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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Potential use of engineered nanoparticles in ocean fertilization for large-scale atmospheric carbon dioxide removal

Artificial ocean fertilization (AOF) aims to safely stimulate phytoplankton growth in the ocean and enhance carbon sequestration. AOF carbon sequestration efficiency appears lower than natural ocean fertilization processes due mainly to the low bioavailability of added nutrients, along with low export rates of AOF-produced biomass to the deep ocean. Here we explore the potential application of engineered nanoparticles (ENPs) to overcome these issues. Data from 123 studies show that some ENPs may enhance phytoplankton growth at concentrations below those likely to be toxic in marine ecosystems. ENPs may also increase bloom lifetime, boost phytoplankton aggregation and carbon export, and address secondary limiting factors in AOF. Life-cycle assessment and cost analyses suggest that net CO 2 capture is possible for iron, SiO 2 and Al 2 O 3 ENPs with costs of 2–5 times that of conventional AOF, whereas boosting AOF efficiency by ENPs should substantially enhance net CO 2 capture and reduce these costs. Therefore, ENP-based AOF can be an important component of the mitigation strategy to limit global warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e., conversion from detected signals to descriptors specific to tip–surface interactions and subsequently to material’s properties. In this work, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We explore the nonlinear mechanical behaviors spatially in a classical ferroelectric material, PbTiO 3 . We observe the non-trivial evolution of the Duffing stiffness term and the nonlinearity of the sample surface, determine spatial clustering of the nonlinear response, and perform a Landau analysis on predicting the nonlinear coefficient, which indicates that ferroelectric behavior can be a cause of the observed results. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls may be broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Privacy Preservation from High-Performance Computing to Autonomous Science [Industrial and Governmental Activities]

High-Performance Computing (HPC) and Leadership-Class Supercomputing are driving forces behind scientific advancements, enabling researchers to tackle complex challenges in physics, chemistry, biology, and engineering. These systems power vast simulations and data analyses, fueling discoveries in fields ranging from materials science to climate modeling. However, their use often involves processing sensitive data—such as proprietary industry simulations, biomedical records, and national security computations—posing significant privacy concerns. In conclusion, this issue is amplified in collaborative environments like Department of Energy (DOE) user facilities, where HPC resources are shared across institutions to foster innovation.

Kotevska, Olivera [Oak Ridge National Laboratory (↗
DOE OSTI2016

Hot-Carrier Degradation in GaN HEMTs Due to Substitutional Iron and Its Complexes

We report the experimental data, quantum-mechanical calculations, and engineering-level modeling that provide insight into the atomic-scale processes that underlie the hot-electron degradation of AlGaN/GaN high-electron-mobility transistors during electrical stress at moderate drain bias. There is relatively large degradation (up to 20%) of the peak transconductance (g m ) in the semi-ON state, along with a small shift of the threshold voltage (V T ). The V T shift and g m degradation increase with temperature. A model of the degradation is presented, based on the hot-carrier-induced defect generation. The model considers carrier energy distributions for different temperatures obtained using an ensemble Monte Carlo (EMC) approach. The Ensemble Monte-Carlo results show that the concentration of energetic carriers is maximum at the end of the gate on the gate-drain access side in the semi-ON state of operation. The degradation is directly related to the number of carriers with sufficient energy to generate defects, and the carrier energy distribution depends strongly on the device temperature. The first-principles density functional theory calculations and the analysis suggest that the dehydrogenation of substitutional iron and its complexes is the dominant cause of the observed degradation in these devices.

Engineering↗
DOE OSTI2016

Introduction: The 2014 Sandia Verification and Validation Challenge Workshop [The 2014 Sandia Verification and Validation Challenge Workshop]

The 2014 Sandia Verification & Validation Challenge Workshop was held at the 3rd ASME Verification & Validation Symposium in Las Vegas, on May 5-8, 2014. The workshop was built around a challenge problem, formulated as an engineering investigation that required integration of experimental data, modeling and simulation, and verification and validation. The challenge problem served as a common basis for the ASME Journal of Verification, Validation, and Uncertainty Quantification participants to both demonstrate methodology and explore a critical aspect of the field: the role of verification and validation in establishing credibility and supporting decision making. Ten groups presented responses to the challenge problem at the workshop, and the follow-on efforts are documented in this special edition of the ASME Journal of Verification, Validation, and Uncertainty Quantification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Large eddy simulation of a turbulent non-reacting spray jet

