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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 631 records · Page 35
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NASA NTRS2023

A Machine Learning-Based Approach to Time-Series Wave Identification in the Solar Wind

The Wind spacecraft has yielded several decades of high-resolution magnetic field data, a large fraction of which displays small-scale structures. In particular, the solar wind is full of wavelike fluctuations that appear in both the field magnitude and its components. The nature of these fluctuations can be tied to the properties of other structures in the solar wind, such as shocks, that have implications for the time evolution of the solar wind. As such, having a large collection of wave events would facilitate further study of the effects that these fluctuations have on solar wind evolution. Given the large volume of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed. To this end, a subset of Wind data is labeled and used as a training set for a multi-branch 1D convolutional neural network aimed at classifying circularly polarized wave modes. Using this algorithm, a preliminary statistical study of one year of data is performed, yielding about 300,000 wave intervals out of about 5,000,000 solar wind intervals. The wave intervals come about more often in the fast solar wind and at higher temperatures, and the number of waves per day is highly periodic. This machine learning-based approach to wave detection has the potential to be a powerful, inexpensive way to catalog waves throughout decades of spacecraft data.

Samuel Fordin↗
NASA NTRS2023

Two-Dimensional Network Flow Modeling of No-Vent Tank Filling of A Cryogenic Tank With Thermo-Dynamic Vent System Assisted Injector

A finite volume-based network flow modeling tool, Generalized Fluid System Simulation Program (GFSSP), was used to simulate the no-vent filling of a cryogenic tank with a thermodynamic vent system assisted injector. The tank was discretized into nodes in the radial and axial directions to calculate two-dimensional axisymmetric flow with heat transfer between the solid wall and fluid. The nodes are connected by branches where momentum equations are solved to calculate the flowrates. The mass and energy conservation equations and the equation of state are solved to calculate pressure, temperature, and resident mass at the nodes. The energy conservation equations at the solid nodes were solved to calculate the temperature of the solid wall. The system of equations was solved by a combination of successive substitution and simultaneous Newton-Raphson method. The numerical model accounts for a) different regimes of pool boiling heat transfer, b) condensation of vapor around the spray droplets, c) condensation of vapor at the interface of the cooled injector and ullage, and d) condensation at the liquid-vapor interphase. The numerical predictions of tank pressure, filling rate, and wall temperatures were compared with the test data. The paper also presents the effect of nodal discretization by comparing the results of the 1D and 2D models and examines the effect of the droplet diameter on tank filling.

Nodal Model↗
NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRS2023

Distorted-Toroidal Flux Rope Model

The 3D characterization of magnetic flux ropes observed in the heliosphere has been a challenging task for decades. This is mainly due to the limitations on inferring the 3D global topology and physical properties from the 1D time series from any spacecraft. To advance our understanding of magnetic flux ropes whose configuration departs from the typical stiff geometries, here we present an analytical solution for a 3D flux rope model with an arbitrary cross section and a toroidal global shape. This constitutes the next level of complexity following the elliptic-cylindrical (EC) geometry. The mathematical framework was established by Nieves-Chinchilla et al. with the EC flux rope model, which describes a magnetic topology with an elliptical cross section as a first approach to changes in the cross section. In the distorted-toroidal flux rope model, the cross section is described by a general function. The model is completely described by a nonorthogonal geometry and the Maxwell equations can be consistently solved to obtain the magnetic field and relevant physical quantities. As a proof of concept, this model is generalized in terms of the radial dependence of current density components. The last part of this paper is dedicated to a specific function, F (φ) = δ (1 - λ cosφ), to illustrate possibilities of the model. This model paves the way toward the investigation of complex distortions of magnetic structures in the solar wind. Future investigations will explore these distortions in depth by analyzing specific events; studying implications for physical quantities, such as magnetic fluxes, helicity, or energy; and evaluating the force balance with the ambient solar wind that allows such distortions.

Solar wind↗
NASA NTRS2023

Structured Type III Radio Bursts Observed in Interplanetary Space

Context . The last few decades have seen numerous studies dedicated to fine structures of type III radio bursts observed in the meter–decameter wavelengths. Most of the explanations of the structured radio emission involve the propagation of electron beams through the strongly inhomogeneous plasma in the low corona. To date, only a few type III bursts with fine structures, observed at hecto-kilometric wavelengths, have been reported. Aims . We report here the existence of numerous structured type III radio bursts observed during the STEREO era by all three WAVES instruments on board STEREO A, B, and Wind. The aim of the study is to report and classify structured type III bursts, and to present the characteristics of their fine structures. The final goal is to try to understand the physical mechanism responsible for the generation of structured radio emission. Methods . In this study we used data from all available spacecraft, specifically STEREO and Wind. We employed 1D density models to obtain the speed of the source of type III radio emission, the electron beam. We also performed a spectral analysis of the fine structures in order to compare their characteristics with the metric–decametric fine structures. Results . The presented similarities of the type III fine structures in the metric to decametric and interplanetary wavelengths indicate that the physical processes responsible for the generation of structured type III radio bursts could be the same, at heights from the low corona to the interplanetary range. We show that the observed structuring and intermittent nature of the type III bursts can be explained by the variation in the level of density fluctuations, at different distances from the Sun.

