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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Data for Rapid and High-Throughput Determination of Sorghum ( Sorghum bicolor ) Biomass Composition using Near Infrared Spectroscopy and Chemometrics

Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum ( Sorghum bicolor ) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models’ effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.

Biomass Analytics↗
DOE OSTI2021

Accelerated Creep Testing of Inconel 718 Using the Stepped Isostress Method (SSM)

This paper demonstrates the stepped isostress method (SSM); an accelerated creep test (ACT) for the rapid assessment of metallic materials. The SSM test is based on the time-temperature-stress superposition principle (TTSSP) where temperature and/or stress are step increased to accelerate the time-to-rupture. The SSM test has proven successful for the ACT of polymers and polymeric composites but has yet to be proven for metallic materials. In this study, new test matrix design rules for the SSM of metals are established based on deformation mechanism, time-temperature transformation, and time-temperature precipitation maps. A test matrix of SSM and conventional creep tests (CCTs) are executed for alloy Inconel 718 at 750°C (1382°F) with stress levels ranging from 100 to 350 MPa. Validation CCT data is gathered from the Japan National Institute of Material Science (NIMS). Material constants for the Sine-hyperbolic (Sinh) constitutive model are calibrated using the SSM data and employed to predict the conventional creep response. Here, when blindly compared to the CCT data, the SSM calibrated Sinh model can accurately predict the conventional creep response across logarithmic decades and thus accelerate the capture of conventional creep data. Fractography indicates ductile fracture by transgranular microvoid coalescence (TMVC) with the same fracture mode observed in both SSM and CCT specimens. Creep cavitation is indicated by the population, smoothness, and size of microvoids.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Neutron and Thermal Embrittlement of RPV Steels: An Overview

Because the reactor pressure vessel (RPV) represents the first structural line of defense against the release of radiation to the public, the design and fabrication of the RPV for any nuclear reactor facility is performed at very high standards in accordance with consensus codes that are based on mechanical and physical properties of the steels used to construct the vessel. Nuclear RPVs may weigh up to 800 tons with wall thicknesses up to approximately 330 mm and are clad on the inside with stainless-steel weld metal and given a final post-weld heat treatment. The RPV is a unique structural component in that it operates under high pressures and temperatures and is exposed to relatively high neutron radiation. Although typical RPV steels and welds have excellent fracture toughness at room temperature and above when put into service, the degrading effects of high-energy neutron irradiation can cause levels of irradiation-induced embrittlement in radiation-sensitive materials of concern for the structural integrity of the RPV. In recent decades, remarkable progress has been made in developing a mechanistic understanding of irradiation embrittlement. This progress includes developing physically based and statistically calibrated models of Charpy V-notch-indexed transition temperature shifts based on results from RPV surveillance programs complemented by significant results from comprehensive research experiments performed in test reactors. In addition, advances in elastic-plastic fracture mechanics allow for a relatively small number of relatively small specimens to characterize the fracture toughness of RPV steels with statistical confidence. This paper presents a review of the primary mechanical properties, test procedures, examples of applicable codes and standards, and specimen types used to characterize RPV steels and welds, the effects of neutron irradiation on those most relevant mechanical properties, and a brief review of the effects of thermal aging on RPV materials. The paper closes with a summary.

Nanstad, Randy K.↗
DOE OSTI2025

Foliar element determination from field survey in association with the National Ecological Observatory Network Airborne Observation Platform survey, East River, Colorado 2018

The purpose of this dataset is to support research aimed at understanding the coupling between hydrologic and biogeochemical processes at watershed scale, particularly the relationship between aboveground vegetation characteristics and subsurface soil properties. These data are intended to inform and calibrate models of catchment-scale biogeochemical fluxes, including rock-derived nutrient cycling, and they were procured to address the following questions: (1) What is the distribution of vegetation characteristics across the study catchments? (2) Are foliar concentrations of rock-derived nutrients related to underlying lithology and soil availability, or are these signals masked by biotic nutrient cycling and retention processes?This data package contains foliar elemental data collected during the 2018 National Ecological Observatory Networks (NEON) Airborne Observation Platform (AOP) imaging spectroscopy and lidar surveys in Gunnison County, Colorado. Folair samples were collected across the East River, Washington Gulch, Slate River, and Coal Creek watersheds and contain a mixture of vegetation including meadow, shrub, and tree foliar samples. The samples were processed using aqua regia digestion and analyzed for elemental determination on inductively coupled plasma optical emission spectrometry (ICP-OES).The data package includes: (1) raw foliar elemental data files in CSV and PDF formats, (2) quality control certificates in PDF format, and (3) an aggregated CSV file containing all elemental measurements compiled across samples. No specialized software is required to access or use these files.

