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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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NASA NTRS2018

The Ability of NASA's Meteoroid Engineering Model to Replicate in Situ Impact Data

Moorhead, Althea V.↗

NASA NTRS2018

The Ability of NASA's Meteoroid Engineering Model (MEM) 3 to Replicate in Situ Impact Data

Meteoroid environment models must describe the mass, directionality, velocity, and density distributions of meteoroids in order to correctly predict the rate at which meteoroids impact spacecraft. We present an updated version of NASA's Meteoroid Engineering Model (MEM) that better captures the correlation between directionality and velocity and incorporates a bulk density distribution. We compare the resulting model with the rate of large particle impacts seen on the Long Duration Exposure Facility (LDEF) and the Pegasus I and II satellites. Impact crater counts are modeled using two different ballistic limit equations (BLEs), one of which assumes a constant depth-to-diameter ratio and one of which yields depth-to-diameter ratios that depend on meteoroid speed and density. MEM agrees with the in situ crater record to within the range of values associated with different BLEs, but our preliminary analysis indicates that the level of agreement could be improved by reducing the strength of the toroidal meteoroid population and increasing the strength of the helion/antihelion and apex populations.

Moorhead, Althea↗

DOE OSTI2018

The ATLAS Data Management System Rucio: Supporting LHC Run-2 and beyond

With this contribution we present some recent developments made to Rucio, the data management system of the High-Energy Physics Experiment ATLAS. Already managing 300 Petabytes of both official and user data, Rucio has seen incremental improvements throughout LHC Run-2, and is currently laying the groundwork for HEP computing in the HL-LHC era. The focus of this contribution are (a) the automations that have been put in place such as data rebalancing or dynamic replication of user data, as well as their supporting infrastructures such as real-time networking metrics or transfer time predictions; (b) the flexible approach towards inclusion of heterogeneous storage systems, including object stores, while unifying the potential access paths using generally available tools and protocols; (c) machine learning approaches to help with transfer throughput estimation; and (d) the adoption of Rucio for two other experiments, AMS and Xenon1t. We conclude by presenting operational numbers and figures to quantify these improvements, and extrapolate the necessary changes and developments for future LHC runs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2011

Use of Heated Helium to Simulate Surface Pressure Fluctuations on the Launch Abort Vehicle During Abort Motor Firing

The solid-rocket plumes from the Abort motor of the Multi-Purpose Crew Vehicle (MPCV, also know as Orion) were simulated using hot, high pressure, Helium gas to determine the surface pressure fluctuations on the vehicle in the event of an abort. About 80 different abort situations over a wide Mach number range, (0.3< or =M< or =1.2) and vehicle attitudes (+/-15deg) were simulated inside the NASA Ames Unitary Plan, 11-Foot Transonic Wind Tunnel. For each abort case, typically two different Helium plume and wind tunnel conditions were used to bracket different flow matching critera. This unique, yet cost-effective test used a custom-built hot Helium delivery system, and a 6% scale model of a part of the MPCV, known as the Launch Abort Vehicle. The test confirmed the very high level of pressure fluctuations on the surface of the vehicle expected during an abort. In general, the fluctuations were found to be dominated by the very near-field hydrodynamic fluctuations present in the plume shear-layer. The plumes were found to grow in size for aborts occurring at higher flight Mach number and altitude conditions. This led to an increase in the extent of impingement on the vehicle surfaces; however, unlike some initial expectations, the general trend was a decrease in the level of pressure fluctuations with increasing impingement. In general, the highest levels of fluctuations were found when the outer edges of the plume shear layers grazed the vehicle surface. At non-zero vehicle attitudes the surface pressure distributions were found to become very asymmetric. The data from these wind-tunnel simulations were compared against data collected from the recent Pad Abort 1 flight test. In spite of various differences between the transient flight situation and the steady-state wind tunnel simulations, the hot-Helium data were found to replicate the PA1 data fairly reasonably. The data gathered from this one-of-a-kind wind-tunnel test fills a gap in the manned-space programs, and will be used to establish the acoustic environment for vibro-acoustic qualification testing of the MPCV.

Panda, Jayanta↗

NASA NTRS2014

Architectures Toward Reusable Science Data Systems

Data Processing↗

NASA NTRS2015

Architectures Toward Reusable Science Data Systems

Science Data Systems (SDS) comprise an important class of data processing systems that support product generation from remote sensors and in-situ observations. These systems enable research into new science data products, replication of experiments and verification of results. NASA has been building systems for satellite data processing since the first Earth observing satellites launched and is continuing development of systems to support NASA science research and NOAAs Earth observing satellite operations. The basic data processing workflows and scenarios continue to be valid for remote sensor observations research as well as for the complex multi-instrument operational satellite data systems being built today. System functions such as ingest, product generation and distribution need to be configured and performed in a consistent and repeatable way with an emphasis on scalability. This paper will examine the key architectural elements of several NASA satellite data processing systems currently in operation and under development that make them suitable for scaling and reuse. Examples of architectural elements that have become attractive include virtual machine environments, standard data product formats, metadata content and file naming, workflow and job management frameworks, data acquisition, search, and distribution protocols. By highlighting key elements and implementation experience we expect to find architectures that will outlast their original application and be readily adaptable for new applications. Concepts and principles are explored that lead to sound guidance for SDS developers and strategists.

