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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression

Background Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype. Results We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics (“inertia”) alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation. Conclusions Overall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.

Mathematical & Computational Biology↗
DOE OSTI2026

Machine learning of factors for improving oyster hatchery production

Oyster aquaculture and restoration in the Chesapeake Bay are vital, yet hatcheries frequently struggle with inconsistent larval growth and sudden mass mortality events. Unpredictable disruptions in larval production cause large economic losses, represent a perceived risk to growers, and impede industry expansion. To better understand associations between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming to identify key yield predictors and develop a robust forecasting tool. We used recursive Boruta algorithm for variable selection, pinpointing critical predictors, and employed cross-validation to fine-tune model settings. Shapley value analysis offered crucial insights into model interpretations, highlighting week number, Normalized Difference Vegetation Index, salinity, turbidity, and fecundity as primary drivers of yield variability. For low-yield cases, salinity-related variables were particularly important. Our findings provide an early warning system for potential production downturns, empowering hatchery operators to make data-driven decisions for optimizing water conditions, feeding schedules, and broodstock management. By boosting predictability and efficiency, this research directly supports economic stability of the oyster industry and ecological health of the Chesapeake Bay.

Vishwakarma, Srishti [Oak Ridge National Laborator↗
DOE OSTI2026

Linear viscoelastic measurements of commerical therapy putties for design and intuition

This dataset complements the publication Mapping Linear Viscoelasticity for Design and Tactile Intuition (Corman & Ewoldt, 2019). It contains rigorous shear rheometry measurements for six commercial silicone putties, including creep recovery, SAOS, LAOS, and stress relaxation. Additional, unpublished data was generated as part of a graduate rheology course taught by Randy Ewoldt at University of Illinois Urbana-Champaign. Together, these datasets provide a comprehensive example of linear viscoelasticity of a commercial material. The collection is intended both as a pedagogical resource for developing physical intuition in linear viscoelasticity and as a reference dataset for data interpretation, model fitting, and comparative rheological analysis.

C. Marsh, Maxwell↗
DOE OSTI2019

Phase changes in fluoropolymer binders

Motivation: Understanding fluoropolymer binder structure and aging response continues to be an important goal for the prediction and assessment of polymer bonded explosive compositions within the stockpile. As part of ongoing efforts within NNM, we are developing new methods to quantify crystallization and chemical degradation within aged Fluoropolymer materials. Significance: Time, temperature and radiation are known to alter the mechanical properties of fluoropolymers as crystalline domains nucleate and grow within the material. However, current methods do not accurately quantify and assess the degree or rapidity of these physical aging processes. For this reason, we are developing methods that directly measure crystalline domain, composition, size and shape and cross-correlating them with traditional techniques. Progress: Most recently, we thermally aged fluoropolymer binder, FK800, for various durations up to 23 days at several temperatures including: 25C (red), 35C (green), and 60C (blue). We used wide angle X-ray scattering (WAXS) to observe when crystalline peaks emerged, signaling a phase transition from amorphous to partially crystalline. Some of these data are summarized in Figure 1. As expected, samples held at 25C (red), which is below the glass transition temperature at 28C, remain amorphous – the WAXS data (Figure 1b) lacks sharp peaks indicative of crystallinity and the ultra-small angle X-ray scattering (USAXS) also has a smooth background without peaks indicative of structure (Figure 1a). By contrast, samples held at 35C and 60C have new peaks in both the USAXS and WAXS data. The structure is clearer after subtracting off the amorphous background from the WAXS data (Figure 1c). These data show that samples aged at 60C are more crystalline as determined by the more defined peaks and also have a larger lattice constant as seen by the shift in peak position as compared to the 35C samples. The corresponding USAXS data (Figure 1a) indicate that the size of the crystals was larger at 60C than at 35C as seen by the shift in the knee position to lower q-values. Interestingly, the shape of the scatter curve also changed, which indicates a change in morphology. Atomic force microscopy (AFM) was used to image the fluoropolymer (Figure 1c-e). At 35C, ~50nm long stacked crystal plates are observed. High resolution imaging shows that the stacks consist of 2-6 lamellae that are spaced approximately 3nm apart. At 60C, the individual lamella appear both thicker and longer and are intertwined to fill the surface. Morphological data such as these images help to interpret & model volume averaging techniques like X-ray scattering and NMR. We also expect the shift from isolated islands to intertwined clusters to impact mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Sandia-UT Academic Alliance Project Summary

