SEARCH · Engineering Papers

Results for “permutation”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 505 records · Page 28

All sources NASA NTRS DOE OSTI

NASA NTRS2012

Electric Field Reconstruction in the Image Plane of a High-Contrast Coronagraph Using a Set of Pinholes around the Lyot Plane

In a setup similar to the self coherent camera, we have added a set of pinholes in the diffraction ring of the Lyot plane in a high-contrast stellar Lyot coronagraph. We describe a novel complex electric field reconstruction from image plane intensity measurements consisting of light in the coronagraph's dark hole interfering with light from the pinholes. The image plane field is modified by letting light through one pinhole at a time. In addition to estimation of the field at the science camera, this method allows for self-calibration of the probes by letting light through the pinholes in various permutations while blocking the main Lyot opening. We present results of estimation and calibration from the High Contrast Imaging Testbed along with a comparison to the pair-wise deformable mirror diversity based estimation technique. Tests are carried out in narrow-band light and over a composite 10% bandpass.

coronagraphs↗

NASA NTRS2014

Focal Gray Matter Plasticity as a Function of Long Duration Head Down Tilted Bed Rest: Preliminary Results

Long duration spaceflight (i.e., 22 days or longer) has been associated with changes in sensorimotor systems, resulting in difficulties that astronauts experience with posture control, locomotion, and manual control. The microgravity environment is an important causal factor for spaceflight induced sensorimotor changes. Whether these sensorimotor changes are solely related to peripheral changes from reduced vestibular stimulation, body unloading, body fluid shifts or that they may be related to structural and functional brain changes is yet unknown. However, a recent study reported associations between microgravity and flattening of the posterior eye globe and protrusion of the optic nerve [1] possibly as the result of increased intracranial pressure due to microgravity induced bodily fluid shifts [3]. Moreover, elevated intracranial pressure has been related to white matter microstructural damage [2]. Thus, it is possible that spaceflight may affect brain structure and thereby cognitive functioning. Long duration head down tilt bed rest has been suggested as an exclusionary analog to study microgravity effects on the sensorimotor system [4]. Bed rest mimics microgravity in body unloading and bodily fluid shifts. In consideration of the health and performance of crewmembers both in- and post-flight, we are conducting a prospective longitudinal 70-day bed rest study as an analog to investigate the effects of microgravity on brain structure [5]. Here we present results of the first six subjects. Six subjects were assessed at 12 and 7 days before-, at 7, 30, and ~70 days in-, and at 8 and 12 days post 70 days of bed rest at the NASA bed rest facility in UTMB, Galveston, TX, USA. At each time point structural MRI scans (i.e., high resolution T1-weighted imaging and Diffusion Tensor Imaging (DTI)) were obtained using a 3T Siemens scanner. Focal changes over time in gray matter density were assessed using the voxel based morphometry 8 (VBM8) toolbox under SPM. Longitudinal processing in VBM8 includes linear registration of each scan to the mean of the subject and subsequently transforming all scans in to MNI space by applying the warp from the mean subject to MNI to the individual gray matter segmentations. Modulation was applied so that all images represented the volume of the original structure in native space. Voxel wise analysis was carried out on the gray matter images after smoothing, using a flexible factorial design with family wise error correction. Focal changes in white matter microstructural integrity were assessed using tract based spatial statistics (TBSS) as part of FMRIB software library (FSL). TBSS registers all DTI scans to standard space. It subsequently creates a study specific white matter skeleton of the major white matter tracts. For each subject, for each DTI metric (i.e. fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD)), the maximum value in a line perpendicular to the skeleton tract is projected to the skeleton. Non-parametric permutation based t-tests and ANOVA's were used for voxel-wise comparison of the skeletons. For both VBM and TBSS, comparison of pre bed rest measurements did not show significant differences. VBM analysis revealed decreased gray matter density in bilateral areas including the frontal medial cortex, the insular cortex and the caudate (see Figure) from 'pre to in bed rest'. Over the same time period, there was an increase in gray matter density in the cerebellum, occipital-, and parietal cortex, including the precuneus (see Figure). The majority of these changes did not recover from 'during to post bed rest'. TBSS analysis did not reveal significant changes in white matter microstructural integrity after correction for multiple comparisons. Uncorrected analyses (p<.015) revealed an increase in RD in the cerebellum and brainstem from pre bed rest to the first week in bed rest that did not recover post bed rest. Extended bed rest, which is an analog for microgravity, can result in gray matter changes and potentially in microstructural white matter changes in areas that are important for neuro motor behavior and cognition. These changes did not recover at two weeks post bed rest. Whether the effects of bed rest wear off at longer times post bed rest, and if they are associated with behavior are important questions that warrant further research.

