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NASA NTRSDate not supplied

Validation of Actuator Disk, Actuator Line and Sliding Mesh Methods within the LAVA Solver

In this study the implementation of actuator-disk, actuator-line and sliding-mesh methodologies in the Launch Ascent and Vehicle Aerodynamics (LAVA) solver is described and validated against several test-cases. The different models are validated against available numerical as well as experimental data. Both steady and unsteady Reynolds-Averaged Navier-Stokes (RANS) simulations using the Spallart Allmaras (SA) turbulence model are performed for several different configurations representative of aeroscience applications. The first part of the paper is focused on the verification and validation of the implemented propulsor models. The first validation case is a theoretical rotor in hover, compared with the 1D analytical solution derived from momentum theory. The second validation is the Rotor-Airframe Interaction Model of Georgia Institute of Technology (GIT), representing the application of actuator disks to top-mounted rotorcraft vehicles or unmanned aerial vehicles (UAVs). The third case is a representative configuration for tip-mounted rotor-craft vehicles such as NASA’s X57 airplane. The second part of the paper shows the implemented models applied to realistic engineering configurations such as NASA’s X57 airplane and the R4 Advanced Ducted Propellor (ADT). A modification to the actuator line method in order to represent the blade geometry more closely is proposed and comparisons with simulations modeling the blade utilizing a sliding-mesh approach are made.

ARMD↗

NASA NTRSDate not supplied

Heliospheric energetic particle transport: Analysis of near-field-aligned particle propagation for SEP events observed by Wind and MAVEN

Magnetic field alignments of spacecraft over large distances in the heliosphere are rare and are usually very limited in duration. Cruise phases of planetary transfers, however, are an exception to this rule, given the Hohmann-Parker effect. The transfer of the MAVEN s/c in 2014 is one such example. Multiple (~10) solar particle events occurred and were detected at both MAVEN and the Wind s/c, originating from solar activity near the foot points of both s/c. We show initial analysis results of the data collected by the solar wind and energetic particle instruments on both s/c while they were more than 0.2 AU apart, but practically Parker field aligned. Using a 1D model, we present initial simulations in qualitative agreement with energetic electron measurements. Next step is to implement the 2D model with approximate particle release from the Sun, and transport durations between Sun, Earth, and MAVEN. We will discuss implications for this data-model comparison, including the possibility to constrain particle scattering inside 1 AU, as well as its radial dependence between Earth and MAVEN.

Solar Energetic Particles↗

NASA NTRS2022

Markovian Statistical Model of Cloud Optical Thickness. Part I: Theory and Examples

We present a generalization of the binary-value Markovian model previously used for statistical characterization of cloud masks to a continuous-value model describing 1D fields of cloud optical thickness (COT). This model has simple functional expressions and is specified by four parameters: the cloud fraction, the autocorrelation (scale) length, and the two parameters of the normalized probability density function of (non-zero) COT values (this PDF is assumed to have gamma-distribution form). Cloud masks derived from this model by separation between the values above and below some threshold in COT appear to have the same statistical properties as in binary-value model described in our previous publications. We demonstrate the ability of our model to generate examples of various cloud-field types by using it to statistically imitate actual cloud observations made by the Research Scanning Polarimeter (RSP) during two field experiments.

binary-value Markovian model↗

NASA NTRSDate not supplied

Enumeration and Fluorescence In Situ Hybridization of Microbial Bioburden on Cleanroom Surfaces

