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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-D-207B-07: Development of a CT-Radiomics Based Early Response Prediction Model During Delivery of Chemoradiation Therapy for Pancreatic Cancer

Purpose: Pilot study developing a CT-texture based model for early assessment of treatment response during the delivery of chemoradiation therapy (CRT) for pancreatic cancer. Methods: Daily CT data acquired for 24 pancreatic head cancer patients using CT-on-rails, during the routine CT-guided CRT delivery with a radiation dose of 50.4 Gy in 28 fractions, were analyzed. The pancreas head was contoured on each daily CT. Texture analysis was performed within the pancreas head contour using a research tool (IBEX). Over 1300 texture metrics including: grey level co-occurrence, run-length, histogram, neighborhood intensity difference, and geometrical shape features were calculated for each daily CT. Metric-trend information was established by finding the best fit of either a linear, quadratic, or exponential function for each metric value verses accumulated dose. Thus all the daily CT texture information was consolidated into a best-fit trend type for a given patient and texture metric. Linear correlation was performed between the patient histological response vector (good, medium, poor) and all combinations of 23 patient subgroups (statistical jackknife) determining which metrics were most correlated to response and repeatedly reliable across most patients. Control correlations against CT scanner, reconstruction kernel, and gated/nongated CT images were also calculated. Euclidean distance measure was used to group/sort patient vectors based on the data of these trend-response metrics. Results: We found four specific trend-metrics (Gray Level Coocurence Matrix311-1InverseDiffMomentNorm, Gray Level Coocurence Matrix311-1InverseDiffNorm, Gray Level Coocurence Matrix311-1 Homogeneity2, and Intensity Direct Local StdMean) that were highly correlated with patient response and repeatedly reliable. Our four trend-metric model successfully ordered our pilot response dataset (p=0.00070). We found no significant correlation to our control parameters: gating (p=0.7717), scanner (p=0.9741), and kernel (p=0.8586). Conclusion: We have successfully created a CT-texture based early treatment response prediction model using the CTs acquired during the delivery of chemoradiation therapy for pancreatic cancer. Future testing is required to validate the model with more patient data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-EF-207A-01: Respiratory Motion Correction in 4D-Multi Pinhole Small Animal SPECT

Purpose: Respiration causes lesion smearing, image blurring and quality degradation, affecting lesion contrast and the ability to define correct lesion size. The spatial resolution of current multi pinhole SPECT (MPHS) scanners is sub-millimeter. Therefore, the effect of motion is more noticeable in comparison to conventional SPECT scanner. Gated imaging aims to reduce motion artifacts. A major issue in gating is the lack of statistics and individual reconstructed frames are noisy. The increased noise in each frame, deteriorates the quantitative accuracy of the MPHS Images. The objective of this work, is to enhance the image quality in 4D-MPHS imaging, by 4D image reconstruction. Methods: The new algorithm requires deformation vector fields (DVFs) that are calculated by non-rigid Demons registration. The algorithm is based on the motion-incorporated version of ordered subset expectation maximization (OSEM) algorithm. This iterative algorithm is capable to make full use of all projections to reconstruct each individual frame. To evaluate the performance of the proposed algorithm a simulation study was conducted. A fast ray tracing method was used to generate MPHS projections of a 4D digital mouse phantom with a small tumor in liver in eight different respiratory phases. To evaluate the 4D-OSEM algorithm potential, tumor to liver activity ratio was compared with other image reconstruction methods including 3D-MPHS and post reconstruction registered with Demons-derived DVFs. Results: Image quality of 4D-MPHS is greatly improved by the 4D-OSEM algorithm. When all projections are used to reconstruct a 3D-MPHS, motion blurring artifacts are present, leading to overestimation of the tumor size and 24% tumor contrast underestimation. This error reduced to 16% and 10% for post reconstruction registration methods and 4D-OSEM respectively. Conclusion: 4D-OSEM method can be used for motion correction in 4D-MPHS. The statistics and quantification are improved since all projection data are combined together to update the image.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Peer Review Lessons Learned - DOE and Regulator Perspectives - 17140

