Search NASA⌕ Search

SEARCH · Search NASA

Results for “Resource Characterization”

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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Genetic dissection of natural variation in oilseed traits of camelina by whole‐genome resequencing and QTL mapping

Abstract Camelina [ Camelina sativa (L.) Crantz] is an oilseed crop in the Brassicaceae family that is currently being developed as a source of bioenergy and healthy fatty acids. To facilitate modern breeding efforts through marker‐assisted selection and biotechnology, we evaluated genetic variation among a worldwide collection of 222 camelina accessions. We performed whole‐genome resequencing to obtain single nucleotide polymorphism (SNP) markers and to analyze genomic diversity. We also conducted phenotypic field evaluations in two consecutive seasons for variations in key agronomic traits related to oilseed production such as seed size, oil content (OC), fatty acid composition, and flowering time. We determined the population structure of the camelina accessions using 161,301 SNPs. Further, we identified quantitative trait loci (QTL) and candidate genes controlling the above field‐evaluated traits by genome‐wide association studies (GWAS) complemented with linkage mapping using a recombinant inbred line (RIL) population. Characterization of the natural variation at the genome and phenotypic levels provides valuable resources to camelina genetic studies and crop improvement. The QTL and candidate genes should assist in breeding of advanced camelina varieties that can be integrated into the cropping systems for the production of high yield of oils of desired fatty acid composition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Gradient process parameter optimization in additive friction stir deposition of aluminum alloys

As one of the most novel additive manufacturing methods, currently selection and optimization of processing parameters in additive friction stir deposition (AFSD) have mainly relied on experiments and subsequent characterization of microstructural and mechanical properties. Such approaches are both time- and resource-consuming. Therefore, an ultrasound elastography enhanced gradient process parameter optimization method was applied in the present work to obtain a window of optimized processing parameters for AFSD processing of aluminum alloy by varying both rotational and linear deposition speeds. Further, the quality of AFSD processed layer was investigated for physical nature of surface, dynamic elastic modulus, and microstructural aspects in cross-sections of the deposited layer. The efficiency in exploring process parameters was significantly enhanced by implementing a high-throughput screening experimental design based on application of gradient process parameters and continuous ultrasound elastographs. In addition, the applied ultrasonic elastography technique assisted in evaluating the homogeneity in microstructure and mechanical properties of AFSD sample over the entire gradients of the process parameters. The techniques adopted in current work can be further extended to identify suitable parameters for AFSD fabrication of components with desired mechanical properties such as hardness, fatigue, etc.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Chlamydomonas CHT7 is involved in repressing DNA replication and mitotic genes during synchronous growth

In the green alga Chlamydomonas reinhardtii, regulation of the cell cycle in response to external cues is critical for survival in a changing environment. The loss of the nuclear COMPROMISED HYDROLYSIS OF TRIACYLGLYCEROLS 7 (CHT7) protein affects the expression of many genes especially in response to nitrogen availability. Cells lacking CHT7 exhibit abnormal cell morphology following nitrogen deprivation and fail to resume normal cell division after N resupply. To investigate the function of CHT7 in the regulation of cell cycle-related pathways, cells were synchronized, and RNA-seq analysis was performed during various stages of the cell cycle. In the cht7 mutant following nitrogen deprivation, the cells were not dividing, but a subset of cell cycle genes involved in DNA replication and mitosis were found to be derepressed, suggesting that the CHT7 protein plays a role in cell cycle regulation that is opposite to that of the mitotic cyclin-dependent kinases. Furthermore, genes for cell wall synthesis and remodeling were found to be abnormally induced in nondividing cht7 cells; this misregulation may deplete cellular resources and thus contribute to cell death following nitrogen deprivation. Lastly, 43 minimally characterized kinases were found to be highly misregulated in cht7. Further analysis suggested that some of these CHT7-regulated kinases may be related to the MAP3K and Aurora-like kinases, while others are unique. Together, these results suggest a role of CHT7 in transcriptional regulation of the cell cycle and reveal several pathways and genes whose expression appears to be subject to a CHT7-mediated regulatory network.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Characterization of Rare-Earth Elements in Lignite Coal of the Williston Basin: Past Efforts and Ongoing Work

