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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

CFD validation experiments for internal flows

Computational Fluid Dynamics (CFD) validation experiments at NASA Lewis Research Center are described. The material presented summarizes the research in three areas: Inlets, Ducts and Nozzles; Turbomachinery; and Chemically Reacting Flows. The specific validation activities are concerned with shock-boundary layer interactions, vortex generator effects, large low speed centrifugal compressor measurements, transonic fan shock structure, rotor/stator kinetic energy distributions, stator wake shedding characteristics, boundary layer transition, multiphase flow and reacting shear layers. These experiments are intended to provide CFD validation data for the internal flow fields within aerospace propulsion system components.

Povinelli, Louis A.↗
NASA NTRS1988

CFD validation experiments for internal flows

Computational Fluid Dynamics (CFD) validation experiments at NASA Lewis are described. The material presented summarized the research in 3 areas: Inlets, ducts and nozzles; Turbomachinery; and Chemically reacting flows. The specific validation activities are concerned with shock boundary layer interactions, vortex generator effects, large low speed centrifugal compressor measurements, transonic fan shock structure, rotor/stator kinetic energy distributions, stator wake shedding characteristics, boundary layer transition, multiphase flow and reacting shear layers. These experiments are intended to provide CFD validation data for the internal flow fields within aerospace propulsion system components.

Povinelli, Louis A.↗
NASA NTRS1993

Molecular processes in comets

Classical trajectory calculations of the cross sections for vibrational and rotational energy exchange in direct and reactive collisions of hydrogen atoms and hydrogen molecules have been carried out. To test the sensitivity, three potential energy surfaces have been used. For the exchange transitions which occur at small internuclear distances, the rate coefficients for the three surfaces agree quite well. For the direct transitions, there are significant differences for the pure rotational transitions from j=0 to 2 and from j=1 to j=3 in which there is no change in vibration. For higher j the differences tend to disappear, suggesting that the rotational angular momentum can couple to the orbital angular momentum to overcome the centrifugal barrier. Complete numerically exact quantum mechanical calculations for the process in which vJ changes have been performed. Dr. M. A'Hearn has provided data on the fluorescent population of the NH rotational and fine-structure levels from which we should be able to predict accurate photodissociation lifetimes. The distribution rate of C2 is being investigated. A review of H3(+) in terrestrial and extraterrestrial environments was prepared for a volume of Advances in Atomic, Molecular and Optical Physics.

Dalgarno, A.↗
NASA NTRS1994

Design and performance/measurement simulation studies for a ER-2 sunphotometer

Work and results, including developed software, obtained under this grant (NAG 2-620) have been reported in various progress reports, an M.S. Thesis, and software programs. The purpose of this final report is simply to provide a documented listing of what has already been reported. This includes the following: (1) Conceptual Design and Simulation of a Spectral Sunphotometer for the ER-2 Aircraft; (2) Continued Simulation and Design Studies for an ER-2 Sunphotometer; and (3) Simulation and Performance Studies of a Spectra Sunphotometer for the ER-2 Aircraft. Also, software and computational results for modeling airmass and diffuse light effects due to aerosols were provided in support of NASA ARC research on the effects of aerosols resulting from the Pinatubo volcanic eruptions. The references for these articles are as follows: (1) Post Pinatubo Optical Depth Spectra vs. Latitude and Vortex Structure; and (2) Pinatubo and Pre-Pinatubo Optical-Depth Spectra -- Mauna Loa Measurements, Comparisons, Inferred Particle Size Distributions, Radiative Effects, and Relationships to Lidar Data.