Non-thermal radiation mechanisms↗
NASA NTRS2022

Interrogating Solar Flare Loop Models With IRIS Observations 1: Overview of the Models, and Mass Flows

Solar flares are transient yet dramatic events in the atmosphere of the Sun, during which a vast amount of magnetic energy is liberated. This energy is subsequently transported through the solar atmosphere or into the heliosphere, and together with coronal mass ejections flares comprise a fundamental component of space weather. Thus, understanding the physical processes at play in flares is vital. That understanding often requires the use of forward modelling in order to predict the hydrodynamic and radiative response of the solar atmosphere. Those predictions must then be critiqued by observations to show us where our models are missing ingredients. While flares are of course 3D phenomenon, simulating the flaring atmosphere including an accurate chromosphere with the required spatial scales in 3D is largely beyond current computational capabilities, and certainly performing parameter studies of energy transport mechanisms is not yet tractable in 3D. Therefore, field-aligned 1D loop models that can resolve the relevant scales have a crucial role to play in advancing our knowledge of flares. In recent years, driven in part by the spectacular observations from the Interface Region Imaging Spectrograph (IRIS), flare loop models have revealed many interesting features of flares. For this review I highlight some important results that illustrate the utility of attacking the problem of solar flares with a combination of high quality observations, and state-of-the-art flare loop models, demonstrating: 1) how models help to interpret flare observations from IRIS, 2) how those observations show us where we are missing physics from our models, and 3) how the ever increasing quality of solar observations drives model improvements. Here in Paper one of this two part review I provide an overview of modern flare loop models, and of electron-beam driven mass flows during solar flares.

solar flares↗
NASA NTRS2023

Interrogating Solar Flare Loop Models with IRIS Observations 2: Plasma Properties, Energy Transport, and Future Directions

During solar flares a tremendous amount of magnetic energy is released and transported through the Sun’s atmosphere and out into the heliosphere. Despite over a century of study, many unresolved questions surrounding solar flares are still present. Among those are how does the solar plasma respond to flare energy deposition, and what are the important physical processes that transport that energy from the release site in the corona through the transition region and chromosphere? Attacking these questions requires the concert of advanced numerical simulations and high spatial-, temporal-, and spectral-resolution observations. While flares are 3D phenomenon, simulating the NLTE flaring chromosphere in 3D and performing parameter studies of 3D models is largely outwith our current computational capabilities. We instead rely on state-of-the-art 1D field-aligned simulations to study the physical processes that govern flares. Over the last decade, data from the Interface Region Imaging Spectrograph (IRIS) have provided the crucial observations with which we can critically interrogate the predictions of those flare loop models. Here in Paper 2 of a two-part review of IRIS and flare loop models, I discuss how forward modelling flares can help us understand the observations from IRIS, and how IRIS can reveal where our models do well and where we are likely missing important processes, focussing in particular on the plasma properties, energy transport mechanisms, and future directions of flare modelling.

solar flares↗
NASA NTRSDate not supplied

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRS2024

NASA HECC Geometry and Performance Review Part 1: Validation of a Computational Model for the Vaneless Diffuser Configuration with As-Manufactured Impeller Geometry

An investigation of the NASA High Efficiency Centrifugal Compressor (HECC) vaneless diffuser configuration was performed. This multipart investigation presents an extensive validation of the computational model against experimental data. The validated model was then used to explore the effects geometric differences between the as-manufactured and design-intent impellers. Lastly, the validated model was used for comparison against an experimental tip clearance study. Part I of the investigation focused on describing the HECC vaneless computational model and its validation against the experimental data published in GT2023-103128. To generate the model, a verification of the manufactured impeller was performed and was confirmed to match the impeller CAD. Section data from the impeller CAD, referred to as the As-Manufactured geometry, were used for development of the computational model. Steady RANS simulations utilizing the Wilcox K-Omega 98 turbulence model and a multiblock structured mesh were used to conduct a mesh convergence study. The resultant mesh was then used for simulations at both design-speed and off-design speed conditions. The simulations compared well to data in both 1D and 2D terms for the major performance metrics of efficiency, total pressure rise, and total temperature rise. Based on the observed results the model was deemed validated and was used in the latter two parts of the multipart investigation.

