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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Relationship Between Iron Valence States of Serpentine in CM Chondrites and Their Aqueous Alteration Degrees

The 0.6-0.7 micron absorption band observed for C-type asteroids is caused by the presence of Fe(3+) in phyllosilicates . Because Fe-bearing phyllosilicates, especially serpentine, are the most dominant product of aqueous alteration in the most abundant carbonaceous chondrites, CM chondrites, it is important to understand the crystal chemistry of serpentine in CM chondrites to better understand spectral features of C-type asteroids. CM chondrites show variable degrees of aqueous alteration, which should be related to iron valences in serpentine. It is predicted that the Fe(3+)/Sum of (Fe) ratios of serpentine in CM chondrites decrease as alteration proceeds by Si and Fe(3+) substitutions from end-member cronstedtite to serpentine, which should be apparent in the absorption intensity of the 0.6-0.7 micron band from C-type asteroids. In fact, the JAXA Hayabusa 2 target (C-type asteroid: 1993 JU3) exhibits heterogeneous spectral features (0.7 micron absorption band disappears by rotation). From these points of view, we have analyzed iron valences of matrix serpentine in several CM chondrites which span the entire observed range of aqueous alteration using Synchrotron Radiation X-ray Absorption Near-Edge Structure (SR-XANES). In this abstract we discuss the relationship between obtained Fe(3+)/Sum of (Fe) ratios and alteration degrees by adding new data to our previous studies

Mikouchi, T.↗
NASA NTRS2016

Selected Scientific and Technical Contributions of Edward C. Polhamus

Edward C. Polhamus joined the NACA Langley Research Center staff in 1944 and was active in a broad range of aerodynamic research related to high-speed aircraft technology, aerodynamic prediction methods, and cryogenic wind-tunnel development. This lecture will focus on his 'leading-edge suction analogy' for the prediction of vortex-lift effects on slender wings. Briefer treatment of his contributions to variable-sweep aircraft and cryogenic wind tunnels is also included.

Luckring, James M.↗
NASA NTRS2020

Test-Analysis Modal Correlation of Rocket Engine Structures in Liquid Hydrogen - Phase II

Many structures in a launch vehicle operate in liquid hydrogen, from the hydrogen fuel tanks through the ducts and valves and into the turbopumps. Calculating the structural dynamic response of these structures is critical for successful qualification, but accurate knowledge of the natural frequencies is based entirely on numerical or analytical predictions since testing in operating conditions is problematic. A comprehensive test/analysis program has therefore been performed at NASA/MSFC to enable accurate prediction of the modal characteristics of the Space Launch System’s (SLS) RS-25 Low Pressure Fuel Turbopump Inducer including the effects of fluid-added mass, mechanical property change at cryogenic temperatures, operation within tight tip clearances, acoustic/structure interaction, and hydroelasticity. The process also has to account for complicated cyclic symmetry mode shapes which can be easily mistuned in test, and geometric, boundary condition, and material modifications between the sub-scale inducer used as a test article and the actual flight component. The first phase of the program, documented previously, focused on testing of a cantilever beam in a number of fluids and temperatures to isolate the effects of fluid-added mass and temperature, while the second phase reported here documents the additional issues associated with the more realistic inducer test article. Preliminary structural dynamic analysis of the flight hardware including variability for the above parameters indicated potential severe resonances, requiring implementation of undesired programmatic constraints, so the improved predictive capability may allow removal of these constraints.

Brown, Andrew M.↗
NASA NTRSDate not supplied

SpaceX Dragon 1 Post Flight MMOD Inspection Campaign

Introduction The SpaceX Dragon 1 spacecraft performed 20 resupply missions to the International Space Station (ISS) between 2012 and 2020. After each mission, a team from the NASA John-son Space Center Hypervelocity Impact Technology Group inspected each Dragon 1 capsule for hypervelocity impact damage features. Data from these inspections are collected into a database that includes impact feature dimensions as well as the location on the vehicle. Additional details on the type and size of particle that produced the damage site are provided when sampling data and definitive spectroscopic analysis results are available. Observation data can be compared with impact estimates from risk assessment codes as a check on the micrometeoroid and orbital debris (MMOD) environment predictions. Scope A general description of the areas of the vehicle that were inspected are provided as well as mission details such as exposure duration and launch dates. The paper documents the general inspection procedure for collection of data and the post inspection data analysis process. It also provides details of the observation data collected as well as the results of analysis of intact samples collected for spectroscopic analysis to discern the source of the impacting particle. A comparison between observed impacts and the expected number of damage features calculated by Bumper 3 with the latest MMOD environments are also presented. Findings Statistics on the >300 impact features documented in the database will provide insight into the depth to diameter ratios and other relationships. The quality of the comparison between the observations and code predictions are dependent on several factors. The paper provides de-tails of each of these variables. (1) Damage equations (2) Impact condition assumptions a. Projectile density b. Impact speed c. Impact angle (3) Analysis results a. pre-flight vs. on-orbit damage b. MMOD vs. non-MMOD Conclusions and Recommendations The ISS visiting vehicle impact database is an ongoing project. SpaceX provides crew rotations as well as resupply and cargo return with the Dragon 2 spacecraft. Sierra Space is manifested for resupply and cargo return services starting in 2023. Boeing is expected to provide crew rotations as well. All of these spacecraft will provide additional opportunities for post flight MMOD inspections of space exposed hardware. Acknowledgements Over the course of the inspection campaign various personnel at the SpaceX Texas Test Site in McGregor and at the SpaceX headquarters in Hawthorne CA provided valuable support to this activity.