Koppelmans, V.↗

NASA NTRS2014

Focal Gray Matter Plasticity as a Function of Long Duration Bedrest: Preliminary Results

Long duration spaceflight (i.e., 22 days or longer) has been associated with changes in sensorimotor systems, resulting in difficulties that astronauts experience with posture control, locomotion, and manual control. It is unknown whether and how spaceflight impacts sensorimotor brain structure and function, and whether such changes may potentially underlie behavioral effects. Long duration head down tilt bed rest has been used repeatedly as an exclusionary analog to study microgravity effects on the sensorimotor system [1]. Bed rest mimics microgravity in body unloading and bodily fluid shifts. We are currently testing sensorimotor function, brain structure, and brain function pre and post a 70‐day bed rest period. We will acquire the same measures on NASA crewmembers starting in 2014. Here we present the results of the first eight bed rest subjects. Subjects were assessed at 12 and 7 days before‐, at 7, 30, and ~70 days in‐, and at 8 and 12 days post 70 days of bed rest at the NASA bed rest facility, UTMB, Galveston, TX, USA. At each time point structural MRI scans (i.e., high resolution T1‐weighted imaging and Diffusion Tensor Imaging (DTI)) were obtained using a 3T Siemens scanner. Focal changes over time in gray matter density were assessed using the voxel based morphometry 8 (VBM8) toolbox under SPM. Focal changes in white matter microstructural integrity were assessed using tract based spatial statistics (TBSS) as part of the FMRIB software library (FSL). TBSS registers all DTI scans to standard space. It subsequently creates a study specific white matter skeleton of the major white matter tracts. Non‐parametric permutation based t‐tests and ANOVA's were used for voxel‐wise comparison of the skeletons. For both VBM and TBSS, comparison of the two pre bed rest measurements did not show significant differences. VBM analysis revealed decreased gray matter density in bilateral areas including the frontal medial cortex, the insular cortex and the caudate nucleus from pre to in bed rest. Over the same time period, there was an increase in gray matter density in the cerebellum, occipital, and parietal cortices. The majority of these changes did not recover from during to post bed rest. TBSS analyses will also be presented. Extended bed rest, which is an analog for microgravity, can result in gray matter changes and potentially in microstructural white matter changes in areas that are important for neuromotor behavior and cognition. These changes did not recover at two weeks post bed rest. These results have significant public health implications, and will also aid in interpretation of our future data obtained pre and post spaceflight. Whether the effects of bed rest wear off at longer times post bed rest, and if they are associated with behavior are important questions that warrant further research.

