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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Grandparent Inference from Genetic Data: The Potential for Parentage-Based Tagging Programs to Identify Offspring of Hatchery Strays

Abstract Fisheries managers routinely use hatcheries to increase angling opportunity. Many hatcheries operate as segregated programs where hatchery-origin fish are not intended to spawn with natural-origin conspecifics in order to prevent potential negative effects on the natural-origin population. Currently available techniques to monitor the frequency with which hatchery-origin strays successfully spawn in the wild rely on either genetic differentiation between the hatchery- and natural-origin fish or extensive sampling of fish on the spawning grounds. We present a method to infer grandparent–grandchild trios by using only genotypes from two putative grandparents and one putative grandchild. We developed estimators of false-positive and false-negative error rates and showed that genetic panels containing 500–700 single-nucleotide polymorphisms or 200–300 microhaplotypes are expected to allow application of this technique for monitoring segregated hatchery programs. We discuss the ease with which this technique can be implemented by pre-existing parentage-based tagging programs and provide an R package that applies the method.

Delomas, Thomas A. (ORCID:000000015154759X)↗
DOE OSTI2016

Oxygen potential measurement and point defect chemistry of UO{sub 2}

Many studies on the oxygen potential of UO{sub 2} have been carried out so far. However, the oxygen potential data for UO{sub 2} near the stoichiometric composition in the high temperature region (1673-1873 K) are limited. Recently, Kato's group had investigated oxygen potential of (U, Pu)O{sub 2} as functions of Pu content, oxygen-to-metal (O/M) ratio and temperature. They evaluated the data based on defect chemistry. The UO{sub 2} data are fundamental to evaluate oxygen potential in UO{sub 2}-PuO{sub 2} solid solution. In this work, the oxygen potential data of UO{sub 2+x} were extended to the high temperature range of 1673-1873 K by gas equilibrium method. The measured data were analyzed based on a defect chemistry model. Kroeger-Vink's diagram were obtained in UO{sub 2+x}. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Supplementary spatial data for Available Land for Cellulosic Biofuel Production: A Supply Chain Centered Comparison

Collection of the underlying spatially explicit data for Available Land for Cellulosic Biofuel Production: A Supply Chain Centered Comparison. Includes raw biomass yield data and soil carbon sequestration potential data for three types of marginal land for the USA midwest at the field level including field areas. Collection also includes raw land rasters for the three types of marginal land, model parameters for the MILP model used in the study, and results used to generate the figures in the paper.

bioenergy landscape design↗
DOE OSTI2016

Quantitative Infrared Absorption Spectra and Vibrational Assignments of Crotonaldehyde and Methyl Vinyl Ketone Using Gas-Phase Mid-Infrared, Far-Infrared, and Liquid Raman Spectra: s-cis vs s-trans Composition Confirmed via Temperature Studies and ab Initio Methods

Methyl vinyl ketone (MVK) and crotonaldehyde are chemical isomers; both are also important species in tropospheric chemistry. We report quantitative vapor-phase infrared spectra of crotonaldehyde and MVK vapors over the 540-6500 cm-1 range. Vibrational assignments of all fundamental modes are made for both molecules based on far- and mid-infrared vapor-phase spectra, liquid Raman spectra, along with density functional theory and ab initio MP2 and high energy-accuracy compound theoretical models (W1BD). Theoretical results indicate that at room temperature the crotonaldehyde equilibrium mixture is approximately 97% s-trans and only 3% s-cis conformer. Nearly all observed bands are thus associated with the s-trans conformer, but a few appear to be uniquely associated the s-cis conformer, notably ν16c at 730.90 cm-1, which displays a substantial intensity increase with temperature (62% upon going from 5 to 50 oC). The intensity of the corresponding mode of the s-trans conformer decreases with temperature. Under the same conditions, the MVK equilibrium mixture is approximately 69% s-trans conformer and 31% s-cis. W1BD calculations indicate that for MVK this is one of those (rare) cases where there are comparable populations of both conformers, ~doubling the number of observed bands and exacerbating the vibrational assignments. We uniquely assign the bands associated with both the MVK s-cis conformer as well as those of the s-trans, thus completing the vibrational analyses of both conformers from the same set of experimental spectra. Integrated band intensities are reported for both molecules along with global warming potential values. Using the quantitative IR data, potential bands for atmospheric monitoring are also discussed.

