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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Spin-Up and Tuning of the Global Carbon Cycle Model Inside the GISS ModelE2 GCM

Planetary carbon cycle involves multiple phenomena, acting at variety of temporal and spacial scales. The typical times range from minutes for leaf stomata physiology to centuries for passive soil carbon pools and deep ocean layers. So, finding a satisfactory equilibrium state becomes a challenging and computationally expensive task. Here we present the spin-up processes for different configurations of the GISS Carbon Cycle model from the model forced with MODIS observed Leaf Area Index (LAI) and prescribed ocean to the prognostic LAI and to the model fully coupled to the dynamic ocean and ocean biology. We investigate the time it takes the model to reach the equilibrium and discuss the ways to speed up this process. NASA Goddard Institute for Space Studies General Circulation Model (GISS ModelE2) is currently equipped with all major algorithms necessary for the simulation of the Global Carbon Cycle. The terrestrial part is presented by Ent Terrestrial Biosphere Model (Ent TBM), which includes leaf biophysics, prognostic phenology and soil biogeochemistry module (based on Carnegie-Ames-Stanford model). The ocean part is based on the NASA Ocean Biogeochemistry Model (NOBM). The transport of atmospheric CO2 is performed by the atmospheric part of ModelE2, which employs quadratic upstream algorithm for this purpose.

Climate model↗
NASA NTRS2016

Tracking Blade Tip Vortices for Numerical Flow Simulations of Hovering Rotorcraft

Blade tip vortices generated by a helicopter rotor blade are a major source of rotor noise and airframe vibration. This occurs when a vortex passes closely by, and interacts with, a rotor blade. The accurate prediction of Blade Vortex Interaction (BVI) continues to be a challenge for Computational Fluid Dynamics (CFD). Though considerable research has been devoted to BVI noise reduction and experimental techniques for measuring the blade tip vortices in a wind tunnel, there are only a handful of post-processing tools available for extracting vortex core lines from CFD simulation data. In order to calculate the vortex core radius, most of these tools require the user to manually select a vortex core to perform the calculation. Furthermore, none of them provide the capability to track the growth of a vortex core, which is a measure of how quickly the vortex diffuses over time. This paper introduces an automated approach for tracking the core growth of a blade tip vortex from CFD simulations of rotorcraft in hover. The proposed approach offers an effective method for the quantification and visualization of blade tip vortices in helicopter rotor wakes. Keywords: vortex core, feature extraction, CFD, numerical flow visualization

Numerical↗
NASA NTRS2016

Model Predictions and Observed Performance of JWST's Cryogenic Position Metrology System

The James Webb Space Telescope cryogenic testing requires measurement systems that both obtain a very high degree of accuracy and can function in that environment. Close-range photogrammetry was identified as meeting those criteria. Testing the capability of a close-range photogrammetric system prior to its existence is a challenging problem. Computer simulation was chosen over building a scaled mock-up to allow for increased flexibility in testing various configurations. Extensive validation work was done to ensure that the actual as-built system meet accuracy and repeatability requirements. The simulated image data predicted the uncertainty in measurement to be within specification and this prediction was borne out experimentally. Uncertainty at all levels was verified experimentally to be less than 0.1 millimeters.

Lunt, Sharon R.↗
NASA NTRS2016

Model Predictions and Observed Performance of JWST's Cryogenic Position Metrology System

The James Webb Space Telescope cryogenic testing requires measurement systems that both obtain a very high degree of accuracy and can function in that environment. Close-range photogrammetry was identified as meeting those criteria. Testing the capability of a close-range photogrammetric system prior to its existence is a challenging problem. Computer simulation was chosen over building a scaled mock-up to allow for increased flexibility in testing various configurations. Extensive validation work was done to ensure that the actual as-built system meet accuracy and repeatability requirements. The simulated image data predicted the uncertainty in measurement to be within specification and this prediction was borne out experimentally. Uncertainty at all levels was verified experimentally to be less than 0.1 millimeters.