2018 National Ecological Observatory Network Campa↗
DOE OSTI2025

Unified differentiable digital twin for the IOTA/FAST facility

As the design complexity of modern accelerators grows, there is more interest in using advanced simulations that have fast execution time or produce insights about accelerator state. One notable example of additional information are gradients of physical observables with respect to design parameters produced by differentiable simulations. The IOTA/FAST facility has recently begun a program to implement and experimentally validate a unified start-to-end differentiable digital twin to serve as a virtual accelerator test stand, allowing for rapid prototyping of new software and experiments with minimal beam time costs. In this contribution we will discuss our plans and progress. Specifically, we will cover the selection and benchmarking of both physics and ML codes, the development of generic interfaces between device models and surrogate or physics-based sections, and the export of the parameters through either a deterministic event loop or a fully asynchronous EPICS soft input/output controller. We will also discuss challenges in model calibration and uncertainty quantification, as well as future plans to support larger proton accelerators like PIPII and Booster.

Kuklev, Nikita [Fermilab]↗
DOE OSTI2015

A Surrogate-based Adaptive Sampling Approach for History Matching and Uncertainty Quantification

A critical procedure in reservoir simulations is history matching (or data assimilation in a broader sense), which calibrates model parameters such that the simulation results are consistent with field measurements, and hence improves the credibility of the predictions given by the simulations. Often there exist non-unique combinations of parameter values that all yield the simulation results matching the measurements. For such ill-posed history matching problems, Bayesian theorem provides a theoretical foundation to represent different solutions and to quantify the uncertainty with the posterior PDF. Lacking an analytical solution in most situations, the posterior PDF may be characterized with a sample of realizations, each representing a possible scenario. A novel sampling algorithm is presented here for the Bayesian solutions to history matching problems. We aim to deal with two commonly encountered issues: 1) as a result of the nonlinear input-output relationship in a reservoir model, the posterior distribution could be in a complex form, such as multimodal, which violates the Gaussian assumption required by most of the commonly used data assimilation approaches; 2) a typical sampling method requires intensive model evaluations and hence may cause unaffordable computational cost. In the developed algorithm, we use a Gaussian mixture model as the proposal distribution in the sampling process, which is simple but also flexible to approximate non-Gaussian distributions and is particularly efficient when the posterior is multimodal. Also, a Gaussian process is utilized as a surrogate model to speed up the sampling process. Furthermore, an iterative scheme of adaptive surrogate refinement and re-sampling ensures sampling accuracy while keeping the computational cost at a minimum level. The developed approach is demonstrated with an illustrative example and shows its capability in handling the above-mentioned issues. Multimodal posterior of the history matching problem is captured and are used to give a reliable production prediction with uncertainty quantification. The new algorithm reveals a great improvement in terms of computational efficiency comparing previously studied approaches for the sample problem.

Li, Weixuan↗
DOE OSTI2018

Comparing vis–NIRS, LIBS, and Combined vis–NIRS‐LIBS for Intact Soil Core Soil Carbon Measurement

Core Ideas First rigorous integration of VisNIR and LIBS spectroscopic techniques for soil profile C measurement. Field‐moist intact soil cores scanned with LIBS and VisNIR simulating in situ measurements. Combined VisNIR‐LIBS wavelengths selected by MRCE were split nearly equally between sensors, suggesting molecular and elemental information was successfully integrated for predicting TC, IC, and SOC. Stoichiometric relationships in LIBS elemental emissions are more strongly correlated to IC concentration than were carbonate absorptions in VisNIR. Lack of variation and consistent, definable chemical composition and stoichiometry likely contributed to poor LIBS and combined VisNIR – LIBS prediction accuracy for SOC. Soil organic carbon (SOC) measurement is critically important to quantify regional and global soil C stocks and better understand soil C biogeochemical processes. Recent studies employing laser‐induced breakdown spectroscopy (LIBS) and visible‐near infrared diffuse reflectance spectroscopy (vis–NIRS) indicate their potential for in situ soil C determination. Visible and near infrared diffuse reflectance spectroscopy and LIBS spectroscopy fundamentally differ and we hypothesize that their integration would provide improved soil C predictions. We report the first rigorous integration of vis–NIRS and LIBS, evaluating the precision of vis–NIRS, LIBS, and combined vis–NIRS‐LIBS spectra for simulated in situ soil profile total C (TC), inorganic C (IC) and SOC measurement. Three multivariate variable selection and regression approaches were evaluated for soil C prediction. The highest soil C prediction accuracies were observed using multivariate regression with covariance estimation (MRCE). Inorganic C was best predicted by LIBS, vis–NIRS provided better SOC predictions, and TC was best predicted using combined vis–NIRS‐LIBS data. Combined vis–NIRS‐LIBS did not consistently increase soil C prediction accuracy. Soil organic C was not well predicted, presumably due to challenges associated with scanning surfaces of intact soil cores, variable SOC chemistries, and low SOC variation in the dataset. Considering the challenging conditions under which combined vis–NIRS– LIBS was tested for soil C measurement, data integration and model calibrations had acceptable performance. Further testing under more controlled soil conditions with samples containing greater SOC diversity is necessary to determine the technical potential of combined vis–NIRS/LIBS for soil C determination.