Data Processing↗

DOE OSTI2025

The Natural Products Magnetic Resonance Database (NP-MRD) for 2025

The Natural Products Magnetic Resonance Database or NP-MRD (https://np-mrd.org) is a comprehensive, freely accessible, web-based resource for the deposition, distribution, extraction and retrieval of nuclear magnetic resonance (NMR) data on natural products. The NP-MRD was initially established to support compound de-replication and data dissemination for the natural products community. However, that community has now grown to include many users from the metabolomics, microbiomics, foodomics and nutrition science fields. Indeed, since its launch in 2021, the NP-MRD has expanded enormously in size, scope and popularity. The current version of NP-MRD now contains nearly 7X more compounds (281,859 vs. 40,908) and 7X more NMR spectra (5.1 million vs. 817,000) than the first release. More specifically, an additional 4.6 million predicted spectra and another 11,000 spectra simulated from experimental chemical shifts were deposited into the database. Likewise, the number of NMR raw spectral data depositions has grown from a 165 spectra per year to more than 10,000 per year. As a result of this expansion, the number of monthly webpage views has grown from 55 to 20,000 and the number of monthly visitors has increased from 7 to 2500. To address this growth and to better support the expanding needs of its diverse community of users, many additional improvements to the NP-MRD have been made. These include significant enhancements to the data submission process, important improvements to the visualization and display of NMR spectra, notable updates to the database’s spectral search utilities and useful additions to support better NMR spectral analysis/prediction. Significant efforts have also been undertaken to remediate and update many of NP-MRD’s database entries. This manuscript describes these database improvements and expansion efforts, along with how they have been implemented and what future upgrades to the NP-MRD are planned.

Artifical Intelligence↗

DOE OSTI2024

BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data

Abstract BindingDB (bindingdb.org) is a public, web-accessible database of experimentally measured binding affinities between small molecules and proteins, which supports diverse applications including medicinal chemistry, biochemical pathway annotation, training of artificial intelligence models and computational chemistry methods development. This update reports significant growth and enhancements since our last review in 2016. Of note, the database now contains 2.9 million binding measurements spanning 1.3 million compounds and thousands of protein targets. This growth is largely attributable to our unique focus on curating data from US patents, which has yielded a substantial influx of novel binding data. Recent improvements include a remake of the website following responsive web design principles, enhanced search and filtering capabilities, new data download options and webservices and establishment of a long-term data archive replicated across dispersed sites. We also discuss BindingDB’s positioning relative to related resources, its open data sharing policies, insights gleaned from the dataset and plans for future growth and development.

Liu, Tiqing↗

NASA NTRS1989

Software reliability growth models dominated by randomness

The Jelinski-Moranda and Geometric models for software reliability failed the consistency test which was proposed. These models were challenged to take data which comes from a process which they have correctly modeled and to make predictions about the reliability of that process. It was found that either model, given data precisely from a process it correctly models, will usually fail to make good predictions. These problems are attributed to randomness in the data used as input to the models and a remedy is indicated for this lack of robustness, namely replication of data.

Shen, Wenhui↗

DOE OSTI2018

Correlation of Platinum Cytotoxicity to Drug-DNA Adduct Levels in a Breast Cancer Cell Line Panel

Platinum drugs, including carboplatin and oxaliplatin, are commonly used chemotherapy drugs that kill cancer cells by forming toxic drug-DNA adducts. These drugs have a proven, but modest, efficacy against several aggressive subtypes of breast cancer but also cause several side effects that can lead to the cessation of treatment. There is a clinical need to identify patients who will respond to platinum drugs in order to better inform clinical decision making. Diagnostic microdosing involves dosing patients or patient samples with subtherapeutic doses of radiolabeled platinum followed by measurement of platinum-DNA adducts in blood or tumor tissue and may be used to predict patient response. We exposed here a panel of six breast cancer cell lines to 14 C-labeled carboplatin or oxaliplatin at therapeutic and microdose (1% therapeutic dose) concentrations for a range of exposure lengths and isolated DNA from the cells. The DNA was converted to graphite, and measurement of radiocarbon due to platinum-DNA adduction was quantified via accelerator mass spectrometry (AMS). We observed a linear correlation in adduct levels between the microdose and therapeutic dose, and the level of platinum-DNA adducts corresponded to cell line drug sensitivity for both carboplatin and oxaliplatin. These results showed a clear separation in adduct levels between the sensitive and resistant groups of cell lines that could not be fully explained or predicted by changes in DNA repair rates or mutations in DNA repair genes. Further, we were able to quantitate oxaliplatin-DNA adducts in the blood and tumor tissue of a metastatic breast cancer patient. Together, these data support the use of diagnostic microdosing for predicting patient sensitivity to platinum. Future studies will be aimed at replicating this data in a clinical feasibility trial.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Structural and mechanistic insights into Mcm2-7 double-hexamer assembly and function