James L Hyde↗
NASA NTRSDate not supplied

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
NASA NTRSDate not supplied

Capturing Multivariate Time Series Interactions to Detect High‑Risk Instability During Approach

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
NASA NTRS2026

LAI Assimilation Schedules to Constrain Uncertain Cultivars and Soils in CERES-Maize

In anticipation of large-domain crop model applications where precise local configuration and calibration is not possible, we describe benefits and potential drawbacks of employing a crop pest module to achieve leaf area index (LAI) assimilation into a high performing CERES-Maize crop model configuration at a field experiment site in Perry, Iowa. Simulation experiments explore the use of MODIS satellite-derived LAI to constrain and adjust LAI to counter imprecise cultivar and soil configurations often occurring in the absence of high-quality local information. Simulations using single-day, window, and continuous LAI replacement across 5 cultivars and 2 soil calibration approaches for nine corn rotation years from 2004 to 2020 led to different yield outcomes and reverberations throughout the field environment. Evaluating variance and mean bias, results indicate minimal interventions in early vegetative and grain-filling stages were more beneficial than use of continuous LAI adjustments, as they minimized disruptions to the internal resource balances governing plant stresses and grain production. LAI adjustment was particularly helpful in constraining growth related to uncertain thermal unit requirements and leaf tip appearance rates (P1 and PHINT cultivar parameters, respectively). Findings underscore the need to assimilate additional state variables to ensure internal biophysical coherence. This approach shows promise for applications spanning wider domains with prediction time pressure where detailed configuration, more complex assimilation methods, or recalibration of crop model parameters may not be practical.

Phenology↗
NASA NTRS1986

Thermophoretically augmented mass transfer rates to solid walls across laminar boundary layers

Predictions of mass transfer (heavy vapor and small particle deposition) rates to solid walls, including the effects of thermal (Soret) diffusion ('thermophoresis' for small particles), are made by numerically solving the two-dimensional self-similar forced convection laminar boundary-layer equations with variable properties, covering the particle size range from vapor molecules up to the size threshold for inertial (dynamical nonequilibrium) effects. The effect of thermophoresis is predicted to be particularly important for submicron particle deposition on highly cooled solid surfaces, with corresponding enhancement factors at atmospheric conditions being over a thousand-fold at T(w)/T(e) equal to about 0.6. As a consequence of this mass transfer mechanism, the particle size dependence of the mass transfer coefficient to a cooled wall will be much weaker than for the corresponding case of isothermal capture by Brownian-convective diffusion.

Gokoglu, S. A.↗
NASA NTRS1976

Inlet spillage drag tests and numerical flow-field analysis at subsonic and transonic speeds of a 1/8-scale, two-dimensional, external-compression, variable-geometry, supersonic inlet configuration

Accurate spillage drag and pressure data are presented for a realistic supersonic inlet configuration. Results are compared with predictions from a finite-differencing, inviscid analysis computer procedure. The analytical technique shows good promise for the evaluation of inlet drag, but necessary refinements were identified. A detailed description of the analytical procedure is contained in the Appendix.

Hawkins, J. E.↗
NASA NTRS2018

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Our coupled model is composed of a primitive equation ocean model for the tropical Indo-Pacific region that is coupled with the global SPEEDY atmospheric model (Molteni, 2003). The Ensemble Reduced Order Kalman Filter is used to assimilate observations to constrain dynamics and thermodynamics for initialization of the coupled model. The baseline experiment assimilates satellite sea level, SST, and in situ subsurface temperature and salinity observations. This baseline is then compared with experiments that additionally assimilate Aquarius (version 4.0) and SMAP (version 2.0) SSS. Twelve-month forecasts are initialized for each month from Sep. 2011 to Dec. 2016. We find that including satellite SSS significantly improves NINO 3.4 sea surface temperature anomaly validation after 1 out to 12 month forecast lead times. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10 degrees South latitude-5 degrees North latitude. SST differences at initialization force wide-spread downwelling favorable curl over most of the tropical Pacific. Over an average forecast, SST remains warmer with SSS assimilation at the eastern edge of the warm pool. This warm SST propagates into the eastern Pacific and drags westerly wind anomalies eastward into the NINO 3.4 region. In addition, salting near the ITCZ (Intertropical Convergence Zone) leads to a deepening of the mixed layer and thermocline near 8 degrees North latitude. These patterns together lead to a funneling effect that provides the background state to amplify equatorial Kelvin waves. We show that the downwelling Kelvin waves are amplified by assimilating satellite SSS and lead to significantly improved forecasts particularly for the 2015 El Nino.