Bricklemyer, Ross S.↗
DOE OSTI2013

A comparison of methods for representing sparsely sampled random quantities.

This report discusses the treatment of uncertainties stemming from relatively few samples of random quantities. The importance of this topic extends beyond experimental data uncertainty to situations involving uncertainty in model calibration, validation, and prediction. With very sparse data samples it is not practical to have a goal of accurately estimating the underlying probability density function (PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a specified percentile range of the actual PDF, say the range between 0.025 and .975 percentiles, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the desired percentile range of the actual PDF. The presence of the two opposing objectives makes the sparse-data uncertainty representation problem interesting and difficult. In this report, five uncertainty representation techniques are characterized for their performance on twenty-one test problems (over thousands of trials for each problem) according to these two opposing objectives and other performance measures. Two of the methods, statistical Tolerance Intervals and a kernel density approach specifically developed for handling sparse data, exhibit significantly better overall performance than the others.

Romero, Vicente Jose↗
DOE OSTI2014

Biomass Compositional Analysis: NIR Rapid Methods (Fact Sheet)

NREL biomass analysis scientists use near-infrared (NIR) spectroscopy correlated with compositional data, produced using traditional wet chemical techniques, to develop rapid calibration models. These models dramatically decrease the time required for (and the cost of) routine compositional analyses. NREL has models to predict the composition of a variety of biomass types.

09 BIOMASS FUELS↗
DOE OSTI2014

CASL Institutional Computing Report, CY2013 [Slides]

• FY14: Mini-PIRTs conducted on challenge problems to provide subject matter input on important physics models, experimental data, and figures of merit – Progression problem 6, CIPS, PCI – RIA and LOCA up next • Tailor “generic” VUQ plan (FY14 milestone) to each challenge problem – Appendix to challenge problem charters • FY15: Full implementation of probabilistic model calibration capability – Complete implementation of GPMSA in QUESO – Complete verification tests of sampling algorithms in QUESO – Complete Dakota-QUESO interface • FY15: Demonstrate dimension reduction capability for large input spaces – Hybrid sampling methods for ESM dimension reduction and probabilistic calibration.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Development of Geomechanical Screening Tools to Identify Risk: An Experimental and Modeling Approach for Secure CO 2 Storage Final Technical Report

CO 2 injection in the subsurface introduces additional complexity in terms of interactions of the reservoir host pore fluid with natural heterogeneity of hydro-mechanical properties of the rock. Natural heterogeneity of the reservoir rock and caprock formations includes spatial variation of hydro-mechanical properties as well as the presence of natural fractures. We developed a coupled mathematical modeling and experimental framework that takes into account the effect of rock heterogeneity on effective mechanical properties of the rock in contact with CO 2 . We performed accurate laboratory experiments to determine the changes in rock mechanical properties due to mineral dissolution in the presence of carbonic acid generated by CO 2 injection. A two-scale adaptive homogenization framework was then developed to consistently upscale petrophysical and geomechanical properties to the field scale. Further, we also developed high-fidelity, numerical solution schemes, non-linear and linear solvers and preconditioners to solve the coupled flow, reactive transport and geomechanical system. The numerical model was then used to study field scale CO 2 sequestration problems for the two selected field sites: (1) Frio formation and (2) Cranfield site. We also developed robust schemes for field data assimilation, model calibration such that the residual uncertainty at the end of the data assimilation procedures can be faithfully represented while taking into account the coupled geochemical and geomechanical processes.

