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NASA NTRS2020

Modeling the smoky troposphere of the southeast Atlantic: a comparison to ORACLES airborne observations from September of 2016

In the southeast Atlantic, well-defined smoke plumes from Africa advect over marine boundary layer cloud decks; both are most extensive around September, when most of the smoke resides in the free troposphere. A framework is put forth for evaluating the performance of a range of global and regional atmospheric composition models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016. A strength of the comparison is a focus on the spatial distribution of a wider range of aerosol composition and optical properties than has been done previously. The sparse airborne observations are aggregated into approximately 2° grid boxes and into three vertical layers: 3–6 km, the layer from cloud top to 3 km, and the cloud-topped marine boundary layer. Simulated aerosol extensive properties suggest that the flight-day observations are reasonably representative of the regional monthly average, with systematic deviations of 30 % or less. Evaluation against observations indicates that all models have strengths and weaknesses, and there is no single model that is superior to all the others in all metrics evaluated. Whereas all six models typically place the top of the smoke layer within 0–500 m of the airborne lidar observations, the models tend to place the smoke layer bottom 300–1400 m lower than the observations. A spatial pattern emerges, in which most models underestimate the mean of most smoke quantities (black carbon, extinction, carbon monoxide) on the diagonal corridor between 16° S, 6° E, and 10° S, 0° E, in the 3–6 km layer, and overestimate them further south, closer to the coast, where less aerosol is present. Model representations of the above-cloud aerosol optical depth differ more widely. Most models overestimate the organic aerosol mass concentrations relative to those of black carbon, and with less skill, indicating model uncertainties in secondary organic aerosol processes. Regional-mean free-tropospheric model ambient single scattering albedos vary widely, between 0.83 and 0.93 compared with in situ dry measurements centered at 0.86, despite minimal impact of humidification on particulate scattering. The modeled ratios of the particulate extinction to the sum of the black carbon and organic aerosol mass concentrations (a mass extinction efficiency proxy) are typically too low and vary too little spatially, with significant inter-model differences. Most models overestimate the carbonaceous mass within the offshore boundary layer. Overall, the diversity in the model biases suggests that different model processes are responsible. The wide range of model optical properties requires further scrutiny because of their importance for radiative effect estimates.

Yohei Shinozuka↗
NASA NTRS2021

Estimated Ambient Sonic Boom Metric Levels and X-59 Signal-to-Noise Ratios across the USA

NASA is building the X-59 Quiet Supersonic Technology aircraft to produce low noise sonic booms for a series of community noise surveys across the USA. Survey participants will rate their perception of the low-booms from supersonic X-59 flyovers. Several noise metrics are proposed to quantify the noise dose: A-, B-, D-, and E-weighted Sound Exposure Level, Stevens Perceived Level, and Indoor Sonic Boom Annoyance Predictor. Sparse measurements across the survey area will be used to estimate community noise exposure. The level of these low-booms may be comparable to the ambient noise level in some locations, leading to uncertainty in noise exposure estimations. This uncertainty may necessitate increased reliance on sonic boom propagation predictions for exposure estimation. Low-boom signal to ambient noise ratio is one way to quantify uncertainty in measured sonic boom levels. An empirical relationship between A-weighted ambient level and sonic boom metric levels is used in conjunction with the National Park Service’s L50 SPL map to estimate ambient noise levels expressed in terms of sonic boom noise metrics across the USA. These estimates of ambient levels will aid in X-59 community test planning and execution. The signal-to-noise ratio for the undertrack X-59 sonic boom is also estimated, and an example application of these data is presented for comparing potential noise monitor sites prior to a community noise test.