Reagan, J. A.↗
NASA NTRS2001

Multi-Epoch XMM Observations of NGC4258

The goal of this project is to better understand whether the well known, warped accretion disk around the supermassive black hole in NGC4258 is responsible for the observed X-ray absorbing column of gas. The active galactic nuclei (AGN) paradigm suggests that doughnut-like tori obscure black holes, not warped disks. Using radio interferometry, we have mapped the disk and its orientation. Through time monitoring radio observations we are following the rotation and variability in the disk structure. The disk crosses the line of sight to the black hole, if it is clumpy, then we will observe variations in the X-ray spectrum of NGC4258 on month to year time scales. We have reduced 14 epochs of very long base interferometry (VLBI) observations that we use to characterize the structure of the maser-laden accretion disk and its evolution. Five epochs of data remain to be reduced in the next year. We have received distributions of data for two of our four intended epochs of X-ray Multimirror Mission (XMM) observation. Because the first two epochs are relatively close in time, and because community understanding of how to calibrate XMM data has until recently been relatively immature, we are waiting until we receive the third epoch of data before we reduce the several datasets. Delivery of the third epoch should take place late this year. The last epoch of XMM observation will take place in mid-2002. In the next year, we will begin to combine constraints on disk clumpiness with estimates of X-ray variability observed by XMM.

Greenhill, Lincoln J.↗
NASA NTRS2023

Oblique Propagation and Temperature Effects on the Resonant Right-Hand Ion Beam Instability

The resonant right-hand instability (RHI) is often the dominant mode driven by reflected ions upstream of Earth’s quasi-parallel bow shock. In the tradition of Peter Gary, this paper further explores the right-hand instability using numerical solutions of the plasma dispersion relation and non-linear kinetic simulations, with parameters inspired by observations from NASA’s Magnetospheric Multiscale (MMS) mission. Agreement is found between the ion distributions in the particle-in-cell simulations and Magnetospheric Multiscale spacecraft data, which show the gyrophase bunching characteristic of the instability. The non-linear structures created by right-hand instability tend to be stronger when the plasma beta is lower. These structures have sizes of around 100 to 200 ion inertial lengths perpendicular to the magnetic field, presenting planet-sized disturbances to the magnetosphere. 2d and 3D hybrid particle-in-cell simulations show that modes with a range of propagation angles oblique to the magnetic field are excited, providing a ground to understand previous statistical studies of observed foreshock waves.

Ari Le↗
DOE OSTI2023

Ensemble models for circuit topology estimation, fault detection and classification in distribution systems

This paper presents a methodology for simultaneous fault detection, classification, and topology estimation for adaptive protection of distribution systems. The methodology estimates the probability of the occurrence of each one of these events by using a hybrid structure that combines three sub-systems, a convolutional neural network for topology estimation, a fault detection based on predictive residual analysis, and a standard support vector machine with probabilistic output for fault classification. The input to all these sub-systems is the local voltage and current measurements. A convolutional neural network uses these local measurements in the form of sequential data to extract features and estimate the topology conditions. The fault detector is constructed with a Bayesian stage (a multitask Gaussian process) that computes a predictive distribution (assumed to be Gaussian) of the residuals using the input. Since the distribution is known, these residuals can be transformed into a Standard distribution, whose values are then introduced into a one-class support vector machine. The structure allows using a one-class support vector machine without parameter cross-validation, so the fault detector is fully unsupervised. Finally, a support vector machine uses the input to perform the classification of the fault types. All three sub-systems can work in a parallel setup for both performance and computation efficiency. In conclusion, we test all three sub-systems included in the structure on a modified IEEE123 bus system, and we compare and evaluate the results with standard approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Bridging the Gap between Cosmological Simulations with Graph Neural Networks and Domain Adaptation

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross dataset tasks (up to 28% better relative error and up to almost an order of magnitude better χ 2 ). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models

Abstract Background A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. Results Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp 614 Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro 323 Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. Conclusions These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Interface properties and built-in potential profile of a LaCr O 3 / SrTi O 3 superlattice determined by standing-wave excited photoemission spectroscopy