42 ENGINEERING↗
DOE OSTI2018

FY18 J23X PSDT Final Report: Shock Initiation of PBX 9502 Lot HOL88H891-008

This report details a series of plate impact experiments on the insensitive explosive PBX 9502 (95% TATB, 5% Kel-F 800) with lot number HOL88H891-008. This is a ’virgin’ lot (no recycled material used), for which many detonation performance measurements are available; the addition of these 1-D shock initiation experiments will aid model calibration efforts for this material. Samples were machined from isostatically-pressed billets with anominal density of 1.89 g/cc. The evolution of the shock to a detonation was characterized by 9 embedded magnetic particle velocity gauges, and 116 embedded magnetic time of arrival gauges (AKA shock trackers). Pho-ton Doppler velocimetry (PDV) with a LiF window was used at the back surface of the samples to capture the wave profile exiting the sample. Six experiments were performed, covering a pressure range of 10.8–18.6 GPa and run distances to detonation of 2.7–12.2+ mm. The shock initiation of this lot is found to be consistent with Pop plots reported for other lots of PBX 9502. All data have been uploaded to the LANL Small Scale Database for use by the research community.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2018

FY18 J23X PDZG Final Report: Double Shock Initiation of PBX 9502 Lot HOL88H891-008

Heterogeneous high explosives subjected to sub-critical shock waves are known to exhibit desensitization to later shock inputs. While it is believed that this phenomenon is due to the removal of hot spot forming features in the explosive by the initial sub-critical shock, the effect of the time delay between the shocks is poorly understood. This report details a series of double shock plate impact experiments that vary the time between similar shocks to explore the time dependance of this shock desensitization process. These experiments were performed on the insensitive explosive PBX 9502 (95% TATB, 5% Kel-F 800) with lot number HOL88H891-008. The evolution of the shocks to a detonation was characterized by 9 embedded magnetic particle velocity gauges, and 116 embedded magnetic time of arrival gauges (AKA shock trackers). Photon Doppler velocimetry (PDV) with a LiF window was used at the back surface of the samples to capture the wave profile exiting the sample. Three double shock experiments were performed, with first and second shock pressures of 8.3 GPa and 16.5 GPa, respectively. An analogous single shock experiment at 16.3 GPa was also performed. The double shock experiments exhibited small levels of reaction during the initial shock,and shock desensitization to the second shock. Run distances after shock coalescence were shorter than those expected for a single shock, indicating the shock to detonation transition (SDT) process was slightly further along due to partial reaction in the first shock wave. These data will be used to calibrate model behavior under multiple-shock conditions. Additional double shock data will be collected to further explore this area in FY2019. All data have been uploaded to the LANL Small Scale Database for use by the research community.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2016

Online mapping and forecasting of epidemics using open-source indicators

Open-source indicators have been proposed as a way of tracking and forecasting disease outbreaks. Some, such are meteorological data, are readily available as reanalysis products. Others, such as those derived from our online behavior (web searches, media article etc.) are gathered easily and are more timely than public health reporting. In this study we investigate how these datastreams may be combined to provide useful epidemiological information. The investigation is performed by building data assimilation systems to track influenza in California and dengue in India. The first does not suffer from incomplete data and was chosen to explore disease modeling needs. The second explores the case when observational data is sparse and disease modeling complexities are beside the point. The two test cases are for opposite ends of the disease tracking spectrum. We find that data assimilation systems that produce disease activity maps can be constructed. Further, being able to combine multiple open-source datastreams is a necessity as any one individually is not very informative. The data assimilation systems have very little in common except that they contain disease models, calibration algorithms and some ability to impute missing data. Thus while the data assimilation systems share the goal for accurate forecasting, they are practically designed to compensate for the shortcomings of the datastreams. Thus we expect them to be disease and location-specific.

42 ENGINEERING↗
DOE OSTI2020

Evaluation of Changes to the MTR Fuel Dissolution Flowsheet to Increase the Number of L-Bundles Charged to a Dissolver