Koppelmans, V.↗

NASA NTRS2015

A Metric to Quantify Shared Visual Attention in Two-Person Teams

Introduction: Critical tasks in high-risk environments are often performed by teams, the members of which must work together efficiently. In some situations, the team members may have to work together to solve a particular problem, while in others it may be better for them to divide the work into separate tasks that can be completed in parallel. We hypothesize that these two team strategies can be differentiated on the basis of shared visual attention, measured by gaze tracking. 2) Methods: Gaze recordings were obtained for two-person flight crews flying a high-fidelity simulator (Gontar, Hoermann, 2014). Gaze was categorized with respect to 12 areas of interest (AOIs). We used these data to construct time series of 12 dimensional vectors, with each vector component representing one of the AOIs. At each time step, each vector component was set to 0, except for the one corresponding to the currently fixated AOI, which was set to 1. This time series could then be averaged in time, with the averaging window time (t) as a variable parameter. For example, when we average with a t of one minute, each vector component represents the proportion of time that the corresponding AOI was fixated within the corresponding one minute interval. We then computed the Pearson product-moment correlation coefficient between the gaze proportion vectors for each of the two crew members, at each point in time, resulting in a signal representing the time-varying correlation between gaze behaviors. We determined criteria for concluding correlated gaze behavior using two methods: first, a permutation test was applied to the subjects' data. When one crew member's gaze proportion vector is correlated with a random time sample from the other crewmember's data, a distribution of correlation values is obtained that differs markedly from the distribution obtained from temporally aligned samples. In addition to validating that the gaze tracker was functioning reasonably well, this also allows us to compute probabilities of coordinated behavior for each value of the correlation. As an alternative, we also tabulated distributions of correlation coefficients for synthetic data sets, in which the behavior was modeled as a first-order Markov process, and compared correlation distributions for identical processes with those for disparate processes, allowing us to choose criteria and estimate error rates. 3) Discussion: Our method of gaze correlation is able to measure shared visual attention, and can distinguish between activities involving different instruments. We plan to analyze whether pilots strategies of sharing visual attention can predict performance. Possible measurements of performance include expert ratings from instructors, fuel consumption, total task time, and failure rate. While developed for two-person crews, our approach can be applied to larger groups, using intra-class correlation coefficients instead of the Pearson product-moment correlation.

Gontar, Patrick↗

NASA NTRS2016

Automation of Flight Software Regression Testing

NASA is developing the Space Launch System (SLS) to be a heavy lift launch vehicle supporting human and scientific exploration beyond earth orbit. SLS will have a common core stage, an upper stage, and different permutations of boosters and fairings to perform various crewed or cargo missions. Marshall Space Flight Center (MSFC) is writing the Flight Software (FSW) that will operate the SLS launch vehicle. The FSW is developed in an incremental manner based on "Agile" software techniques. As the FSW is incrementally developed, testing the functionality of the code needs to be performed continually to ensure that the integrity of the software is maintained. Manually testing the functionality on an ever-growing set of requirements and features is not an efficient solution and therefore needs to be done automatically to ensure testing is comprehensive. To support test automation, a framework for a regression test harness has been developed and used on SLS FSW. The test harness provides a modular design approach that can compile or read in the required information specified by the developer of the test. The modularity provides independence between groups of tests and the ability to add and remove tests without disturbing others. This provides the SLS FSW team a time saving feature that is essential to meeting SLS Program technical and programmatic requirements. During development of SLS FSW, this technique has proved to be a useful tool to ensure all requirements have been tested, and that desired functionality is maintained, as changes occur. It also provides a mechanism for developers to check functionality of the code that they have developed. With this system, automation of regression testing is accomplished through a scheduling tool and/or commit hooks. Key advantages of this test harness capability includes execution support for multiple independent test cases, the ability for developers to specify precisely what they are testing and how, the ability to add automation, and the ability of the harness and cases to be executed continually. This test concept is an approach that can be adapted to support other projects.

Tashakkor, Scott B.↗

NASA NTRS2017

Titan Atmospheric Entry Radiative Heating

Detailed spectrally and spatially resolved radiance has been measured in the Electric Arc Shock Tube for conditions relevant to Titan entry, varying atmospheric composition, free-stream density (equivalent to altitude) and shock velocity. Permutations in atmospheric composition include 1.1, 2, 5 and 8.6 CH4 by mole with a balance of N2 and 1.5 CH4 0.5 Ar 98 N2 by mole, which is consistent with the current understanding of Titan's atmosphere. The effect of gas impurities identified in previous shock tube studies were also examined by testing in pure N2 and deliberate addition of air to the CH4N2 mixtures. The test campaign measured radiation at velocities from 4.7 kms to 8 kms and free-stream pressures from 0.1 to 0.47 Torr. These conditions cover a range of potential trajectories for flight missions, including a direct ballistic trajectory, a fly by or an extremely high speed entry. Radiances measured in this work are substantially larger compared to that reported both in past EAST test campaigns and other shock tube facilities. Depending on the metric used for comparison, the discrepancy can be as high as an order of magnitude. Potential causes for the discrepancy, such as the effect of oxygen due to Air leakage, gas composition and purity are discussed. The present work provides a new benchmark set of data to replace those published in previous studies.

