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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Utility of Physiologically Based Pharmacokinetic Modeling to Investigate the Impact of Physiological Changes of Pregnancy and Cancer on Oncology Drug Pharmacokinetics

The treatment of cancer during pregnancy remains challenging with knowledge gaps in drug dosage, safety, and efficacy due to the under-representation of this population in clinical trials. Our aim was to investigate physiological changes reported in both pregnancy and cancer populations into a PBPK modeling framework that allows for a more accurate estimation of PK changes in pregnant patients with cancer. Methods: Paclitaxel and docetaxel were selected to validate a population model using clinical data from pregnant patients with cancer. The validated population model was subsequently used to predict the PK of acalabrutinib in pregnant patients with cancer. Results: The Simcyp pregnancy population model reasonably predicted the PK of docetaxel in pregnant patients with cancer, while a modified model that included a 2.5-fold increase in CYP2C8 abundance, consistent with the increased expression during pregnancy, was needed to reasonably predict the PK of paclitaxel in pregnant patients with cancer. Changes in protein binding levels of patients with cancer had a minimal impact on the predicted clearance of paclitaxel and docetaxel. PBPK modeling predicted approximately 60% lower AUC and Cmax for acalabrutinib in pregnant versus non-pregnant patients with cancer. Conclusions: Our results suggest that PBPK modeling is a promising approach to investigate the effects of pregnancy and cancer on the PK of oncology drugs and potentially inform dosing for pregnant patients with cancer. Further evaluation and refinement of the population model are needed for pregnant patients with cancer with additional compounds and clinical PK data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data

In this work, we propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized in an image. Using an out-of-the-box implementation of Mask R-CNN, instance segmentation is applied to images of metal powder particles produced through gas atomization. Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images. As well as providing another method for measuring the particle size distribution, we demonstrate the first direct measurements of the satellite content in powder samples. After analyzing the results for the labeled data dataset, the trained model was used to generate measurements for a much larger set of unlabeled images. The resulting particle size measurements showed reasonable agreement with laser scattering measurements. The satellite measurements were self-consistent and showed good agreement with the expected trends for different samples. Finally, we present a small case study showing how instance segmentation can be used to measure spheroidite content in the UltraHigh Carbon Steel DataBase, demonstrating the flexibility of the technique.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Measurement of free chlorine levels in water using potentiometric responses of biofilms and applications for monitoring and managing the quality of potable water

We report residual free chlorine is not monitored continuously at scale in drinking water distribution systems because existing real-time sensor technologies require frequent maintenance, cleaning, and calibration, which makes these products too costly to be used throughout a distribution system. As a result, current measurement approaches require manual sampling, which is not feasible for the consistent monitoring of free chlorine because chlorine concentrations vary significantly throughout pipeline distribution and over time and space. This research presents an alternative and cost-effective method of predicting free chlorine levels in drinking water using graphite electrodes coated with naturally grown microbial biofilms. This Microbial Potentiometric Sensor (MPS) array was installed in a Continuously Mixed Batch Reactor (CMBR), and drinking water containing variable free chlorine concentrations. The chlorine concentrations were introduced in a controlled manner, and the MPS signals were monitored over time. MPS signals were measured from the change in Open Circuit Potential (OCP) across the MPS array in real-time. An empirically derived relationship between the normalized change in OCP and free chlorine was established by fitting individual and average MPS data to a decaying exponential growth function in order to predict free chlorine levels. The results show that free chlorine can be predicted with reasonable accuracy, with model validation showing an average absolute error of ±0.09 ppm below 1.1 ppm and ±0.30 ppm between 1.1 and 2.7 ppm. However, the accuracy of predictions was reduced at higher free chlorine levels. The researchers conclude that MPS systems may benefit drinking water distribution systems by measuring free chlorine. These advantages of the MPS are especially pronounced in the developing world because this system is inexpensive and does not require routine maintenance or cleaning. The system relies on a naturally forming and regenerating biofilm and an inexpensive potentiometric meter to produce stable measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Insights into Constraining Rate Coefficients in Fuel Oxidation Mechanisms Using Genetic Algorithm Optimization

