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At least 847 records · Page 47

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NASA NTRS2012

Kinetic Description of Ionospheric Outflows Based on the Exact Form of Fokker-Planck Collision Operator: Electrons

We present the results of a finite difference implementation of the kinetic Fokker-Planck model with an exact form of the nonlinear collisional operator, The model is time dependent and three-dimensional; one spatial dimension and two in velocity space. The spatial dimension is aligned with the local magnetic field, and the velocity space is defined by the magnitude of the velocity and the cosine of pitch angle. An important new feature of model, the concept of integration along the particle trajectories, is discussed in detail. Integration along the trajectories combined with the operator time splitting technique results in a solution scheme which accurately accounts for both the fast convection of the particles along the magnetic field lines and relatively slow collisional process. We present several tests of the model's performance and also discuss simulation results of the evolution of the plasma distribution for realistic conditions in Earth's plasmasphere under different scenarios.

Khazanov, George V.↗

NASA NTRS2015

Experimental Constraints on the Chemical Differentiation of Mercurys Mantle

Mercury is known as being the most reduced terrestrial planet with the highest core/mantle ratio. Results from MESSENGER spacecraft have shown that its surface is FeO-poor (2-4 wt%) and S-rich (up to 6-7 wt%), which confirms the reducing nature of its silicate mantle. In addition several features suggest important melting stages of the Mercurian mantle: widespread volcanic deposits on its surface, a high crustal thickness (approximately 10% of the planet's volume) and chemical compositions of its surface suggesting several stages of differentiation and remelting processes. Therefore it is likely that igneous processes like magma ocean crystallization and continuous melting have induced chemical and mineralogical heterogeneities in the Mercurian mantle. The extent and nature of compositional variations produced by partial melting remains poorly constrained for the particular compositions of Mercury (very reducing conditions, low FeO-contents and high sulfur-contents). Melting experiments with bulk Mercury-analogue compositions are scarce and with poorly con-trolled starting compositions. Therefore additional experimental data are needed to better understand the differentiation processes that lead to the observed chemical compositions of Mercury's surface.

Boujibar, A.↗

NASA NTRS2008

Time-Dependent Erosion of Ion Optics

The accurate prediction of thruster life requires time-dependent erosion estimates for the ion optics assembly. Such information is critical to end-of-life mechanisms such as electron backstreaming. CEX2D was recently modified to handle time-dependent erosion, double ions, and multiple throttle conditions in a single run. The modified code is called "CEX2D-t". Comparisons of CEX2D-t results with LDT and ELT post-tests results show good agreement for both screen and accel grid erosion including important erosion features such as chamfering of the downstream end of the accel grid and reduced rate of accel grid aperture enlargement with time.

ion thrusters↗

NASA NTRS2015

Method to Generate Full-Span Ice Shape on Swept Wing Using Icing Tunnel Data

There is a collaborative research program by NASA, FAA, ONERA, and university partners to improve the fidelity of experimental and computational simulation methods for swept-wing ice accretion formulations and resultant aerodynamic effects on large transport aircraft. This research utilizes a 65 scale Common Research Model as the baseline configuration. In order to generate the ice shapes for the aerodynamic testing, ice-accretion testing will be conducted in the NASA Icing Research Tunnel utilizing hybrid model from the 20, 64, and 83 spanwise locations. The models will have full-scale leading edges with truncated chord in order to fit the IRT test section. The ice shapes from the IRT tests will be digitized using a commercially available articulated-arm 3D laser scanning system. The methodology to acquire 3D ice shapes using a laser scanner was developed and validated in a previous research effort. Each of these models will yield a 1.5ft span of ice than can be used. However, a full-span ice accretion will require 75 ft span of ice. This means there will be large gaps between these spanwise ice sections that must be filled, while maintaining all of the important aerodynamic features. A method was developed to generate a full-span ice shape from the three 1.5 ft span ice shapes from the three models.