Rare-earth elements (REEs) have been a subject area of high interest for their unique properties. REEs are crucial materials used in an incredible array of consumer goods, energy system components, and military defense applications. While the United States has one operating REE mine, the product is sent overseas for refining into usable metals making the United States 100% import-reliant on these critical materials. This has led the Federal Government to declare the REE market an issue of national security. The Energy and Environmental Research Center (EERC) under funding provided by the Department of Energy (DOE) has undertaken efforts to determine if the lignite coal found in the Williston Basin has the potential to be an ore body containing sufficient quantities of REEs and Critical Minerals (CM) for extraction and processing. In one such effort, the EERC collected over 400 samples from the Williston Basin lignite coal seams including outcrops as well as active mines. Those efforts have been followed up with ongoing work under the U.S. DOE’s Carbon Ore, Rare Earth and Critical Minerals Initiative (CORE-CM) currently ongoing in the Williston Basin as well as other basins in the United States. This ongoing work in the Williston Basin characterizing REEs in coal has focused on the collection of new sampling and analysis of REEs in coal and building upon previous characterization work. This information is being used to understand the spatial distribution of REEs through mapping and 3D modeling. The goals of these efforts are to better understand the mechanisms for distribution of the REEs in lignite coal as well as their concentration and determine knowledge gaps in characterization and to start to build the database required to understand the potential resource in the Williston Basin.

Feole, Ian K.↗
DOE OSTI2026

Real-Time Inference For MI/RR Deblending

The Fermilab Main Injector (MI) and Recycler Ring (RR) share a common beam loss monitor (BLM) system, making loss events difficult to attribute to their source machine when beam is present in both simultaneously. The Real-time Edge AI for Distributed Systems (READS) project addresses this by deblending BLM readings in real time using machine learning (ML). The current FPGA based implementation meets the sub-3 ms latency requirement but carries a resource intensive hls4ml development cycle, motivating exploration of GPU based deployment. This paper characterizes inference latency on an NVIDIA Jetson Orin Nano and introduces a packet organization scheme for assembling synchronized event frames from seven distributed BLM DAQ streams. Using a Python based DAQ simulation with injected timing jitter in place of unavailable live beam data, the pipeline achieved an average end to end latency of 0.456 ms (σ = 0.122 ms) across 167,000 test frames, comfortably meeting the timing constraint. Early outliers were attributed to TensorRT warm-up rather than steady state limitations, suggesting GPU based inference is a viable alternative to the existing FPGA implementation.

Yu, Kellen [Cornell U.]↗
DOE OSTI2022

Impact of Tree Age and Anatomical Fraction on Forest Residue Pyrolysis and Hydrotreating

Utilization of cost-advantaged biomass and waste resources to produce clean, domestic biofuels will be a key factor in decarbonizing transportation in the United States. This study investigates how the distribution of anatomical fractions (stem wood, bark, needles, branches) from pine forest residues of different ages impacts pyrolysis and hydrotreating processes, including product composition, carbon efficiency, economics, and overall sustainability. Commercially sourced 13- and 23-year-old loblolly pine residues were chosen to represent available resources; whole tree thinnings, and tops and branches from merchantable timber, respectively. Detailed characterization of the residue samples, product distributions, bio-oil, char, and gas characterization data are presented for 13 experiments, including the individual fractions, blends, and air-classified samples to reduce ash content. Pyrolysis mass balances averaged 97 +/- 3%. As expected, bio-oil yields were lower for the bark, needles, and blends with high proportions of these (53-56% dry basis), and highest for the debarked stem wood (67-71% dry basis). Lower yields were attributed to the high ash content of the needles, but also to the high lignin content in the bark (>50%), which is hypothesized to have led to increased char formation during pyrolysis. Higher-oil yield was generally correlated with total volatiles, hydrogen, oxygen, glucan, and mannan; somewhat correlated with xylan and acetyl content; and negatively correlated with total carbon, fixed carbon, nitrogen, sulfur, and extractives. Air classification of the residues was effective in reducing ash content (1.5% to 0.64%), extractives (~7% to 2%), and sulfur (0.04% to 0.02%), resulting in a small increase in yield, a significant increase in the GC-detectable proportion of oil (27% to 34%) and a net economic benefit. These experimental results were used to conduct technoeconomic and life cycle analyses, which are also summarized here.

biofuels↗
DOE OSTI2023

From Well Log to Formation Model: A Novel Laboratory Calibrated Methodology with Demonstration