Lunt, Sharon R.↗
NASA NTRS2019

Photoionization Calculations of the Radiation Force Due To Spectral Lines in AGNs

One of the main mechanisms that could drive mass outflows in active galactic nuclei (AGNs) is radiation pressure due to spectral lines. Although straightforward to understand, the actual magnitude of the radiation force is challenging to compute because the force depends on the physical conditions in the gas, as well as the strength, spectral energy distribution (SED), and geometry of the radiation field. We present results from our photoionization and radiation transfer calculations of the force multiplier, M(ξ, t), using the same radiation field to compute the gas photoionization and thermal balance. We assume low gas density (n = 104 per cu.cm) and column density (N ≤ 1017 per sq.cm), a Boltzmann distribution for the level populations, and the Sobolev approximation. Here, we describe results for two SEDs corresponding to an unobscured and obscured AGN in NGC 5548. Our main results are the following: (1) although M(ξ, t) starts to decrease with ξ for ξ gsim 1 as shown by others, this decrease in our calculations is relatively gradual and could be nonmonotonic as M(ξ, t) can increase by a factor of a few for ξ ≈ 10–1000; (2) at these same ξ for which the multiplier is higher than in previous calculations, the gas is thermally unstable by the isobaric criterion; (3) non-local thermodynamic equilibrium effects reduce M(t, ξ) by over two orders of magnitude for ξ gsim 100. The dynamical consequence of result (1) is that line driving can be important for ξ as high as 1000 when the LTE approximation holds, while result (2) provides a natural cloud formation mechanism that may account for the existence of narrow line regions. Result (3) suggests that line driving may not be important for ξ gsim 100 in tenuous plasma.

Dannen, Randall C.↗
NASA NTRSDate not supplied

REACTER: A Versatile Tool for Large-Scale Reactive Molecular Dynamics

Accurately describing reactive events over long length and time scales remains a grand challenge of computational materials science. REACTER is a general protocol for modeling chemical reactions using classical force fields, and is implemented in the popular molecular dynamics software LAMMPS. REACTER has a growing user base and has been used as a model-building tool for a variety of materials, including thermoplastics, thermosets, glassy materials and composites. The method has also been applied to accelerated modeling of reversible chemical reactions, such as the formation of electrochemical components for batteries. Recently, the REACTER protocol has received some major upgrades to enhance its ability to predict when reactions occur and to make it easier to use. Force field parameters can now be automatically assigned to newly created bonds, angles and other interactions. Advanced reaction constraints have also been added, including an Arrhenius constraint to enforce an effective activation energy, a root-mean-square-deviation option for complex geometrical constraints, and a constraint based on the total potential energy of the atoms involved in a reactive site. This potential energy constraint allows for the accurate reproduction of DFT-based tight-binding (DFTB3) predicted bond dissociation curves for mechanically induced bond breaking.

polymer simulations, molecular dynamics↗
NASA NTRSDate not supplied

Overview of Mars Sample Return – Earth Entry System Woven Roughness Heating Augmentation Test in NASA Langley’s Mach 6 Wind Tunnel

The Mars Sample Return Mission (MSR) is a planned NASA flagship mission in which a sample retrieval lander (SRL) with a rover will be flown to Mars to obtain sample tubes on the surface that were dropped by the Mars 2020 rover [1]. After obtaining the sam-ples, the rover will return and ascend back to Martian orbit onboard the Mars Ascent Vehicle (MAV). Upon return to Earth orbit, the samples will perform Entry, Descent, and Landing (EDL) with the Earth Entry Sys-tem (EES) architecture, and land in Utah. The EES vehicle will utilize a HEEET-variant as its TPS, which will be the first time a woven TPS will be used on a flagship NASA mission [2]. This TPS offers a unique challenge for Computational Fluid Dynamics (CFD) modeling of the aerothermal envi-ronment of the vehicle, as woven roughness heating augmentation has not been extensively investigated experimentally. As a result, in order to validate com-putational models for woven roughness heating aug-mentation, a wind tunnel test campaign at NASA Langley Research Center’s Mach 6 wind tunnel was performed in April of 2023. This test campaign consisted of over a hundred runs with Reynolds numbers spanning from 1-7 mil-lion 1/ft and with six separate wind tunnel models used. A second campaign with a suite of new models will be conducted in Summer 2023 as well as a cam-paign with a flat plate model, both of which are of great interest to the MSR-EES project. The data obtained from this test are extremely vital for the MSR mission, as they will validate CFD roughness heating models which will be directly used to design the TPS of the EES portion of MSR and characterize the heating environment that the entry ve-hicle will experience. Further extensions of the MSR-EES test campaign will continue to provide validation data for developing more effective computational tools.