X-59↗
NASA NTRS2019

The Global Fire Atlas of Individual Fire Size, Duration, Speed and Direction

Natural and human-ignited fires affect all major biomes, altering ecosystem structure, biogeochemical cycles and atmospheric composition. Satellite observations provide global data on spatiotemporal patterns of biomass burning and evidence for the rapid changes in global fire activity in response to land management and climate. Satellite imagery also provides detailed information on the daily or sub-daily position of fires that can be used to understand the dynamics of individual fires. The Global Fire Atlas is a new global dataset that tracks the dynamics of individual fires to determine the timing and location of ignitions, fire size and duration, and daily expansion, fire line length, speed, and direction of spread. Here, we present the underlying methodology and Global Fire Atlas results for 2003–2016 derived from daily moderate-resolution (500 m) Collection 6 MCD64A1 burned-area data. The algorithm identified 13.3 million individual fires over the study period, and estimated fire perimeters were in good agreement with independent data for the continental United States. A small number of large fires dominated sparsely populated arid and boreal ecosystems, while burned area in agricultural and other human-dominated landscapes was driven by high ignition densities that resulted in numerous smaller fires. Long-duration fires in boreal regions and natural landscapes in the humid tropics suggest that fire season length exerts a strong control on fire size and total burned area in these areas. In arid ecosystems with low fuel densities, high fire spread rates resulted in large, short-duration fires that quickly consumed available fuels. Importantly, multiday fires contributed the majority of burned area in all biomass burning regions. A first analysis of the largest, longest and fastest fires that occurred around the world revealed coherent regional patterns of extreme fires driven by large-scale climate forcing. Global Fire Atlas data are publicly available through http://www.globalfiredata.org (last access: 9 August 2018) and https://doi.org/10.3334/ORNLDAAC/1642, and individual fire information and summary data products provide new information for benchmarking fire models within ecosystem and Earth system models, understanding vegetation–fire feedbacks, improving global emissions estimates, and characterizing the changing role of fire in the Earth system.

global fire atlas↗
NASA NTRS2021

Investigation of High Incidence Angle Propeller Aerodynamics for Subscale eVTOL Aircraft

Propellers used for electric vertical takeoff and landing (eVTOL) aircraft propulsion systems experience a wide range of aerodynamic conditions, including significant incidence angles relative to oncoming airflow. Propellers in oblique flow exhibit deviations in thrust and torque oriented along their axis of rotation, as well as significant off-axis forces and moments. Although important for understanding eVTOL aircraft aerodynamics, sparse experimental data exist for propellers operating at incidence. This report describes an experimental wind tunnel study of isolated propeller aerodynamics across a wide range of flight conditions expected to be experienced by the LA-8 tandem tilt-wing, eVTOL aircraft. The experimental data obtained from the study are graphically presented and a discussion of observed aerodynamic phenomena is compared to theoretical expectations and past experimental work. The content of this report is intended to provide guidance to future propeller testing efforts and describe pertinent propeller aerodynamic behavior expected to be experienced by eVTOL vehicles.

propeller aerodynamics↗
NASA NTRS2019

Mapping the Interstellar Reddening and Extinction toward Baade's Window Using Minimum Light Colors of ab-type RR Lyrae Stars: Revelations from the De-reddened Color–Magnitude Diagrams

We have obtained repeated images of six fields toward the Galactic bulge in five passbands (u, g, r, i, z) with the DECam imager on the Blanco 4 m telescope at CTIO. From more than 1.6 billion individual photometric measurements in the field centered on Baade’s window, we have detected 4877 putative variable stars. A total of 474 of these have been confirmed as fundamental mode RR Lyrae stars, whose colors at minimum light yield line-of- sight reddening determinations, as well as a reddenning law toward the Galactic Bulge, which differs significantly from the standard R(V) = 3.1 formulation. Assuming that the stellar mix is invariant over the 3 square-degree field, we are able to derive a line-of-sight reddening map with sub-arcminute resolution, enabling us to obtain de-reddened and extinction corrected color–magnitude diagrams (CMDs) of this bulge field using up to 2.5 million well-measured stars. The corrected CMDs show unprecedented detail and expose sparsely populated sequences: for example, delineation of the very wide red giant branch, structure within the red giant clump, the full extent of the horizontal branch, and a surprising bright feature that is likely due to stars with ages younger than 1 Gyr. We use the RR Lyrae stars to trace the spatial structure of the ancient stars and find an exponential decline in density with Galactocentric distance. We discuss ways in which our data products can be used to explore the age and metallicity properties of the bulge, and how our larger list of all variables is useful for learning to interpret future LSST alerts.