This project seeks to leverage various hyperspectral tensor products for the purposes of target classification/detection/prediction. In addition to hyperspectral, these products may be images, time series, geometries, or other modalities. The scenarios in which the targets of interest must be identified are typically from remote sensing platforms such as satellites. As such, there are numerous real-world constraints that drive algorithmic formulation. Cost, complexity, and feasibility of the algorithm should all be considered. Targets of interest are exceedingly rare, and collecting many data samples is prohibitively expensive. Furthermore, model interpretability is paramount due to the application space. The goal of this project is to develop a constrained supervised tensor factorization framework for use on hyperspectral data products. Supervised tensor factorizations already exist in the literature, although they have not seen widespread adoption in the remote sensing domain. The novelty of this project will be the formulation and inclusion of constraints that take into account mission considerations and physics based limits to learn a factorization that is both physically interpretable and mission deployable. This will represent a new contribution to the field of remote sensing for performing supervised learning tasks with hyperspectral data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Ex-Situ Surface Characterization Studies and Boundary Plasma Diagnostic Development for DIII-D (Final Report)

This report details the accomplishments for award DE-SC0016318, which sponsored collaborative research activities between the University of Tennessee-Knoxville and the DIII-D experiment at General Atomics, led by the PI (Donovan), which officially began on August 1, 2016 and ended July 31, 2020. Though the official start date for the award was August 1, 2016, the PI had already initiated collaborative activities with DIII-D during 2015 supported by internal UTK start-up funds utilized by the PI. This prior work enabled UTK to have a substantial role in the June 2016 Metal Rings Campaign (MRC). The DOE funds then provided the opportunity to expand the UTK team with funding for students and a postdoc to perform ex-situ analysis on the wide array of samples exposed during the MRC and develop more sophisticated analysis tools and interpretive modeling techniques.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗
NASA NTRS1971

Analysis of solar ultraviolet lines

The formation of the strongest ultra-violet emission lines of Mg II, O I, C II, and C III in the solar atmosphere is studied in detail. The equations of statistical equilibrium and radiative transfer for each ion are solved using a general computer program that is capable of solving non-LTE line-formation problems for arbitrary atmospheric and atomic models. Interpreting the results in terms of the structure of the solar atmosphere, it is concluded that the HSRA atmosphere has a temperature too low by about 500 K near h = 1100 km and that a temperature plateau with T sub e approximately = 18,000 K and width close to 60 km exists in the upper chromosphere. The structure of the solar atmosphere in the range 20,000 to 100,000 K and the effects of microturbulence on the formation of lines are also investigated. Approximate analytic line-formation problems are solved, and more exact solutions are derived later. An attempt is made to make the best possible fit to the Ca II K line center-to-limb profiles with a one-component atmosphere, with an assumed source function and microturbulent velocity.

Chipman, E.↗
NASA NTRS1974

The X-ray spectra of the Vela and Puppis supernova remnants and the shock-wave model of supernova remnants

The structure and spectrum of the Vela and Puppis supernova remnants (SNRs) were observed in soft (0.1 to 1.5 keV) X rays from a rocket. The spectral data of both objects below 1.5 keV are consistent with thermal radiation from a hot plasma and are not consistent with a simple power-law function. Fitting the data to a Tucker-Koren model results in values of T = (4.3 plus or minus 0.3) x 1,000,000 K for Vela and T = (7 plus or minus 1) x 1,000,000 K for Puppis. The present data, earlier data from the Cygnus Loop, and results on Cas A and Tycho reported by others are considered within the context of a simple model in which X rays are thermally produced as a result of a shock wave expanding into the interstellar medium. X-ray data on the temperature, intrinsic diameter, and intrinsic luminosity of the five SNRs are used to compute the age, energy release, and initial interstellar density for each one. From measurements of X-ray absorption along the line of sight, and also from shock-wave model interpretations of temperature-diameter-luminosity relations, it is concluded that the average density of interstellar material is about 0.1 per cu cm between the sun and the nearest X-ray emitting SNR, Vela.