Introduction: Microorganisms are everywhere on Earth, even in the cleanest of places. Spacecraft assembly cleanrooms can harbor low levels of living and dead microbial cells (e.g., [1,2]), and cleanroom bioburden can also include organic molecules from industrial sources and in situ biomass. Life detection missions require careful attention to avoid contaminants that can be easily convoluted with analytical targets. We are evaluating epifluorescent microscopy and fluorescence in situ hybridization (FISH) as methods to complement organic contamination detection techniques. Epifluorescent cell counting offers an accurate and cost-effective way to quantify low levels of surface biomass. FISH could allow for the identification of residual organisms, and can be targeted to detect active populations of specific organisms such as bacteria known to resist cleaning procedures. This effort is part of a larger study that is concentrated on characterizing the surface and airborne molecular organic contamination background in Johnson Space Center (JSC) Astromaterials curation laboratories and Goddard Space Flight Center (GSFC) spacecraft assembly rooms, and understanding contaminants in the context of cleaning procedures and residual bioburden. Methods: Samples were collected by swabbing surfaces in ISO 5 and ISO 7 equivalent cleanrooms at JSC. Swabs for FISH were fixed in 4% paraformaldehyde (PFA) for 3 hours and then stored in 1:1 ethanol:PBS, while swabs for cell counting were stored in 4% PFA until analysis to avoid any cell loss during centrifugation that could impact quantification of very low biomass samples. Cell counting was performed with SYBR Gold as in [3], but adapted for very low biomass. FISH was performed as in [4], using DAPI as a counterstain for all DNA-containing cells. Negative controls included wells with no probe applied, to test for natural fluorescence, as well as the nonsense probe NONEUB (reverse complement of EUB338) to evaluate non-specific probe binding. Results and Discussion: Cleanroom surfaces had 102-103 cells cm-2. The extremely low biomass of these samples was challenging for enumeration, and required careful and routine use of “field” and laboratory blanks. FISH was performed with the general archaeal and bacterial probes ARCH915 and EUB338 (EUBMIX, [4]), probe GAMBET ([4]), and PSE227, which targets the genus Pseudomonas [5]). The latter two probes were selected because Pseudomonas spp. and other Gammaproteobacteria have not been isolated from cleanroom surfaces but do appear frequently in rRNA gene libraries from these surfaces. While some active bacteria were identified (Fig. 1c), most cells detectable by DAPI did not have a strong or any fluorescent signal (e.g., Fig. 1d), indicating that the vast majority of cells are dead or inactive. This suggests that cleaning protocols are effective at inactivating microbial contaminants, but that dead or inactive cells can remain on surfaces. Cells were often clumped in a weakly autofluorescent matrix, possibly biofilm material (Fig. 1c,d). We also observed other particulate material that was collected by the swabs, including apparent textile fibers (Fig. 1b). Our results are consistent with other studies that show that the bioburden present in clean rooms includes active, dormant, and dead cells. We will discuss how FISH and epifluorescent cell counting could be applied in planetary protection protocols, including the advantages and disadvantages of FISH and cell counting for routine use, as well as different possible applications for more specialized FISH procedures. References: [1] Moissl-Eichinger et al. (2015) Sci Rep, 5, 9156 [2] Hendrickson et al. (2021) Microbiome, 9, 238 [3] Jones et al. (2017) Appl Environ Microbiol, 83, e00909-17 [4] Jones et al. (2015) Appl Environ Microbiol, 81, 1242-1250. [5] Watt et al. (2006) Environ Microbiol, 8, 871-884

C J Huff↗

NASA NTRSDate not supplied

Continuous Habitable Zones: Pairing a GCM and Bayesian Framework to Predict Habitable Zone Evolution

In the near-future, new space telescopes like JWST, LUVOIR, and HabEx will begin attempting to explore the properties of atmospheres of potentially habitable planets. This will require a significant amount of time and resources for even a single planet, which makes it essential to prioritize observations by those most-likely to have detectable life. Here we present a statistical method to estimate the probabilities that specific exoplanets have been continuously in the habitable zone of their host stars for more than 2 billion years, the approximate time it took life on Earth to significantly increase the oxygen content of the atmosphere. We introduce the use of statistics of an ensemble of 3D planetary general circulation models to estimate these probabilities, replacing prior 1D model estimates.