Abhijit Saha↗
NASA NTRS2020

A Brief Assessment of the Impact of Nearly 40 Years of Assimilated Observations Over the Amazon Basin

Adding a Forecast Sensitivity-based Observation Impact component to Version 2 of the Modern Era Retrospective-analysis for Research and Applications, the present study provides an assessment of the impact of nearly 40 years of observations on short-range (24-hr) forecasts over the Amazon basin. Under self-verification, forecast errors are found to slightly increase from the early data-sparse days to the more recent years, when data dramatically increase. Throughout the reanalysis, satellite radiances dominate in volume, but only before 1999 they dominate the impacts. Beyond 1999, over 50% of forecast error reduction is associated with conventional observations (radiosondes). Atmospheric Motion Vectors are also found to be large contributors to error reduction, but their contribution reduces in dry periods. In opposition to Atmospheric Motion Vectors, satellite radiances tend to contribute more in the dry season. Results provide motivation for additional conventional observations and the use of all-sky treatment of radiances.

F. L. R. Diniz↗
NASA NTRS2021

Validation of Soil Moisture Data Products from the NASA SMAP Mission

The National Aeronautics and Space Administration (NASA)Soil Moisture Active Passive(SMAP) mission has been validatingits soil moisture (SM) products since the start of data production onMarch 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement(unbiased root-mean-square error <0.04 m3/m3). Thevalidation approach also includes other (“sparse network”) in situSM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to thesereference data,and the analysis approaches themselves have been scrutinizedin an effort to best understand the products’ performance. Validation of themost recent SMAP Level 2 and 3 SMretrievalproducts (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products aregenerallyconsistentwith SM retrievals from the European Space Agency (ESA)Soil Moisture Ocean Salinity mission, althoughthere aredifferencesin some regions. The high-resolution (3-km) SM retrieval product,generated by combining Copernicus Sentinel-1 data with SMAP observations,performswithin expectations. Currently, however,there is limited availability of3-kmCVSdatato support extensive validation at this spatial scale. The most recent (version 5)SMAP Level4 SMdata assimilation productprovidingsurface and root-zone SM with complete spatio-temporal coverageat 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; futureplans include expanding ittoforestedand high-latituderegions

SMAP↗
NASA NTRSDate not supplied

Compressive Sensing Application for Transient Photometric Measurements

Compressive Sensing (CS) is a mathematical theory for simultaneous data acquisition and compression. Natural phenomena may be sparse in some physical or temporal domain. If we exploit this sparsity by applying the technique of CS to obtain information, how do our measurements change as a function of domain and measurement systematics? What are the specific implications for the science and for the sensing infrastructure? In this talk, we will discuss the generalized systematic effects consequent to the application of CS to time-series photometric measurements. We assess implications for observability, sparsification, and information loss in the detection, retrieval and reconstruction process. To study time-series photometry, we explore the field of gravitational microlensing. A source star, typically in the galactic bulge, gets microlensed when there is a precise alignment of a lensing star and its planetary system, with the source star. The microlensed source star changes in flux magnification as the lensing system crosses the precise path of alignment, resulting in a microlensing curve in time domain. A high-cadence, high-resolution system, which uses low power and bandwidth is essential to obtain valuable science measurements. Hence, we discuss application of CS to gravitational microlensing data sets, which in turn can be generalized to any time-ordered photometric measurements.

Asmita Abhay Korde↗
NASA NTRSDate not supplied

Constraining Venus Cloud Habitability: Earth's Aerobiosphere as an Analogue Environment