We performed Large Eddy Simulation (LES) of a turbulent non-reacting n-Heptane spray jet, referred to as Spray H in the Engine Combustion Network (ECN), and executed a data analysis focused on key LES metrics such as fraction of resolved turbulent kinetic energy and similarity index. In the simulation, we used the dynamic structure model for the sub-grid stress, and the Lagrangian-based spray-parcel models coupled with the blob-injection model. The finest mesh-cell size used was characterized by an Adaptive Mesh Refinement (AMR) cell size of 0.0625 mm. To obtain ensemble statistics, we performed 28 numerical realizations of the simulation. Demonstrated by the comparison with experimental data in a previous study [7], this LES has accurately predicted global quantities, such as liquid and vapor penetrations. The analysis in this work shows that 14 realizations of LES are sufficient to provide a reasonable representation of the average flow behavior that is benchmarked against the 28-realization ensemble. With the current mesh, numerical schemes, and sub-grid scale turbulence model, more than 95% of the turbulent kinetic energy is directly resolved in the flow regions of interest. The large-scale flow structures inferred from a statistical analysis reveal a region of disorganized flow around the peripheral region of the spray jet, which appears to be linked to the entrainment process.

Hu, Bing↗
DOE OSTI2016

Nuclear-level densities in the {sup 49}V and {sup 57}Co nuclei on the basis of evaporated-neutron spectra in (p, n) and (d, n) reactions

The spectra of neutrons from the reactions {sup 49}Ti(p, n){sup 49}V and {sup 57}Fe (p, n){sup 57}Co were measured in the range of proton energies between 8 and 11 MeV along with their counterparts from the reactions {sup 48}Ti(d, n){sup 49}V and {sup 56}Fe (d, n){sup 57}Co at the deuteron energies of 2.7 and 3.8 MeV. These measurements were conducted with the aid of a time-of-flight fast-neutron spectrometer on the basis of the EGP-15 pulsed tandem accelerator of the Institute for Physics and Power Engineering (IPPE, Obninsk). An analysis of measured data was performed within the statistical equilibrium and preequilibrium models of nuclear reactions. The respective calculations based on the Hauser–Feshbach formalism of statistical theory were carried out with nuclear-level densities given by the generalized superfluid model of the nucleus, the backshifted Fermi-gas model, and the Gilbert–Cameron composite formula. The nuclear-level densities of {sup 49}V and {sup 57}Co and their energy dependences were determined. The results were discussed together with available experimental data and data recommended by model systematics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

xxing9703/PAVE

Untargeted metabolomics can detect more than 10 000 peaks in a single LC–MS run. The correspondence between these peaks and metabolites, however, remains unclear. Here, we introduce a Peak Annotation and Verification Engine (PAVE) for annotating untargeted microbial metabolomics data. The workflow involves growing cells in 13C and 15N isotope-labeled media to identify peaks from biological compounds and their carbon and nitrogen atom counts. Improved deisotoping and deadducting are enabled by algorithms that integrate positive mode, negative mode, and labeling data. To distinguish metabolites and their fragments, PAVE experimentally measures the response of each peak to weak in-source collision induced dissociation, which increases the peak intensity for fragments while decreasing it for their parent ions. The molecular formulas of the putative metabolites are then assigned based on database searching using both m/z and C/N atom counts. Application of this procedure to Saccharomyces cerevisiae and Escherichia coli revealed that more than 80% of peaks do not label, i.e., are environmental contaminants. More than 70% of the biological peaks are isotopic variants, adducts, fragments, or mass spectrometry artifacts yielding ∼2000 apparent metabolites across the two organisms. About 650 match to a known metabolite formula based on m/z and C/N atom counts, with 220 assigned structures based on MS/MS and/or retention time to match to authenticated standards. Thus, PAVE enables systematic annotation of LC–MS metabolomics data with only ∼4% of peaks annotated as apparent metabolites.

Xing, Xi↗
DOE OSTI2026

MCNPy

SAND2026-20425O MCNPy runs and analyzes simulations from MCNP, a software that models radiation transport of neutrons and gamma rays. MCNPy uses Python to start MCNP, retrieve event data files, and convert them into graph structures for detailed analysis. It offers visualization tools, including 2D views of particle histories, making complex simulation data easier to interpret for researchers and engineers. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Nowack, Aaron [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2024

Computational study on the impact of gasoline-ethanol blending on autoignition and soot/NO x emissions under low-load gasoline compression ignition conditions