This work demonstrates how the characterization and modeling of both elastic and creep properties are essential to describe zones in layered rock formations as either low-stress targets for stimulation or high-stress barriers to fracture growth. Prediction of fracture height is critical for designing stimulation operations in oil and gas wells. Ideally, fractures are placed in target zones which will produce hydrocarbons and should not propagate into zones expected to be unproductive or to produce unwanted fluids such as water which in turn must be treated and/or disposed. The essential task in designing stimulation plans is predicting which zones have low horizontal stresses and which will be high-stress barriers to fracture growth. Despite this importance, there are gaps in current knowledge and a complete workflow from laboratory characterization to a finite element model which includes time dependent rock deformation is required. While the research and methodology presented here also have application to CO 2or hydrogen storage, wastewater injection, and geothermal applications, the focus will be on hydrocarbon extraction. This thesis presents the results of a characterization-to-prediction workflow for the Caney shale, which is an emerging hydrocarbon resource in Oklahoma, USA. It begins with an investigation to enable critical evaluation of the Caney zonation into nominally “brittle” and “ductile” zones based on properties observed from well logs. It shows none of the zones are consistently “brittle” or “ductile” mechanical behavior based on the variety of definitions of these terms. However, the nominally ductile zones are weaker and more prone to creep. A laboratory investigation of samples including strength, elastic, and creep properties, is then used in a finite element model of stress evolution. The model includes both elastic deformation and viscoplastic creep. Results predict the least creep-prone layers to have the lowest horizontal stresses, therefore comprising hydraulic fracturing targets. The most creep-prone layers attain a horizontal stress similar to the vertical stress and therefore are predicted to be high stress barriers to hydraulic fracture stimulation. In addition to defining stimulation target intervals, the model shows how as tectonic strain rate increases, there is a transition from creep-dominated stresses to stresses dominated by elasticity.

Benge, Margaret↗
DOE OSTI2026

Exploration of Real Time Inference for MI-RR Deblending on GPU/TPU Systems

The Fermilab Main Injector (MI) and Recycler Ring (RR) share a common beam loss monitor (BLM) system, making loss events difficult to attribute to their source machine when beam is present in both simultaneously. The Real-time Edge AI for Distributed Systems (READS) project addresses this by deblending BLM readings in real time using machine learning (ML). The current FPGA based implementation meets the sub-3 ms latency requirement but carries a resource intensive hls4ml development cycle, motivating exploration of GPU based deployment. This paper characterizes inference latency on an NVIDIA Jetson Orin Nano and introduces a packet organization scheme for assembling synchronized event frames from seven distributed BLM DAQ streams. Using a Python based DAQ simulation with injected timing jitter in place of unavailable live beam data, the pipeline achieved an average end to end latency of 0.456 ms (σ = 0.122 ms) across 167,000 test frames, comfortably meeting the timing constraint. Early outliers were attributed to TensorRT warm-up rather than steady state limitations, suggesting GPU based inference is a viable alternative to the existing FPGA implementation.

Yu, Kellen [Fermilab; Cornell U.]↗
DOE OSTI2024

Performance Characterization and Provenance of Distributed Task-based Workflows on HPC Platforms

Understanding performance and provenance of task-based workflows poses significant challenges, particularly in distributed configurations where resources are shared by multiple applications. Task-based workflow management systems further complicate performance predictability because of their dynamicity that subtly alters task execution order from run to run. In this paper we propose a layered characterization framework for performance and task provenance for Dask.distributed workflows running on high-performance computing (HPC) platforms. It collects data from jobs, the workflow management system, and the operating system to aid in understanding the performance of these workflows. Our approach encompasses three main contributions: first, an extension of Dask.distributed to capture high-fidelity task provenance using Mochi data services; second, the adaptation of the established HPC I/O characterization tool Darshan to gather high-fidelity I/O data, thereby enhancing the granularity of our analysis; and third, a framework to combine and process the collected data and provide helpful insights into performance characterization and reproducibility, alongside our lessons learned.

Dask↗
DOE OSTI2023

Using the Shallow Strain Tensor to Characterize Deep Geologic Reservoirs

Abstract Storing and recovering water, carbon, and heat from geologic reservoirs is central to managing resources in a changing climate. We tested the hypothesis that the strain tensor caused by injecting or producing fluids can be measured at shallow depths and interpreted to advance understanding of underlying deep aquifers or reservoirs. Geodetic‐grade strainmeters were deployed at 30 m depth overlying the Bartlesville Formation, a 500‐m‐deep sandstone near Tulsa, OK. The strainmeters are 220 m east of injection well 9A completed in a permeable lens at the base of the Bartlesville Formation. Water was injected into well 9A at approximately 1.0 L/s during four tests that ranged in duration from a few hours to a few weeks. The horizontal strain increased (tension) and the circumferential strain was a few times larger than the radial strain. The vertical strain decreased (compression) during injection. Strain rates were approximately 100 nε/day during the first few hours, but the rates decreased and were approximately 10 nε/day during most of the tests. Four independent methods of poroelastic simulation and inversion predict reservoir properties and geometries that are similar to each other and consistent with independent information about the reservoir. All strain interpretations predict that a boundary to the permeable lens occurs beneath the vicinity of the strainmeters, which is consistent with core data from the site. The boundary of the permeable lens is located by matching the vertical, radial and circumferential strains, which demonstrates the value of measuring the strain tensor.

