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NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗
NASA NTRSDate not supplied

Effect of Rain-Adjusted Satellite Sea Surface Salinity on ENSO Predictions from the GMAO S2S Forecast System

ENSO has a significant impact on climate variability throughout the world and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of altimetry and in situ data lead to improved short-term forecasts of the coupled system. However, few studies have focused on improving the near-surface density/mixing through assimilation of satellite sea surface salinity (SSS). For expediency, most projects that do assimilate SSS do so as if these data were observed at the top model layer instead of at the surface. In rainy regions where buoyant water sits as a fresh lens at the surface, this assumption is likely invalid. Therefore, we adjust SSS so that it more accurately represents the salinity at the first model layer. The Rain Impact Model (RIM) uses a simple diffusion model to determine the near surface salinity gradient (i.e., 1 cm to 5 m). Satellite SSS data are modified using this near-surface salinity gradient, so the salinity values are now valid at the first model layer (we call this SSS@5m). We assess the impact of satellite SSS observations for near-surface dynamics within ocean reanalyses and how these impact dynamical ENSO forecasts using the NASA GMAO S2S coupled system. For all reanalysis experiments, all available along-track sea level and in situ observations are assimilated for 2011 to 2020 using the LETKF scheme (Penny et al., 2013). One reanalysis assimilates Aquarius /SMAP SSS as before. An additional reanalysis is performed assimilating the SSS@5m data. Validation statistics are compared for experiments that assimilate SSS (sub-optimally as before) versus the SSS@5m. We also compare results of coupled forecasts that are initialized from these reanalyses in spring. For all but the big La Niña in 2017, all NINO3.4 forecasts were improved by using the RIM. We will show that improved SSS estimates upgrades density and near-surface mixing leading to more accurate coupled air/sea interaction and better forecasts.

ENSO↗
NASA NTRSDate not supplied

Towards Automated Analytics of Research Publications

For readers of scientific publications it remains a big challenge to unambiguously relate the published research with the data used. To a substantial degree it is attributed to authors, journals, editors, and reviewers not prioritizing correct data citation, which impacts traceability, repeatability, and giving credits to published authors and their funding sources. Furthermore, uniform classification of the content of the published research is hampered by journals using journal specific topics and letting authors to assign free text keywords to their papers. We demonstrate automated analytics methods for extracting and relating datasets used and the research application areas by processing 1,300 research papers that referenced the NASA Giovanni service (but probably not the datasets in particular) as supporting their publication process. This presentation was given during the 2022 ESIP January meeting held virtually in January 2022.

Irina Gerasimov↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, the re-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA’s Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related ‘omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata ‘omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data re-use, resulting in 38 additional publications derived from the original 67 publication over the past four years. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA “Open Science Data Repositories (OSDR)” and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Fluorescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to “big data” from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology. Several other talks will cover these topics in this conference.

life sciences↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, there-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA's Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomatic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related 'omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata 'omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data-use, resulting in 40 enabled publications by open data. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA "Open Science Data Repositories (OSDR)" and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Flourescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to "big data" from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology.

omics↗
NASA NTRS2020

Cascade Model for Planetesimal Formation by Turbulent Clustering.

We use a newly developed cascade model of turbulent concentration of particles in protoplanetary nebulae to calculate several properties of interest to the formation of primitive planetesimals and to the meteorite record. The model follows, and corrects, calculations of the primary planetesimal Initial Mass Function (IMF) by Cuzzi et al. (2010), in which an incorrect cascade model was used. Here we use the model of Hartlep et al. (2017), which has been validated against several published numerical simulations of particle concentration in turbulence. We find that, for a range of nebula and particle properties, planetesimals may be “born big”, formed as sandpiles with diameters in the 10 − 100 km range, directly from freely floating particles. The IMFs have a modal nature, with a well-defined peak rather than a powerlaw size dependence. Predictions for the inner and outer nebula behave similarly in these regards, and observations of inner and outer nebula primitive bodies support such modal IMFs. Also, we present predictions of local particle concentrations on several lengthscales in which particles “commonly” find themselves, which have significance for meteoritical observations of the redox state and isotopic fractionation in regions of chondrule formation. An important difference between these results, and those of Cuzzi et al. (2010), is that particle growth-by-sticking must proceed to at least the 1−few cm radius range for the IMF and meteoritical properties to be most plausibly satisfied. That is, as far as the inner nebula goes, the predominant “particles” must be aggregates of chondrules (or chondrule-size precursors) rather than individual chondrules themselves.