Earth↗

NASA NTRS2017

Titan Atmospheric Entry Radiative Heating

Brandis, A. M↗

NASA NTRS2017

Towards the Development of a Global, Satellite-Based, Terrestrial Snow Mission Planning Tool

A global, satellite-based, terrestrial snow mission planning tool is proposed to help inform experimental mission design with relevance to snow depth and snow water equivalent (SWE). The idea leverages the capabilities of NASA's Land Information System (LIS) and the Tradespace Analysis Tool for Constellations (TAT-C) to harness the information content of Earth science mission data across a suite of hypothetical sensor designs, orbital configurations, data assimilation algorithms, and optimization and uncertainty techniques, including cost estimates and risk assessments of each hypothetical permutation. One objective of the proposed observing system simulation experiment (OSSE) is to assess the complementary or perhaps contradictory information content derived from the simultaneous collection of passive microwave (radiometer), active microwave (radar), and LIDAR observations from space-based platforms. The integrated system will enable a true end-to-end OSSE that can help quantify the value of observations based on their utility towards both scientific research and applications as well as to better guide future mission design. Science and mission planning questions addressed as part of this concept include: What observational records are needed (in space and time) to maximize terrestrial snow experimental utility? How might observations be coordinated (in space and time) to maximize this utility? What is the additional utility associated with an additional observation? How can future mission costs be minimized while ensuring Science requirements are fulfilled?

Mission Desig↗

NASA NTRS2017

Navigating the Deployment and Downlink Tradespace for Earth Imaging Constellations

Distributed Spacecraft Missions (DSMs) are gaining momentum in their application to Earth Observation (EO) missions owing to their unique ability to increase observation sampling in spatial, spectral, angular and temporal dimensions simultaneously. DSM design includes a much larger number of variables than its monolithic counterpart, therefore, Model-Based Systems Engineering (MBSE) has been often used for preliminary mission concept designs, to understand the trade-offs and interdependencies among the variables. MBSE models are complex because the various objectives a DSM is expected to achieve are almost always conflicting, non-linear and rarely analytical. NASA Goddard Space Flight Center (GSFC) is developing a pre-Phase A tool called Tradespace Analysis Tool for Constellations (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper will describe the different types of constellations that TAT-C’s Tradespace Search Iterator is capable of enumerating (homogeneous Walker, heterogeneous Walker, precessing type, ad-hoc) and their impact on key performance metrics such as revisit statistics, time to global access and coverage. We will also discuss the ability to simulate phased deployment of the given constellations, as a function of launch availabilities and/or vehicle capability, and show the impact on performance. All performance metrics are calculated by the Data Reduction and Metric Computation module within TAT-C, which issues specific requests and processes results from the Orbit and Coverage module. Our TSI is also capable of generating tradespaces for downlinking imaging data from the constellation, based on permutations of available ground station networks - known (default) or customized (by the user). We will show the impact of changing ground station options for any given constellation, on data latency and required communication bandwidth, which in turn determines the responsiveness of the space system.

Nag, Sreeja↗

NASA NTRS2019

Earthdata Search UX Lessons Learned

Crafting a great user experience is hard. Crafting a great user experience for Earth science applications is fraught with challenges. From the variability in metadata to the experience profile of various users the possible permutations of use cases introduce layer upon layer of complexities that must be designed against. In this session, the Earthdata Search team would like to highlight lessons learned over the lifespan of the application the good, the bad, and the ugly.