The potential habitability of Venus's cloud and haze layers has been debated for decades, with interest fueled by observations including disequilibria in atmospheric chemistry, strongly UV-absorbing particles, and (most recently) a controversial phosphine detection. Venus cloud temperature and pressure are clement by Earth standards; however, the calculated high acid activity and low water activity may exceed the limits of Earth biochemistry, based on observed life in extreme environments. A significant challenge in understanding Venus's habitability is the lack of an appropriate terrestrial analogue environment. Life has not been found in hydrothermal systems approaching Venus aerosol acidities, but these systems also contain high levels of other harsh solutes. Earth's stratospheric sulfate layer is a partial match in terms of acidity, desiccation, size, and isolation from the surface, but the few samples returned from these altitudes have yielded only sparse, inactive cells. Earth's tropospheric cloud droplets have an active microbial presence, but are larger and far more water-rich than Venus aerosols; tropospheric times aloft are also similar to microbial generation times, making Earth's aerobiosphere dependent on continuous surface replenishment. Direct detection of potential biosignatures from a single transect is challenging; Earth's cloud microbiota yields 102 - 108 viable cells per mL, equivalent to one per 103 - 109 particles, with highly dynamic and heterogeneous distribution. The most important in situ measurement for Venus cloud habitability is detailed aerosol composition: confirmation of acid and water activity, presence of potentially bioavailable nutrients and energy, and potential presence of organic matter. Modeling Venus cloud aerosol residence time in comparison to periodic influxes of water and photochemical energy, and thus the ability of an ecosystem to maintain itself over time, would also address a significant habitability constraint particular to aerobiospheres.

Venus↗
NASA NTRSDate not supplied

Development of the GeoNEX Level 2G Products: Exploiting the Diurnal Variability of TOA Reflectance in Atmospheric Correction

This study develops a new atmospheric correction algorithm to generate the Level 2G products, in particular the gap-filled Surface Reflectance at 10-minute time steps, for the Geostationary-NASA Earth Exchange (GeoNEX) project. The algorithm is based on the MODIS MAIAC (Multi-Angle Implementation of Atmospheric Correction) framework but with significant modifications to exploit angular/temporal information from the diurnal variability of the GeoNEX L1G TOA (Top-of-Atmosphere) reflectance. The algorithm starts by evaluating the roughness/smoothness of the diurnal time series of the TOA reflectance. Because rapid changes in TOA reflectance are generally caused by passing clouds or shadows, rough segments of the time series are automatically filtered out while the smooth segments are further tested for brightness and temperature to identify clear-sky and snow-free observations. Next the algorithm runs the MAIAC RTM (Radiative Transfer Model) to retrieve the Ross-Thick-Li-Sparse (RTLS) BRDF model parameters and the daily-mean atmospheric optical depth (AOD) that allow the RTM to optimally simulate the observed diurnal variability of clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they are used as the prior information to predict the AOD level for the next days, while the subsequent clear-sky observations are used to make necessary adjustments to the RTLS parameters in an continuous fashion. This “prediction-analysis” cycle is then iterated to process the full time series of the L1G data, skipping only total-cloudy days or when surface snow is detected. We tested the algorithm over a list of selected AERONET sites. The retrieved results (the daily mean AOD and the RTLS parameters) reasonably agree with the ground-based measurements. Importantly, the results indicate that the diurnal cycles of surface reflectance are continuous functions of the illumination-view geometry. Thus we can use the retrieved RTLS model to accurately fill in data gaps on partial cloudy days. Also, our algorithm is totally independent from the traditional approaches based on the use of spectral band ratios between the shortwave infrared (e.g., 2.2µm) and the visible (e.g., 0.47µm and 0.64µm) bands. Our results thus demonstrate that the high-frequent diurnal geostationary observations contain unique information that helps us improve atmospheric correction of remote sensing data.

GeoNEX↗
NASA NTRSDate not supplied

Remote Sensing of Lineage Functional Types for Modeling and Monitoring Biodiversity

Hyperspectral remote sensing has the potential to continuously scale plant function and plant diversity information from landscape to global extents. Numerous studies have indicated that VSWIR (400-2500 nm) reflectance properties of vegetation capture evolutionarily conserved biochemical, structural, and other functional attributes of plant species. Spectral properties conserved in plants provide the opportunity to both 1) aggregate species into lineages with improved classification accuracy and 2) link those lineages directly to plant traits. Full realization of this goal will enable parameterization of Land Surface Models (LSMs) with remotely sensed information, e.g., canopy nitrogen, and better representations of biodiversity and functional diversity in biogeographic studies. In this study, we use hyperspectral AVIRIS data from the 2013 HyspIRI campaign over the Southern Sierra Nevada, California flight box to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from roughly 1372 surveys and a phylogeny representing 1361 species. We aggregate species into lineages ranging from species level groups down to similar number of Plant Functional Types as often used in LSMs. We assessed the ability of Random Forest and Partial Least Squares Discriminant Analysis to discriminate across these different phylogenetic scales and determine the optimal number of lineages to classify. Although there are some temporal and spatial differences in our training data, our best approaches achieved moderate classification accuracy (Kappa > 0.65). Given an optimal number of lineages, we explored approaches to improve classifications including machine learning and unmixing approaches. This work suggests that lineage-based methods may be a promising way to leverage the huge amounts of data that will come from high resolution and high return interval hyperspectral data planned for the Surface Biology and Geology mission with sparsely sampled existing ground-based ecological data.