LaCrO 3 (LCO)/SrTiO 3 (STO) heterojunctions are intriguing due to a polar discontinuity along [001], exhibiting two distinct and controllable charged interface structures [(LaO) + /(TiO 2 ) 0 and (SrO) 0 /(CrO 2 ) - ] with induced polarization, and a resulting depth-dependent potential. In this study, we have used soft- and hard-x-ray standing-wave excited photoemission spectroscopy (SW-XPS) to quantitatively determine the elemental depth profile, interface properties, and depth distribution of the polarization-induced built-in potentials. We observe an alternating charged interface configuration: a positively charged (LaO) + /(TiO 2 ) 0 intermediate layer at the LCO top /STO bottom interface and a negatively charged (SrO) 0 /(CrO 2 ) - intermediate layer at the STO top /LCO bottom interface. Using core-level SW data, we have determined the depth distribution of species, including through the interfaces, and these results are in excellent agreement with scanning transmission electron microscopy and electron energy-loss spectroscopy mapping of local structure and composition. SW-XPS also enabled deconvolution of the LCO and STO contributions to the valence-band (VB) spectra. Using a two-step analytical approach involving first SW-induced core-level binding-energy shifts and then VB modeling, the variation in potential across the complete superlattice is determined in detail. As a result, this potential is in excellent agreement with density functional theory models, confirming this method as a generally useful tool for interface studies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2012

Large Impact Basins on Mercury: Global Distribution, Characteristics, and Modification History from MESSENGER Orbital Data

The formation of large impact basins (diameter D greater than or equal to 300 km) was an important process in the early evolution of Mercury and influenced the planet's topography, stratigraphy, and crustal structure. We catalog and characterize this basin population on Mercury from global observations by the MESSENGER spacecraft, and we use the new data to evaluate basins suggested on the basis of the Mariner 10 flybys. Forty-two certain or probable impact basins are recognized a few additional basins that may have been degraded to the point of ambiguity are plausible on the basis of new data but are classified as uncertain. The spatial density of large basins (D greater than or equal to 500 km) on Mercury is lower than that on the Moon. Morphological characteristics of basins on Mercury suggest that on average they are more degraded than lunar basins. These observations are consistent with more efficient modification, degradation, and obliteration of the largest basins on Mercury than on the Moon. This distinction may be a result of differences in the basin formation process (producing fewer rings), greater relaxation of topography after basin formation (subduing relief), and/or higher rates of volcanism during the period of heavy bombardment on Mercury compared to the Moon (burying basin rings and interiors).

Fassett, Caleb I.↗
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $\chi^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2011

Vertical Distribution of Aersols and Water Vapor Using CRISM Limb Observations

Near-infrared spectra taken in a limb-viewing geometry by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on-board the Mars Reconnaissance Orbiter (MRO) provide a useful tool for probing atmospheric structure. Specifically, the observed radiance as a function of wavelength and height above the limb allows the vertical distribution of both dust and ice aerosols to be retrieved. These data serve as an important supplement to the aerosol profiling provided by the MRO/MCS instrument allowing independent validation and giving additional information on particle physical and scattering properties through multi-wavelength studies. A total of at least ten CRISM limb observations have been taken so far covering a full Martian year. Each set of limb observations nominally contains about four dozen scans across the limb giving pole-to-pole coverage for two orbits at roughly 100 and 290 W longitude over the Tharsis and Syrtis/Hellas regions, respectively. At each longitude, limb scans are spaced roughly 10 degrees apart in latitude, with a vertical spatial resolution on the limb of roughly 800 m. Radiative transfer modeling is used to model the observations. We compute synthetic CRISM limb spectra using a discrete-ordinates radiative transfer code that accounts for multiple scattering from aerosols and accounts for spherical geometry of the limb observations by integrating the source functions along curved paths in that coordinate system. Retrieved are 14-point vertical profiles for dust and water ice aerosols with resolution of 0.4 scale heights between one and six scale heights above the surface. After the aerosol retrieval is completed, the abundances of C02 (or surface pressure) and H20 gas are retrieved by matching the depth of absorption bands at 2000 nm for carbon dioxide and at 2600 run for water vapor. In addition to the column abundance of water vapor, limited information on its vertical structure can also be retrieved depending on the signal available from aerosol scattering.