Earthdata Search↗

NASA NTRS2016

On the Maneuvers Operational Response for NASA’s Soil Moisture Active-Passive (SMAP) Mission

The Soil-Moisture Active-Passive (SMAP) spacecraft requires various kinds of in-orbit maneuvers over the course of its three-year mission. The types of maneuvers include pre-planned commissioning maneuvers to reach its science orbit, regularly executed orbit maintenance maneuvers to overcome drag and other nominally occurring phenomena, as well as (the possibility of) collision avoidance maneuvers. The architecture of the spacecraft – in terms of availability of commanding via ground assets, the inherited avionics' ability to sequence and execute commands, and the capability of available subsystems able to carry out maneuvers – was well defined early in the development of the spacecraft and mission, well before the operational plan for responding to maneuver requests was cemented. The systems engineering challenge became: how to accommodate all three types of maneuvers in the confines of this well-defined architecture. This paper will describe how the operations team on SMAP successfully met this challenge. Specifically, it will dive into the three pronged approach that SMAP developed to handle each type of maneuver described above – to meet the timeliness requirements leveraged on the operations team to execute said maneuvers, while continuing to fit within the allotted staffing profile during nominal operations. Defining this paradigm to fit the mission’s architecture meant re-defining the original paradigm, (planned maneuvers being thought of separately than collision avoidance maneuvers), and re-classifying all responses to maneuver requests as variations and permutations of a singular operational response to a maneuver request. This paper will also describe the tools that were created to simplify the human interface and automate as much of the response as was possible. Finally, this paper will describe, at a very high level, some of the problems encountered and lessons learned by the operations team when this process was executed the first four times during the first ninety days of operations. Though the architecture of the operations team's response to maneuver requests will never be repeated exactly, the flexibility that was inserted via redefining the scope of the problem and by redefining the human interfaces should influence future projects' architectures earlier in their development – in the hopes that said influence will save time and money in the future.

Tirona, Joseph↗

NASA NTRSDate not supplied

Next Generation Big Data Storage for Long Space Missions

This paper presents the results of the HELIOS (Hardened Extremely Long Life In-formation Optical Storage) mission on the International Space Station (ISS) which tested a unique solution for the long-term storage and retrieval of data in space. For this mission Creative Technology (CTech) developed test media—termed WORF (Write Once, Read Forever)—to validate whether this patented technology will survive all critical parameters for harsh space-based environments including microgravity and ionizing radiation. The HELIOS experiment confirmed that the WORF media is impervious to ionizing radiation, microgravity, solar (plasma) eruptions, and the stress from 8 Gs of the launch including extreme temperature expo-sure. The principal results indicate that there has been no discernible degradation of the media after 8 months on the ISS as compared to a control set of media stored on the ground. This data validated the media’s survivability for harsh space environments for long-term and deep space missions. In addition to the space environment, we are confident that WORF technology can be used for data storage where space-related and other long-term or archival integrity is critical such as: geospatial collections from satellites; space weather archives; past, ongoing, and future space mission media and documentation files; the deep space Gate-way program; as well as Big Data applications such as the Vera C. Rubin astronomical observatory (formerly the LSST). WORF technology for the HELIOS experiment uses a proven archival media, redesigned, re-purposed and patented by CTech to store digital data for long periods, measured in decades and possibly centuries. The media stores standing waves embedded in a substrate that capture the precise col-ors or wavelengths projected onto the media. The colors represent numerical data, with each data location storing multiple superimposed wavelengths, which facilitate the storage of multiple data bytes (rather than just zeros and ones); advanced mathematical permutations allow for extremely large data density equal to or greater than contemporary data storage de-vices. These colors cannot fade or degrade over time since the standing waves are physically stabilized (fully oxidized) metallic silver; no dyes are embedded for this storage system, and silver ions resist micro-bacterial and fungal contamination

Rodney Grubbs↗

NASA NTRS2021

Characterizing the Aging of Alphasense NO2 Sensors in Long-term Field Deployments