Jonathan Cheatwood↗
NASA NTRS2025

PSE-Based Aerodynamic Flow Transition Prediction Using Automated Unstructured CFD Integration

Accurate, robust, and efficient prediction of transition in viscous flows is a significant challenge in computational fluid dynamics. We present a coupled, high-fidelity, iterative approach that leverages the FUN3D flow solver and the LASTRAC stability code to predict transition in low-disturbance environments, initiated by the linear growth of boundary-layer instability modes. Our method integrates the ability of FUN3D to compute mixed laminar-transitional-turbulent mean flows via transition-sensitized Reynolds-Averaged Navier-Stokes equations with the ability of LASTRAC to perform linear stability analysis, all within an automated framework that requires no intermediate user involvement. Unlike conventional frameworks that rely on classical stability theory or reduced-order metamodels, our approach employs the parabolized stability equations to provide more accurate and reliable estimates of disturbance growth for multiple instability mechanisms, including Tollmien-Schlichting, Kelvin-Helmholtz, and crossflow modes. By accounting for the effects of mean-flow nonparallelism as well as the surface curvature, this approach lays the foundation for improved N-factor correlations for transition onset prediction in a broad class of flows. We apply this method to three distinct flow configurations: 1) flow over a zero-pressure-gradient flat plate, 2) the NLF-0416 airfoil with both natural and separation-induced transitions, and 3) a 6:1 prolate spheroid, where transition is primarily driven by crossflow instability. For the two-dimensional cases, a formulated intermittency distribution is used to model the transition zone in between the laminar and fully turbulent flows. The results include comparisons with experimental measurements, similar numerical approaches, and transport-equations-based models, demonstrating good agreement in surface pressure coefficients, transition onset locations, and skin-friction coefficients for all three configurations. Besides contributing a couple of new insights into boundary-layer transition in these canonical cases, this study provides a powerful tool for transition modeling in both research and design applications in aerodynamics.

Transition Prediction↗
NASA NTRS2024

3D Cloud Masking Across A Broad Swath Using Multi-Angle Polarimetry and Deep Learning

Understanding the 3-dimensional structure of clouds is of crucial importance to modeling our changing climate. Active sensors, such as radar and lidar, provide accurate vertical cloud profiles, but are mostly restricted to along-track sampling. Passive sensors can capture a wide swath, but struggle to see beneath cloud tops. In essence, both types of products are restricted to two dimensions: as a cross-section in the active case, and an image in the passive case. However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information can be challenging, requiring computationally expensive radiative transfer techniques. Machine learning, as an alternative, may be able to capture some of the complexity of a full 3D radiative transfer solution with significantly less computational expense. In this work, we make three contributions towards understanding 3D cloud structure from multi-angle polarimetry. First, we introduce a large-scale, publicly available dataset that fuses existing cloud products into a format more amenable to machine learning. This dataset treats multi-angle polarimetry as an input, and radar-based vertical cloud profiles as an output. Second, we describe and evaluate strong baseline machine learning models based that predict these profiles from the passive imagery. Notably, these models are trained only on center-swath labels, but can predict cloud profiles over the entire passive imagery swath. Third, we leverage the information-theoretic nature of machine learning to draw conclusions about the relative utility of various sensor configurations, including spectral channels, viewing angles, and polarimetry. These findings have implications for Earth-observing missions such as NASA’s Plankton, Aerosol, Cloud-ocean Ecosystem (PACE) and Atmosphere Observing System (AOS) missions, as well as in informing future applications of computer vision to atmospheric remote sensing.

clouds↗
DOE OSTI2012

Transversity from First Principles in QCD

Transversity observables, such as the T-odd Sivers single-spin asymmetry measured in deep inelastic lepton scattering on polarized protons and the distributions which are measured in deeply virtual Compton scattering, provide important constraints on the fundamental quark and gluon structure of the proton. In this talk I discuss the challenge of computing these observables from first principles; i.e.; quantum chromodynamics, itself. A key step is the determination of the frame-independent light-front wavefunctions (LFWFs) of hadrons - the QCD eigensolutions which are analogs of the Schroedinger wavefunctions of atomic physics. The lensing effects of initial-state and final-state interactions, acting on LFWFs with different orbital angular momentum, lead to T-odd transversity observables such as the Sivers, Collins, and Boer-Mulders distributions. The lensing effect also leads to leading-twist phenomena which break leading-twist factorization such as the breakdown of the Lam-Tung relation in Drell-Yan reactions. A similar rescattering mechanism also leads to diffractive deep inelastic scattering, as well as nuclear shadowing and non-universal antishadowing. It is thus important to distinguish 'static' structure functions, the probability distributions computed the target hadron's light-front wavefunctions, versus 'dynamical' structure functions which include the effects of initial- and final-state rescattering. I also discuss related effects such as the J = 0 fixed pole contribution which appears in the real part of the virtual Compton amplitude. AdS/QCD, together with 'Light-Front Holography', provides a simple Lorentz-invariant color-confining approximation to QCD which is successful in accounting for light-quark meson and baryon spectroscopy as well as hadronic LFWFs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