Gorenstein, P.↗
NASA NTRS1985

Subionospheric VLF/LF phase perturbations produced by lightning-whistler induced particle precipitation

Rapid phase perturbations of subionospherically propagating VLF/LF signals are associated with lightning-generated whistlers which propagate in the magnetosphere. The perturbations, called 'Trimpi events' or 'Trimpi effects' after their discoverer, are attributed to alterations in the earth-ionosphere waveguide caused by localized enhancements in the ionospheric D region. These enhancements are produced by bursts of high-energy electrons which are precipitated out of the earth's radiation belts by the whistler waves. The present paper has the objective to present and discuss new evidence of whistler-associated phase perturbations of subionospheric signals. Events of this type, called phase Trimpi's were first reported by Lohrey and Kaiser (1979). Attention is given to occurrence statistics in terms of the seasonal distribution and hourly rate of events, possible effects on global VLF/LF navigation, and an interpretive model.

Inan, U. S.↗
NASA NTRS1991

Estimation of porphyrin concentration in the kerogen fraction of shales using high-resolution reflectance spectroscopy

An interpretive model for estimating porphyrin concentration in bitumen and kerogen from spectral reaflectance data in the visible and near-ultraviolet region of the spectrum is derived and calibrated. Preliminary results obtained using the model are consistent with concentrations determined from the bitumen extract and suggest that 40 to 60 percent of the total porphyrin concentration remains in the kerogen after extraction of bitumen from thermally immature samples. The reflectance technique will contribute to porphyrin and kerogen studies and can be applied at its present level of development to several areas of geologic and paleo-oceanographic research.

Holden, Peter N.↗
NASA NTRS1992

The narrow emission lines from SN 1987 A

Observations of the narrow lines from SN 1987 A have been reviewed, along with model interpretations of these observations. In addition, a discussion is provided on characteristics of the radiation from the supernova outburst, and how this affects the thermal structure of the circumstellar gas. Preliminary results from 2D hydrodynamical calculations for the circumstellar structure are also presented.

Lundqvist, Peter↗
NASA NTRS1993

A simple model of clastic sediments on Mars

In preparation for the start of Mars Observer operations at Mars later this year, Viking Infrared Thermal Mapper (IRTM) observations were synthesized into a simple but geologically coherent conceptual model for use in establishing targets for coordinated Thermal Emission Spectrometer (TES) and Mars Observer Camera (MOC) observations. The model is based on three assumptions that are, at best, only partly true: that albedo is a measure of the presence or absence of dust; that thermal inertia is a measure of the weighted average particle size; and that rock abundance is a measure of the statistical thickness of fine sediment (i.e., that the observed areal abundance of rock reflects the whole or partial burial of rocks). Using this model, it is possible to show that, on the scale of 30 km, mantles of wind-transportable sediment (dust and sand) are at most about 1 m thick, and that on a global average such mantles are about 35 cm thick. It is shown that 3.8 x 10(exp 19) cu cm of such sediment covers Mars equatorward of +/- 60 deg latitude. Using the model, interpretation of digital maps of IRTM data focus attention not only on areas where dust is the primary sediment (e.g., Arabia Terra), but also on areas where sand is the primary sediment (e.g., eastern Valles Marineris) and where rocks and other coarse materials are abundant (e.g., eastern Kasei Vallis). Three IRTM data sets are used in the analysis: Viking 1 and 2 pre-dawn observations mosaiced into a global map of single-point thermal inertias at 0.5 deg/bin resolution; Pleskot and Miner's global albedo map using the best available, clear-period IRTM observations (1x/bin); and Christensen's 1 deg/bin rock abundance map. Uncertainty analyses indicate these data sets to be accurate to 5 percent, 2 percent, and 20 percent, respectively.