Thomas Hartlep↗
NASA NTRSDate not supplied

SWIPE: Spectral Water Inversion Processor and Emulator

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.

SWIPE↗
NASA NTRS2023

The Hazard Components of Representative Key Risks. The Physical Climate Perspective

The framework of Representative Key Risks (RKRs) has been adopted by the Intergovernmental Panel on Climate Change Working Group II (WGII) to categorize, assess and communicate a wide range of regional and sectoral key risks from climate change. These are risks expected to become severe due to the potentially detrimental convergence of changing climate conditions with the exposure and vulnerability of human and natural systems. Other papers in this special issue treat each of eight RKRs holistically by assessing their current status and future evolution as a result of this convergence. However, in these papers, such assessment cannot always be organized according to a systematic gradation of climatic changes. Often the big-picture evolution of risk has to be extrapolated from either qualitative effects of “low”, “medium” and “high” warming, or limited/focused analysis of the consequences of particular mitigation choices (e.g., benefits of limiting warming to 1.5 or 2C), together with consideration of the socio-economic context and possible adaptation choices. In this study we offer a representation – as systematic as possible given current literature and assessments – of the future evolution of the hazard components of RKRs. We identify the relevant hazards for each RKR, based upon the WGII authors’ assessment, and we report on their current state and expected future changes in magnitude, intensity and/or frequency, linking these changes to Global Warming Levels (GWLs) to the extent possible. We draw on the assessment of changes in climatic impact-drivers relevant to RKRs described in the 6th Assessment Report by Working Group I supplemented when needed by more recent literature. For some of these quantities - like regional trends in oceanic and atmospheric temperature and precipitation, some heat and precipitation extremes, permafrost thaw and Northern Hemisphere snow cover - a strong and quantitative relationship with increasing GWLs has been identified. For others - like frequency and intensity of tropical cyclones and extra-tropical storms, and fire weather - that link can only be described qualitatively. For some processes - like the behavior of ice sheets, or changes in circulation dynamics - large uncertainties about the effects of different GWLs remain, and for a few others - like ocean pH and air pollution - the composition of the scenario of anthropogenic emissions is most relevant, rather than the warming reached. In almost all cases, however, the basic message remains that every small increment in CO2 concentration in the atmosphere and associated warming will bring changes in climate phenomena that will contribute to increasing risk of impacts on human and natural systems, in the absence of compensating changes in these systems’ exposure and vulnerability, and in the absence of effective adaptation. Our picture of the evolution of RKR-relevant climatic impact-drivers complements and enriches the treatment of RKRs in the other papers in at least two ways: by filling in their often only cursory or limited representation of the physical climate aspects driving impacts, and by providing a fuller representation of their future potential evolution, an important component – if never the only one – of the future evolution of risk severity.

Representative Key Risks↗
NASA NTRS2023

Machine Intelligence for Radiation Science: Summary of the Radiation Research Society 67th Annual Meeting Symposium

The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how those large-scale data sets are processed, analyzed, and interpreted. The 67 th Annual Meeting of the Radiation Research Society featured a symposium on MI approaches to highlight recent advancements in the radiation sciences and their clinical applications. This article summarizes three of those presentations regarding recent developments for metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.

radiation↗
NASA NTRS2020

Common Origin of Warm Dark Matter and Dark Radiation

We consider a cosmological scenario where a relativistic particle and a stable massive particle are simultaneously produced from the decay of a late-decaying particle after Big-Bang Nucleosynthesis but before matter-radiation equality. The relativistic and massive particles behave as dark radiation and warm dark matter, respectively. Due to a common origin, the warmness and abundances are closely related. We refer to the models that lead to such a scenario as Common Origin of Warm and Relativistic Decay Products (COWaRD). We show that COWaRD predicts a correlation between the amount of dark radiation and suppression of the large scale structure, which can be tested in future precision cosmology observations. We demonstrate that COWaRD is realized, as an example, in a class of supersymmetric axion models and that future observations by the next generation Cosmic Microwave Background, Large Scale Structure, and 21-cm surveys can reveal the structure of the theory.