centrifugal compressor↗
NASA NTRSDate not supplied

Scoping of an Air Supply Configuration for AFC on a Commercial Transport Airplane High Lift System

A scoping study is being conducted on the air supply for a microjet-based active flow control (AFC) system on a twin-turbofan commercial transport airplane. Microjets provide circulation control using small surface-normal pneumatic jets located near the trailing edge of a lifting surface such as a wing or flap. When located on the pressure side of the lifting surface they increase the lift, and when located on the suction side they decrease lift. In this study, microjets are considered for installation in the flaps of the high-lift version of the Common Research Model (CRM-HL). An air supply system involving bleed air from the airplane’s auxiliary power unit (APU) plus ram air provides the air to the microjets. A model based on the 1D compressible flow equations is applied to analyze the air supply system configuration and predict the microjet flow rate with the resulting airplane performance changes based on Reynolds-averaged Navier-Stokes modeling of microjets on the CRM-HL. The results of this scoping study are encouraging in that APU air can be used to entrain ram air and thereby increase the AFC mass flow rate to achieve effective lift control and airplane performance enhancement during takeoff and landing.

AATT↗
NASA NTRSDate not supplied

Heating of Coronal X-Ray Bright Points

The X-ray bright points (XBPs) are consistently present on the solar disk throughout time and space. Studying their contribution to overall coronal emissions and heating is intriguing. XBPs are prominently visible during the solar minimum, especially in the absence of bright active regions (ARs). In this discussion, we'll talk about the contribution of XBPs to coronal X-ray emissions during the cycle minimum. This analysis is based on spectroscopic observations by Solar X-ray Monitor (XSM) onboard Chandrayaan 2, alongside concurrent observations from the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO) and the X-Ray Telescope (XRT) on the Hinode observatory. Similar to ARs, XBPs consist of coronal loops visible in EUV/X-ray images. We investigated the possibility of nanoflares sustaining the heating of these XBP loops by comparing observed emissions with field-aligned hydrodynamic simulations. Our results suggest that nanoflares could indeed maintain the heating of the XBPs. Furthermore, we conducted a detailed study of a single XBP observed during the successful flight of the Marshall Grazing Incidence Spectrometer (MaGIXS). The unique observations made by MaGIXS, combined with 1D hydrodynamic simulations, enabled us to determine the nanoflare heating frequency for this particular XBP.

coronal heating↗
NASA NTRSDate not supplied

Cryogenic Extension of NASA Species Polynomials Using Hydrogen and Oxygen at Stoichiometry

NASA has been conducting research in Rotating Detonation Rocket Engines (RDREs) for several years. A recent test at the Marshall Spaceflight Center has successfully shown operation of a RDRE that ran for 251 seconds which had combustion chamber cryogenic inlet conditions [1]. However, an overall pressure gain was not observed in the performance of the engine. Such an indication could mean an optimal chamber design is yet to be discovered. Such a design could significantly reduce parasitic pressure loses and improve overall engine efficiency beyond that of conventional rockets. To investigate different chamber configurations, new CFD capability is needed to accurately capture cryogenic thermophysical and transport property data. Such an effort is currently part of NASA’s Early Career Initiative (ECI) program. This paper focuses on the thermophysical property mixture model approach that makes use of NASA’s polynomial fits of species and another cryogenic model chosen from NIST’s Refprop program. Presented in this paper are results of a 1D denotation CFD simulation of stoichiometric Hydrogen and Oxygen mixture at a cryogenic upstream condition.

Combustion↗
NASA NTRS2024

Accounting for 3D Radiative Effects in MODIS Aerosol Retrievals near Clouds using CALIPSO Observations