The DOE NNSA Nevada Field Office (NFO) Underground Test Area (UGTA) Activity requires an external peer review to complete an extensive stage of site characterization and groundwater flow and contaminant transport model(s) development. The external peer review evaluates whether assumptions, methods, and conclusions derived from model(s) outputs are based on sound scientific principles and examines the scientific appropriateness of the model(s) outputs for informing the regulatory decision. The regulatory decision which is made by the Nevada Division of Environmental Protection (NDEP) is whether the model is acceptable to advance to the next UGTA strategy stage. Depending on the specific corrective action unit, the next stage is a Corrective Action Decision Document/Corrective Action Plan or Closure Stage. Three external peer reviews have taken place to date for the UGTA Activity. The first one resulted in a second phase of characterization and model development. The second external peer review resulted in advancement to the next UGTA strategy stage. While advancement to the next strategy stage was recommended by the third external peer review, a series of supplemental data collection and analyses and model simulation activities to address external peer review comments, including drilling three new wells and sampling multiple wells, occurred before moving into the next UGTA strategy stage. Twelve participants representing different aspects of the peer reviews (NDEP, NNSA/NFO, science advisors, presenters, and external peer reviewers) were interviewed to generate a combined lessons learned. Questions were asked regarding their experience with the process including panel selection, focused questions, presentations, interactions between UGTA Activity participants and reviewers, and results. The interview results were combined with the outcome of a published lessons learned report developed following the first external peer review. In general, those interviewed believed that all three external peer reviews were satisfactory and met the goals of the FFACO process. The following seven lessons learned were identified: (1) Ensure the panel is not biased and has a balance of technical and regulatory background; (2) Clearly state the review objective(s) at the start of the review; (3) Ask focused questions that support the peer review objective(s) but that do not ask the specific regulatory question; (4) Establish a peer review panel earlier in the strategy process; (5) Establish one point of contact for the entire review and conduct periodic meetings with the peer review panel to ensure focus remains on the review objective(s) and to ensure that pertinent information is provided; (6) Ensure that presentations are well coordinated, anomalous data or analysis results are addressed, and are focused on supporting the peer review objective(s); and (7) Carefully consider the panel's recommendations prior to embarking on a particular path forward. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

In-Situ Calibrated Modeling of Residual Stresses Induced in Machining under Various Cooling and Lubricating Environments

Although many functional characteristics, such as fatigue life and damage resistance depend on residual stresses, there are currently no industrially viable ‘Digital Process Twin’ models (DPTs) capable of efficiently and quickly predicting machining-induced stresses. By leveraging advances in ultra-high-speed in-situ experimental characterization of machining and finishing processes under plane strain (orthogonal/2D) conditions, we have developed a set of physics-based semi-analytical models to predict residual stress evolution in light of the extreme gradients of stress, strain and temperature, which are unique to these thermo-mechanical processes. Initial validation trials of this novel paradigm were carried out in Ti-6Al4V and AISI 4340 alloy steel. A variety dry, cryogenically cooled and oil lubricated conditions were evaluated to determine the model’s ability to capture the tribological changes induced due to lubrication and cooling. The preliminarily calibrated and validated model exhibited an average correlation of better than 20% between the predicted stresses and experimental data, with calculation times of less than a second. Based on such fast-acting DPTs, the authors envision future capabilities in pro-active surface engineering of advanced structural components (e.g., turbine blades).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Towards Interactive, Reproducible Analytics at Scale on HPC Systems

The growth in scientific data volumes has resulted in a need to scale up processing and analysis pipelines using High Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform provides core capabilities for interactivity but was not designed for HPC systems. In this paper, we outline our efforts that bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC systems. Our work is grounded in a real world science use case-applying geophysical simulations and inversions for imaging the subsurface. Our core platform addresses three key areas of the scientific analysis workflow-reproducibility, scalability, and interactivity. We describe our implemention of a system, using Binder, Science Capsule, and Dask software. We demonstrate the use of this software to run our use case and interactively visualize real-Time streams of HDF5 data.

containers↗
DOE OSTI2023

Binder Jet Additive Manufacturing Process and Material Characterization for High Temperature Heat Exchangers Used in Concentrated Solar Power Applications