Manuel A. Buen-Abad↗
NASA NTRS2022

Coevolution of Machine Learning and Process-Based Modelling to Revolutionize Earth and Environmental Sciences: A Perspective

Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co-creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high-dimensional mapping, while remaining faithful to process-based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.

Saman Razavi↗
NASA NTRSDate not supplied

Flow Regime Transitions and Pressure Drop in Gas-Liquid Flows through Packed Beds in Microgravity

The Packed Bed Reactor Experiment (PBRE) focused on the hydrodynamics of gas-liquid flow in porous media, specifically on flow regime transition and pressure drop correlation. The reactor included a cylindrical test section of 60 cm in length, 5 cm in diameter and filled with 2 mm spherical glass beads. Liquid water and Nitrogen gas were flowed into a centrifugal phase separator after exiting the test section. Five absolute pressure transducers and two high-speed video cameras spanned the test section to measure the pressure drop and capture flow regime behavior. Video observations, analysis of pressure traces, and pressure drop data were used to identify flow patterns. It was found that the slope of pressure gradient data when plotted versus the gas or liquid Reynolds numbers changed when the flow regime changed. This observation was used to identify the flow regime boundaries. At high liquid flow rates (high interaction regime), dispersed bubble and pulse flow regimes were detected. At low liquid flow rates (low interaction regime), large bubble and gas channeling regimes were observed. The transition from the big bubble regime to the gas channeling regime occurred at a critical gas flow rate independent of the liquid flow rate. Similarly, the transition from the low interaction to the high interaction regime occurred at a critical liquid flow rate. For these different flow regimes, different two-phase flow friction factors and pressure gradient correlations were fitted as a function of the gas and liquid modified Reynolds numbers and Suratman number. It was found that in all the flow regimes the capillary contribution to the pressure drop is significant and can be as high as 90% of the total pressure drop.

Brian Motil↗
NASA NTRSDate not supplied

Breakout Reconnection and Eruption in a Double Null-point Topology

Basic Question: Why are some eruptions successful while others remain confined? • We analyzed an eruption associated with a C-class flare in a null-point topology (fan-spine/embedded bipole configuration) within NOAA AR 11791, embedded within a large pseudostreamer between two coronal holes. • About 4 hours prior, interchange reconnection at the null produced a faint jet and a B-class flare without any filament eruption. • Continued reconnection below the filament built a flux rope (FR) that rose slowly and later generated the C1.4 flare. The flux rope reconnected with pseudostreamer flux near inner null, producing a remote (third) ribbon at the base of the fan and partial ejection of the flux rope. • Multiple plasmoids appeared in two places: above the rising flux rope in the breakout current sheet (the deformed null), and in a vertical flare current sheet below the flux rope (quasiperiodic flare reconnection). • Weak, narrow, coronal and interplanetary Type-III radio bursts were also detected, suggesting that electron beams from the null were ejected into the heliosphere. • The partial FR ejected from the inner system encountered the overlying arcade and null of the big pseudostreamer higher in the corona and oscillated, but did not escape. • EUV and coronagraph images revealed a wave and a faint ejection from the pseudostreamer that propagated laterally southward with ~270 km s-1. • We suggest that the main factors leading to the confined filament eruption are (i) lack of significant breakout reconnection between the rising flux rope and the overlying arcade due to an unfavorable relative orientation of these flux systems; and/or (ii) insufficient free energy of the flux rope to overcome the tension forces of the overlying arcades.

J Karpen↗
NASA NTRS2022

SNPP and N20 VIIRS Thermal Emissive Bands Calibration Comparison Using the GEO-LEO Double Difference Method