Murdoch, Lawrence C.↗
DOE OSTI2025

Integrating and Characterizing HPC Task Runtime Systems for hybrid AI-HPC workloads

Scientific workflows increasingly involve both HPC and machine-learning tasks, combining MPI-based simulations, training, and inference in a single execution. Launchers such as Slurm’s srun constrain concurrency and throughput, making them unsuitable for dynamic and heterogeneous workloads. We present a performance study of RADICAL-Pilot (RP) integrated with Flux and Dragon, two complementary runtime systems that enable hierarchical resource management and high-throughput function execution. Using synthetic and production-scale workloads on Frontier, we characterize the task execution properties of RP across runtime configurations. RP+Flux sustains up to 930 tasks/s, and RP+Flux+Dragon exceeds 1,500 tasks/s with over 99.6% utilization. In contrast, srun peaks at 152 tasks/s and degrades with scale, with utilization below 50%. For IMPECCABLE.v2 drug discovery campaign, RP+Flux reduces makespan by 30–60% relative to srun/Slurm and increases throughput more than four times on up to 1,024. These results demonstrate hybrid runtime integration in RP as a scalable approach for hybrid AI-HPC workloads.

HPC-AI↗
DOE OSTI2023

Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is forecast to collect a large sample of Type Ia supernovae (SNe Ia) expected to be instrumental in unveiling the nature of dark energy. The feat, however, requires accurately measuring the two components of the Hubble diagram, distance modulus and redshift. Distance is estimated from SN Ia parameters extracted from light-curve fits, where the average quality of light curves is primarily driven by survey parameters. An optimal observing strategy is thus critical for measuring cosmological parameters with high accuracy. We present in this paper a three-stage analysis to assess the impact of the deep drilling (DD) strategy parameters on three critical aspects of the survey: redshift completeness, the number of well-measured SNe Ia, and cosmological measurements. We demonstrate that the current DD survey plans (internal LSST simulations) are characterized by a low completeness (z ~ 0.55–0.65), and irregular and low cadences (several days), which dramatically decrease the size of the well-measured SN Ia sample. We propose a method providing the number of visits required to reach higher redshifts. We use the results to design a set of optimized DD surveys for SN Ia cosmology taking full advantage of spectroscopic resources for host galaxy redshift measurements. The most accurate cosmological measurements are achieved with deep rolling surveys characterized by a high cadence (1 day), a rolling strategy (at least two seasons of observation per field), and ultradeep (z ≳ 0.8) and deep (z ≳ 0.6) fields. A deterministic scheduler including a gap recovery mechanism is critical to achieving a high-quality DD survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Perennial grass root system specializes for multiple resource acquisitions with differential elongation and branching patterns

Roots optimize the acquisition of limited soil resources, but relationships between root forms and functions have often been assumed rather than demonstrated. Furthermore, how root systems co-specialize for multiple resource acquisitions is unclear. Theory suggests that trade-offs exist for the acquisition of different resource types, such as water and certain nutrients. Measurements used to describe the acquisition of different resources should then account for differential root responses within a single system. To demonstrate this, we grew Panicum virgatum in split-root systems that vertically partitioned high water availability from nutrient availability so that root systems must absorb the resources separately to fully meet plant demands. We evaluated root elongation, surface area, and branching, and we characterized traits using an order-based classification scheme. Plants allocated approximately 3/4th of primary root length towards water acquisition, whereas lateral branches were progressively allocated towards nutrients. However, root elongation rates, specific root length, and mass fraction were similar. Our results support the existence of differential root functioning within perennial grasses. Similar responses have been recorded in many plant functional types suggesting a fundamental relationship. Root responses to resource availability can be incorporated into root growth models via maximum root length and branching interval parameters.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Characterizing the Performance of Executing Many-tasks on Summit