Lindenmaier, Rodica↗
DOE OSTI2025

The Carbon Storage Technical Viability Approach (CS TVA)

The Carbon Storage Technical Viability Approach (CS TVA) StoryMap provides an in-depth overview of the products created during the CS TVA research effort. In detail, the StoryMap addresses the CS TVA Matrix, Database Version 2.0, Database Catalog, Workflow, and Data Availability Result Database, discussing how each was developed and implemented. Information on how the matrix, database, and database catalog are interconnected, and their usage is also explained. The workflow section provides information on the CS TVA product development from the data-gathering stage to the final data availability results. A section on an expansion of the CS TVA workflow that utilizes Natural Language processing (NLP) section was included. Finally, the Data Availability Results Database is discussed. These results provide data science-informed insights into potential data gaps when assessing the viability of carbon storage in a given area or region.

Carbon Storage↗
DOE OSTI2020

Constraining the Milky Way Mass Profile with Phase-space Distribution of Satellite Galaxies

We estimate the Milky Way (MW) halo properties using satellite kinematic data including the latest measurements from Gaia DR2. With a simulation-based 6D phase-space distribution function (DF) of satellite kinematics, we can infer halo properties efficiently and without bias, and handle the selection function and measurement errors rigorously in the Bayesian framework. Applying our DF from the EAGLE simulation to 28 satellites, we obtain an MW halo mass of $M={1.23}_{-0.18}^{+0.21}\times {10}^{12}{M}_{\odot }$ and a concentration of $c={9.4}_{-2.1}^{+2.8}$ with the prior based on the M–c relation. The inferred mass profile is consistent with previous measurements but with better precision and reliability due to the improved methodology and data. Potential improvement is illustrated by combining satellite data and stellar rotation curves. Using our EAGLE DF and best-fit MW potential, we provide much more precise estimates of the kinematics for those satellites with uncertain measurements. Compared to the EAGLE DF, which matches the observed satellite kinematics very well, the DF from the semi-analytical model based on the dark-matter-only simulation Millennium II (SAM-MII) over-represents satellites with small radii and velocities. We attribute this difference to less disruption of satellites with small pericenter distances in the SAM-MII simulation. Finally, by varying the disruption rate of such satellites in this simulation, we estimate a ~5% scatter in the inferred MW halo mass among hydrodynamics-based simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Characterizing the performance of node-aware strategies for irregular point-to-point communication on heterogeneous architectures

Supercomputer architectures are trending toward higher computational throughput due to the inclusion of heterogeneous compute nodes. These multi-GPU nodes increase on-node computational efficiency, while also increasing the amount of data to be communicated and the number of potential data flow paths. In this work, we characterize the performance of irregular point-to-point communication with MPI on heterogeneous compute environments through performance modeling, demonstrating the limitations of standard communication strategies for both device-aware and staging-through-host communication techniques. Presented models suggest staging communicated data through host processes then using node-aware communication strategies for high inter-node message counts. Notably, the models also predict that node-aware communication utilizing all available CPU cores to communicate inter-node data leads to the most performant strategy when communicating with a high number of nodes. Furthermore, model validation is provided via a case study of irregular point-to-point communication patterns in distributed sparse matrix–vector products. Importantly, we include a discussion on the implications model predictions have on communication strategy design for emerging supercomputer architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Data for Optimization of Pre-Commercial Enzymes Dosage for a Potential Lignocellulosic Biorefinery

Lignocellulolytic enzymes remain one of the primary cost constraints in second-generation (2G) ethanol biorefineries. Achieving efficient hydrolysis of structural carbohydrates with minimal enzyme dosage, maintaining slurry fermentability for industrially relevant ethanol titers, and maximizing ethanol yield per ton of biomass are among the major challenges in 2G processes. In this study, we optimized the dosages of pre-commercial cellulase (NS22257) and hemicellulase (NS22244) on pilot-scale, hydrothermally pretreated lignocellulosic substrates. Enzyme dosages were evaluated at three levels: 20 mg of cellulase with 7.25 mg of hemicellulase (ED-1), 40 mg with 14.5 mg (ED-2), and 60 mg with 21.75 mg (ED-3). As expected, the highest sugar yields were obtained with ED-3; however, for sweet sorghum, oilcane, and miscanthus, sugar yields from ED-2 and ED-3 were not significantly different (p < 0.05). For example, sweet sorghum produced 123.78 ± 1.54 g L−1 and 125.76 ± 0.46 g L−1 of total sugars (glucose and xylose) with ED-2 and ED-3, respectively. Although energycane exhibited a statistically significant difference between ED-2 and ED-3, the incremental gain with ED-3 was modest, increasing sugar release by only 9.02 g L−1 relative to ED-2. Importantly, ED-1 resulted in sugar yields of 88.88 ± 3.64 to 106.86 ± 1.21 g L−1, sufficient to achieve ethanol titers ≥40 g L−1, the threshold required for industrial relevance. A semi-integrated bioprocess validated this outcome, producing 42.09 ± 2.38 g L−1 ethanol and an estimated yield of 213.38 L of ethanol per dry ton of pretreated biomass, requiring only 20.83 L of cellulase and 6.25 L of hemicellulase per ton. Remarkably, these enzyme dosages were approximately tenfold lower than those reported in prior studies.

