Search NASA⌕ Search

SEARCH · Search NASA

Results for “analysis process”

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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Thinking Bayesian for plasma physicists

Bayesian statistics offers a powerful technique for plasma physicists to infer knowledge from the heterogeneous data types encountered. To explain this power, a simple example, Gaussian Process Regression, and the application of Bayesian statistics to inverse problems are explained. The likelihood is the key distribution because it contains the data model, or theoretic predictions, of the desired quantities. By using prior knowledge, the distribution of the inferred quantities of interest based on the data given can be inferred. Because it is a distribution of inferred quantities given the data and not a single prediction, uncertainty quantification is a natural consequence of Bayesian statistics. The benefits of machine learning in developing surrogate models for solving inverse problems are discussed, as well as progress in quantitatively understanding the errors that such a model introduces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Bayesian calibration of irradiated graphite property models under high temperatures

Graphite under high temperatures and irradiation is central to advanced reactors. We develop a Bayesian calibration framework for graphite property models that explicitly represents model-data mismatch via a Gaussian-process discrepancy. The approach propagates uncertainty from parameters, experimental noise, and model form, with a hierarchical variance structure to capture group and cross-group noise. Using two predictive models across five grades (IG-110, NBG-18, PCEA, NBG-17, 2114) and four properties-irradiation-induced dimension change, creep, Young’s modulus change ratio, and coefficient of thermal expansion change ratio-we obtain average predictive-error reductions of 54%, 65%, 17%, and 17% when discrepancy is included. We illustrate engineering impact with a multiphysics model of a very-high-temperature reactor prismatic reflector brick, analyzing stresses under high fluence and temperature. Accounting for model discrepancy markedly improves predictive accuracy and provides a robust basis for reliable graphite component design in advanced reactors.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Proactive Regulatory Approaches to Electrification and Load Growth: Workshop Report

On July 10 and 11, 2024, Pacific Northwest National Laboratory and RMI led a workshop in Aurora, Colorado, to explore novel and proactive approaches to electrification and load growth while minimizing risks and costs to customers. Over the next decade, a unique opportunity exists to invest strategically in the electricity system to enable electrification across the transportation, industrial, and building sectors and respond to data and technology-based load growth. However, current utility and regulatory planning practices are insufficient to identify and enable the right investments, and work must be done to reduce the risk and decisional uncertainty faced by utility regulatory commissions and utilities. Ensuring timely electrification investments may require new approaches to address risk, uncertainty, prudence, and cost recovery. Understanding the decision-making process and information needs of utilities and regulators is critical. New policies (or application of policies), financial tools, systems analysis, regulatory mechanisms, and enhanced process transparency may be required. The workshop's goal was to identify proactive regulatory approaches for electrification and load growth that minimize costs and risks to customers. Our intention was that the conversations and the resulting solutions and takeaways would be specific and tactical rather than general and theoretical and that together we would create actionable next steps for key actors in the system, including utilities, regulators, thought leaders, researchers, and the U.S. Department of Energy (DOE). This report is intended to provide workshop attendees with a record and summary of the discussion and proposals raised at the workshop and to provide interested entities who did not attend, such as other regulators, policymakers, utilities, and U.S. DOE offices, with an understanding of what was discussed and with ideas to explore in their organizations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Adapt: A Weather Radar Data Analysis and Nowcasting Platform for Informed Adaptive Scanning

SF-26-021 Adapt is a data processing platform for real-time data analysis, short term prediction of targets convective cells and tracking for archived data. It provides tools for downloading, processing, segmenting, projecting, analyzing, and visualizing storm cell data from weather radar. The pipeline includes cell detection, motion estimation using optical flow, cell property extraction, and persistence to NetCDF and SQLite/Parquet for guiding adaptive scanning.