The U.S. Department of Energy’s (DOE) Sunshot 2030 initiative has a goal of reducing the cost of concentrating solar power (CSP) to 5 cents per kWh for baseload power plants. One of the potential pathways to this goal includes a reduction in the cost of the supercritical CO2 (sCO2) power block to 0.9 cents per kWh. Recuperators—high and low temperatures, used in the sCO2 power cycle, contribute to >50% of the cost of the power cycle. This work studies the feasibility towards a ≥10% cost reduction for High Temperature Recuperators (HTR) used in the sCO2 power cycle. One way to address the cost reduction is by leveraging low-cost additive manufacturing, specifically, Binder Jet Additive Manufacturing (BJAM) to 3D print HTRs at scale. This study focuses on the development of a BJAM process towards 3D printing HTR cores using Stainless Steel alloy 316L (SS316L). To evaluate the suitability of the BJ process towards the HTR, high level specifications of the application are translated to materials capability requirements. Subsequently, at-temperature materials testing is conducted on as-printed and sintered additively manufactured coupons. Data from the coupons are compared against cast and wrought SS316L data obtained from the literature. Results show that the tensile properties from the BJ process compare well against cast properties. Furthermore, a baseline analysis of creep testing data is established for the BJ process, and insights are drawn from the results towards future improvements of the process.

14 SOLAR ENERGY↗
DOE OSTI2021

Cleaning Images with Gaussian Process Regression

Many approaches to astronomical data reduction and analysis cannot tolerate missing data: corrupted pixels must first have their values imputed. This paper presents astrofix, a robust and flexible image imputation algorithm based on Gaussian process regression. Through an optimization process, astrofix chooses and applies a different interpolation kernel to each image, using a training set extracted automatically from that image. It naturally handles clusters of bad pixels and image edges and adapts to various instruments and image types. For bright pixels, the mean absolute error of astrofix is several times smaller than that of median replacement and interpolation by a Gaussian kernel. We demonstrate good performance on both imaging and spectroscopic data, including the SBIG 6303 0.4 m telescope and the FLOYDS spectrograph of Las Cumbres Observatory and the CHARIS integral-field spectrograph on the Subaru Telescope.

42 ENGINEERING↗
DOE OSTI2020

A database of battery materials auto-generated using ChemDataExtractor

A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database.

25 ENERGY STORAGE↗
DOE OSTI2015

ABSOLUTE PROPER MOTIONS OUTSIDE THE PLANE (APOP)—A STEP TOWARD THE GSC2.4

We present a new catalog of absolute proper motions and updated positions derived from the same Space Telescope Science Institute digitized Schmidt survey plates utilized for the construction of Guide Star Catalog II. As special attention was devoted to the absolutization process and the removal of position, magnitude, and color dependent systematic errors through the use of both stars and galaxies, this release is solely based on plate data outside the galactic plane, i.e., |b|≥27°. The resulting global zero point error is less than 0.6 mas yr{sup -1}, and the precision is better than 4.0 mas yr{sup -1} for objects brighter than R{sub F} = 18.5, rising to 9.0 mas yr{sup -1} for objects with magnitudes in the range 18.5 < R{sub F} < 20.0. The catalog covers 22,525 square degrees and lists 100,774,153 objects to the limiting magnitude of R{sub F} ~ 20.8. Alignment with the International Celestial Reference System was made using 1288 objects common to the second realization of the International Celestial Reference Frame (ICRF2) at radio wavelengths. As a result, the coordinate axes realized by our astrometric data are believed to be aligned with the extragalactic radio frame to within ±0.2 mas at the reference epoch J2000.0. This makes our compilation one of the deepest and densest ICRF-registered astrometric catalogs outside the galactic plane. Although the Gaia mission is poised to set the new standard in catalog astronomy and will in many ways supersede this catalog, the methods and procedures reported here will prove useful to remove astrometric magnitude- and color-dependent systematic errors from the next generation of ground-based surveys reaching significantly deeper than the Gaia catalog.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The Target-selection Pipeline for the Dark Energy Spectroscopic Instrument