Retrievals of aerosol properties near clouds from passive remote sensing is challenging. Sunlight scattered by clouds into nearby clear regions can effectively enhance the clear area reflectance. These cloud 3D radiative effects may lead to large biases in aerosol retrievals if uncorrected, risking an incorrect interpretation of satellite observations for aerosol-cloud interaction in cloudy atmosphere. In earlier studies, we developed a simple two-layer model (2LM) to estimate the cloud-induced clear-sky radiance enhancements in cloud fields. In this study, we take advantage of CALIPSO lidar observations, which should not be affected by the 3D radiative effect, to study passive aerosol retrievals in cloud fields in the Amazon region, specifically those produced by the operational Dark Target algorithm applied to Aqua-MODIS. From two-years’ worth of co-located CALIPSO/MODIS aerosol retrievals, we find a larger increase in operationally retrieved MODIS AOD from clear to cloudy regions (~0.075 or ~40%) than for the CALIPSO AOD (~0.021 or ~20%). The much larger increase in MODIS AOD is mainly due to the 3D radiative effects. After using the 2LM model to account for cloud 3D radiative effects, the clear to cloudy increase in MODIS AOD was reduced to ~0.043 (~23%), much closer to CALIPSO observations. The 3D corrected average MODIS AOD for cloudy scenes is significantly larger than AOD for clear conditions, even for cloud fraction (CF) less than 0.1, suggesting aerosols in cloudy scenes are characteristically different from aerosols in clear conditions. Furthermore, the 3D correction of AOD (i.e., τ_1D-τ_3D) increases linearly with CF for a large range of CF. We have also examined the impact of the 3D effect on aerosol Ångström Exponent (AE) and fine model fraction (FMF) of AOD. We found that the uncorrected average AE and FMF depend strongly on CF, ~25% increase in AE (decrease in particle size) and ~60% increase in FMF as CF increases from 0.05 to 0.45. The 3D correction leads to smaller average AE (bigger particle size) and FMF that are almost independent of CF. Thus, the 3D corrected aerosol properties are expected to provide more accurate information for better understanding aerosol-cloud interactions.

MODIS↗
NASA NTRSDate not supplied

Cryogenic Extension of NASA Species Polynomials Using Hydrogen and Oxygen at Stoichiometry

The Helmholtz free energy for a mixture has been used in many chemistry and combustion studies for mixtures. An approach is described where polynomial fits intended for a thermally perfect ideal gases can be modified and implemented into a Helmholtz free energy model, such as those used by NIST in their REFPROP program. However, the limitation of the polynomial fits must be respected and should only used in a valid temperature interval where the flow field can be treated as a thermally perfect ideal gas when these species are present. This approach can allow a CFD simulation to have cryogenic regions of a flow field where high fidelity cryogenic species models are needed and allows for the use of species in other areas at higher temperatures where combustion species do not have such a model available or is not needed. Presented in the paper are CFD 1D detonation wave and species mass fraction simulation results which have cryogenic upstream conditions of Hydrogen and Oxygen at stoichiometry.

Combustion↗
NASA NTRSDate not supplied

Heating of Coronal X-Ray Bright Points

The X-ray bright points (XBPs) are consistently present on the solar disk throughout time and space. Studying their contribution to overall coronal emissions and heating is intriguing. XBPs are prominently visible during the solar minimum, especially in the absence of bright active regions (ARs). In this discussion, we'll talk about the contribution of XBPs to coronal X-ray emissions during the cycle minimum. This analysis is based on spectroscopic observations by Solar X-ray Monitor (XSM) onboard Chandrayaan 2, alongside concurrent observations from the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO) and the X-Ray Telescope (XRT) on the Hinode observatory. Similar to ARs, XBPs consist of coronal loops visible in EUV/X-ray images. We investigated the possibility of nanoflares sustaining the heating of these XBP loops by comparing observed emissions with field-aligned hydrodynamic simulations. Our results suggest that nanoflares could indeed maintain the heating of the XBPs. Furthermore, we conducted a detailed study of a single XBP observed during the successful flight of the Marshall Grazing Incidence Spectrometer (MaGIXS). The unique observations made by MaGIXS, combined with 1D hydrodynamic simulations, enabled us to determine the nanoflare heating frequency for this particular XBP.

coronal heating↗
NASA NTRS2024

Correction of Cloud Optical Thickness Retrievals from Nadir Reflectances in the Presence of 3D Radiative Effects. Part I: Concept and Tests on 3D RT Simulations

3D effects cause substantial underestimation of cloud optical thickness (COT) in airborne and satellite retrievals based on 1D radiative transfer computations (such as in the case of widely used bispectral technique). For a single-layer isolated cloud we propose a simple linear correction of the retrieved COT with the renormalization factor dependent on the cloud’s aspect ratio (the ratio between vertical and horizontal dimensions of the cloud). This is an empirical assumption which we successfully test using synthetic 3D RT data. We introduce a heuristic “block model” of 3D radiative effects and show that the functional form of the renormalization factor is consistent with the process of radiation escape from cloud sides in an essentially 3D geometry. We also extend the block model to the case of single-layer broken cloud field with radiative interaction between the neighboring clouds. In this case the renormalization factor depends also on the distance between clouds.

cloud optical thickness↗