Raut, Bhupendra Ashokrao [Argonne National Laborat↗
DOE OSTI2026

Electrostatic‐Attraction‐Driven Self‐Assembled Graphene‐Disordered Rocksalt Composite Cathode for Lithium‐Ion Batteries

Disordered rocksalt cathodes hold promise for achieving high-capacity lithium-ion batteries while using low-cost, earth-abundant elements. However, their electrochemical performance remains critically limited by their poor electronic conductivity. Conventional strategies such as high-energy ball milling with excess carbon additives can improve conductivity but remain challenging to scale and often produce defects and increase surface area, thereby accelerating capacity degradation. Herein, we report an alternative approach of electrostatic-attraction-driven self-assembly to fabricate Li 1.2 Mn 0.6 Ti 0.2 O 1.8 F 0.2 (LMTOF) particles uniformly wrapped with electronically conductive graphene sheets without associated materials degradation. The graphene-wrapped LMTOF demonstrates significantly improved cycling stability (89% capacity retention after 100 cycles) and superior rate capability compared with an LMTOF-carbon composite electrode fabricated using the conventional high-energy ball-milling process. Post-cycling analysis reveals reduced oxygen evolution, suppressed unwanted side reactions, and improved structural integrity for the graphene-LMTOF composite. This work highlights the advantages of solution-based carbon wrapping and offers a scalable strategy to prepare high-performance DRX cathodes for lithium-ion batteries.

carbon composite↗
DOE OSTI2026

Highly Anisotropic Quasi‐Direct Organic Metal Halide Hybrids: A Platform for Polarization‐Sensitive Optoelectronics

Low-dimensional organic–inorganic metal halide hybrids (OMHHs) exhibit remarkable optical properties and enhanced environmental stability. We investigate a 1D OMHH with formula C 4 N 2 H 14 PbBr 4 , consisting of Pb–Br chains separated by organic cations, which shows a large Stokes shift (0.83 eV) and broadband emission. Through first-principles calculations and polarized Raman spectroscopy, we characterize the material's vibrational properties and identify the specific phonon modes that drive exciton self-trapping. Our novel GW/Bethe-Salpeter equation force formalism reveals that low-frequency phonons (∼ 100 cm −1 , primarily involving Pb–Br motions) couple strongly with excitons, with a remarkably high Huang-Rhys factor of 137 ± 4, and gives a pathway for ultrafast structural analysis during the absorption process. This phonon-exciton coupling mechanism explains the material's broadband emission and provides a pathway for controlling optical properties through vibrations and for tuning vibrations through optical excitations. The material also exhibits highly anisotropic optical properties and electronic transport, with bands that are dispersive along the Pb–Br chains but nearly flat in perpendicular directions, resulting in direction-dependent electrical conductivity that is calculated to be an order of magnitude higher along the chain direction and consistent with measurements. These combined properties make this system an excellent platform for polarization-sensitive optoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Assessment of frequency and amplitude dependence on the cyclic degradation of polyurethane foams

Many energy absorption applications utilize flexible polymeric foams for their viscoelastic properties. It is desired that the material will perform consistently across repeated compression cycles. This study examines the effect of fatigue at low strain rates on the viscoelasticity of open-cell polyurethane foam. Six polyurethanes of the same base composition with two porosities (70% and 80%) and three chemical indexes (79i, 100i, and 121i) are tested. Large deformation cyclic compression of the foams is conducted on a universal testing system (UTS). These data are then post-processed leveraging dynamic mechanical analysis Fourier transform rheology to characterize changes in the viscoelasticity of the materials over fatigue cycles. Results show that foams can increase or decrease in stiffness up to 10% over 10 4 cycles. Specifically, higher chemical index, higher excitation frequency, and larger excitation amplitude correlate with a more pronounced decrease in stiffness. Damping can also change by 15% and correlates with chemical index and excitation frequency. Consequently, the findings suggest that internal foam structure and bulk material properties as well as applied loading parameters affect the viscoelastic fatigue response of flexible polymeric foams.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Chemically and mechanically recyclable polyester-based multilayer plastics