Accurate fuel oxidation mechanisms can enable predictive capabilities that aid in advancing combustion technologies. High-level computational kinetics can yield reasonable rate coefficients with uncertainties, in some cases, below a factor of 2. Computed rate coefficients can be constrained further by optimizing against experimental data. Here, we explore the application of genetic algorithm (GA) optimization to constrain computed rate coefficients in complex fuel oxidation mechanisms in conjunction with temperature-dependent species mole fractions from jet-stirred reactor (JSR) measurements. Cyclohexane is a model candidate for understanding the reactivity of cyclic fuels. In this work, we optimize the rate coefficients of the most recent literature cyclohexane mechanism, which incorporates theoretically computed rate coefficients for the reaction networks stemming from the first and second O 2 addition pathways, against the experimental results of two separate literature JSR studies. Optimization consistency is evaluated by carrying out three GA optimizations: fitting to the temperature-dependent species mole fractions in each JSR experiment separately and simultaneously fitting the species mole fractions in both experiments. Local sensitivity analyses are used to identify five influential low-temperature oxidation reactions for optimization. Although the three optimizations do not yield identical rate coefficients, the direction of change in all five rate coefficients is consistent among the three optimizations. Performance of the models from the three optimizations is assessed against literature ignition delay times with differences in the level of agreement observed among the different optimizations. In conclusion, comparisons are made with our recent optimization work of a cyclopentane oxidation master-equation model against time-resolved species concentrations, and insights and improvements of the strategy for constraining rate coefficients using GA optimization are discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

An infrared, Raman, and X-ray database of battery interphase components

Further improvements to lithium-ion and emerging battery technologies can be enabled by an improved understanding of the chemistry and working mechanisms of interphases that form at electrochemically active battery interfaces. However, it is difficult to collect and interpret spectra of interphases for several reasons, including the presence of a variety of compounds. To address this challenge, we herein present a vibrational spectroscopy and X-ray diffraction data library of ten compounds that have been identified as interphase constituents in lithium-ion or emerging battery chemistries. The data library includes attenuated total reflectance Fourier transform infrared spectroscopy, Raman spectroscopy, and X-ray diffraction data, collected in inert atmospheres provided by custom sample chambers. The data library presented in this work (and online repository) simplifies access to reference data that is otherwise either diffusely spread throughout the literature or non-existent, and provides energy storage researchers streamlined access to vital interphase-relevant data that can accelerate battery research efforts.

25 ENERGY STORAGE↗
DOE OSTI2026

A procedure for rule extraction from a Self-Organising plasma disruption predictor for JET

In a previous paper, a Self-Organizing Map had proven to be able to identify the regions of the plasma operative space characterizing the pre-disruptive phase at JET without relying on any a priori information. One of the strengths of this disruption predictor lies in its inherent self-organization capability. The Self-Organizing Map discovers non-trivial relationships and captures the complicated interplay of device diagnostics on the internal plasma states directly from the experimental data. Moreover, the provided model allows the visualization of high-dimensional plasma parameters and facilitates easy interrogation of the model to understand the reasons behind its correlations. In this paper, an additional step is taken towards the interpretability of models for predicting disruptions by training a Decision Tree to classify the plasma states according to the interpretation provided by the Self-Organizing Map (stable or at high risk of disruptions). The Decision tree provides a set of rules which describe the transition of the plasma towards the pre-disruptive phase as visualized in the Self-Organizing Map. The obtained rules for the database explored in the study identify four regions in the map, two of which are at risk of disruption. These regions correspond to partitions of a 3D space based on the peaking factors of the core and divertor radiation, as well as the Locked Mode. The agreement between the Self-Organizing Map answers and the rules supplied by the Decision Tree is confirmed by the comparison of the performance exhibited by the two models in the prediction of disruptions.