Here, in the present work, computational fluid dynamics (CFD) simulations of a single-cylinder gasoline compression ignition (GCI) engine are performed to investigate the impact of gasoline-ethanol blending on autoignition, nitrogen oxide (NO x ), and soot emissions under low-load conditions. In order to represent the test gasoline (RD5-87), a four-component toluene primary reference fuel (TPRF)+ethanol (ETPRF) surrogate (with 10% ethanol by volume; E10) is employed. A three-dimensional (3D) engine CFD model employing finite-rate chemistry with a skeletal kinetic mechanism (including NO x sub-mechanism), adaptive mesh refinement (AMR), and hybrid method of moments (HMOM) is adopted to capture the in-cylinder combustion phenomena and soot/NO x emissions. The engine CFD model is validated against experimental data for three gasoline-ethanol blends: E10, E30 and E100, with varying ethanol content by volume. Model validation is carried out for a broad range of start-of-injection (SOI) timings (−21, −27, −36, and −45 crank angle degrees (°CA) after top-dead-center (aTDC)) with respect to in-cylinder pressure, heat release rate, combustion phasing, NO x and soot emissions. For relatively later injection timings (−21 and −27 °CA aTDC), E30 yields higher amount of soot than E10; while the trend reverses for early injection cases (−36 and −45 °CA aTDC ). On the other hand, E100 yields the lowest amount of soot among all fuels irrespective of SOI timing. Further, E10 shows a non-monotonic trend in soot emissions with SOI timing: SOI-36>SOI-45>SOI-21>SOI-27, while soot emissions from E30 exhibit monotonic decrease with advancing SOI timing. NO x emissions from various fuels follow a trend of E10>E30>E100. On the other hand, NO x emissions increase as SOI timing is advanced for all fuels, with an anomaly for E10 and E100 where NO x decreases when SOI is advanced beyond −36 °CA aTDC. Detailed analysis of the numerical results is performed to investigate the soot/NO x emission trends and elucidate the impact of chemical composition and physical properties on autoignition and emissions characteristics.

Computational fluid dynamics↗
DOE OSTI2012

The Effect of the CO32- to Ca2+ Ion activity ratio on calcite precipitation kinetics and Sr2+ partitioning

Engineering the precipitation of calcium carbonate, which can co-precipitate trace metal contaminants, is a proposed strategy for remediating toxic or radioactive metals in subsurface environments. Engineering precipitation of multi-component minerals will involve injection of chemical amendments that must be mixed at a molecular level to supersaturated conditions that are sufficient to promote rapid mineral precipitation relative to natural systems. In subsurface systems this often means reactant mixing zones will be formed that are characterized by gradients in solute concentrations, saturation state, and solute activity ratios. To better understand the effect of ion activity ratios on CaCO{sub 3} precipitation kinetics and Sr{sup 2+} co-precipitation we experiments were conducted under constant composition conditions where the supersaturation state ({Omega}) with respect to calcite was held constant at 9.4, but the ion activity ratio (r = a{sub co{sub 3}{sup 2-}}/a{sub Ca{sup 2+}}) ranged from 0.003 to 4.15. Results: Under the chosen experimental conditions the CaCO{sub 3} phase formed was calcite and initial precipitation rates varied from a maximum rate of 84.7 {mu}mol/ m{sup 2}/min for a carbonate to calcium activity ratio of (0.21). However, precipitation rates were found to vary with time which could be indicative of variations in precipitation mechanisms that are related to the ion activity ratio. The observed trends in the distribution coefficients for co-precipitated Sr2+ (D{sup P}{sub Sr}{sup 2+}) relative to the calcite precipitation rate (i.e. a positive correlation) indicate that increasing calcite precipitation rates increase the incorporation of Sr{sup 2+}. Conclusion: The observed variation between the rate maxima and minima based on the ion activity ratio could have great deal of implication for sequestering radionuclides (e.g. {sup 90}Sr) and other toxic metals in engineered systems at contaminated sites. Extending our data plot range allowed us to have clues about the differences in mechanism calcite precipitation, which would have significance in our understanding of mineral precipitation modes in subsurface remediation strategies. The positive correlation between D{sup P}{sub Sr}{sup 2+} and calcite precipitation rate could be indicative of the impact calcite precipitation would have on sequestering toxic metals in subsurface environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018

This data package contains mapped trait estimates and their uncertainties, and conifer map, for the National Ecological Observatory Network's Airborne Observation Platform survey data acquired over the Upper East River, Colorado in 2018. For full details, please see associated reference. in brief, trait models were developed independently for needle and non-needle leaf species using partial least squares regression (PLSR) using ground data from additional datasets: doi:10.15485/1618130, doi:10.15485/1618132, and doi:10.15485/1631278, merged with extracted spectral data from doi:10.15485/1618131. We separated vegetated pixels into needle and non-needle classes in order to generate a classification map based on the spectral differences between these leaf types (conifer.tif). We trained a deep learning model with custom architecture, detailed in Chadwick et al. In Press. The model performed with 0.998 true positive rate and 0.982 true negative rate, with ‘positives’ being non-needle identification. We then utilized PLSR to generate models of foliar traits for each leaf type. So that we could also map uncertainty in these predictions, we generated ten different models for needle and non-needle leaf species using different testing holdout sets of discrete sites. Each of these models was developed with a 100-fold cross validation procedure that utilized a 70% training set and 30% validation set with each fold, and then assessed based on the 10% of testing sites that were not included in that model’s development. The mean predicted value across the 10 models is used for the trait estimate in each pixel across the study area. The models are applied according to the leaf type designation in the conifer.tif map. The errors are the standard deviation across the 10 different models developed, with high error suggesting instability in model prediction and areas where values may not be reliable for ecological inference. These maps are only applied to areas with a NDVI > 0.5 to exclude non-vegetated areas. Shade masks could be applied to these data (doi:10.15485/1618131), but have not been for this data package. These data are also available on Google Earth Engine: https://code.earthengine.google.com/?asset=users/kdc/ER_NEON