Material Test Reactor (MTR) fuels are dissolved in the H-Canyon facility using a Hg-catalyzed, HNO₃ dissolution flowsheet. The flowsheet is based on experimental work in which laboratory-scale experiments were used to provide bounding values for both offgas and H₂ generation rates. The generation of H₂ gas, primarily from the dissolution of Al, is controlled by limiting the surface area exposed to the solution. The number of L-Bundles charged and their submergence level defines the exposed surface area which is directly proportional to the H₂ generation rate. For the MTR fuel dissolution flowsheet, the maximum number of L-Bundles which can be charged to a dissolver was specified as a function of the Al concentration in the solution and the submergence level of the bundle. The MTR fuels are currently dissolved in the 6.1D dissolver. To reach the desired terminal Al concentration, a total of 12 L-Bundles of fuel are dissolved per batch. Depending on the submergence level being used in the dissolver, the 12 bundles are dissolved in three charges of four bundles per charge or in a sequence of three, four, and five bundles. However, if six L-Bundles could be dissolved in the initial charge, a dissolver batch could be completed in two dissolutions instead of three. To assess the feasibility of dissolving six bundles in the first charge, the Savannah River National Laboratory evaluated changes to the MTR flowsheet which increase the number of bundles which can be charged to a dissolver. Since the development of the existing MTR fuel dissolution flowsheet, improved offgas measurement methods have been developed to more accurately calculate the offgas composition and generation rate during laboratory-scale dissolutions. In the first part of this study, improvements in the calibration model for the Raman spectrometer (used for offgas characterization) and a more effective data smoothing routine were applied to existing data. The uncertainty in the offgas composition data used to define the MTR fuel dissolution flowsheet were subsequently re-evaluated which resulted in an approximate 50% reduction in the uncertainty of the H₂ gas concentration measurements. The reduced uncertainty was used in new calculations to define the maximum number of L-Bundles which can be charged to either the 6.1D or 6.4D dissolver as a function of the Al concentration. With the reduced uncertainty in the H₂ concentration measurements, a flowsheet was defined which supported initially charging 6 bundles to a dissolver. This flowsheet is based on the use of 0.002 M Hg, an L-Bundle immersion depth of 54 in., an air sparge/purge flowrate of 40 scfm, an iodine reactor operating temperature of 200 °C, and not exceeding 60% of the H₂ LFL in the dissolver offgas. In the second phase of this study, calculations were performed to assess the impact of changing various operating parameters to increase the number of L-Bundles charged to an H-Canyon dissolver. Applying the improved offgas measurement techniques including the reduced uncertainty in the H₂ concentration measurements to the existing data, the number of bundles of MTR fuel which can be charged to either the 6.1D or 6.4D dissolver were calculated based on varying the bundle immersion depth (44-60 in.), air sparge/purge flowrate (40-60 scfm), iodine reactor operating temperature (150-200 °C), and the percentage of the H₂ LFL which must not be exceeded (60-76%). Qualitatively, the percentage of the LFL had the largest influence on the maximum number of bundles which can be charged to a dissolver followed by the air sparge/purge flowrate, L-Bundle immersion depth, and the iodine reactor operating temperature. During the evaluation of each variable, the other variables were held at the baseline flowsheet conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

TChem v2.0 - A Software Toolkit for the Analysis of Complex Kinetic Models

TChem is an open source software library for solving complex computational chemistry problems and analyzing detailed chemical kinetic models. The software provides support for: complex kinetic models for gas-phase and surface chemistry; thermodynamic properties based on NASA polynomials; species production/consumption rates; stable time integrator for solving stiff time ordinary differential equations; and, reactor models such as homogenous gas-phase ignition (with analytical Jacobian matrices), continuously stirred tank reactor, plug-flow reactor. This toolkit builds upon earlier versions that were written in C and featured tools for gas-phase chemistry only. The current version of the software was completely refactored in C++, uses an object-oriented programming model, and adopts Kokkos as its portability layer to make it ready for the next generation computing architectures i.e., multi/many core computing platforms with GPU accelerators. We have expanded the range of kinetic models to include surface chemistry and have added examples pertaining to Continuously Stirred Tank Reactors (CSTR) and Plug Flow Reactor (PFR) models to complement the homogenous ignition examples present in the earlier versions. To exploit the massive parallelism available from modern computing platforms, the current software interface is designed to evaluate samples in parallel, which enables large scale parametric studies, e.g. for sensitivity analysis and model calibration.

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

SAM Co-Development to Support Fluoride-salt-cooled High-temperature Reactor Design and Licensing (CRADA 2018-18177 Final Report)

This project aims to raise the SAM code's technical and commercial maturity level to enable the Kairos Power ("Kairos") to use SAM to support its KP-FHR design analysis and licensing application. Argonne National Laboratory ("Argonne") has actively developed and maintained SAM, a modern system-level analysis tool for advanced nonlight water reactors safety analysis. Kairos is currently actively pursuing development of an FHR design and associated technology, and consequently requires a pedigreed safety analysis tool. The purpose of this project is to increase the maturity of the SAM code for the modeling and simulation of the KP-FHR design, thus enabling its use in safety analyses that support licensing application. Argonne will primarily be responsible for software design, development, and testing; while Kairos will be responsible for software requirements, assessment of software capabilities, needs, gaps, and priorities, and development of proprietary models. The research and development activities for the joint SAM development project include: a series of identification and prioritization studies on design characteristics, event sequences, relevant phenomena, and software capabilities; SAM capability enhancements for specific KP-FHR systems and components; performing code verification and validations; integrating uncertainty quantification (UQ), model calibration, and sensitivity analysis (SA) techniques in safety analyses; and raising the software quality rigor level for commercial-grade applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