Hyperspectral↗
NASA NTRS2021

Peru Health & Air Quality: Land Use Change in the Rapidly Developing Peruvian Amazon and Implications on Zoonotic Disease Incidence

In the Madre de Dios region of the Peruvian Amazon, forests are being cleared for mining, timber harvesting, road construction, and hydroelectric dam development. These rapid land use changes are increasing human presence in previously sparsely populated areas, disrupting ecosystems and increasing the proximity of human settlement to zoonotic disease vectors. Dengue fever and leishmaniasis are two neglected tropical diseases which are prevalent in Madre de Dios and have been associated with urbanization and road construction. In partnership with the Peruvian Ministries of Health (MINSA) and the Environment (MINAM) and other in-country collaborators, our team examined Land Use Land Cover (LULC) correlations with reported dengue and leishmaniasis incidence in the Madre de Dios region to help partners understand the spatial relationship between land use change and zoonotic disease incidence. We created a LULC classification script using Google Earth Engine with Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager imagery to classify land cover in 2010, 2015, and 2020 and evaluate changes over this time period. We then used the quantified results of the LULC assessment in conjunction with reported disease cases to evaluate correlations between disease incidence and key land cover changes across Madre de Dios’s 11 districts. In the second term, the team will use these products to develop more detailed disease incidence risk maps and models. High risk areas will then be classified, using PeruSat-1 that allow for even higher resolution mapping at less than 3 meters. These products will allow the partners to understand hotspots of land cover change in Peru and the relationship with outbreaks to inform public health decision making and environmental policy.

Elizabeth Stapleton↗
NASA NTRS2022

Apollo Crew Loads Data Mining

INTRODUCTION Human exposure data to dynamic loads in spaceflight are sparse not only because very few crewmembers have flown in space, but also because much of the historic data is no longer readily available. Earth and Moon landings of the Apollo missions are unique and valuable for understanding human tolerance in water landing conditions for both short-duration and long-duration crewmembers as well as informing crew tolerance in standing postures during planetary landings. The goal of this task was to find relevant Apollo data to better understand human tolerance in these scenarios, in order to apply this knowledge to future missions. METHODS The goal of this task was to recover time history data from dynamic phases of flight from crewed Apollo missions. The first phase of the task was to search available records at JSC and other centers to locate the appropriate records at the National Archives in Fort Worth, TX. Once the possible data locations were located, it was planned that one researcher would travel to the National Archives in Fort Worth, TX to collect the relevant Apollo data. Once the data was retrieved, they would be digitized for future use and evaluation, and archived within the Human Physiology, Performance, Protection, and Operations (H-3PO) laboratory within the Human Health and Performance Directorate. These data would be used to assess the feasibility and acceptability of current standards and vehicle design requirements for lunar and Mars missions. Additionally, any medical monitoring required, or physiological changes noted in crewmembers based on dynamic loads would be documented for future use. RESULTS Relevant records were found at Ft. Worth, TX and College Park, MD using the National Archives Online Catalog. In addition to the online catalog, an Archives staff member was able to share an additional inventory list of NASA JSC records held in Ft. Worth, TX. Due to COVID-19, NASA has only recently allowed non-mission essential travel. In addition, the National Archives has also been closed to the public since March 2020. Because of this, travel to the National Archives to search records there has not been possible. CONCLUSION The findings include collections at the National Archives at both Ft. Worth, TX and College Park, MD that could possibly hold the data we are looking for. Unfortunately, the descriptions of each collection are very broad. Though we are hopeful that relevant data is stored in one of these collections, we cannot be sure that we will find what we are looking for. At this time, it is not clear when the Archives will reopen to the public or when NASA will allow travel. Travel to the Archives has been postponed, and will be reassessed at a future date.