Low-cost NO2 sensors have been widely deployed for atmospheric sampling. While their initial performance has been characterized, few studies have examined their long-term degradation. This study focused on the performance of Alphasense low-cost NO2 sensors (NO2-B42F and NO2-B43F) over 4 years (2016–2020). A total of 29 NO2 sensors from 10 batches were collocated 78 times at two sites with reference instruments. Raw signals from “functional” NO2 sensors correlated linearly with reference NO2 concentrations. After long-term deployment, sensor raw signals started to deviate from reference NO2 concentrations due to sensor aging, an accumulated effect after sensor unpacking. Several sensors eventually became “non-functional” as sensor raw signals showed no correlation with reference NO2 concentrations. Sensor aging and non-functionality may be primarily caused by expiration of the ozone (O3) scrubber built into these sensors so that sensors responded to both ambient NO2 and O3. The influence of O3 on sensor response is quantified through the permutation importance method. Most of the sensors are non-functional after approximately 200–400 days of deployment, and no sensor was functional after 400 days of deployment. This result agrees well with the estimated lifetime of the built-in ozone scrubbers considering the ambient ozone concentration in the Pittsburgh area where these sensors were deployed. To ensure reliable data quality in long-term field deployments, we recommend collocating NO2 sensors with reference instruments regularly after 200–400 days of deployment to identify and replace non-functional sensors in a timely manner.

NO2sensors↗

NASA NTRSDate not supplied

Strategy for Developing Technologies for Megawatt-class Nuclear Electric Propulsion Systems

In late fiscal year 2020, the Space Nuclear Propulsion (SNP) project began the process of formulating an investment strategy to support development of the technologies required for a high-power (megawatt-class) nuclear electric propulsion (NEP) system capable of performing human-scale missions. This activity was initiated concurrent with several high-level studies and assessments were either under way or had just concluded. Studies of human-scale Mars missions have been performed several times over the past two decades. One of the most recent studies examined opposition-class human Mars missions to occur in the late 2030s timeframe [1,2]. The mission architecture assumed a hybrid NEP/chem-propelled vehicle that used a high specific impulse (Isp) NEP-system and a liquid oxygen (LOx)-liquid methane high thrust chemical stage (two 110 kN (25 klbf) thrust, 365 s Isp engines) for maneuvers performed to enter and exit gravity wells. Trajectory analyses performed in this study showed that such a mission could be performed with 2-4 MWe directed into the electric propulsion system (operating for 20,000+ hours), with the large range representing different opposition-class Mars mission opportunities and permutations on the trajectory design, concept of operations, and technology choices. In 2020, the NASA Engineering and Safety Center (NESC) performed a study to evaluate the maturity of the different technologies required for nuclear propulsion systems [3]. The executive summary of this report provided the following top-level conclusions: • “The majority of critical technologies for… NEP/Chem… systems are relatively immature” • “TRLs [technology readiness levels] in the literature are often overestimated” • “The majority of critical technologies… for NEP/Chem… systems are at a relatively high level of advancement degree of difficulty (AD2 > 4) for maturation, requiring a dual development approach” • “The proper assessment of baseline TRL and AD2 values and the estimation of requirements and resources required for advancement have been consistent issues for NEP,” • “Non-advocate reviews should occur at the start of a technology program and at all key milestones.” In 2021, the National Academies of Science, Engineering, and Medicine (NASEM) issued a separate report [4] identifying the “primary technical and programmatic challenges, merits, and risks for maturing space nuclear propulsion technologies of interest to a future human Mars exploration mission.” That work contained several important findings, including: • “Developing a MWe-class NEP system for the baseline mission would require increasing power by orders of magnitude relative to NEP system flight- or ground-based technology demonstrations completed to date.” • “Subscale in-space flight testing of NEP systems cannot address many of the risks and potential failure modes associated with the baseline mission NEP system. With sufficient M&S [modeling & simulation] and ground testing, including modular subsystem tests at full scale and power, flight qualification requirements can be met by the cargo missions that will precede the first crewed mission to Mars. Fully integrated ground testing may not be required.” • “As a result of low and intermittent investment over the past several decades, it is unclear if even an aggressive program would be able to develop an NEP system capable of executing the baseline mission in 2039.” These efforts motivated the SNP project to investigate the technologies available for a megawatt-class high power nuclear electric propulsion system. That system is illustrated schematically in Figure 1 and is comprised of five separate top-level critical technology elements (CTEs). 1. Nuclear Reactor – Thermal power source for the system, utilizing high-assay low enriched uranium (HALEU) as the nuclear fuel. Reactor radiation shielding is also included in this CTE. 2. Power Conversion – Operates as a thermodynamic cycle, accepting nuclear reactor thermal power as an input and converting it to mechanical power. 3. Power Management and Distribution (PMAD) – Accepts as an input mechanical power from the power conversion system, which is used to generate electrical power. The PMAD system also distributes the generated electrical power to all other parts of the spacecraft, including the high-power EP system. The PMAD system may also perform duties such as isolation, fault detection, and power transformation/rectification for different spacecraft systems, including the thrusters. 4. Electric Propulsion (EP) – Accepts as an input electrical power, which is used to accelerate a propellant to high speeds to produce thrust. This system includes the power processing unit (PPU), which converts the power it receives to the correct current and voltage required by the thrusters, and the propellant storage and feed systems, which contain and meter the flow of propellant to the thrusters. 5. Thermal Management (Radiators/Heat Rejection) – The cold side of the thermodynamic power conversion cycle, accepts thermal power from the power conversion system and radiatively rejects that heat to space. In this paper, we describe the SNP project formulation and investment strategy that aims to accomplish the research and development required to advance the technology readiness for each CTE. The strategy relies heavily upon experimental testing supported by modeling and simulation to yield realistic assessments of the technologies, which in turn will be used to inform future NEP system-level design decisions and any potential technology downselects.