habitable planets↗

NASA NTRSDate not supplied

Pairing a GCM and Bayesian Framework to Predict Habitable Zone Evolution

habitable planets↗

NASA NTRS2022

Impact of 3D Structure on Magnetic Reconnection

Results from 2.5D and 3D studies of the onset and development of the tearing instability are presented, using high fidelity resistive MHD simulations. A limited parameter study of the strength of the reconnecting field (or shear angle) was performed. An initially simple 1D equilibrium was used, consisting of a modified force-free current sheet, with periodic boundary conditions in all directions. In all cases, the linear and nonlinear evolution led to a primary current sheet between two large flux ropes. The global reconnection rate during this later stage was analyzed in all simulations. It was found that in 2.5D the primary current sheet fragmented owing to plasmoids, and as expected, the global reconnection rate, calculated using multiple methods, increases with the strength of the reconnecting field owing to a stronger Alfvén speed. In 3D, the presence of interacting oblique modes of the tearing instability complicates the simple 2.5D picture, entangling the magnetic field of the inflow and introducing a negative effect on the reconnection rate. The two competing effects of stronger Alfvén speed and entangling, which both increase with the strength of the reconnecting field, resulted in a decrease in the reconnection rate with increasing reconnecting field. For all simulations, the 3D rates were less than in 2.5D but suggest that as one goes to weak reconnecting field (or strong guide field) the system becomes more 2.5D-like and the 2.5D and 3D rates converge. These results have relevance to situations like nanoflare heating and flare current sheets in the corona.

Lars K S Daldorff↗

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, sting arm motion, 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.

machine learning↗

NASA NTRSDate not supplied

New Mastcam Multispectral Rock Classes in Sulfate-Bearing Strata, Gale Crater, Mars

Orbital data of Gale Crater, Mars has identified a transition in the stratigraphy of Mt. Sharp indicating an environmental change from a wetter one that accommodated clay mineral formation to a drier environment that led to the precipitation of sulfates. Mt. Sharp is not the only location on Mars for which this transition has been observed which suggests a global environmental change around the Noachian-Hesperian transition. Also visible from orbit is a dark-toned marker band that lies within the sulfate-bearing strata hypothesized to be a volcanic ash deposit. The Mars Science Laboratory (MSL) Curiosity rover is currently exploring this region of Mt. Sharp. In-situ analyses by the rover show changes in morphology and chemistry between the sulfate-bearing layers below and above the marker band and the marker band itself. The MSL instrument payload includes the Mast Camera (Mastcam), which collects multispectral images to provide context for other instruments and broad mineralogic interpretations. In addition to mafic Fe-bearing minerals, Mastcam is sensitive to oxides and some hydrated minerals, important indicators of alteration. In our previous work, we compiled a database of Mastcam spectra through Curiosity’s exploration of Vera Rubin ridge (sols 0-2302), from which 9 spectral classes of rocks were identified (Fig. 1D). Each class has diagnostic spectral features that reflect a common mineralogic interpretation. More recently, as Curiosity traversed through the clay-rich region, Glen Torridon (GT), there was some variation in the rock spectral classes potentially due to nontronite and other phyllosilicates, but not enough to warrant new spectral classes (Fig. 1B). We hypothesize that new classes will appear in accordance with sulfate-bearing strata and the marker band, specifically appearing as a ‘downturn’ in the last two wavelength filters that is inherent, but not unique, to polyhydrated Mg-sulfates. Here, we expand upon our analysis of Mastcam multispectral observations through Curiosity’s second encounter with the marker band (up to sol 3672), comparing the spectral diversity within the sulfate-bearing unit and the marker band to the spectral classes encountered previously in the traverse. These analyses will provide a basis for comparison and aid in selecting targets for in-situ investigations as Curiosity continues its ascent of Mt. Sharp.