Setzu, Samuele [Univ. of Cagliari, Monserrato, Cag↗
DOE OSTI2021

Comparative phylogenetics of repetitive elements in a diverse order of flowering plants (Brassicales)

Genome sizes of plants have long piqued the interest of researchers due to the vast differences among organisms. However, the mechanisms that drive size differences have yet to be fully understood. Two important contributing factors to genome size are expansions of repetitive elements, such as transposable elements (TEs), and whole-genome duplications (WGD). Although studies have found correlations between genome size and both TE abundance and polyploidy, these studies typically test for these patterns within a genus or species. The plant order Brassicales provides an excellent system to further test if genome size evolution patterns are consistent across larger time scales, as there are numerous WGDs. This order is also home to one of the smallest plant genomes, Arabidopsis thaliana—chosen as the model plant system for this reason—as well as to species with very large genomes. With new methods that allow for TE characterization from low-coverage genome shotgun data and 71 taxa across the Brassicales, we confirm the correlation between genome size and TE content, however, we are unable to reconstruct phylogenetic relationships and do not detect any shift in TE abundance associated with WGD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Gas‐Phase Ion‐Molecule Interactions in a Collision Reaction Cell with ICP‐MS / MS : Investigations with CO 2 as the Reaction Gas

Carbon dioxide (CO 2 ) was used as a reaction gas to investigate the gas‐phase ion‐molecule interactions using the Agilent 8900 ICP‐MS/MS. A solution containing forty‐five elements representative of the periodic table was used to supply the ions to react with CO 2 in the collision/reaction cell (CRC). The only significant product ions formed were monoxides. The general reactivity was shown to be consistent with density functional theory (DFT)‐predicted reaction enthalpies, such that all predicted exothermic reactions produced product ions at levels of at least 1% of the unreacted ion. Most endothermic reactions observed had sufficient kinetic energy in excess of the reaction enthalpies. Our results suggest that reaction enthalpy is a reasonable predictor of reactivity with CO 2 on the timescales of the interactions in non‐thermal ICP‐MS/MS systems. The ease and rapidity of data collection with the ICP‐MS/MS and DFT calculations using the NWChem suite has value given the scarcity of thermochemical data of CO 2 reactions in the literature. These studies are especially useful for the identification of targeted reaction chemistries to be leveraged for analytical method development, such as for the inline separation of isobaric interferences from analytes of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2021

CO 2 Storage Site Screening Platform Development and CO 2 Storage Resource Analysis in SECARB Offshore Reservoirs Using SAS Viya

A major goal of the SECARB Offshore Partnership (DE-FE0031557) is to screen deep saline aquifers and hydrocarbon reservoirs in the central Gulf of Mexico for CO 2 sequestration and CO 2 -enhanced oil and gas recovery (EOR/EGR) and estimate the corresponding CO 2 storage resources for select reservoirs. CO 2 storage potential associated with offshore CO 2 -EOR is considerable and likely represents “low hanging fruit” for near-term CO 2 storage given the in-place infrastructure in the region. It is for these reasons that this assessment focuses on oil and gas fields. To this end, three major objectives have been completed and include (1) managing geological data derived from different sources, (2) building a reservoir screening platform for CO 2 storage, and (3) ranking the reservoirs based on the estimated CO 2 storage resources. The SAS ® Viya platform was used for data management and analytics. The Viya platform is a cloud service platform that provides data integration, data management, quick analytics, data visualization, machine learning functions, and application programming interfaces (APIs) for multi-programming languages. Different sources of data containing geologic information, reservoir properties, and EOR/EGR information were collected, cleaned, formatted, and loaded into the SAS ® Viya platform for evaluation. The major geological characteristics of both shelf and deep-water areas of the central Gulf were examined and compared to define the appropriate reservoir screening criteria. Next, a CO 2 storage site screening system was built in the SAS ® Viya platform with the pre-defined criteria. Finally, the CO 2 storage resources of the screened reservoirs were calculated and reported at the BOEM field level to identify fields with the highest estimated CO 2 storage resource. The fields with the largest total estimated CO 2 storage resource are located in the Mississippi Canyon protraction area. Due to proximity to the Mississippi Delta (indicative of less infrastructure) and large estimated CO 2 storage resources, future development activities may wish to focus efforts in the Mississippi Canyon protraction area.

