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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Relativistic pion loop corrections to the electromagnetic nucleon coupling

We present a relativistic formulation of pion loop corrections to the coupling of photons with nucleons on the light-front. Vertex and wave function renormalization constants are computed to lowest order in the pion field, including their nonanalytic behavior in the chiral limit, and studied numerically as a function of the ultraviolet cutoff. Particular care is taken to explicitly verify gauge invariance and Ward-Takahashi identity constraints to all orders in the m? expansion. The results are used to compute the chiral corrections to matrix elements of local operators, related to moments of deep-inelastic structure functions. Finally, comparisons of results for pseudovector and pseudoscalar coupling allows the resolution of a long-standing puzzle in the computation of pion cloud corrections to structure function moments.

Ji, Chueng↗
DOE OSTI2015

Enabling Exascale Hardware and Software Design through Scalable System Virtualization (Final Report)

The purpose of this project has been to extend the state of the art of systems software for high-end computing (HEC) platforms, and to use systems software to better enable the evaluation of potential future HEC platforms, for example exascale platforms. Such platforms, and their systems software, have the goal of providing scientific computation at new scales, thus enabling new research in the physical sciences and engineering. Over time, the innovations in systems software for such platforms also become applicable to more widely used computing clusters, data centers, and clouds. This was a five-institution project, centered on the Palacios virtual machine monitor (VMM) systems software, a project begun at Northwestern, and originally developed in a previous collaboration between Northwestern University and the University of New Mexico. In this project, Northwestern (including via our subcontract to the University of Pittsburgh) contributed to the continued development of Palacios, along with other team members. We took the leadership role in (1) continued extension of support for emerging Intel and AMD hardware, (2) integration and performance enhancement of overlay networking, (3) connectivity with architectural simulation, (4) binary translation, and (5) support for modern Non-Uniform Memory Access (NUMA) hosts and guests. We also took a supporting role in support for specialized hardware for I/O virtualization, profiling, configurability, and integration with configuration tools. The efforts we led (1-5) were largely successful and executed as expected, with code and papers resulting from them. The project demonstrated the feasibility of a virtualization layer for HEC computing, similar to such layers for cloud or datacenter computing. For effort (3), although a prototype connecting Palacios with the GEM5 architectural simulator was demonstrated, our conclusion was that such a platform was less useful for design space exploration than anticipated due to inherent complexity of the connection between the instruction set architecture level and the microarchitectural level. For effort (4), we found that a code injection approach proved to be more fruitful. The results of our efforts are publicly available in the open source Palacios codebase and published papers, all of which are available from the project web site, v3vee.org. Palacios is currently one of the two codebases (the other being Sandia’s Kitten lightweight kernel) that underlies the node operating system for the DOE Hobbes Project, one of two projects tasked with building a systems software prototype for the national exascale computing effort.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1979

Visible flux variations across finite clouds

A radiative transfer model developed to computer visible flux variations across and within finite clouds of varying shapes is described. The technique has much of the versatility of Monte Carlo models, as well as the speed of the analytical finite cloud models.

Mosher, F. R.↗
NASA NTRS1980

Fragmentation of rotating protostellar clouds

With a three-dimensional hydrodynamic computer code, the behavior of rotating, isothermal gas clouds as they collapse from Jeans unstable configurations is examined in order to determine whether they are susceptible to fragmentation during the initial dynamic collapse phase of evolution. It is found that a gas cloud will not fragment unless (1) it begins collapsing from a radius much smaller than the Jeans radius (i.e., the cloud initially encloses many Jeans masses) and (2) irregularities in the cloud's initial structure (specifically, density inhomogeneities) enclose more than one Jeans mass of material. Instead of fragmenting, most of the models collapse to a ring configuration. The rings appear to be less susceptible to fragmentation from arbitrary perturbations in their structure than has previously been indicated in other work. Because the models, which include the effects of gas pressure, do not readily fragment during a phase of dynamic collapse, it is suggested that gas clouds in the galactic disk undergo fragmentation only during quasi-equilibrium phases of their evolution.