PCM-Based Durable Write Cache for Fast Disk I/O

Flash based solid-state devices (FSSDs) have been adopted within the memory hierarchy to improve the performance of hard disk drive (HDD) based storage system. However, with the fast development of storage-class memories, new storage technologies with better performance and higher write endurance than FSSDs are emerging, e.g., phase-change memory (PCM). Understanding how to leverage these state-of-the-art storage technologies for modern computing systems is important to solve challenging data intensive computing problems. In this paper, we propose to leverage PCM for a hybrid PCM-HDD storage architecture. We identify the limitations of traditional LRU caching algorithms for PCM-based caches, and develop a novel hash-based write caching scheme called HALO to improve random write performance of hard disks. To address the limited durability of PCM devices and solve the degraded spatial locality in traditional wear-leveling techniques, we further propose novel PCM management algorithms that provide effective wear-leveling while maximizing access parallelism. We have evaluated this PCM-based hybrid storage architecture using applications with a diverse set of I/O access patterns. Our experimental results demonstrate that the HALO caching scheme leads to an average reduction of 36.8% in execution time compared to the LRU caching scheme, and that the SFC wear leveling extends the lifetime of PCM by a factor of 21.6.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Big Data Processing for Power Grid Event Detection

In this paper we present the application of big data processing for the development of machine learning (ML) models to detect relevant events in power grid operations. This is based on almost 20TB of phasor measurement unit data corresponding to up to two years of operation of three grid interconnections which provide power to most of the United States. A significant aspect of the work consists in having all data processing performed on a single standard GPU server, from pre-processing to ML model training and testing. We describe the data and computational infrastructure, challenges faced and methods used in data processing, main findings and results. The ML approach employed for best utilization of the big data is also discussed, including sample results.

Paes Leao, Bruno↗
DOE OSTI2020

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction

Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank in order to study the expressiveness of different structure-based prediction schemes. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis to capture a complex set of both local and global structural features. While variations of these techniques have been successfully applied to proteins before, we demonstrate that our hybridized approach, PersGNN, outperforms either method on its own as well as a baseline neural network that learns from the same information. PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR) compared to the best individual model, as well as high F1 scores across different gene ontology categories, indicating the transferability of this approach.

Swenson, Nicolas↗
DOE OSTI2021

Real-Time XFEL Data Analysis at SLAC and NERSC: a Trial Run of Nascent Exascale Experimental Data Analysis

X-ray scattering experiments using Free Electron Lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to computing in two ways: i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; ii) data collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround time from data acquisition to full molecular reconstruction in as little as 10 min -- sufficient time for the experiment's operators to make informed decisions. By hosting the data analysis on Cori, and by automating LCLS-NERSC interoperability, we achieved a data analysis rate which matches the data acquisition rate. Furthermore, completing data analysis with 10 mins is a first for XFEL experiments and an important milestone if we are to keep up with data collection trends.