Approximately 100 million tons of multilayered plastics (MLPs) are produced each year worldwide but are not recycled due to their complex structure. Here, this work aims to design polyester-based multilayer plastics (80–100 % polyester) that provide barrier performance comparable to typical 9–12-layer commercial MLPs, while also enabling both chemical recycling (back to parent monomer) and mechanical recycling (grind-and-melt reprocessing). Such dual recyclability is not achievable with non-polyester multilayers, such as all-polyolefin systems. Furthermore, we emphasize how the multilayer architecture was tailored to balance barrier properties, mechanical integrity, and end-of-life recyclability for both flexible and rigid packaging applications. Two main categories of polyester-based MLPs are reported; in the first type, poly(butylene adipate-co-terephthalate) (PBAT)-70 % polyglycolic acid (PGA) is used as middle barrier layer, while in second type, middle barrier layer is Ethylene-vinyl alcohol (EVOH) copolymers. Polyethylene terephthalate (PET) was used as a structural layer, while either PBAT or poly(butylene succinate) (PBS) was used to enable thermal sealing and serve as the product contact layer. These MLPs are recycled by both chemical and mechanical recycling processes. Techno-economic analysis (TEA) shows that MLPs incorporating EVOH as barrier layer have similar or lower selling costs (0.32 $\$$/m 2 ) than commercial MLPs. Life cycle assessment (LCA) indicates EVOH-based MLPs have a lower carbon footprint and lower energy consumption relative to commercial MLP benchmarks. This work offers simplified MLPs that are easy to manufacture and ready to recycle, which will significantly reduce environmental impact of MLP packaging while also providing a cost-effective and practical solution for industry.

Barrier properties↗
DOE OSTI2026

William A. Bardeen: A life in physics and the legacy of the chiral anomaly

William Allan Bardeen (September 15, 1941 − November 18, 2025) was an American theoretical physicist who worked at the Fermi National Accelerator Laboratory. He is renowned for his foundational work on the chiral anomaly, the Adler-Bardeen theorem, the non-Abelian anomaly and gravitational anomalies. He was instrumental in the development of quantum chromodynamics and its applications, such as semileptonic decays and the Λ $\overline{MS}$ scheme frequently used in perturbative analysis of high energy processes involving strong interactions. Bardeen also played a major role in developing a theory of dynamical breaking of electroweak symmetry via top quark condensates, leading to one of the first composite Brout-Englert-Higgs boson models. His work on the chiral symmetry dynamics of heavy-light quark bound states correctly predicted abnormally long-lived resonances which are chiral symmetry partners of the ground state.

Hill, Christopher T. [Fermi National Accelerator L↗
DOE OSTI2024

Reversible Disorder-to-Order Transition Induced by Aqueous Lithiation in Vanadate Electrode Materials

Vanadium-based oxides are intriguing electrode materials in aqueous electrochemical systems owing to their low cost and high theoretical capacity for alkali storage, especially lithium (Li) ions. However, a sequence of phase transformations and irreversible structure distortion upon Li-ion intercalation causes structural instability and has been a lingering problem for vanadium oxide electrodes. Here, in this work, we investigate lithium vanadate (Li–V 3 O 8 ) for aqueous Li-ion intercalation and deintercalation processes. Unlike its crystalline V 2 O 5 polymorph, Li–V 3 O 8 retains monophasic lithiation, which is attributed to its disordered crystalline nature and large interplanar distance. Importantly, we show a unique and reversible sequence of disorder-to-order structural transition induced by the extent of lithiation, which indicates sequential interlayer and intralayer lithiation process, and vice versa in delithiation process, supported by electrokinetic analysis, in situ X-ray diffraction (XRD), and Debye scattering simulations. The absence of distortive phase transitions and multilithiation pathways facilitates Li-ion diffusion across the vanadate electrode materials to improve storage capacity. This work opens a new dimension for vanadium-based disordered oxides, accelerating the development of low-cost, aqueous electrochemical systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Characterizing the Potential for Sustainable Azelaic Acid Production from High-Oleic Vegetable Oil Using Two-Step Oxidative Cleavage