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

MyCrunchGPT: A LLM Assisted Framework for Scientific Machine Learning

Scientific machine learning (SciML) has advanced recently across many different areas in computational science and engineering. Here, the objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing, and analysis are still time- consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a web app based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus on fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology, and bioinformatics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Extraction-Scrub-Strip test results from the interim Salt Disposition Program Macrobatch 9 Tank 21H qualification samples

Savannah River National Laboratory (SRNL) analyzed samples from Tank 21H in support of qualification of Macrobatch (Salt Batch) 9 for the Interim Salt Disposition Program (ISDP). The Salt Batch 9 characterization results were previously reported. An Extraction-Scrub-Strip (ESS) test was performed to determine cesium distribution ratios (D (Cs) ) and cesium concentration in the strip effluent and decontaminated salt solution (DSS) streams; this data will be used by Tank Farm Engineering to project a cesium decontamination factor (DF). This test used actual Tank 21H material, and a blend solvent prepared by SRNL that mimics the solvent composition currently being used at the Modular Caustic-Side Solvent Extraction Unit (MCU). The ESS test showed acceptable performance with an extraction D (Cs) value of 52.4. This value is consistent with results from previous salt batch ESS tests using similar solvent formulations. This compares well against the predicted value of 56.5 from a recently created D (Cs) model

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Extraction, -scrub, -strip test results from the interim salt disposition program macrobatch 10 tank 21H qualification samples

Savannah River National Laboratory (SRNL) analyzed samples from Tank 21H in support of qualification of Macrobatch (Salt Batch) 10 for the Interim Salt Disposition Program (ISDP). The Salt Batch 10 characterization results were previously reported.ii,iii An Extraction, -Scrub, -Strip (ESS) test was performed to determine cesium distribution ratios (D(Cs)) and cesium concentration in the strip effluent (SE) and decontaminated salt solution (DSS) streams; this data will be used by Tank Farm Engineering to project a cesium decontamination factor (DF). This test used actual Tank 21H material, and a sample of the NGS Blend solvent currently being used at the Modular Caustic-Side Solvent Extraction Unit (MCU). The ESS test showed acceptable performance with an extraction D(Cs) value of 110. This value is consistent with results from previous salt batch ESS tests using similar solvent formulations. This is better than the predicted value of 39.8 from a recently created D(Cs) model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Extraction, Scrub, and Strip Test Results from The Interim Salt Disposition Program Macrobatch 11 Tank 21H Qualification Samples

Savannah River National Laboratory (SRNL) analyzed samples from Tank 21H in support of qualification of Macrobatch (Salt Batch) 11 for the Interim Salt Disposition Program (ISDP). The Salt Batch 11 characterization results were previously reported. An Extraction, Scrub, and Strip (ESS) test was performed to determine cesium distribution ratios (D(Cs)) and cesium concentration in the strip effluent (SE) and decontaminated salt solution (DSS) streams; this data will be used by Tank Farm Engineering to project a cesium decontamination factor (DF). This test used actual Tank 21H material, and a sample of the Next Generation Solvent (NGS) Blend solvent currently being used at the Modular Caustic-Side Solvent Extraction Unit (MCU). The ESS test showed acceptable performance with an extraction D(Cs) value of 117. This value is consistent with results from previous salt batch ESS tests using similar solvent formulations. This cesium removal efficiency is better than the predicted value of 50.8 from a recently created D (Cs) model.

Peters, T.↗
DOE OSTI2020

SNL will be able to provide well-calibrated models to weapons systems analysts by integrating an engine for constitutive model calibration with the GRANTA materials database

Researchers at Sandia National Laboratories have integrated the GRANTA materials database with the MatCal calibration engine to calibrate material models from characterization data. GRANTA is gaining acceptance across the NNSA Tri-lab complex and is being populated with weapons-specific test data by Sandia experimentalists. To use that data to create material models for use by weapons systems analysts, MatCal has been enabled import calibration data and test conditions from GRANTA to quickly and reproducibly produce a calibrated set of parameters for a given constitutive model. The team is currently working to store the parameters characterizing material behavior in GRANTA to make them accessible by all weapons analysts.

36 MATERIALS SCIENCE↗