A M Eng↗

NASA NTRSDate not supplied

Using XR for Improving Scientific Discovery With Numerical Weather Models

Earth science (ES) digital twins will help us understand the complex interactions and interrelationships that make up our Earth system and the impacts of earth science phenomena on it. Our work addresses two underdeveloped areas in current ES digital twin work: improving the understanding and interaction with ES model outputs by using Virtual and Mixed Reality (XR) tools and improving the non-intuitive mapping of continuous ES natural phenomena to gridded reference frames in current numerical models. Traditionally, scientists working on ES view and analyze the results of calculated or measured observables with static 1-dimensional (1D), 2D or 3D plots displayed on flat computer screens or paper. Using such limited mediums, it can be very difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. In addition, numerical models, such as the NASA Goddard Earth Observing System (GEOS) ES model, are almost exclusively formulated, visualized and analyzed in an Eulerian reference frame with fixed grid points in space and time. However, ES phenomena such as convective clouds, hurricanes and wildfire smoke plumes are visualized and analyzed in a Lagrangian reference frame: therefore it is often difficult and unnatural to understand these phenomena in relation to each other, visualized either in an Eulerian or Lagrangian context. In 3D visualizations, data generally takes one of three forms: gridded (e.g., voxelized) data, where space is divided into regions; point clouds, where data is represented as a set of points; and meshes, where objects are rendered as surfaces composed of small polygons (usually triangles). A gridded, Eulerian reference frame has been the default representation for the 2D visual analysis of atmospheric data in part because the numerical methods used to generate atmospheric model data in the first place use a gridded approach, with equations defining the relationships between the physical variables in each of a grid's cells across successive timesteps. In our work, we are particularly interested in data from GEOS. Another reason why gridded representations tend to be used for visualizing data from such models is because trajectories are difficult to interpret from representations on 2D surfaces, due to line-of-sight ambiguity. Instead of a fixed grid from GEOS, we embed a trajectory model to simulate particles' movement throughout a GEOS run. We then ingest these particle trajectories as animated point clouds with a NASA open source XR toolkit, the Mixed Reality Exploration Toolkit (MRET), and merge GEOS data with ES phenomena data onto one combined visualization that the user can intuitively interact with. Efficient rendering of arbitrarily large point clouds is an ongoing challenge being addressed by the computer science community, with the GPU-based optimizations and efficient GPU memory utilization a common theme of recent advances, especially for XR, where sustained high frame rate is mandatory to save the user from suffering due to simulation sickness. In this work, we describe and evaluate our progress in choosing and implementing appropriate methods for rendering arbitrarily large point clouds within MRET for XR. While tracking the XR headset enables the immersion of a user within a 3D scene of a data visualization, tracking of XR handheld controllers or user’s hands enables us to implement intuitive user interactions with the visualized datasets. Conventional tools require a user working with an ES visualization to conduct many interactions to commit their intended selections or manipulations with a visualized dataset; for example to specify a set of points in 3D space. Doing so in a 2D flat screen interface has traditionally required specifying a set of points in three distinct 2D coordinate systems (XY, XZ, and YZ), which is cumbersome. In other scientific domains, it has been shown that specifying or selecting a location or volume in XR using handheld controllers or tracked hands allows for greater speed and accuracy. We anticipate the same will hold true for atmospheric data, and we will share initial results of measuring the utility of such an interface. Notably, as the data being visualized is generated by GEOS as a prediction based on initial conditions, an intended application of our tool is to serve as part of an iterative feedback loop. Through XR, a scientist will review and manipulate a GEOS model run, modifying the conditions as needed to do subsequent runs of GEOS. Thereby, XR-based improvements to speed and accuracy of 3D tagging of points minimizes the effort required by both the scientist and the computer cluster conducting the necessary calculations.

Thomas Grubb↗

NASA NTRS2015

Stratospheric Temperatures and Water Loss From Moist Greenhouse Atmospheres of Earth-Like Planets

A radiative-convective climate model is used to calculate stratospheric temperatures and water vapor concentrations for ozone-free atmospheres warmer than that of modern Earth. Cold, dry stratospheres are predicted at low surface temperatures, in agreement with recent 3D calculations. However, at surface temperatures above 350 K, the stratosphere warms and water vapor becomes a major upper atmospheric constituent, allowing water to be lost by photodissociation and hydrogen escape. Hence, a moist greenhouse explanation for loss of water from Venus, or some exoplanet receiving a comparable amount of stellar radiation, remains a viable hypothesis. Temperatures in the upper parts of such atmospheres are well below those estimated for a gray atmosphere, and this factor should be taken into account when performing inverse climate calculations to determine habitable zone boundaries using 1D models.