Abstract In 2021 May, the Dark Energy Spectroscopic Instrument (DESI) began a 5 yr survey of approximately 50 million total extragalactic and Galactic targets. The primary DESI dark-time targets are emission line galaxies, luminous red galaxies, and quasars. In bright time, DESI will focus on two surveys known as the Bright Galaxy Survey and the Milky Way Survey. DESI also observes a selection of “secondary” targets for bespoke science goals. This paper gives an overview of the publicly available pipeline ( desitarget ) used to process targets for DESI observations. Highlights include details of the different DESI survey targeting phases, the targeting ID ( TARGETID ) used to define unique targets, the bitmasks used to indicate a particular type of target, the data model and structure of DESI targeting files, and examples of how to access and use the desitarget code base. This paper will also describe “supporting” DESI target classes, such as standard stars, sky locations, and random catalogs that mimic the angular selection function of DESI targets. The DESI target-selection pipeline is complex and sizable; this paper attempts to summarize the most salient information required to understand and work with DESI targeting data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Nuclear Protein-Only Ribonuclease P2 Structure and Biochemical Characterization Provide Insight into the Conserved Properties of tRNA 5' End Processing Enzymes

Protein-only RNase Ps (PRORPs) are a recently discovered class of RNA processing enzymes that catalyze maturation of the 5' end of precursor tRNAs in Eukaryotes. PRORPs are found in the nucleus and/or organelles of most eukaryotic organisms. Arabidopsis thaliana is a representative organism that contains PRORP enzymes (PRORP1, PRORP2 and PRORP3) in both its nucleus and its organelles; PRORP2 and PRORP3 localize to the nucleus and PRORP1 localizes to the chloroplast and the mitochondria. Apart from their identification, almost nothing is known about the structure and function of PRORPs that act in the nucleus. Here, we use a combination of biochemical assays and X-ray crystallography to characterize A. thaliana PRORP2. We solved the crystal structure of PRORP2 (3.2 Å) revealing an overall V-shaped protein and conserved metallonuclease active-site structure. Our biochemical studies indicate that PRORP2 requires Mg 2+ for catalysis and catalyzes the maturation of nuclear encoded substrates up to 10-fold faster than mitochondrial encoded precursor nad6 t-element under single-turnover conditions. We also demonstrate that PRORP2 preferentially binds precursor tRNAs containing short 5' leaders and 3' trailers; however, leader and trailer lengths do not significantly alter the observed rate constants of PRORP2 in single-turnover cleavage assays. In conclusion, our data provide a biochemical and structural framework to begin understanding how nuclear localized PRORPs recognize and cleave their substrates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Final Report - Phylogenomic tools and web resources for the Systems Biology Knowledgebase

The major advance during this last reporting period (8/15/12 to present) is our release of data on the PhyloFacts website: phylogenetic trees, multiple sequence alignments and other data for protein families are now available for download from http://phylogenomics.berkeley.edu/data/. This project as a whole aimed to develop high-throughput functional annotation systems that exploit information from protein 3D structure and evolution to provide highly precise inferences of various aspects of gene function, including molecular function, biological process, pathway association, Pfam domains, cellular localization and so on. We accomplished these aims by developing and testing different systems on a database of protein family trees: the PhyloFacts Phylogenomic Encyclopedia (at http://phylogenomics.berkeley.edu/phylofacts/ ).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Rethinking the Default Construction of Multimodel Climate Ensembles

Here, we discuss the current code of practice in the climate sciences to routinely create climate model ensembles as ensembles of opportunity from the newest phase of the Coupled Model Intercomparison Project (CMIP). We give a two-step argument to rethink this process. First, the differences between generations of ensembles corresponding to different CMIP phases in key climate quantities are not large enough to warrant an automatic separation into generational ensembles for CMIP3 and CMIP5. Second, we suggest that climate model ensembles cannot continue to be mere ensembles of opportunity but should always be based on a transparent scientific decision process. If ensembles can be constrained by observation, then they should be constructed as target ensembles that are specifically tailored to a physical question. If model ensembles cannot be constrained by observation, then they should be constructed as cross-generational ensembles, including all available model data to enhance structural model diversity and to better sample the underlying uncertainties. To facilitate this, CMIP should guide the necessarily ongoing process of updating experimental protocols for the evaluation and documentation of coupled models. Finally, with an emphasis on easy access to model data and facilitating the filtering of climate model data across all CMIP generations and experiments, our community could return to the underlying idea of using model data ensembles to improve uncertainty quantification, evaluation, and cross-institutional exchange.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Additive Manufacturing of Nickel Superalloys: Opportunities for Innovation and Challenges Related to Qualification