Energycane↗
DOE OSTI2026

FIRM: federated image reconstruction using multimodal tomographic data

Here, we propose a federated algorithm for reconstructing images using multimodal tomographic data sourced from dispersed locations, addressing the challenges of traditional unimodal approaches that are prone to noise and reduced image quality, as well as the limitations of centralized multimodal approaches that require extensive data transfer, leading to significant communication overhead, storage demands, and potential data privacy concerns. Our approach formulates a joint inverse optimization problem incorporating multimodality constraints and solves it in a federated framework through local gradient computations complemented by lightweight central operations, thereby ensuring data decentralization. Leveraging the connection between our federated algorithm and the quadratic penalty method, we introduce an adaptive step-size rule with guaranteed sublinear convergence. Numerical results demonstrate superior computational efficiency and improved image reconstruction quality compared to existing approaches.

federated algorithm↗
DOE OSTI2024

Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.

asset health management↗
DOE OSTI2025

47 Tuc in Rubin Data Preview 1. Exploring Early LSST Data and Science Potential

We present analyses of the early data from Rubin Observatory’s Data Preview 1 (DP1) for the field of the globular cluster 47 Tuc. The DP1 data set for 47 Tuc includes four nights of observations from the Rubin Commissioning Camera (LSSTComCam), covering multiple bands (ugriy). We address challenges of crowding in the inner region of the cluster and toward the SMC in DP1, and demonstrate improved star–galaxy separation by fitting fifth-degree polynomials to the stellar loci in color–color diagrams and applying multidimensional sigma clipping. We compile a catalog of 3576 probable 47 Tuc member stars selected via a combination of isochrone, Gaia proper-motion, and color–color space matched filtering. We explore the sources of photometric scatter in the 47 Tuc color–color sequence, evaluating contributions from various potential sources, including differential extinction within the cluster. Finally, of the 72 well-characterized variables in the field, we recover three known variable stars, including two RR Lyrae and one eclipsing binary, in the coadd-based object catalog, and identify 62 in the difference image-based object catalog. Although the DP1 lightcurves have sparse temporal sampling, they appear to follow the patterns of densely sampled literature lightcurves well. Despite some data limitations for crowded-field stellar analysis, DP1 demonstrates the promising scientific potential for future LSST data releases.

Choi, Yumi [NSF National Optical-Infrared Astronom↗
DOE OSTI2020

Event Definition for the Automated Detection of Nuclear Proliferation Activities

In FY2020, Savannah River National Laboratory (SRNL) in collaboration with the Discovery Analytics Center (DAC) at Virginia Polytechnic Institute and State University (VT) began developing a demonstration prototype system that uses multiple machine learning and data analytic methods on largescale open data sources to identify new, developing, or undeclared nuclear programs. One of the most challenging aspects of applying machine learning techniques to such a problem is the high likelihood of extremely sparse data from disparate sources. To overcome this challenge, the current work will use a strategic combination of supervised, semi-supervised, and unsupervised learning techniques to ingest and fuse data streams to make a forecast of nuclear activities in a targeted geospatial location. Identifying potential data sources and training supervised learning algorithms is dependent upon the development of a robust foundation of targeted event domains that fundamentally define the nuclear activities of interest. This report documents the definition of a hierarchical structure for both nuclear activity and event domains that will be used to guide the research team in development or use of existing semantic dictionaries that are instrumental to searching, parsing, and categorizing events for the forecasting system’s use.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Digital Twin Empowered PV Power Prediction

The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper proposes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical data set, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long-short-term memory model) for capturing the hidden spatial-temporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real data set is conducted to access the effectiveness of the proposed method.

14 SOLAR ENERGY↗
DOE OSTI2022

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich data challenges existing data storage and access technologies, therefore precluding informatics advancements. Here we present MZA™ (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed by a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and efficient programmatic access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