Astrobiology↗

NASA NTRSDate not supplied

Evaluation of Human Spaceflight-Related Tissue Weight Relief Using Whole Body Finite Element Model Simulations

Tissue Weight Relief (TWR) is a physiological condition observed in human spaceflight. It not only impacts the injury biomechanics of soft tissue but also the physiological responses of the cardiovascular system both due to fluid redistribution and the effect of tissue-related transmural pressure on the large venous blood vessels. Understanding the effects of tissue weight relief is especially important because of the role it may play in understanding the cause of Space Associated Neuro-Ocular Syndrome (SANS). SANS can be characterized by a number of ocular changes which reduce visual acuity and SANS related symptoms occur in up to 51% of astronauts. A prevailing theory for the causation of SANS is that of headward (cephalad) fluid shift and a prolonged increase of Intracranial Pressure (ICP) similar to intracranial hypertension, which is not fully supported by the experimental data or astronaut symptom reporting. However, it is still believed that SANS is caused by a pressure change in the eye and the surrounding tissues. It has been proposed that TWR plays a substantial role in affecting internal pressures and fluid shifts in microgravity. In this effort, two whole-body Finite Element (FE) models – Elemance and THUMS – are used to ascertain the microgravity-associated TWR of the musculature surrounding the lower body veins. Elemance and THUMS are physics-based computational models that have been validated and verified for several automotive and domestic applications, and as such, can simulate the relief of soft tissue weight due to changes in the gravitation vector. Specifically, the current effort modeled the transition of the gravitational vector from 1G to 0G, applied across the whole-body model in a supine position. For each 1G to 0G transition simulation, the lower body vein’s transmural pressure-time profile was extracted and averaged around the anterior portion of the thigh muscle. The ascertained transmural pressure changes from 1G to 0G transition are given in Figure 1 for the Elemance and the THUMS FE models. The transmural pressure changes of 10 mmHg and 21 mmHg are in the same order of magnitude as Lu’s value of 44 mmHg. It is to be noted that Lu implemented a 0D to 1D lumped parameter model and the Elemance and THUMS are 3D higher order computational models. This proof-of-concept approach demonstrates that TWR pressure can be adequately estimated with in silico techniques however, further in silico investigations need to be conducted to address the unique contributions to the transmural pressure from each of the computational models.

Finite element modeling↗

NASA NTRS2022

The Impact of Firn Models on Ultrawideband Brightness Temperatures in the Partially Coherent Model

The density profile of a polar ice sheet is an important parameter for the estimation of ice mass balance. Wave reflections caused by density variations are also a key uncertainty in the retrieval of ice sheet temperature profiles in Ultra-Wide band radiometry. In this paper, we examine different firn density profile models and analyze the subsurface reflections they cause using an analytical partially coherent approach. We also examine firn density profiles obtained from borehole measurements, from past UWBRAD modeling studies, from a community firn model, and from snow radar echo measurements. In previous studies, the ice sheet has been model as a 1D random medium with density variations in depth. However, horizontal density variations also exist, so that the ice sheet is a 3D random medium. Analyses using the partially coherent model show that in the presence of horizontal fluctuations, contributions from short scale variations vanish as the horizontal correlation length decreases due to the diffraction of waves.