swept↗

NASA NTRS2016

T-Cap Pull-Off and Bending Behavior for Stitched Structure

The Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) is a structural concept that was developed by The Boeing Company to address the complex structural design aspects associated with a pressurized hybrid wing body aircraft configuration. An important design feature required for assembly is the integrally stitched T-cap, which provides connectivity of the corner (orthogonal) joint between adjacent panels. A series of tests were conducted on T-cap test articles, with and without a rod stiffener penetrating the T-cap web, under tension (pull-off) and bending loads. Three designs were tested, including the baseline design used in largescale test articles. The baseline had only the manufacturing stitch row adjacent to the fillet at the base of the T-cap web. Two new designs added stitching rows to the T-cap web at either 0.5- or 1.0-inch spacing along the height of the web. Testing was conducted at NASA Langley Research Center to determine the behavior of the T-cap region resulting from the applied loading. Results show that stitching arrests the initial delamination failures so that the maximum strength capability exceeds the load at which the initial delaminations develop. However, it was seen that the added web stitching had very little effect on the initial delamination failure load, but actually decreased the initial delamination failure load for tension loading of test articles without a stiffener passing through the web. Additionally, the added web stitching only increased the maximum load capability by between 1% and 12.5%. The presence of the stiffener, however, did increase the initial and maximum loads for both tension and bending loading as compared to the stringerless baseline design. Based on the results of the few samples tested, the additional stitching in the T-cap web showed little advantage over the baseline design in terms of structural failure at the T-cap web/skin junction for the current test articles.

Lovejoy, Andrew E.↗

NASA NTRS2016

Tension and Bending Testing of an Integral T-Cap for Stitched Composite Airframe Joints

The Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) is a structural concept that was developed by The Boeing Company to address the complex structural design aspects associated with a pressurized hybrid wing body aircraft configuration. An important design feature required for assembly is the integrally stitched T-cap, which provides connectivity of the corner (orthogonal) joint between adjacent panels. A series of tests were conducted on T-cap test articles, with and without a rod stiffener penetrating the T-cap web, under tension (pull-off) and bending loads. Three designs were tested, including the baseline design used in large-scale test articles. The baseline had only the manufacturing stitch row adjacent to the fillet at the base of the T-cap web. Two new designs added stitching rows to the T-cap web at either 0.5- or 1.0-inch spacing along the height of the web. Testing was conducted at NASA Langley Research Center to determine the behavior of the T-cap region resulting from the applied loading. Results show that stitching arrests the initial delamination failures so that the maximum strength capability exceeds the load at which the initial delaminations develop. However, it was seen that the added web stitching had very little effect on the initial delamination failure load, but actually decreased the initial delamination failure load for tension loading of test articles without a stiffener passing through the web. Additionally, the added web stitching only increased the maximum load capability by between 1% and 12.5%. The presence of the stiffener, however, did increase the initial and maximum loads for both tension and bending loading as compared to the stringerless baseline design. Based on the results of the few samples tested, the additional stitching in the T-cap web showed little advantage over the baseline design in terms of structural failure at the T-cap web/skin junction for the current test articles.

Lovejoy, Andrew E.↗

NASA NTRS2016

Processing and Managing the Kepler Mission's Treasure Trove of Stellar and Exoplanet Data