T Reiber↗
NASA NTRS2021

Dynamic Ensemble Prediction of Cognitive Performance in Space

Astronauts are exposed to a unique set of stressors in spaceflight. Microgravity, isolation, confinement, and environmental and operational hazards: all of these can impact sleep, vigilant attention, and alertness, which are critical to mission success. In this paper, we seek to understand the most important predictors of alertness over the course of a space mission, using self-reported, cognitive, and environmental data collected from 24 astronauts on 6-month missions to the International Space Station (ISS). Alertness was repeatedly and objectively assessed on the ISS with a brief 3-minute Psychomotor Vigilance Test (PVT) that is highly sensitive to sleep deprivation. To relate PVT performance to time-varying and sparsely-measured environmental, operational, and psychological covariates, we propose a n ensemble prediction model comprising of linear mixed effects regression, random forest, and functional concurrent regression models. An extensive cross-validation procedure reveals that this ensemble outperforms any one of its components alone. We also discover that a participant’s past performance, reported fatigue and stress, and temperature and radiation exposure were among the most important variables associated with alertness. This method is broadly applicable to environmental studies where the main goal is accurate, individualized prediction involving a mixture of person-level traits and irregularly measured time series.

Danni Tu↗
NASA NTRSDate not supplied

Advances to a Global Agroclimatology Solar Insolation and Meteorological Parameter Data Base: Improved Solar Irradiance up to Hourly Temporal Resolution

A primary objective of NASA’s Prediction of Worldwide Energy Resource (POWER) project is to facilitate the use of NASA Earth Science data holdings within the energy, agricultural, and architectural industries. To this end daily averaged solar data from several NASA projects and metrological data from a NASA assimilation model have long been reformatted and via a user friendly web based data portal (https://power.larc.nasa.gov) at the native resolution of each data products. Potential users can access solar and metrological data in a column formatted DSSAT ASCII format by entering single site specific coordinates or from an area by entering the appropriate area coordinates. Upgrades to the POWER data portal have been implemented that result in a complete upgrade of the base solar insolation data products. From the years 1984 through 2000, a new version of the NASA/GEWEX Surface Radiation Budget (SRB) and Clouds is utilized that reduces the RMS relative to surface measurements. Additionally, Clouds and the Earth’s Radiant Energy System (CERES) SYNoptic 1x1 Degree (SYN1Deg) data products are utilized starting from January 1, 2001 through 3 months of real-time. The CERES Fast Longwave and SHortwave radiative Fluxes (FLASHFlux) is still used to provided daily data spanning from the end of SYN1Deg to within 7 days of real-time. Meteorological parameters now are taken from the NASA Modern Era Retrospective-analysis for Research and Applications (MERRA-2) data set which provides higher resolution data products (hourly and 0.5 x 625 degree) covering the entire globe. Besides updating the solar and meteorological data products, this new version features new data products such as photosynthetically active radiation (PAR), more cloud information, estimates of soil temperatures and improved options for long-term climatological data requests. More than 36+ years daily time slices are included in the combined solar and meteorological data sets. However, perhaps the most important innovation of the POWER GIS-enabled Web Services is the provision of hourly solar and meteorological data products beginning in Jan 1, 2001. The hourly values will enable more detailed modeling and crop analysis that incorporates the diurnal variability of these parameters. The new capability is made possible by utilizing both the CERES SYN1Deg and MERRA-2 data products that include parameters at these resolutions. An assessment of both the meteorological and the solar irradiance data are based upon comparisons with globally distributed surface observations. The meteorological observations from the National Center for Environmental Information’s “Integrated Surface Database” as well as the “Global Summary of the Day” (GSOD) data files. The estimates of solar insolation are compared to the Baseline Solar Radiation Network (BSRN) and other high quality surface measurement networks. Results from the uncertainty assessments demonstrates that the NASA’s meteorological and solar irradiance data can represent a viable alternative to surface observations, particularly in data sparse regions of the world.