02 PETROLEUM↗
DOE OSTI2022

Gamma-ray spectra analyses of molten salts in spent nuclear fuels pyroprocessing facilities for mass measurement

To evaluate radioactive tracer dilution and gamma spectroscopy as a safeguards technique for monitoring the mass of liquid salt in molten salt systems, the gamma spectroscopy data of salt samples from the pyroprocessing facilities in Idaho National Laboratory were acquired and analyzed. The primary focus is the uncertainty analysis of the gamma radioactivity of 154 Eu isotope in the molten salts. The facilities include an electrorefiner for pyroprocessing spent oxide fuels (SOF-ER) and Mark-IV ER for processing the metallic fuels from Experimental Breeder Reactor-II (EBR-II). The relative gamma radioactivity uncertainty was consistently at 3% for 137 Cs for both Mark-IV ER and SOF-ER salts. However, for 154 Eu, it was 8% for SOF-ER salt and 7% for Mark-IV ER salt. Here, the main reason for the higher uncertainty for 154 Eu is believed to be the lower counting statistics in gamma spectroscopy. To improve the gamma radioactivity uncertainty of isotope 154 Eu, a longer data acquisition time (12 h instead of 4 h) and a higher count rate gamma detector for gamma spectroscopy were tested and it was found that the uncertainty for 154 Eu was significantly improved from 7% or 8–3%.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecasting model (HWRF) model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-based three-dimensional ensemble-variational (GSI-3DEnVar) hybrid data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble Kalman filtering data assimilation system. In addition, our proposed initialization framework discards the empirical intensity correction in the vortex initialization package that is employed by the GSI-3DEnVar initialization framework in operational HWRF. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, minimum sea level pressure (MSLP), and maximum surface wind (MSW) forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. With assimilating high-resolution observations within the hurricane inner-core region, GSI-4DEnVar can produce the initial hurricane intensity reasonably well without the empirical vortex intensity correction. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Fog particle concentrations

ngVIPS metadata and caveats: Column 1: Time, seconds after midnight UTC of beginning of time period. (60 sec values) Column 2: Relative concentration. Notes: Concentration values are observed values and assume ~4.0 m/s wind speed. (#/cm^3) Caveats: Observed particles with a very few exceptions were on the order of a few pixels (less than 10 microns). The size differential was insufficient to determine a particle size distribution. As the temperatures were relatively warm (and it can be 10C warmer inside the instrument) it is anticipated that some solid ice particles could have melted and some smaller liquid drops could have evaporated. A cyclic nature to the high resolution data was observed and determined to be caused by a focus issue. Each 60 second period included ~25 images. Particles are collected on a glass disk, but the disk isn't perfectly centered causing small particles to be slightly out of focus during part of the rotation. For that reason, we chose to report the average of the five highest values in a time period rather the average of all values. Higher resolution can be made available upon request, but a conversation with the PI would be important to fully understand the planned usage and if the higher resolution data were to be useful for the task. Please contact instrument PI, Carl Schmitt (naturalsystemsresearch@gmail.com) with any questions about these data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Announcing the Biomedical Data Translator: Initial Public Release

ABSTRACT The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open‐source knowledge graph–based system—Translator—designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real‐world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

Research & Experimental Medicine↗
DOE OSTI2021

Fractal dimension of optical cirrus in Stripe82

ABSTRACT The geometric characteristics of dust clouds provide important information on the physical processes that structure such clouds. One of such characteristics is the 2D fractal dimension D of a cloud projected on to the sky plane. In previous studies, which were mostly based on infrared (IR) data, the fractal dimension of individual clouds was found to be in a range from 1.1 to 1.7 with a preferred value of 1.2–1.4. In this work, we use data from Stripe82 of the Sloan Digital Sky Survey to measure the fractal dimension of the cirrus clouds. This is done here for the first time for optical data with significantly better resolution as compared to IR data. To determine the fractal dimension, the perimeter-area method is employed. We also consider IR (IRAS and Herschel) counterparts of the corresponding optical fields to compare the results between the optical and IR. We find that the averaged fractal dimension across all clouds in the optical is $\langle D \rangle =1.69^{+0.05}_{-0.05}$ which is significantly larger than the fractal dimension of its IR counterparts $\langle D\rangle =1.38^{+0.07}_{-0.06}$. We examine several reasons for this discrepancy (choice of masking and minimal contour level, image and angular resolution, etc.) and find that for approximately half of our fields the different angular resolution (point spread function) of the optical and IR data can explain the difference between the corresponding fractal dimensions. For the other half of the fields, the fractal dimensions of the IR and visual data remain inconsistent, which can be associated with physical properties of the clouds, but further physical simulations are required to prove it.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The edge-on Galaxies in the Pan-STARRS survey (EGIPS)