Haokui Xu↗

NASA NTRSDate not supplied

Aerothermodynamic Measurements in Hypersonic Non-Equilibrium Flows

High enthalpy arc-jets are unique facilities particularly suited for simulating complex flows in the aerospace field, such as the aerothermodynamics of a re-entry vehicle. Arc-jets are often used to evaluate important design factors such as heat shield materials and vehicle design. Characterization of these facilities is important, as studies often match specific in-flight environments during experiments. Due to the complex environment produced by an arc-jet, with effects such as thermodynamic and chemical non-equilibrium occurring in the flow, characterization experiments are significantly more difficult than in traditional blow-down wind tunnels. The current work aims to characterize an arc-jet facility in terms of flow temperature, velocity and unsteadiness, in a spatially-resolved analysis. To achieve this goal, a non-intrusive imaging technique called “planar laser-induced fluorescence” was performed in the NASA Langley Hypersonic Materials Environ-mental Test System arc-jet facility. The experimental data was analyzed to produce the quantitative measurements in multiple regions of the flow around a blunt body sample. A quasi-1D low fidelity solver was created to numerically simulate the flow to investigate non-equilibrium effects occur-ring outside the imaging region in the arc-jet nozzle. Unsteadiness in the test section was minimized by analyzing a subset of data assessing the gas injection location. Radial velocity, rotational temperature and translational temperature measurements are provided that can be used to validate future high-fidelity computational studies. The temperature measurements revealed rotational non-equilibrium potentially occurring in the flow. This work provides a methodology for characterizing arc-jet facilities, identifying and correcting sources of systematic error, and estimating the uncertainty associated with the quantitative measurements.

laser induced fluorescence↗

NASA NTRS2022

Beyond Basic Drag in Interplanetary CME Modeling: Effects of Solar Wind Pileup and High-Speed Streams

Coronal mass ejections (CMEs) cause severe space weather effects throughout our solar system. As a fast CME propagates through interplanetary space, it accumulates solar wind materials at its front. This pileup of materials, or CME-driven sheath, can be important in determining the geoeffectiveness of a CME. We take an existing arrival time model that includes expansion and deformation of the CME flux rope (ANTEATR; Kay & Gopalswamy, 2018, https://doi.org/10.1029/2018JA025780; Kay & Nieves-Chinchilla, 2021a, https://doi.org/10.1029/2020JA028911) and add a pileup procedure (PUP) as a physics-based approach to modeling the CME-driven sheath. ANTEATR-PUP solves the Rankine-Hugoniot equations for an oblique shock to determine the shock speed and sheath density, magnetic field, and temperature. The extra sheath mass affects the background drag calculation. Additionally, ANTEATR can now use any 1D profile for the background solar wind as opposed to the simple empirical models it previously relied upon. We present initial results from ANTEATR-PUP and compare with previous ANTEATR findings. Using results from an MHD simulation, we explore the effects of interactions with a static high-speed stream (HSS) on the CME's and sheath's interplanetary evolution. The drag forces essentially disappear while a CME remains within the HSS, but reappear stronger once the CME exits. The HSS-CME interaction produces the largest changes in the CME and sheath properties at 1 au when it occurs either close to the Sun near the inner simulation boundary at 0.1 au or right before the CME reaches 1 au. We estimate that these changes could significantly affect the geoeffectiveness.

C Kay↗

NASA NTRS2021

3D Cloud Tomography and Droplet Size Retrieval from Multi-Angle Polarimetric Imaging of Scattered Sunlight from Above

Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging,it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomographyof cloud droplet distributions via passive remote sensing. We use multi-view polarimetric images to fit a 3Dpolarized radiative transfer (RT) forward model. Our motivation is 3D volumetric probing of vertically-developedconvectively-driven clouds that are ill-served by current methods in operational passive remote sensing. Currenttechniques are indeed based on strictly 1D RT modeling and applied to a single cloudy pixel, where cloud geometrydefaults to that of a plane-parallel slab. Incident unpolarized sunlight, once scattered by cloud droplets, changesits polarization state according to droplet size. Therefore, polarimetric measurements in the rainbow and gloryangular regions can be used to infer the droplet size distribution. This work defines and derives a framework for afull 3D tomography of cloud droplets for both their mass concentration in space and their distribution across arange of sizes. This gridded 3D retrieval of key microphysical properties is made tractable by our novel approachthat involves a restructuring and partial linearization of an open-source polarized 3D RT code to accommodate aspecial two-step iterative optimization technique. Physically-realistic synthetic clouds are used to demonstrate themethodology with rigorous uncertainty quantification, while a real-world cloud imaged by AirMSPI is processedto illustrate the new remote sensing capability

Schechner, Yoav Y.↗

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

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↗