Azelaic acid is a renewable monomer conventionally produced via the energy-intensive ozonolysis of oleic acid. Recent advancements have enabled the use of high-oleic vegetable oils (rather than tallow-derived oleic acid) and replaced ozonolysis with two-step oxidative cleavage using hydrogen and oxygen. Although this shift would improve process safety, the financial viability and environmental implications remain uncertain. In this study, we characterized the sustainability of azelaic acid production from high-oleic vegetable oil using two-step oxidative cleavage. Process design, simulation, technoeconomic analysis (TEA), and life cycle assessment (LCA) were executed under uncertainty using BioSTEAM. The modeled system produces azelaic acid at a market-competitive minimum selling price (MSP) of 8.32 [4.93−13.34] $\$kg$ −1 (median 5th−95th percentiles), below the minimum estimated market price of 9.93 $\$kg$ −1 . Further, it has the potential to approach carbon neutrality (0.0 [−5.5 to 5.6] kg of CO 2 -eq kg −1 ) under displacement allocation. Improvements to dihydroxylation (86 to 99%) and oxidative cleavage conversions (93 to 99%) would reduce MSP to $\$5.24$ kg −1 and carbon intensity to −1.90 kg of CO 2 -eq kg −1 (displacement). Additionally, increasing the feedstock triolein content (75 to 85%) lowers MSP by $\$0.82$ kg −1 . Overall, this research demonstrates the potential for financially viable production of azelaic acid from vegetable oils and the utility of agile TEA/LCA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗
DOE OSTI2024

Scalable learning of potentials to predict time-dependent Hartree–Fock dynamics

We propose a framework to learn the time-dependent Hartree–Fock (TDHF) inter-electronic potential of a molecule from its electron density dynamics. Although the entire TDHF Hamiltonian, including the inter-electronic potential, can be computed from first principles, we use this problem as a testbed to develop strategies that can be applied to learn a priori unknown terms that arise in other methods/approaches to quantum dynamics, e.g., emerging problems such as learning exchange–correlation potentials for time-dependent density functional theory. We develop, train, and test three models of the TDHF inter-electronic potential, each parameterized by a four-index tensor of size up to 60 × 60 × 60 × 60. Two of the models preserve Hermitian symmetry, while one model preserves an eight-fold permutation symmetry that implies Hermitian symmetry. Across seven different molecular systems, we find that accounting for the deeper eight-fold symmetry leads to the best-performing model across three metrics: training efficiency, test set predictive power, and direct comparison of true and learned inter-electronic potentials. All three models, when trained on ensembles of field-free trajectories, generate accurate electron dynamics predictions even in a field-on regime that lies outside the training set. To enable our models to scale to large molecular systems, we derive expressions for Jacobian-vector products that enable iterative, matrix-free training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ion velocity effect governs damage annealing process in defective KTaO 3

Effects of electronic to nuclear energy losses (S e /S n ) ratio on damage evolution in defective KTaO 3 have been investigated by irradiating pre-damaged single crystal KTaO 3 with intermediate energy O ions (6 MeV, 8 MeV and 12 MeV) at 300 K. By exploring these processes in pre-damaged KTaO 3 containing a fractional disorder level of 0.35, the results demonstrate the occurrence of a precursory stage of damage production before the onset of damage annealing process in defective KTaO 3 that decreases with O ion energy. The observed ionization-induced annealing process by ion channeling analysis has been further mirrored by high resolution transmission electron microscopy analysis. In addition, the reduction of disorder level is accompanied by the broadening of the disorder profiles to greater depth with increasing ion fluence, and enhanced migration is observed with decreasing O ion energy. Since S e (~3.0 keV nm –1 ) is nearly constant for all 3 ion energies across the pre-damaged depth, the difference in behavior is due to the so-called 'velocity effect': the lower ion velocity below the Bragg peak yields a confined spread of the electron cascade and hence an increased energy deposition density. Here, the inelastic thermal spike calculation has further confirmed the existence of a velocity effect, not previously reported in KTaO 3 or very scarcely reported in other materials for which the existence of ionization-induced annealing has been reported. In other words, understanding of ionization-induced annealing has been advanced by pointing out that ion velocity effect governs the healing of pre-existing defects, which may have significant implication for the creation of new functionalities in KTaO 3 through atomic-level control of microstructural modifications, but may not be limited to KTaO 3 .