El Nino↗
NASA NTRS2018

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Our coupled model is composed of a primitive equation ocean model for the tropical Indo-Pacific region that is coupled with the global SPEEDY atmospheric model (Molteni, 2003). The Ensemble Reduced Order Kalman Filter is used to assimilate observations to constrain dynamics and thermodynamics for initialization of the coupled model. The baseline experiment assimilates satellite sea level, SST, and in situ subsurface temperature and salinity observations. This baseline is then compared with experiments that additionally assimilate Aquarius (version 4.0) and SMAP (version 2.0) SSS. Twelve-month forecasts are initialized for each month from Sep. 2011 to Dec. 2016. We find that including satellite SSS significantly improves NINO3.4 sea surface temperature anomaly validation after 1 out to 12 month forecast lead times. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10S-5N. SST differences at initialization force wide-spread downwelling favorable curl over most of the tropical Pacific. Over an average forecast, SST remains warmer with SSS assimilation at the eastern edge of the warm pool. This warm SST propagates into the eastern Pacific and drags westerly wind anomalies eastward into the NINO3.4 region. In addition, salting near the ITCZ leads to a deepening of the mixed layer and thermocline near 8N. These patterns together lead to a funneling effect that provides the background state to amplify equatorial Kelvin waves. We show that the downwelling Kelvin waves are amplified by assimilating satellite SSS and lead to significantly improved forecasts particularly for the 2015 El Nino.

SST↗
NASA NTRS1975

Investigation to develop a multistage forest sampling inventory system using ERTS-1 imagery

The author has identified the following significant results. The annotation system produced a RMSE of about 200 m ground distance in the MSS data system with the control data used. All the analytical MSS interpretation models tried were highly significant. However, the gains in forest sampling efficiency that can be achieved by using the models vary from zero to over 50 percent depending on the area to which they are applied and the sampling method used. Among the sampling methods tried, regression sampling yielded substantial and the most consistent gains. The single most significant variable in the interpretation model was the difference between bands 5 and 7. The contrast variable, computed by the Hadamard transform was significant but did not contribute much to the interpretation model. Forest areas containing very large timber volumes because of large tree sizes were not separable from areas of similar crown cover but containing smaller trees using ERTS image interpretation only. All correlations between space derived timber volume predictions and estimates obtained from aerial and ground sampling were relatively low but significant and stable. There was a much stronger relationship between variables derived from MSS and U2 data than between U2 and ground data.

Langley, P. G.↗
NASA NTRS2001

Interannual Variability of Tropical Precipitation: How Well Do Climate Models Agree With Current Satellite Estimates?

Since the beginning of the World Climate Research Program's Global Precipitation Climatology Project (GPCP) satellite remote sensing of precipitation has made dramatic improvements, particularly for tropical regions. Data from microwave and infrared sensors now form the most critical input to precipitation data sets and can be calibrated with surface gauges to so that the strengths of each data source can be maximized in some statistically optimal sense. Recent availability of the TRMM (Tropical Rainfall Measuring Mission) has further aided in narrowing uncertainties in rainfall over die tropics and subtropics. Although climate modeling efforts have long relied on space-based precipitation estimates for validation, we now are in a position to make more quantitative assessments of model performance, particularly in tropical regions. An integration of the CCM3 using observed SSTs as a lower boundary condition is used to examine how well this model responds to ENSO forcing in terms of anomalous precipitation. An integration of the NCEP spectral model used for the Reanalysis-H effort is also examined. This integration is run with specified SSTs, but with no data assimilation. Our analysis focuses on two aspects of inter-annual variability. First are the spatial anomalies that are indicative of dislocations in Hadley and Walker circulations. Second, we consider the ability of models to replicate observed increases in oceanic precipitation that are noted in satellite observations for large ENSO events. Finally, we consider a slab ocean version of the CCM3 model with prescribed ocean beat transports that mimic upwelling anomalies, but which still allows the surface energy balance to be predicted. This less restrictive experiment is used to understand why model experiments with specified SSTs seem to have noticeably less interannual variability in precipitation than do the satellite observations.