Eukaryotic cells license each DNA replication origin during G1 phase by assembling a prereplication complex that contains a Mcm2–7 (minichromosome maintenance proteins 2–7) double hexamer. During S phase, each Mcm2–7 hexamer forms the core of a replicative DNA helicase. However, the mechanisms of origin licensing and helicase activation are poorly understood. The helicase loaders ORC–Cdc6 function to recruit a single Cdt1–Mcm2–7 heptamer to replication origins prior to Cdt1 release and ORC–Cdc6–Mcm2–7 complex formation, but how the second Mcm2–7 hexamer is recruited to promote double-hexamer formation is not well understood. Here, structural evidence for intermediates consisting of an ORC–Cdc6–Mcm2–7 complex and an ORC–Cdc6–Mcm2–7–Mcm2–7 complex are reported, which together provide new insights into DNA licensing. Detailed structural analysis of the loaded Mcm2–7 double-hexamer complex demonstrates that the two hexamers are interlocked and misaligned along the DNA axis and lack ATP hydrolysis activity that is essential for DNA helicase activity. Moreover, we show that the head-to-head juxtaposition of the Mcm2–7 double hexamer generates a new protein interaction surface that creates a multisubunit-binding site for an S-phase protein kinase that is known to activate DNA replication. The data suggest how the double hexamer is assembled and how helicase activity is regulated during DNA licensing, with implications for cell cycle control of DNA replication and genome stability.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications

A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB 2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

The Effects of Assumed Source Depth and Shear-Wave Velocity on Moment Tensors Estimated for Small, Contained Chemical Explosions in Granite

ABSTRACT The Source Phenomenology Experiment (SPE-Arizona) included of a series of chemical explosions detonated within a copper mine in Arizona. This study focuses on ground motions from detonations in the copper mine, which are analyzed to assess the uniqueness of the resulting source representation when the source region propagation characteristics have a range of possible models. P-wave velocities are well constrained by refraction data with less constraint of the S-wave velocities. The effects of explosion source depth and VS are assessed with Green’s functions for a range of models in which VP is held constant. Propagation models with a Poisson’s value of 0.25 and a source depth 30–60 m most accurately replicate the data. The explosion was detonated at a centroid depth of 30 m, so trade-offs in depth are demonstrated. The compensated linear vector dipole and explosion components of the Green’s functions convolved with a Mueller–Murphy source function are compared. Both produce significant energy in the 2–12 Hz band, due to surface-wave contributions with no clear depth dependencies above 20 Hz. The range of propagation models is used with the observational data to invert for the frequency-domain moment tensor. Fits to the data from these inversions have cross-correlation values of 0.64, demonstrating effectiveness in replicating the observations with the assumed propagation path effects and resulting source function. Inversions produce horizontal dipoles (Mxx and Myy), roughly half the maximum amplitude of Mzz, consistent with a compensated linear vector dipole source, which is frequency dependent. Denny and Johnson, Mueller–Murphy, Walter and Ford, and the revised Mueller–Murphy source models, parameterized for granite, are compared to the moment tensors. Despite a nonisotropic moment tensor source, the revised Mueller–Murphy isotropic source model best replicates the long-period moments, overshoot, and corner frequency.

Geochemistry & Geophysics↗

DOE OSTI2021

Integrating Cambium Marginal Costs into Electric Sector Decisions: Opportunities to Integrate Cambium Marginal Cost Data into Berkeley Lab Analysis and Technical Assistance