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Bayesian inference of nuclear incompressibility from collective flow in mid-central Au+Au collisions at 400–1500 MeV/nucleon

The incompressibility K of symmetric nuclear matter (SNM) is determined through a Bayesian analysis of collective flow data from Au + Au collisions at beam energies $E = 400 -1500$ MeV/nucleon. This analysis utilizes a Gaussian process (GP) emulator applied to the isospin-dependent quantum molecular dynamics (IQMD) model for heavy-ion collisions, both with and without incorporating the momentum dependence of the single-nucleon potentials. Specifically, at the 68% confidence level, using rapidity and transverse velocity dependence of proton elliptic flow data with and without consideration of the momentum dependence, the inferred incompressibility values are $K=188.9^{+2.9}_{-4.5}$ MeV and $256.1^{+8.2}_{-8.7}$ MeV at $E = 400$ MeV/nucleon, respectively. When the transverse momentum dependence of proton-like directed flow data is included, the inferred incompressibility values become $K=222.3^{+9.0}_{-9.9}$ MeV and $K=285.5^{+6.7}_{-7.3}$ MeV, respectively. Furthermore, we found that the value of K derived from observables of proton elliptic flow increases with beam energy. Finally, this indicates that the equation of state (EoS) of nuclear matter hardens at higher densities and temperatures in reactions with higher beam energies.

Bayesian inference↗
DOE OSTI2024

Enhancing Discoverability and Management of Atmospheric Data at Scale: Solutions from the ARM Data Center

The Atmospheric Radiation Measurement (ARM) is a multi-laboratory and multi-institutional U.S. Department of Energy (DOE) Office of Science National User Facility. The ARM Data Center (ADC), located at Oak Ridge National Laboratory, collects, archives, and shares vast atmospheric data crucial for climate research. The ADC manages over 7 PB of data from 460 instruments worldwide, processing it into more than 11,000 diverse data products using the Network Common Data Form (NetCDF) for machine-independent accessibility. The primary challenge addressed in this paper is the efficient management and distribution of vast and diverse datasets essential for the climate research community, enhancing accessibility through advanced tools like Data Discovery. The ADC has developed advanced infrastructure and software architecture to handle the continuous influx of heterogeneous data to enhance data discoverability, resulting in increased scientific collaboration. In 2023, users from over 34 countries downloaded and utilized ARM data, resulting in 1,455 publications. The ADC’s efforts have significantly improved the discoverability and usability of atmospheric data, fostering extensive scientific research and collaboration. This paper details the solutions implemented by the ADC team for efficient data discovery and distribution, and it demonstrates ARM’s capability of staging processed data for scientific analysis.

Shah, Chirag [ORNL] (ORCID:0000000203145737)↗
DOE OSTI2024

Mbin v1.0

The Mbin software, is a software toolkit that implements the IMG metagenome binning pipeline. The software allows the user to process input metagenome contigs, and produces metagenome assembled genomes (metagenome bins) and valuation metrics per bin including completion and contamination estimates, quality assignment, predicted lineage and eukaryotic potential. It is currently packed as a portable docker container and provides the advantage of running the process of binning and analysis of the bins generated, using a suite of tools run sequentially with controls in place to capture errors and optional arguments to run a modified version depending on individual needs and capabilities.

Varghese, Neha↗