Robertson, Franklin R.↗
NASA NTRSDate not supplied

Interpretable Machine Learning Models for Autonomous Characterization of Analogue Ocean World Seawater Chemistry and Biosignature Potential Using Isotope Ratio Data

Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.

geochemistry↗
NASA NTRS2011

An Extended View of Ozone and Chemistry in the Atmosphere of Mars

We present an ongoing effort to characterize chemistry in Mars' atmosphere in multiple seasons on timescales longer than spaceflight missions through coordinated efforts by GSFC's HIPWAC spectrometer and Mars Express SPICAM, archival measurements, and tests/application of photochemical models. The trace species ozone (03) is an effective probe of Mars' atmospheric chemistry because it is destroyed by odd-hydrogen species (HOx, from water vapor photolysis). Observed ozone is a critical test for specific predictions by 3-D photochemical models (spatial, diurnal, seasonal). Coordinated measurements by HIPWAC and SPICAM quantitatively linked mission data to the 23-year GSFC ozone data record and also revealed unanticipated inter-decadal variability of same-season ozone abundances, a possible indicator of changing cloud activity (heterogeneous sink for HOx). A detailed study of long-term conditions is critical to characterizing the predictability of Mars' seasonal chemical behavior, particularly in light of the implications of and the lack of explanation for reported methane behavior.

Smith, Ramsey L.↗
NASA NTRS2000

Interannual Variability in the Meridional Transport of Water Vapor

The zonal-mean meridional transport of water vapor across the globe is evaluated using the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis for 1948-97. The shape of the meridional profile of the climatological mean transport closely resembles that of previous mean climate descriptions, but values tend to be notably larger than in climatologies derived from radiosonde-only-based analyses. The unprecedented length of the NCEP-NCAR dataset invites a focus on interannual variations in the zonal-mean moisture transport, and these results for northern winter are highlighted here. Although interannual variability in the transport is typically small at most latitudes, a significant ENSO signal is present, marked by a strengthening of water vapor transports over much of the winter hemisphere during warm events. Because of an increase in tropical sea surface temperatures and in the frequency of warm events relative to cold events in the latter half of the 50-yr record, this interannual signal projects onto an overall trend toward enhanced meridional moisture transports in the global hydrological cycle.

Cohen, Judah L.↗
NASA NTRS2018

Modeling the Distribution and Type of High-Latitude Natural Wetlands for Methane Studies

High latitude (>50N) natural wetlands emit a substantial amount of methane to the atmosphere, and are located in a region of amplified warming. Northern hemisphere high latitudes are characterized by cold climates, extensive permafrost, poor drainage, short growing seasons, and slow decay rates. Under these conditions, organic carbon accumulates in the soil, sequestering CO2 from the atmosphere. Methanogens produce methane from this carbon reservoir, converting stored carbon into a powerful greenhouse gas. Methane emission from wetland ecosystems depends on vegetation type, climate characteristics (e.g, precipitation amount and seasonality, temperature, snow cover, etc.), and geophysical variables (e.g., permafrost, soil type, and landscape slope). To understand how wetland methane dynamics in this critical region will respond to climate change, we have to first understand how wetlands themselves will change and therefore, what the primary controllers of wetland distribution and type are. Understanding these relationships permits data-anchored, physically-based modeling of wetland distribution and type in other climate scenarios, such as paleoclimates or future climates, a necessary first step toward modeling wetland methane emissions in these scenarios. We investigate techniques and datasets for predicting the distribution and type of high latitude (>50N) natural wetlands from a suite of geophysical and climate predictors. Hierarchical clustering is used to derive an empirical methane-centric wetland model. The model is applied in a multistep process first to predict the distribution of wetlands from relevant geophysical parameters, and then, given the predicted wetland distribution, to classify the wetlands into methane-relevant types using an expanded suite of climate and biogeophysical variables. As the optimum set of predictor variables is not known a priori, the model is applied iteratively, and each simulation is evaluated with respect to observed high-latitude wetlands.

Romanski, Joy↗
NASA NTRS1977

Systems theoretic model of stratospheric pollution on ozone

Discrete state space analysis is applied to the development of a model for predicting the reduction in the amount of stratospheric ozone due to stratospheric pollution. The modeling approach is to segment the stratosphere into a number of altitudinal bands and to associate state variables with the state of ozone reduction in each band due to NO(x) injections. The ozone reductions in each band are then combined into an estimate of total global ozone reduction. The model proposed predicts changes in stratospheric ozone based on altitude of pollutant injection and volume of pollutant injected at that altitude. The present approach is not a detailed analysis of stratospheric constituents and their interactions, but rather an analysis procedure for modeling the results of other researchers in the field.

Wilkins, R. D.↗