Tohline, J. E.↗
DOE OSTI2022

Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets

The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ~40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ~3.7 billion candidate molecules.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Biomass Harmonization and SAR Analysis with the Multi-mission Algorithm and Analysis Platform (MAAP)

The Multi‐mission Algorithm and Analysis Platform (MAAP) is a collaborative effort between NASA and the European Space Agency (ESA) to support above ground biomass (AGB) research in an open science framework. MAAP brings together relevant data, algorithms, and computing capabilities in a common cloud environment to address the challenges of sharing and processing data from field, airborne and satellite measurements. MAAP was publicly released in October 2021, providing computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of data. MAAP has allowed scientists from both North America and Europe to collaborate on the generation and analysis/visualization of data derived from multiple, discipline-adjacent missions in an open, collaborative environment that has reached beyond traditional scientific investigation. MAAP has been used to support multiple scientific activities. To date, existing LiDAR data from multiple platforms has been calibrated with field measurements and combined for more comprehensive and accurate estimates of above ground biomass AGB; these LiDAR platforms include airborne (e.g. LVIS), the International Space Station (NASA’s Global Ecosystem Dynamics Investigation (GEDI), and satellites (e.g. ICESat-2). The current challenge is to effectively and seamlessly combine the aforementioned LiDAR-based data with new data sources such as P-band RADAR from ESA’s upcoming BIOMASS mission, existing ESA Sentinel-1 C-band SAR, and the 30 PB/yr of high cadence global coverage L-band SAR data from the upcoming NASA-ISRO SAR (NISAR) mission. Recent analysis using MAAP merged ICESat-2 and optical data (Harmonized Landsat Sentinel) produced the most comprehensively precise estimate of boreal-wide AGB to date. Another effort using MAAP is the production and open distribution of global comparisons of AGB map estimates, including from ICESat-2 and GEDI, to bolster stakeholder uptake for policy applications. These map estimates will feed into the Intergovernmental Panel on Climate Change (IPCC) database, likely aiding the next Global Carbon Stocktake of the UNFCCC. Furthermore, the biomass retrieval intercomparison exercise BRIX-2 could benefit from the MAAP providing standardized test cases (based on airborne campaign and spaceborne data) allowing the community to develop and apply retrieval algorithms based on these test cases, while forthcoming SAR data training curricula could also use the MAAP as a teaching and learning platform. The MAAP is meeting the challenges inherent in international, open science collaboration and large scale computing with a platform that is entirely open source and cloud native, using open standards for data access, manipulation, protocols, and formats. The MAAP data system consists of a dedicated data store whose data is indexed in an online catalog conforming to established metadata, application programmatic interfaces (APIs), and service interface standards, using an implementation of the open sourced NASA Common Metadata Repository. Federation of user identities allows users from either NASA or ESA to access and consume services from the other using a unified metadata catalog for the data utilized across the ESA and NASA MAAP platforms. Similarly, we are exploring how to increase interoperability to achieve a common approach to packaging, orchestrating and executing algorithms, with interoperable access to data for subsetting, fast browse, and cloud-optimized access, all using interoperable standards such as those from the Open Geospatial Consortium (OGC). Designed for interoperability, ESA and NASA utilize a common architecture for the software platform. It provides a cloud-based algorithm development environment (ADE) that enables scientists to develop algorithms collaboratively with access to the MAAP data catalog as well as other data archives. MAAP provides an Eclipse Che-based ADE supporting both Python and R languages, popular in this biomass community. Algorithms developed and containerized within the ADE can be deployed to run to thousands of computational nodes in the MAAP’s data processing system (DPS), dramatically speeding up processing and giving scientists a rapid, iterative turnaround of results. NASA’s implementation of the DPS is based on the Hybrid Science Data System (HySDS) framework, used by NASA flight projects to produce Earth science standard products.

cloud computing↗
NASA NTRS2018

Range and Intensity Image-Based Terrain and Vehicle Relative Pose Estimation System

A navigation system includes an image acquisition device for acquiring a range image of a target vehicle, at least one processor, a memory including a target vehicle model and computer readable program code, where the processor and the computer readable program code are configured to cause the navigation system to convert the range image to a point cloud having three dimensions, compute a transform from the target vehicle model to the point cloud, and use the transform to estimate the target vehicle's attitude and position for capturing the target vehicle.

Gill, Nathaniel↗
DOE OSTI2016

Final Technical Report for "High-resolution global modeling of the effects of subgrid-scale clouds and turbulence on precipitating cloud systems"

The Multiscale Modeling Framework (MMF) embeds a cloud-resolving model in each grid column of a General Circulation Model (GCM). A MMF model does not need to use a deep convective parameterization, and thereby dispenses with the uncertainties in such parameterizations. However, MMF models grossly under-resolve shallow boundary-layer clouds, and hence those clouds may still benefit from parameterization. In this grant, we successfully created a climate model that embeds a cloud parameterization (“CLUBB”) within a MMF model. This involved interfacing CLUBB’s clouds with microphysics and reducing computational cost. We have evaluated the resulting simulated clouds and precipitation with satellite observations. The chief benefit of the project is to provide a MMF model that has an improved representation of clouds and that provides improved simulations of precipitation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Prospective Validation of a High Dimensional Shape Model for Organ Motion in Intact Cervical Cancer