Kurt A Polzin↗

NASA NTRS2022

A Benchmark Example for Delamination Propagation Predictions Based on the Calibrated End-Loaded Split Specimen

A benchmark example based on the Calibrated End-Loaded Split (C-ELS) specimen is developed and used to assess the performance of recently developed delamination propagation capabilities in the Abaqus/Standard finite element code. The C-ELS specimen has the advantage of a longer region of stable delamination propagation compared to the existing mode II benchmark case. The new benchmark example may therefore provide a better assessment tool by enabling more stable crack growth in regions further away from the boundary conditions or load application. First, a benchmark result is created manually using two-dimensional finite element models of the C-ELS specimen with different delamination lengths. Second, the performance of the delamination propagation capabilities in the Abaqus/Standard finite element code are assessed by comparing the results to the benchmark case. Two examples with different starter delamination lengths are studied. A shorter starter length is chosen to create a scenario with unstable delamination propagation. A longer delamination causes stable delamination propagation. Detailed results from three-dimensional analyses with aligned and mis-aligned meshes and two levels of mesh refinement are provided for several permutations of numerical input parameters. In general, good agreement can be achieved between the results obtained from the quasi-static propagation analysis and the benchmark analysis. However, particular non-default settings are found to be most reliable, accurate, and numerically efficient. Numerical artifacts including anomalous unreleased nodes in the crack wake and zig-zag crack fronts occur, and further development of the Abaqus/Standard VCCT propagation may be required. Use of the benchmark case to assess the continuous improvements in one finite element code illustrates the value of establishing benchmark solutions.

delamination↗

NASA NTRS2022

Acoustic Modeling of Novel Over-The-Rotor Acoustic Liner Concepts Using COMSOL Multiphysics Simulation Software

This paper presents simulation predictions of novel over-the-rotor acoustic liner configurations compared with experimental data. This is a follow on to the investigation on the effect of grazing flow on grooved over-the-rotor casing treatments. The purpose of this modeling investigation stems from the determination that traditional liner modeling techniques are inadequate for nontraditional internal liner cavity geometries. A series of these expansion chamber type liner design concepts were previously developed by Glenn Research Center and tested using the Langley Research Center Normal Incidence Tube. The measured normalized impedance and absorption coefficient is compared with that obtained from the COMSOL Multiphysics® simulation. Additionally, using a parametric case study, another series of simulations were conducted to determine what additional benefits, if any, this genre of liner has to offer the acoustics community. A total of 53 permutations of this concept was investigated. The results indicate that with longer internal fins, the resonance is sifted to lower frequencies.

Acoustics↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