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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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DOE OSTI2020

BEPAM Model Code and CABBI Simulation Results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land"

This dataset contains BEPAM model code and input data to the replicate the results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land." The dataset consists of: (1) The replication codes and data for the BEPAM model. The code file is named as output_0213-2020_Complete_daycent-agversion-[rental payment level]%_[biomass price].gms. (BEPAM-CRP model-Sep2020.zip) (2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv) * Item (1) is in GAMS format. Item (2) is in text format.

biomass price↗

NASA NTRS2015

Simulated Transmission of the Dengue Virus Across the US-Mexico Border Using Remotely Sensed and Ground Based Weather Data

Incidence of dengue fever, caused by a mosquito transmitted virus, have increased in the Americas during recent decades. In the US, local transmission has been reported in southern Texas and Florida. However, despite its close proximity to dengue endemic areas in Mexico and the presence of a primary mosquito vector, there are no reports of local transmission in Arizona. Many studies have demonstrated that weather influences dengue virus transmission by regulating vector development rates, vector habitat availability, and the duration of the virus extrinsic incubation period (EIP). The EIP, the period between mosquito infection and the ability for it to retransmit the virus, is especially important given its high sensitivity to temperature and the short lifespan of mosquitoes. Other studies, however, have suggested that human related factors such as socioeconomic status and herd immunity may explain much of the disparity in dengue incidence in the US‐Mexico border region. Using a meteorologically driven model of vector population dynamics and virus transmission we compare simulations of dengue fever cases in southern Arizona and northern Mexico. A Monte Carlo approach is employed to select parameter values by evaluating simulations in Hermosillo Mexico with reported dengue fever case data. Simulations that replicate the case data best are retained and rerun using remotely sensed climate data from other Arizona and Mexico locations to determine the relative influence of weather on virus transmission. Although human and environmental factors undoubtedly influence dengue transmission in the US‐Mexico border regions, weather is a major facilitator of the transmission process.

Dengue↗

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↗

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 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 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↗

NASA NTRS1996

Distributed Large Data-Object Environments: End-to-End Performance Analysis of High Speed Distributed Storage Systems in Wide Area ATM Networks

We have developed and deployed a distributed-parallel storage system (DPSS) in several high speed asynchronous transfer mode (ATM) wide area networks (WAN) testbeds to support several different types of data-intensive applications. Architecturally, the DPSS is a network striped disk array, but is fairly unique in that its implementation allows applications complete freedom to determine optimal data layout, replication and/or coding redundancy strategy, security policy, and dynamic reconfiguration. In conjunction with the DPSS, we have developed a 'top-to-bottom, end-to-end' performance monitoring and analysis methodology that has allowed us to characterize all aspects of the DPSS operating in high speed ATM networks. In particular, we have run a variety of performance monitoring experiments involving the DPSS in the MAGIC testbed, which is a large scale, high speed, ATM network and we describe our experience using the monitoring methodology to identify and correct problems that limit the performance of high speed distributed applications. Finally, the DPSS is part of an overall architecture for using high speed, WAN's for enabling the routine, location independent use of large data-objects. Since this is part of the motivation for a distributed storage system, we describe this architecture.

Johnston, William↗

DOE OSTI2023

Uncertainty quantification for high explosive reactant and product equations of state

Equations of state (EOSs) are typically represented as physics-informed models with tunable parameters that are adjusted to replicate calibration data as closely as possible. Uncertainty quantification (UQ) allows for the development of an ensemble of EOS parameters that are consistent with the calibration data instead of a single EOS. In this work, we perform UQ for the reactant and product EOSs for a variety of high explosives (HEs). In doing so, we demonstrate a strategy for dealing with heterogeneous (both experimental and calculated) data. We also use a statistical distance metric to quantify the differences between the various HEs using the UQ results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