Many scientific workloads are comprised of many tasks, where each task is an independent simulation or analysis of data. The execution of millions of tasks on heterogeneous HPC platforms requires scalable dynamic resource management and multi-level scheduling. RADICAL-Pilot (RP) -- an implementation of the Pilot abstraction, addresses these challenges and serves as an effective runtime system to execute workloads comprised of many tasks. In this paper, we characterize the performance of executing many tasks using RP when interfaced with JSM and PRRTE on Summit: RP is responsible for resource management and task scheduling on acquired resource; JSM or PRRTE enact the placement of launching of scheduled tasks. Our experiments provide lower bounds on the performance of RP when integrated with JSM and PRRTE. Specifically, for workloads comprised of homogeneous single-core, 15 minutes-long tasks we find that: PRRTE scales better than JSM for > O(1000) tasks; PRRTE overheads are negligible; and PRRTE supports optimizations that lower the impact of overheads and enable resource utilization of 63% when executing O(16K), 1-core tasks over 404 compute nodes.

Matteo, Turilli↗
DOE OSTI2023

A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films

Abstract Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Detection of surface water temperature variations of Mongolian lakes benefiting from the spatially and temporally gap-filled MODIS data

Lakes provide critical water resources for human activities and ecosystems, particularly in the Mongolian Plateau (MP), which is characterized by a dry climate and a harsh environment. As a region that is sensitive to anthropogenic warming, tracking lake surface water temperature (LSWT) changes in Mongolian lakes is crucial for understanding the consequences of a warming climate on lake ecosystems. However, the long-term monitoring of LSWT is restricted by the spatiotemporal gaps in the raw imagery of remote sensing-based land surface temperature (LST), e.g., the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. This study applied an improved gap-filling method by utilizing the discrete cosine transform-based penalized least squares (DCT-PLS) strategy in the spatial domain combined with the linear interpolation (LI) algorithm in the temporal domain. The method was applied to fill gaps in the LSWT imagery of 12 representative lakes across MP. The randomly sampled high-quality MODIS LSWT values in the spatial and temporal domains were excavated as false data gaps and considered “virtual true” validation datasets. The spatial validation results showed that the estimated LSWT for all the lake cases were comparable with the “virtual true” LSWT values, with the average values of the coefficient of determination, mean absolute error, mean square error, and root mean square error being 0.98, 0.38 °C, 0.45 °C, and 0.59 °C, respectively. Meanwhile, the error of nighttime LSWT results was relatively lower than that of daytime LSWT. For temporal interpolation validation, the LI algorithm exhibited relatively better performance and could more objectively indicate the variation in LSWT. Benefiting from the spatially and temporally well-constrained data, we analyzed the interannual and intra-annual change characteristics of the LSWTs of the 12 lakes. The long-term variations of annual and seasonal mean LSWTs in the 12 selected lakes exhibited no evident trends in 2000–2020, while presented apparent interannual fluctuations. The slight changes in the average LSWTs of the 12 selected lakes were in excellent synchronization with the surrounding LST derived from the reanalysis datasets, confirming the widely reported phenomenon of “global warming hiatus” that occurred in the early 21st century. This study improves the understanding of the LSWT variations in Mongolian lakes in response to global climate change. It has the potential to provide an effective approach for monitoring LSWT changes in other large-scale studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Automatic information extraction from neutron radiography imaging to estimate axial fuel expansion in $\mathrm{EBR-II}$

Approximately 130,000 metal fuel pins were irradiated in the Experimental Breeder Reactor II (EBR-II) during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed to characterize fuel behavior, like fuel swelling. However, due to the lack of technology or resources, many of the images have not been processed or were processed manually through visual examination. This paper represents first-attempt to develop an image processing algorithm capable of automatically extracting information regarding the degree of fuel swelling from neutron radiography imaging. The algorithm was applied to 120 images of three different metallic fuel pin compositions—U-10Zr, U-8Pu-10Zr, and U-19Pu-10Zr. The algorithm performs operations of image intensity adjustment, image binarization, region finding, and labeling to extract information about fuel swelling. The average growth for U-10Zr was found to be 8.49% with 95% Confidence Interval (CI) [8.33 –8.66%], for U-8Pu-10Zr — 7.50% with 95% CI [7.23 – 7.78%], and for U-19Pu-10Zr — 3.15%, with 95% CI [2.40 – 3.91%]. The results obtained by applying this automatic image processing algorithm are consistent with previously reported studies of the same types of fuels. The automatic image processing algorithm will be expanded to include thousands of available neutron radiography images and different types of fuels to investigate empirical dependencies of fuel swelling, which can be subsequently applied to advanced fuel modeling. Results from this study can later be compared to BISON simulations to further benchmark modeling efforts and develop assessment cases.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