Blaschke, Johannes P.↗
DOE OSTI2016

XAFS Investigation of Uranium Binding by Seawater-Contacted Amidoxime Adsorbents

Extraction of uranium from seawater constitutes an important avenue for ensuring nuclear fuel availability beyond the coming century. With a predicted 100 years of uranium remaining in terrestrial ores, efficient extraction of the 4.5 billion tons of uranium dissolved in ocean water would enable access to an effectively limitless supply. More near-term benefits include establishment of a backstop technology, preventing runaway price inflation for this critical resource. The current state-of-the-art material for seawater extraction of uranium is a polymeric adsorbent composed of a robust trunk material subsequently surface functionalized by copolymerization of polyacrylonitrile and a hydrophilic comonomer. Treatment of this material with hydroxylamine results in the formation of poly-amidoxime functionalities which have been known to bind strongly with uranium since the early 1980's. Despite more than three decades of investigation, many questions remain unanswered regarding how these adsorbents bind uranium in seawater. The physical characteristics of the polymer prohibit many classical characterization techniques, while most spectroscopic approaches are impeded by the relatively low concentration of uranium compared to the polymer itself and other metals extracted by the adsorbent. The traditional approach has been to investigate small molecule analogues of uranyl - dioxouranium (VI), the form of uranium present in seawater - as bound by amidoxime or amidoxime derivatives, and assert the findings to be representative of the larger polymer system. Computational efforts have also been recently leveraged to a similar end. This approach is not without difficulty, as it has been routinely proposed that the treatment with hydroxylamine and subsequent alkaline conditioning results in formation of a cyclic imide dioxime site, which is truly responsible for uranium binding, as opposed to the 'open chain' amidoxime. Further complications ensue from considering the possibility of cooperative binding interactions from the different hydrophilic comonomers which are known to be necessary for optimizing uranium capacity. Finally, application of different surface polymerization techniques has the potential to directly affect the morphology of the surface polymer by influencing the degree of grafting, extent of polymer crosslinking, and polymer density. The impact of these morphological considerations on the uranyl binding environment cannot be easily replicated with small molecule studies or by computation. Ultimately, the precise binding environment of uranium is unknown, but constitutes critical knowledge for the design and engineering of advanced adsorbent materials capable of selective extraction of uranium from seawater. Analysis of the EXAFS fits reveals three of the four polymer morphologies appear to bind uranium in different chemical environments, none of which were predicted by crystallographic study or computational investigation, challenging numerous long-held assumptions. Both AI-8 and AN/HEA spectra possess features which could be attributable to cooperative binding of uranium by comonomers, but this cannot be definitively determined by EXAFS without further investigation. Unlike AF-1, neither AI-8 or AN/HEA require an adjacent μ{sup 2}-oxo-bridged transition metal to obtain a reasonable fit. The fit for Cyclic AF-1 is refined to almost identical parameters to the fit of the original AF-1 adsorbent, with the only difference is the contracting of the light elements in the second coordination sphere. It is thus proposed that the DMSO and heat treatment of the AF-1 fiber does very little to change the uranium binding site, and any differences in uptake are more readily explained by changes in the morphology of the graft polymer. There is no evidence for cyclic amidoxime functionality contributing significantly to the extraction of uranium from seawater, and efforts to maximize the number of these binding sites should be discontinued. While molecular interactions necessarily occur in adsorption processes, these results clearly reveal physical phenomena at the mesoscale play a critical role in adsorbent behavior, even to the extent that the binding environment differs from those predicted by small molecule studies. In contrast to the current paradigm where chemical functionality is varied, improvements in the performance of polymer-based amidoxime-functionalized adsorbents for extraction of uranium from seawater need also be pursued through interrogation and modulation of bulk morphology and improvements in polymer hydrophilicity. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Implementation and Comparison of Different Algorithms for on the fly Doppler Broadening in RMC Code

Accurate cross sections are of great importance to Monte Carlo particle transport calculation codes which use pointwise cross sections. It is well known that cross sections have much dependence on temperature on account of Doppler effect which is particularly vital for resonance absorption cross sections. In general, pre-prepared continuous-energy libraries are used in Monte Carlo codes, which is feasible in many applications. However, in multi-physics calculations where neutronics and thermal hydraulics are coupled, cross sections at a fine interval of temperatures are desired, which brings a great challenge to computer memory. Consequently, different methods about on the fly Doppler broadening for cross sections are proposed, which can generate cross sections at the specific temperature points during neutron simulations. These methods can be roughly classified into two categories: one is to calculate the required cross sections by different algorithms, such as interpolation using a high order function expansion, multipole representation, and different integration algorithms based on Doppler broadening equation. The other, for instance, target motion sampling (TMS), does not calculate the Doppler broadening cross sections but takes into account Doppler effect by rejection technique. In this paper, several on the fly Doppler broadening algorithms based on Doppler broadening equation are implemented in the Reactor Monte Carlo code RMC, and then the accuracy and efficiency are discussed and compared. In this paper, several algorithms for on the fly Doppler broadening based on Doppler broadening equation have been implemented in RMC and the focus falls on the comparison of the Gauss Legendre quadrature and Gauss Hermite quadrature. Gauss Legendre quadrature is more accurate than Gauss Hermite quadrature but also more time consuming. Base temperature has a significant impact on Gauss Hermite quadrature while increasing base temperature to room temperature (300 K) can give satisfactory results. In addition, the proportion of overhead of Doppler broadening treatment in total computational time tends to decrease with an increase number of nuclides in fuel. For depletion calculation, both algorithms are in good agreement with reference and the relative errors of K{sub inf} are within 5 times of standard deviation. As for total computational time, the ratio between Gauss Legendre quadrature or Gauss Hermite quadrature and reference decreases and reaches 9.6 or 3.4 at last, respectively. Four important isotopes are chosen to compare the atom density and all relative errors are within 0.25%. After comparisons of different algorithms and different base temperatures, it can be found that Gauss Hermite quadrature broadening from 300 K can perform on the fly cross section Doppler broadening with least total computational time and acceptable precision. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