which is an amino acid that enhances the reaction kinetics, using a one-hour incubation period. However, as with any spectrophotometric technique, the CASA method depends on a molar absorption coefficient (ε) that varies according to the ratio of the aromatic units within the polymer. To evaluate the suitability of CASA for quantifying lignin in economically significant plant fibers, we investigate the impact of the unit ratios on the accuracy of ε. The results are compared with those of two widely recognized lignin analytical methods: the Klason method, which is a gravimetric reference method, and the acetyl bromide soluble lignin method, which is the most commonly used spectrophotometric approach. The final ε obtained in this study reveals a relative difference in lignin content ranging from –8 % to +9 % based on a comparison between the CASA and Klason methods across different industrial hemp varieties. As a result, using known G:S ratios in annual fibrous plants, ε can be estimated from our results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Fuel properties and steam reactivity of solid waste streams from contingency bases

Highlights: • Composite waste fuels were prepared for characterizing fuel property and reactivity. • Characterization of the composite fuels can be predicted by individual components. • A simple methodology for characterizing multi-component solid wastes was validated. • Plastic components increase carbon content and HHV of solid waste fuels. • Effects of fuel composition on syngas production and composition were explored. - Abstract: The solid waste generated from military contingency bases (CBs) is a potential fuel for appropriately-scaled, waste to energy (WTE) systems. To inform WTE system design, fuel properties of seventeen components of synthetic contingency-base waste recipes from the literature were characterized for ultimate analysis, heating values, proximate analysis and reactivity in a steam atmosphere at three temperature typical of gasification conditions. A simple mathematical approach was applied to predict the properties and reactive behavior of composite mixtures using the weighted sum of the corresponding components, assuming negligible interaction between components. The method was validated using prepared composite mixture samples. In addition, chemical equilibrium calculations for steam gasification of the composite samples were conducted to explore the effects of waste composition variability on syngas production and composition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Decadal Hybrid GCM Simulation Using Deep‐Learning‐Based Cloud and Convection Parameterization Generalized to a Warm Climate

A critical challenge for machine‐learning (ML) parameterization in global climate models (GCMs) is to achieve stable, accurate simulations under climates not seen during training. Previous studies have demonstrated promising offline performance and year‐long online stability in aquaplanet simulations but have encountered difficulties in real geography and under climate warming. Here we report that a GCM with real geography configuration using neural‐network‐based cloud and convection parameterization, trained exclusively with present‐day climate data, successfully performs a stable, decade‐long simulation of a warm climate with +4 K sea surface temperature (SST). The neural network (NN) is based on Han et al. (2023, https://doi.org/10.1029/2022ms003508 ) with additional inputs. The simulation captures the global precipitation distribution, surface temperatures, vertical atmospheric structures, and extreme precipitation very well, closely matching simulations from both the superparameterized CAM (SPCAM) and the conventional CAM5 in the warm climate without accuracy degradation compared to those in the baseline climate. Moreover, it produces a climate response to +4 K SST in atmospheric thermodynamic states and circulations similar to those from SPCAM and CAM5. Prognostic ablation tests on NN input variables show that the NN without convective memory as input suffers from numerical instability, and the NN without considering radiative variables and land fraction as input, or with reduced training samples produce less accurate results. To our knowledge, this is the first time an ML parameterization successfully achieves online extrapolation to a warm climate without using additional warm‐climate data for training. It demonstrates the potential of ML‐driven parameterizations for credible long‐term climate projections.

Atmosphere model↗