Smith, Michael D.↗
NASA NTRS2002

Field-Programmable Gate Array Computer in Structural Analysis: An Initial Exploration

This paper reports on an initial assessment of using a Field-Programmable Gate Array (FPGA) computational device as a new tool for solving structural mechanics problems. A FPGA is an assemblage of binary gates arranged in logical blocks that are interconnected via software in a manner dependent on the algorithm being implemented and can be reprogrammed thousands of times per second. In effect, this creates a computer specialized for the problem that automatically exploits all the potential for parallel computing intrinsic in an algorithm. This inherent parallelism is the most important feature of the FPGA computational environment. It is therefore important that if a problem offers a choice of different solution algorithms, an algorithm of a higher degree of inherent parallelism should be selected. It is found that in structural analysis, an 'analog computer' style of programming, which solves problems by direct simulation of the terms in the governing differential equations, yields a more favorable solution algorithm than current solution methods. This style of programming is facilitated by a 'drag-and-drop' graphic programming language that is supplied with the particular type of FPGA computer reported in this paper. Simple examples in structural dynamics and statics illustrate the solution approach used. The FPGA system also allows linear scalability in computing capability. As the problem grows, the number of FPGA chips can be increased with no loss of computing efficiency due to data flow or algorithmic latency that occurs when a single problem is distributed among many conventional processors that operate in parallel. This initial assessment finds the FPGA hardware and software to be in their infancy in regard to the user conveniences; however, they have enormous potential for shrinking the elapsed time of structural analysis solutions if programmed with algorithms that exhibit inherent parallelism and linear scalability. This potential warrants further development of FPGA-tailored algorithms for structural analysis.

Singleterry, Robert C., Jr.↗
NASA NTRS2013

Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it-an improved and generalized version of Bayesian Blocks [Scargle 1998]-that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piece- wise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by [Arias-Castro, Donoho and Huo 2003]. In the spirit of Reproducible Research [Donoho et al. (2008)] all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.

signal detection↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2019

Wettability Effects on Primary Drainage Mechanisms and NAPL Distribution: A Pore-Scale Study

The pore-scale processes governing water drainage behavior in porous media have implications for geoscience multiphase scenarios including carbon capture and storage, contaminant site remediation, oil recovery, and vadose zone processes. However, few studies report directly observed pore-scale water drainage phenomena in 3-D soils. This knowledge gap limits our ability to verify assumptions underlying existing models and develop optimal solutions. As such, this paper utilizes synchrotron X-ray microtomography to present an experimental pore-scale examination of nonaqueous phase liquid (NAPL)/water distribution along a primary drainage front as dense NAPL was injected upward into water wetting (WW) and intermediate wetting (IW) sand-packed columns. Pore-network structures were extracted from imaged data sets and mapped onto segmented NAPL/water data sets which allowed quantitative examinations of wettability impacts on (a) the extent to which NAPL fills individual pore bodies and (b) relationships between pore size and the phase occupying the pore, with both considered as a function of distance (and capillary pressure) relative to the NAPL front. These results revealed that several hypotheses treating IW sand similarly to WW sands are simplistic. IW systems exhibited a sequence of pore filling that deviated from traditional capillary pressure-based model predictions: NAPL invades smaller pores, while larger, adjacent pores are bypassed leaving multipore residual water ganglia. NAPL pore saturations were close to 1 and did not change with capillary pressure in IW systems. Overall, the results illustrate how a relatively small change in operative contact angle alters NAPL distribution during water drainage, with important implications for geoscience multiphase flow scenarios.

58 GEOSCIENCES↗
DOE OSTI2020

Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning

Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this work, we seek to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs in a probabilistic manner. Furthermore, to solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures, which combines techniques from measure theory and Bayes’ rule to define a unique and numerically stable solution. This approach requires making an initial prediction using an initial guess for the distribution on model inputs and solving a stochastic forward problem. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning Bayesian regression model based on Gaussian processes and demonstrate the proposed methodology on two representative case studies in structure–property linkages.

36 MATERIALS SCIENCE↗