The Kepler telescope launched into orbit in March 2009, initiating NASAs first mission to discover Earth-size planets orbiting Sun-like stars. Kepler simultaneously collected data for 160,000 target stars at a time over its four-year mission, identifying over 4700 planet candidates, 2300 confirmed or validated planets, and over 2100 eclipsing binaries. While Kepler was designed to discover exoplanets, the long term, ultra- high photometric precision measurements it achieved made it a premier observational facility for stellar astrophysics, especially in the field of asteroseismology, and for variable stars, such as RR Lyraes. The Kepler Science Operations Center (SOC) was developed at NASA Ames Research Center to process the data acquired by Kepler from pixel-level calibrations all the way to identifying transiting planet signatures and subjecting them to a suite of diagnostic tests to establish or break confidence in their planetary nature. Detecting small, rocky planets transiting Sun-like stars presents a variety of daunting challenges, from achieving an unprecedented photometric precision of 20 parts per million (ppm) on 6.5-hour timescales, supporting the science operations, management, processing, and repeated reprocessing of the accumulating data stream. This paper describes how the design of the SOC meets these varied challenges, discusses the architecture of the SOC and how the SOC pipeline is operated and is run on the NAS Pleiades supercomputer, and summarizes the most important pipeline features addressing the multiple computational, image and signal processing challenges posed by Kepler.

high performance computing↗

NASA NTRS2018

Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning algorithm requiring a large number of training data. Since the archives of intensity data and tropical cyclone centric satellite images is openly available for use, the training data is easily created by combining the two. Results, case studies, prototypes, and advantages of this approach will be discussed.

tropical cyclone intensity↗

NASA NTRS2018

Dynamics of Gaps Created By Burning in Florida Oak-Saw Palmetto (Quercus, Fagaceae-Serenoa repens, Arecaceae) Scrub

Sandy gaps in the shrub matrix of oak (Quercus L.)-saw palmetto (Serenoa repens (W. Bartram) Small) scrub are created by fire but typically close quickly because of rapid regrowth. Such gaps are important habitat features for rare scrub flora and fauna and appear to have been more common in the historical landscape. We followed, from 1993 to 2016, the dynamics of 12 gaps (32.2-98.1 sq m) created by burning slash piles as part of restoration of long-unburned scrub. Gaps closed slowly, primarily by canopy spread of oaks around the gaps. In the absence of subsequent fire, gaps closed within approximately 12 yr. When burned a second or third time, gap area increased to near the initial after-burn size but then declined in area more rapidly than after the initial fire. Vegetation that reestablished in gaps differed from that of the scrub matrix in having less cover of scrub oaks, less cover of S. repens. > 0.5 m, greater cover of native shrubs and forbs. > 0.5 m, and more bare ground. Soil heating from slash-pile burning killed the roots and rhizomes from which scrub oaks, Serenoa, and ericaceous shrubs sprout; this altered and slowed the after-fire recovery.

Serenoa↗

NASA NTRS2019

Lipid Decontamination Procedures for Life Detection Missions

Molecular biosignatures are key targets for current, proposed, and future life detection missions. With the high accuracy and low limit of detection (LOD) that new and future instruments will require, decontamination of life detection hardware is necessary to prevent false positives. Lipids are a molecular biosignature of interest, as they are ubiquitous to all life as we know it, can survive unaltered in the geologic record for longer than any other biomolecule (i.e. billions of years), and form through both biotic and abiotic processes. Lipids display origin-diagnostic molecular patterns that can reveal biotic or abiotic synthesis, so finding them and ascertaining their molecular features is important for potentially detecting evidence of life elsewhere. Traditional methods of decontamination, or contamination control (CC), primarily clean hardware through fabrication in sterile (cleanroom) environments, killing microbes, and removing/flushing contaminants off instrument and spacecraft components. However, research suggests that some standard cleaning methods are either unlikely to remove lipid contaminants or are incompatible with life detection instrument materials. To solve this problem, I propose to find, test, and verify a decontamination method that thoroughly cleans instruments by destroying lipid molecules, but is simultaneously compatible with major materials used in these instruments. I will study the effects of traditional CC methods (including Dry Heat Microbial Reduction and Vapor phase Hydrogen Peroxide) and experimental CC methods (Electron Beam Irradiation) on lipid molecules for application to life detection instrumentation. I will then develop a CC plan for a novel lipid detector (ExCALiBR, Extractor for Chemical Analysis of Lipid Biomarkers in Regolith) searching for lipids in either soil or icy world scenarios. This plan will uphold planetary protection regulation requirements and validate experimental analyses of in-situ life detection tests.