NREL’s Cambium tool generates forward-looking simulations of marginal wholesale electricity costs associated with NREL’s Standard Scenarios. The scenarios include growing shares of variable renewable energy (VRE, i.e. wind and solar), among other power sector assumptions, between 2018 and 2050. The tool’s primary output—hourly costs at more than 130 balancing areas—could serve as public and transparent data source that supports electric-sector decision-making processes across the U.S. Berkeley Lab conducts a large range of analyses that use historical and forward-looking wholesale electricity prices to inform electric-sector decisions. In this report, Berkeley Lab uses its expertise to evaluate the Cambium cost data. We compare Cambium data with historical wholesale prices for the year 2018 and other modeled prices for the year 2030. We then present eight case studies in which Berkeley Lab researchers use Cambium data to replicate previous analyses based on other price datasets. We describe where primary findings and underlying key price dynamics align or differ, and highlight possible novel insights from the Cambium data. Finally, we qualitatively evaluate the suitability of Cambium costs in ten additional Berkeley Lab studies, though a direct comparison with alternative price data was not feasible at this time. The goal is to inform how electric-sector decision-makers and DOE program offices may be able to use this cohesive dataset, and to highlight what improvements to Cambium may make it even more useful.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1992

Direct numerical simulation of laminar breakdown in high-speed, axisymmetric boundary layers

The compressible Navier-Stokes equations are solved using spectral collocation and high-order compact-difference techniques to simulate the laminar breakdown in high-speed, axisymmetric boundary-layer flow. Mach 4.5 flow along a hollow cylinder and Mach 6.8 flow along a sharp cone are considered. Data obtained replicate two previously unexplained phenomena, namely, the appearance of so-called 'rope-like waves' and 'the precursor transition effect', in which transitional flow originates near the critical layer well upstream of the transition location at the wall. The numerical data also reveal that neither of these effects can be explained, even qualitatively, by linear stability theory alone. It is shown that rope-like appearance arises from secondary instability. Certain features of the precursor transition effect also emerge from secondary instability but its nature is revealed to be fundamentally nonlinear.

Pruett, C. D.↗

DOE OSTI2014

Improved assemblies using a source-agnostic pipeline for MetaGenomic Assembly by Merging (MeGAMerge) of contigs

Assembly of metagenomic samples is a very complex process, with algorithms designed to address sequencing platform-specific issues, (read length, data volume, and/or community complexity), while also faced with genomes that differ greatly in nucleotide compositional biases and in abundance. To address these issues, we have developed a post-assembly process: MetaGenomic Assembly by Merging (MeGAMerge). We compare this process to the performance of several assemblers, using both real, and in-silico generated samples of different community composition and complexity. MeGAMerge consistently outperforms individual assembly methods, producing larger contigs with an increased number of predicted genes, without replication of data. MeGAMerge contigs are supported by read mapping and contig alignment data, when using synthetically-derived and real metagenomic data, as well as by gene prediction analyses and similarity searches. Ultimately, MeGAMerge is a flexible method that generates improved metagenome assemblies, with the ability to accommodate upcoming sequencing platforms, as well as present and future assembly algorithms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Wildfire Smoke PM2.5 per Zipcode

This dataset contains daily aggregated measurements of daily PM2.5 from ambient wildfire smoke in the contiguous United States, spanning from 2006 to 2016. The data is sourced from a study by Childs et al. (2022), titled "Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US" published in Environmental Science & Technology. To compute the standardized weight across different zip codes, we computed the weight calculation on a 10km grids. To compute the weight, we obtain area-weights that add up to 1 in each polygon of zipcodes. This enabled us to calculate the smoke values per zipcode for the aforementioned period. Those interested in replicating our data processing pipeline can access it at https://github.com/NSAPH-Data-Processing/smoke_aggregation.

PM2.5↗

DOE OSTI2021

Discrete-Direct Model Calibration and Uncertainty Propagation Method Confirmed on Multi-Parameter Plasticity Model Calibrated to Sparse Random Field Data

A discrete direct (DD) model calibration and uncertainty propagation approach is explained and demonstrated on a 4-parameter Johnson-Cook (J-C) strain-rate dependent material strength model for an aluminum alloy. The methodology's performance is characterized in many trials involving four random realizations of strain-rate dependent material-test data curves per trial, drawn from a large synthetic population. The J-C model is calibrated to particular combinations of the data curves to obtain calibration parameter sets which are then propagated to “Can Crush” structural model predictions to produce samples of predicted response variability. These are processed with appropriate sparse-sample uncertainty quantification (UQ) methods to estimate various statistics of response with an appropriate level of conservatism. This is tested on 16 output quantities (von Mises stresses and equivalent plastic strains) and it is shown that important statistics of the true variabilities of the 16 quantities are bounded with a high success rate that is reasonably predictable and controllable. The DD approach has several advantages over other calibration-UQ approaches like Bayesian inference for capturing and utilizing the information obtained from typically small numbers of replicate experiments in model calibration situations—especially when sparse replicate functional data are involved like force–displacement curves from material tests. The DD methodology is straightforward and efficient for calibration and propagation problems involving aleatory and epistemic uncertainties in calibration experiments, models, and procedures.

42 ENGINEERING↗