ABSTRACT We present a catalogue of 16 551 edge-on galaxies created using the public DR2 data of the Pan-STARRS survey. The catalogue covers the three quarters of the sky above Dec. = −30°. The galaxies were selected using a convolutional neural network, trained on a sample of edge-on galaxies identified earlier in the SDSS survey. This approach allows us to dramatically improve the quality of the candidate selection and perform a thorough visual inspection in a reasonable amount of time. The catalogue provides homogeneous information on astrometry, SExtractor photometry, and non-parametric morphological statistics of the galaxies. The photometry is reliably for objects in the 13.8–17.4 r-band magnitude range. According to the HyperLeda data base, redshifts are known for about 63 per cent of the galaxies in the catalogue. Our sample is well separated into the red sequence and blue cloud galaxy populations. The edge-on galaxies of the red sequence are systematically Δ(g − i) ≈ 0.1 mag redder than galaxies oriented at an arbitrary angle to the observer. We found a variation of the galaxy thickness with the galaxy colour. The red sequence galaxies are thicker than the galaxies of the blue cloud. In the blue cloud, on average, thinner galaxies turn out to be bluer. In the future, based on this catalogue it is intended to explore the three-dimensional structure of galaxies of different morphologies, as well as to study the scaling relations for discs and bulges.

Makarov, D. (ORCID:0000000191103221)↗
DOE OSTI2021

The physics potential of a reactor neutrino experiment with Skipper CCDs: Measuring the weak mixing angle

We analyze in detail the physics potential of an experiment like the one recently proposed by the vIOLETA collaboration: a kilogram-scale Skipper CCD detector deployed 12 meters away from a commercial nuclear reactor core. This experiment would be able to detect coherent elastic neutrino nucleus scattering from reactor neutrinos, capitalizing on the exceptionally low ionization energy threshold of Skipper CCDs. To estimate the physics reach, we elect the measurement of the weak mixing angle as a case study. We choose a realistic benchmark experimental setup and perform variations on this benchmark to understand the role of quenching factor and its systematic uncertainties, background rate and spectral shape, total exposure, and reactor antineutrino flux uncertainty. We take full advantage of the reactor flux measurement of the Daya Bay collaboration to perform a data driven analysis which is, up to a certain extent, independent of the theoretical un- certainties on the reactor antineutrino flux. We show that, under reasonable assumptions, this experimental setup may provide a competitive measurement of the weak mixing angle at few MeV scale with neutrino-nucleus scattering.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Initial development of a high temperature life prediction method directly accounting for variability in material properties

This report presents the initial development of a life prediction method that accounts for the variability of the material properties in Grade 91 steel. We account for material variability by both fitting a variable 3-parameter Weibull distribution to the rupture data of the material and by quantifying the effect that the creep deformation properties have on stresses via a Monte Carlo approach. To ensure a reasonable time frame when applying Monte Carlo to the creep stress, we propose a methodology that models the material as an extremely viscous Stokes flow with a non-Newtonian viscosity, therefore solving the problem in a single step. We use the final probabilistic model for structural failure developed in this document to evaluate a Flat Head Vessel designed with the Section III, Division 5 ASME Boiler Pressure and Vessel Code rules with a 100,000h design life and assess its probability of premature failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

HYPHY: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics

Generating large-volume hydrodynamical simulations for cosmological observables is a computationally demanding task necessary for next-generation observations. In this work, we construct a novel fully convolutional variational autoencoder (VAE) to synthesize hydrodynamic fields conditioned on dark matter fields from N -body simulations. After training the model on a single hydrodynamical simulation, we are able to probabilistically map new dark-matter-only simulations to corresponding full hydrodynamical outputs. By sampling over the latent space of our VAE, we can generate posterior samples and study the variance of the mapping. We find that our reconstructed field provides an accurate representation of the target hydrodynamical fields as well as reasonable variance estimates. This approach has promise for the rapid generation of mocks as well as for implementation in a full inverse model of observed data.

79 ASTRONOMY AND ASTROPHYSICS↗