Decontamination↗

NASA NTRSDate not supplied

Alternative Setup for Long-Duration Low-Duty-Cycle 600°C Ambient Testing of SiC Integrated Circuits

A scalable, compact oven testbed system for simultaneously evaluating a multitude of high temperature integrated circuits (ICs) for prolonged operating times of up to 600 °C has been prototyped. The new testbed system enables long-duration high temperature testing in sufficient statistical quantities consistent with standard aerospace electronics engineering standards. This setup is comprised of multiple compact ovens housing chips or packages mounted to ceramic circuit boards. Each oven is a compact 15.2 cm length by 15.2 cm width by 12.7 cm depth with a maximum 400 Watts of heating power. The custom-made silicon oxide ceramic heating block inside each oven is based on a 3D printed design adapted for the easy insertion of the IC device under test (DUT). This innovative design provides the quick insertion of ICs with or without a ceramic package into a 600 °C environment by utilizing a movable 11.43 cm long ceramic substrate with electrical traces extending from the oven hot zone to external standard plastic-based board connectors. Another key oven design feature is the minimization of the DUT exposure to electromagnetic interference (EMI) by utilizing a filtered DC power source to reduce heating element noise. Additionally, the ovens can be configured in a parallel arrangement allowing global data monitoring over a single industrial RS422 serial port. This feature is important for scaling up to test multiple ICs semi-simultaneously. A USB serial port is provided to independently control the operating parameters of each oven such as the oven target temperature and oven ramp rate. The oven temperature can reach up to 600 °C with a confirmed +/- 4 °C maximum deviation across the test zone region. The ramp rate can be programmed from 1 °C/minute up to 10 °C/minute. Furthermore, a programmable switchboard is used to interface with the DUT. This switchboard comprises a National Instruments Peripheral Component Interconnect eXtension for Instrumentation (NI PXI) system and a breakout board to send and receive power, analog or digital test signals. By using this unique oven testbed system, a variety of ICs can now be tested in parallel using the same test components configured for a diverse set of requirements.

DC Powered Oven↗

NASA NTRS2023

Predicting Two-Dimensional Airfoil Performance Using Graph Neural Networks

Computer simulations require the use of meshes to simulate geometries. These meshes capture important geometric features of the design and can be used in machine learning modeling. This report explores the use of graph neural networks (GNNs) to learn features from two-dimensional (2D) airfoil designs represented as a set of nodes connected using edges. This type of network is common in aerospace applications: most geometries are represented as a mesh in order to perform analysis. The objective of this work is to use GNNs to predict the performance of 2D airfoils generated using the program XFOIL. The predicted performance parameters include bulk quantities such as coefficients of lift (C L ), drag (C d , C dp ), moment (C m ), and node-specific quantities such as coefficient of pressure (C p ). In this report, a spline convolutional graph-based neural network is compared with deep learning neural networks to predict both bulk and node-specific quantities. The findings indicate the GNNs are able to predict bulk quantities quite well; however, when the number of outputs is increased, the deep neural network (DNN) proves to be better in its prediction capability. Two different normalization strategies were compared in the training of both GNNs and DNNs: minmax and standard deviation. In both types of networks, standard deviation scaling proved to be the best.

machine learning↗

DOE OSTI2022

NRAP-Open-IAM: Open-Source Integrated Assessment Model

NRAP Open IAM is the latest evolution in a series of quantitative models developed under the U.S. Department of Energy’s National Risk Assessment Partnership (NRAP) to address risk assessment, risk management and containment assurance questions of geological carbon storage (GCS). NRAP Open IAM allows a user to define a site specific GCS scenario, characterize important site features, events, and processes (considering uncertainty), couple together reduced order models of various system components to rapidly forecast response to CO 2 injection, and perform stochastic analysis to quantify containment effectiveness, leakage risk, and impact to receptors of concern.