Purpose: Validated models are needed to justify strategies to define planning target volumes (PTVs) for intact cervical cancer used in clinical practice. Our objective was to independently validate a previously published shape model, using data collected prospectively from clinical trials. Methods and Materials: We analyzed 42 patients with intact cervical cancer treated with daily fractionated pelvic intensity modulated radiation therapy and concurrent chemotherapy in one of 2 prospective clinical trials. We collected online cone beam computed tomography (CBCT) scans before each fraction. Clinical target volume (CTV) structures from the planning computed tomography scan were cast onto each CBCT scan after rigid registration and manually redrawn to account for organ motion and deformation. We applied the 95% isodose cloud from the planning computed tomography scan to each CBCT scan and computed any CTV outside the 95% isodose cloud. The primary aim was to determine the proportion of CTVs that were encompassed within the 95% isodose volume. A 1-sample t test was used to test the hypothesis that the probability of complete coverage was different from 95%. We used mixed-effects logistic regression to assess effects of time and patient variability. Results: The 95% isodose line completely encompassed 92.3% of all CTVs (95% confidence interval, 88.3%-96.4%), not significantly different from the 95% probability anticipated a priori (P=.19). The overall proportion of missed CTVs was small: the grand mean of covered CTVs was 99.9%, and 95.2% of misses were located in the anterior body of the uterus. Time did not affect coverage probability (P=.71). Conclusions: With the clinical implementation of a previously proposed PTV definition strategy based on a shape model for intact cervical cancer, the probability of CTV coverage was high and the volume of CTV missed was low. This PTV expansion strategy is acceptable for clinical trials and practice; however, we recommend daily image guidance to avoid systematic large misses in select patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Feasibility and Demonstration of a Cloud-Based RIID Analysis System (Annual Report)

A significant limitation in the operational utility of handheld and backpack radioisotope identifiers (RIIDs) is the ability of their onboard algorithms to accurately and reliably identify the isotopic sources of the measured gamma-ray energy spectrum and thereby separate threat from benign or background sources. Improved algorithms exist but are not implemented on RIIDs due to insufficient computational power or classification, export controls, or other sensitivities. They also have limited ability to use additional contextual information in making a threat no-threat assessment. A possible solution is to move the spectral analysis computations to an external device whose capabilities can be greatly enhanced and whose algorithms can be kept secure. A Cloud-based analysis system can provide this capability. Cloud computing is becoming ubiquitous and affords the national security community an opportunity for tremendous leverage of private sector developments. The goal of this project is to determine if operational performance of RIIDs can be improved by applying these developments in a way that meets the needs of the national security community.

Wright, Michael C.↗
DOE OSTI2024

Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics

Abstract High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Microphysical response of cloud droplets in a fluctuating updraft

The effect of a fluctuating updraft upon a distribution of cloud droplets is examined. Computations are performed for fourteen vertical velocity patterns; each allows a closed parcel of cloud air to undergo downward as well as upward motion. Droplet solution and curvature effects are included. The classical equations for the growth rate of an individual droplet by vapor condensation relies on simplifying assumptions. Those assumptions are isolated and examined. A unique approach is presented in which all energy sources and sinks of a droplet may be considered and is termed the explicit model. It is speculated that the explicit model may enhance the growth of large droplets at greater heights. Such a model is beneficial to the studies of pollution scavenging and acid rain.

Harding, D. D.↗
NASA NTRS2000

Sensitivity of Cirrus Bidirectional Reflectance at MODIS Bands to Vertical Inhomogeneity of Ice Crystal Habits and Size Distribution

A common assumption in satellite imager-based cirrus retrieval algorithms is that the radiative properties of a cirrus cloud may be represented by those associated with a specific ice crystal shape (or habit) and a single particle size distribution. However, observations of cirrus clouds have shown that the shapes and sizes of ice crystals may vary substantially with height within the clouds. In this study we investigate the sensitivity of the top-of-atmosphere bidirectional reflectances at two MODIS bands centered at 0.65 micron and 2.11 micron to the cirrus models assumed to be either a single homogeneous layer or three distinct but contiguous, layers. First, we define the single- and three-layer cirrus cloud models with respect to ice crystal habit and size distribution on the basis of in situ replicator data acquired during the First ISCCP Regional Experiment (FIRE-II), held in Kansas during the fall of 1991. Subsequently, fundamental light scattering and radiative transfer theory is employed to determine the single scattering and the bulk radiative properties of the cirrus cloud. Regarding the radiative transfer computations, we present a discrete form of the adding/doubling principle by introducing a direct transmission function, which is computationally straightforward and efficient an improvement over previous methods. For the 0.65 micron band, at which absorption by ice is negligible, there is little difference between the bidirectional reflectances calculated for the one- and three-layer cirrus models, suggesting that the vertical inhomogeneity effect is relatively unimportant. At the 2.11 micron band, the bidirectional reflectances computed for both optically thin (tau = 1) and thick (tau = 10) cirrus clouds show significant differences between the results for the one- and three-layer models. The reflectances computed for the three-layer cirrus model are substantially larger than those computed for the single-layer cirrus. Finally, we find that cloud reflectance is very sensitive to the optical properties of the small crystals that predominate in the top layer of the three-layer cirrus model. It is critical to define the most realistic geometric shape for the small "quasi-spherical" ice crystals in the top layer for obtaining reliable single-scattering parameters and bulk radiative properties of cirrus.