Christensen, Philip R.↗
NASA NTRS2004

Electrocomposite of Alumina in Nickel Matrix

Nickel/aluminum oxide composite was electroformed in a sulfamate bath with 50 g/L of 0.05-micron aluminum oxide powder. Different plating methods including direct current plating, periodic pulse plating, and periodic reverse pulse plating were used. With conventional direct current plating, the maximum particle inclusion in the nickel matrix remains about 2% (wt). However, much higher percentile particle inclusions were achieved when a specific pulse reversal plating technique was applied. The particle incorporation approaches theoretical maximum when the deposit thickness per cycle approaches the particle diameter size at lower duty cycle. The highest particle incorporation achieved is 23% (by weight). Conceptual models interpreting the dramatic differences in the results of these plating methods were also proposed.

Xiong-Skiba, Pei↗
NASA NTRS2011

Graphical Language for Data Processing

A graphical language for processing data allows processing elements to be connected with virtual wires that represent data flows between processing modules. The processing of complex data, such as lidar data, requires many different algorithms to be applied. The purpose of this innovation is to automate the processing of complex data, such as LIDAR, without the need for complex scripting and programming languages. The system consists of a set of user-interface components that allow the user to drag and drop various algorithmic and processing components onto a process graph. By working graphically, the user can completely visualize the process flow and create complex diagrams. This innovation supports the nesting of graphs, such that a graph can be included in another graph as a single step for processing. In addition to the user interface components, the system includes a set of .NET classes that represent the graph internally. These classes provide the internal system representation of the graphical user interface. The system includes a graph execution component that reads the internal representation of the graph (as described above) and executes that graph. The execution of the graph follows the interpreted model of execution in that each node is traversed and executed from the original internal representation. In addition, there are components that allow external code elements, such as algorithms, to be easily integrated into the system, thus making the system infinitely expandable.

Alphonso, Keith↗
NASA NTRS2016

Faint-Source-Star Planetary Microlensing: The Discovery of the Cold Gas-Giant Planet OGLE-2014-BLG-0676Lb

We report the discovery of a planet OGLE-2014-BLG-0676Lb via gravitational microlensing. Observations for the lensing event were made by the following groups: Microlensing Observations in Astrophysics; Optical Gravitational Lensing Experiment; Wise Observatory; RoboNETLas Cumbres Observatory Global Telescope; Microlensing Network for the Detection of Small Terrestrial Exoplanets; and -FUN. All analyses of the light-curve data favoura lens system comprising a planetary mass orbiting a host star. The most-favoured binary lens model has a mass ratio between the two lens masses of (4.78 +/- 0.13) 10(exp -3). Subject to some important assumptions, a Bayesian probability density analysis suggests the lens system comprises a 3.09(+1.02/-1.12) MJ planet orbiting a 0.62(+0.20/-0.22) solar mass host star at a deprojected orbital separation of 4.40(+2.16/-1.46) au. The distance to the lens system is 2.22(+0.96/-0.83) kpc. Planet OGLE-2014-BLG-0676Lb provides additional data to the growing number of cool planets discover redusing gravitational microlensing against which planetary formation theories may be tested. Most of the light in the baseline of this event is expected to come from the lens and thus high-resolution imaging observations could confirm our planetary model interpretation.

OGLE-2014-BLG-0676Lb↗
NASA NTRS2017

Observations, Modeling, and Predictions of Solar Activity From the Deep Interior to the Corona

Solar activity is a manifestation of magnetic self-organization processes that involve complex dynamical coupling of various layers of the Sun acting over a broad range of spatial and temporal scales. Synergy of observational, theoretical, and modeling efforts is key to understanding solar activity variation, dynamics, and evolution and to developing reliable physics-based forecasts of long-term solar cycles and short-term activity manifestations, “seasons of solar activity”, such as periods of enhanced flaring and CME activity. The session welcomes observers, modelers, and theoreticians to share their results and ideas and to discuss current challenges, development of emerging fields (such as data assimilation), and the analysis of historical and modern observational data using theoretical modeling, interpretations, and predictions.

Deep Interior↗