Vasylkivska, Veronika S.↗

DOE OSTI2024

Modeling for a Digital Twin-Based Remote Operation System Framework

New reactor designs and technologies are being developed to grow and advance the nuclear industry. Microreactors are one of the many new concepts for advancing the industry. Microreactors are very small reactors generally designed to have an operating power of 20 MWth or less. They are ideal for many applications in which it would not be feasible to have a large-scale reactor, such as powering remote communities, military bases, and mining sites. Many of these applications currently rely on diesel generators for power, and replacing those generators with the carbon-free energy of a microreactor is a major driving factor for microreactor development. However, most of the use cases for microreactors are in isolated locations where construction and labor costs are much higher. Microreactors will need to be comparable to other energy production methods for their deployment to be successful. Remotely operating the microreactor has a great potential to benefit the economics and make it more cost competitive. With remote operations, the operation facility location could be strategically chosen based on factors such as construction costs, workforce size, etc. The benefits of remote operations could be leveraged even more if the remote operation system is semi-autonomous. If the remote operation system is semi-autonomous, more microreactors could be operated and monitored from one remote operation facility. Additionally, with the system handling some tasks for the human operator, it could reduce the number of operating staff necessary for the microreactor. Digital twins can be used to introduce a level of automation to the remote operation system. Digital twins are capable of component monitoring, system operation and control, and predictive performance [1]. All of those features are important for a successful semi-autonomous remote operation system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

DETERMINATION OF STRUT QUALITY FACTORS IN ADDITIVELY MANUFACTURED LATTICES USING IN-SITU COMPRESSION TESTING µ-CT

In response to the need for an automated, commercial method to qualify additively manufactured (AM) lattice components, an experiment was conducted to evaluate the effects of defective lattice struts on the structural compression strength. Lattice samples with known defective or missing struts were compressed using a Deben CT5000RT and imaged using xray µ-CT. The compressive force and x-ray computed tomography results were compared to defect free standards to evaluate the impact of each defect type on the overall structure’s compressive strength. This analysis will allow for simplifications to Finite Element Analysis (FEA) on AM parts without sacrificing model fidelity. Understanding the contribution of each defect type and severity will also better inform non-destructive evaluation (NDE) personnel of the inspection parameters necessary to detect the smallest feature of importance.

Dinova, Vincent A.↗

DOE OSTI2020

Arctic Tipping Points Triggering Global Change (LDRD Final Report)

The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Eject, crash, or survive: Using machine learning to predict orbital instability of exoplanetary systems

Astronomers throughout history, including titans like Kepler and Newton, have tackled planetary dynamics and orbital instability. Despite strides taken in research, understanding the evolution of planetary orbits remains an intricate, computationally expensive, and analytically unsolved problem. I apply machine learning classification methods to numerical simulations of planetary systems in order to predict the long-term fate of the planet - whether the planet remains in a stable orbit or not. My method uses the first 41.1 years (≤ 500 orbits) of data from a planet’s simulation to calculate 17 dynamically-motivated metrics; I trained my classifier on these features to predict a planet’s stability after 107 years. At 84.33%, my classifier was comparable in accuracy to pre-existing literature, despite using significantly less computational power than most other methods. In my research, I found that the standard deviation of eccentricity, mass ratios for neighboring planets, and semi-major axis ratio with the outer planet neighbor to be the most predictive features of instability. I propose reasons for the importance of these features, their role in planetary dynamics, as well as possible explanations for why some planets were misclassified. By understanding the important metrics of instability and reasons for misclassification, we can begin to understand more about system architectures, orbital motion and dynamics, and the formation and evolution of the exoplanetary systems. This is applicable in our own Solar System, and with exoplanet discovery missions such as TESS, this research becomes especially relevant in understanding the new exoplanetary systems we discover.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Eject, crash, or survive: Using machine learning to predict orbital instability of exoplanetary systems

79 ASTRONOMY AND ASTROPHYSICS↗