Yang, P.↗
DOE OSTI2019

Expanding the computational frontier of multi-scale atmospheric simulation to advance understanding of low cloud / climate feedbacks: Final Technical Report

Marine boundary layer clouds are a challenge for climate models. They cover much of the oceans, but they are driven by small-scale turbulent eddies only a few hundred meters across, which in turn respond to the cloud formation. Global climate models have a grid spacing that is far too large to simulate such fluid motions, so they parameterize these cloud formation processes. Different climate modeling groups have designed different parameterizations in which the clouds turn out to be differently sensitive to a warming climate. This drives uncertainties in our best guess at the sensitivity of global warming to greenhouse gas increases. If these cloud-forming eddies could be directly simulated using the well-known equations of fluid motion, they would no longer need to be parameterized, removing a major source of climate modeling uncertainty. In this project, we overcame software engineering challenges to successfully implemented ‘ultraparameterization’ (UP), the first global model that does this, and we tested how well it works. We simulated five-year periods with present-day temperatures and with a warmer climate, and we investigated how the UP-simulated clouds responded to climate – the ‘cloud feedback’ problem. We found little response of clouds at all latitudes to the imposed climate change, which is within the range of predictions of conventional global climate models. UP is a variation on superparameterization, in which small cloud-resolving models (CRMs) are embedded in each column of the global model. In UP, the CRM grid is fine enough (250 m horizontal × 20 m vertical) to explicitly capture boundary-layer turbulent eddies and associated clouds. Because only one small columnar patch is simulated within each climate model grid cell, this is a million-fold more efficient than simulating the entire globe on this same CRM grid., but achieves much of the same effect for the cloud properties. It doesn’t work perfectly. For instance, like conventional climate models, UP simulates too little subtropical stratocumulus cloud, a bias that we are continuing to work to reduce. However, because it directly simulates the turbulent cloud-forming processes, UP is inherently more plausible for simulating how clouds will change in a perturbed climate. In future, we hope to apply UP to another key climate modeling issue: cloud-aerosol interaction and the effect of human-produced aerosols on the climate change we have already experienced and that which is likely to come.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Machine Learning for Synchrophasor Analysis

The report presents results from the development of a cloud-based, Big Data analysis framework for power systems. The computational pipeline uses the Apache Spark framework running in an OpenStack cloud infrastructure. A real-world phasor measurement unit (PMU) dataset has been used to carry out the analysis. Several Machine Learning (ML) methods have been developed and implemented for event and anomaly detection and classification. Actual examples of power system events detection and analysis using synchrophasor data are presented. It has been shown that applications of the cloud-based computing environment and the Apache Spark framework enable a significant increase in the computational efficiency of large-scale PMU data analysis.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2017

Cloud Based Analytical Framework for Synchrophasor Data Analysis

The paper presents initial results from the development of a cloud-based, Big Data analysis platform for power systems. The computational pipeline uses the Apache Spark framework running in an OpenStack cloud infrastructure. A real-world phasor measurement unit (PMU) dataset has been used to carry out the analysis. Actual examples of power system events detection using synchrophasor data are presented. It has been shown that applications of the cloud based computing environment and the Apache Spark framework enable a significant increase in the computational efficiency of large-scale PMU data analysis.

PMU, Machine Learning, Big Data, SPARK, Event Dete↗
NASA NTRS2017

Archive Management of NASA Earth Observation Data to Support Cloud Analysis

NASA collects, processes and distributes petabytes of Earth Observation (EO) data from satellites, aircraft, in situ instruments and model output, with an order of magnitude increase expected by 2024. Cloud-based web object storage (WOS) of these data can simplify the execution of such an increase. More importantly, it can also facilitate user analysis of those volumes by making the data available to the massively parallel computing power in the cloud. However, storing EO data in cloud WOS has a ripple effect throughout the NASA archive system with unexpected challenges and opportunities. One challenge is modifying data servicing software (such as Web Coverage Service servers) to access and subset data that are no longer on a directly accessible file system, but rather in cloud WOS. Opportunities include refactoring of the archive software to a cloud-native architecture; virtualizing data products by computing on demand; and reorganizing data to be more analysis-friendly.

Lynnes, Christopher↗