Innovative designs for turbines can be achieved by advances in nickel-based superalloys and manufacturing methods, including the adoption of additive manufacturing. In this regard, selective electron beam melting (SEBM) and selective laser melting (SLM) of nickel-based superalloys do provide distinct advantages. Furthermore, the direct energy deposition (DED) processes can be used for repair and reclamation of nickel alloy components. The current paper explores opportunities for innovation and qualification challenges with respect to deployment of AM as a disruptive manufacturing technology. Here in the first part of the paper, fundamental correlations of processing parameters to defect tendency and microstructure evolution will be explored using DED process. In the second part of the paper, opportunities for innovation in terms of site-specific control of microstructure during processing will be discussed. In the third part of the paper, challenges in qualification of AM parts for service will be discussed and potential methods to alleviate these issues through in situ process monitoring, and big data analytics are proposed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Time-Resolved Geometric Feature Tracking Elucidates Laser-Induced Keyhole Dynamics

In this report, during laser melting of metals, localized metal evaporation resulting in the formation of a keyhole shaped cavity can occur if processing conditions are chosen with high power density. An unstable keyhole can have deleterious effects in certain applications (e.g., laser powder bed fusion) as it increases the likelihood of producing defects such as porosity. In this work, we propose a pipeline that enables complete segmentation and extraction of various geometric features in keyholing conditions. In situ synchrotron high-speed X-ray visualization at the Advanced Photon Source provides large datasets of experimental images with a high spatio-temporal resolution across a range of laser parameters for Ti-6Al- 4 V. Computer vision image processing techniques were used to extract time-resolved quantitative geometric features (e.g., depth, width, front wall angle) throughout keyhole evolution which were subsequently analyzed to understand the relationship between the variation of local keyhole geometry and processing conditions. This analysis is the first to employ a data-driven approach to further our understanding of the keyholing process regime.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Production of soluble Neprilysin by endothelial cells

Highlights: • A soluble full-length form of Neprilysin exists in media of endothelial cells. • Exosomal release is the key mechanism for the production of soluble Neprilysin. • Inhibition of ADAM-17 by specific inhibitors reduce Neprilysin release. • Exosome mediated release of Neprilysin is dependent on ADAM-17 activity. - Abstract: A non-membrane bound form of Neprilysin (NEP) with catalytic activity has the potential to cleave substrates throughout the circulation, thus leading to systemic effects of NEP. We used the endothelial cell line Ea.hy926 to identify the possible role of exosomes and A Disintegrin and Metalloprotease 17 (ADAM-17) in the production of non-membrane bound NEP. Using a bradykinin based quenched fluorescent substrate (40 μM) assay, we determined the activity of recombinant human NEP (rhNEP; 12 ng), and NEP in the media of endothelial cells (10% v/v; after 24 h incubation with cells) to be 9.35 ± 0.70 and 6.54 ± 0.41 μmols of substrate cleaved over 3 h, respectively. The presence of NEP in the media was also confirmed by Western blotting. At present there are no commercially available inhibitors specific for ADAM-17. We therefore synthesised two inhibitors TPI2155-14 and TPI2155-17, specific for ADAM-17 with IC{sub 50} values of 5.36 and 4.32 μM, respectively. Treatment of cells with TPI2155-14 (15 μM) and TPI2155-17 (4.3 μM) resulted in a significant decrease in NEP activity in media (62.37 ± 1.43 and 38.30 ± 4.70, respectively as a % of control; P < 0.0001), implicating a possible role for ADAM-17 in NEP release. However, centrifuging media (100,000g for 1 h at 4 °C) removed all NEP activity from the supernatant indicating the likely role of exosomes in the release of NEP. Our data therefore indicated for the first time that NEP is released from endothelial cells via exosomes, and that this process is dependent on ADAM-17.

60 APPLIED LIFE SCIENCES↗