solar irradiance↗
NASA NTRS2022

Co-leveraging Scientific Advances in Space Biology and Astrobiology Towards Achieving NASA’s Life Science Objectives

Executive Summary: Distinct lines of scientific inquiry drives the separation of NASA’s fundamental life science research into Space Biology and Astrobiology. This division developed as a way to place life scientists alongside experts in the physical constraints that define the acclimation, adaptation and evolution of biology systems relevant to their respective subjects. For astrobiology, integration with disciplines such as geology, geochemistry, astronomy, planetary science, etc., enables a comprehensive assessment of the physical environment and its co-evolution with biological processes. Space Biology’s co-location with Physical Sciences places life science researchers adjacent to experts in the physical phenomena associated with microgravity and spaceflight, enabling an understanding of how the spaceflight environment affects biological systems. Despite this separation, aspects of both disciplines have converged on a similar, fundamental objective: to describe and understand the dynamics of complex living communities in the contexts of their physical environments. While the environmental systems and timescales are dramatically different, continuing to motivate the separation into distinct fields, similarities in the underlying objective present opportunities to find efficiencies, reduce overlap, and minimize duplication of effort. Space Biology and Astrobiology share a common need to understand microbial physiology in extreme environments – whether the ‘built’ spaceflight environment or the natural environments in which many astrobiology studies are conducted. In particular, open questions in each discipline require the development of quantitative frameworks, applicable at the ecosystem level, that support predictive capabilities for environments where observations are sparse. Additionally, both disciplines have a need to prepare, detect, and analyze the (potential) biological signal in complex samples-often in a completely autonomous fashion. The next decade will see NASA Space Biology moving to understand and describe the effects of the beyond low-earth orbit (BLEO) spaceflight environment on living systems. This new direction will dramatically reduce the opportunities for ground-based analysis of space-flown samples, driving space biology investigations towards fully autonomous experiments and missions. At the same time, astrobiology life detection missions aimed at detecting biosignatures on Mars and icy moons in the outer solar system could benefit from fully automated sample processing and analysis. There are opportunities to leverage instrument and method development between both disciplines within the context of these BLEO missions.

Astrobiology↗
NASA NTRSDate not supplied

Component-Based Development of CFD Software FUN3D

FUN3D, a suite of Computational Fluid Dynamics simulation and design tools developed at the NASA Langley Research Center, has undergone continuous development since the late1980s. It contains a large portion of legacy code. Extending it with new capabilities becomes increasingly difficult. To improve the extensibility and reusability, FUN3D is moving toward component-based development. New features, such as Stabilized Finite Elements, Yoga, and Sparse Linear Algebra Toolkit, are integrated into the system as components. Some existing features such as the Node-Centered Finite Volume Solver, are also being refactored to components. The integration of these components poses new requirements on the development workflow. In this paper, we describe the Continuous Integration of FUN3D to support component-based development, and discuss the tools used, the practices followed, and lessons learned during the transition from the traditional approach.

computational fluid dynamics software↗
NASA NTRS2021

System Identification for Propellers at High Incidence Angles

Propellers used for electric vertical takeoff and landing (eVTOL) aircraft propulsion systems experience a wide range of aerodynamic conditions, including large incidence angles relative to oncoming airflow. In oblique flow, propellers exhibit deviations in thrust and torque oriented along the propeller axis of rotation, as well as significant off-axis forces and moments. Although important for modeling eVTOL aircraft aerodynamics, sparse experimental data or mathematical models exist for propellers at incidence. This paper describes a propulsion system modeling methodology for the LA-8 tandem tilt-wing, eVTOL aircraft. System identification methods are applied to isolated propeller wind tunnel data gathered across the vehicle's flight envelope to develop a mathematical model of the propulsion system, including a static motor model, dynamic motor model, and propeller aerodynamic model. Modeling results validated against data withheld from the modeling process indicate good predictive capability and agree with theoretical expectations. The results are followed by a discussion of model implementation strategies into high-fidelity eVTOL aircraft simulations.

system identification↗