The VIIRS instruments onboard the SNPP and NOAA-20 satellites have identical spatial resolutions and the same spectral bands. Similar prelaunch tests and identical on-orbit calibration algorithms established the foundation for their consistent Earth measurements. Calibration assessment and consistency comparisons are useful to maintain their performance and measurement accuracy. Simultaneous nadir overpasses (SNO) between two satellites are commonly used for a direct calibration comparison between sensors. However, there are no SNO between SNPP and NOAA20. Hence, a reference sensor or Earth measurements are normally used to bridge the comparison. As a reference, we focus on the Advanced Baseline Imager (ABI) onboard the GOES-R series spacecraft and its application to the SNPP and NOAA-20 VIIRS comparison. GOES16 and GOES17are the first two satellites of the GOES-R series and were launched on November 19, 2016, and March 12, 2018, respectively. Their operational positions are on the equator with longitudes of 75.2° West over land and 137.2° West over ocean, respectively. The ABI is the primary imaging instrument of these spacecrafts for the Earth’s weather, oceans, and environment, with observations (every 10 minutes) that provide vast data for GEO-Low Earth orbit (LEO)and LEO-LEO comparisons utilizing it as an intermediate reference sensor. VIIRS and ABI have spectrally matched bands and can have simultaneous measurements over any selected site every day. The simultaneous measurements over the same site also have various scan angles. These features provide advantages for a VIIRS-to-ABI comparison. The spectral response function difference between instruments, sites selected, and view angles will have effects on the instrument measurements. Their impacts on the calibration comparison, including the use of double differences, will be discussed. By collecting VIIRS measurements over a large range of view angles, the view angle effect will also be investigated. The collection of an ample amount of data provides an advantage for statistical analyses and potential big data applications to sensor calibration assessments. This method can also be applied to other sensor calibration comparison and performance assessments, such as GOES16 and GOES17 ABI, and Terra and Aqua MODIS.

Tiejun Chang↗
NASA NTRSDate not supplied

Design Development of a Stable, Lightweight, Tall and Self-Deploying Lunar Tower

Deployable composite booms with spaceflight heritage are being investigated at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) and the Massachusetts Institute of Technology (MIT) Space Resources Workshop for their potential to be vertically deployed in the lunar gravity field, in support of the NASA Artemis campaign. This paper reports new design development results—after the original presentation at the NASA 2020 BIG Idea Challenge—for a 16.5-meter-tall, compact, self-deploying composite tower intended to support the exploration of lunar permanently shadowed regions by nearby robotic assets or humans. Possible applications include vertical solar arrays and the provision of elevated lines-of-sight to science or engineering payloads, in support of nearby targets operating in areas of interest that may be hard to reach. Useful elevated payloads include radio repeaters, remote sensing and imaging, navigation and power beaming systems. However, while these lightweight rollable booms have an excellent height to mass ratio, they typically exhibit axial curvature upon deployment resulting in appreciable lateral dead-load deflection of the tip mass relative to the tower base. This static deflection increases with tower height and tip mass, not only constraining the value delivered by the tower but also endangering its integrity. To develop a competitive, lightweight deployable composite boom tower, a capability to correct static deflections during and after deployment will be required. In this paper, a deployable guy wire stability system will be presented for the MIT / LaRC self-erecting composite boom lunar tower that provides real time measurements, maintains tension both actively (during deployment) and passively (post-deployment), and can serve as a reconfigurable platform to test and trade alternative stability system configurations, such as with added spreaders inspired by sailing boat masts. Using a calibrated photogrammetry system, the natural lateral deflection of the boom tip relative to the boom base at different deployed heights was recorded for different configurations. With real-time force measurements it was found that tensioned guy wires can significantly reduce the static tip deflection of a deployable composite boom under dead load and can dampen a dynamic oscillation in under a minute. It was also found that control authority is greatest where it is needed most, i.e., for the lever arm closest to being opposite the direction of deflection. For a tower height of at least 11 m and spreader length of at least 60 cm, a solution of differential tension in all three arms exists and, in principle, provides sufficient control authority to correct or significantly reduce boom tip deflections. Notably, natural deflections occur almost entirely normal to the seams of the boom cross-section, but the natural boom tip lateral deflection under dead load upon deployment was approximately 5% of boom deployed length, exceeding the manufacturing acceptance specification of 1%. Ongoing and future work includes the further investigation towards mitigating manufacture-caused lateral deflection, trading of alternative guy wire system designs, as well as the design development of a second-generation tower incorporating a more capable boom design with the learnings from the proof-of-concept system presented here.

deployable composite boom↗
NASA NTRS2020

Cascade Model for Planetesimal Formation by Turbulent Clustering

We use a newly developed cascade model of turbulent concentration of particles in protoplanetary nebulae to calculate several properties of interest to the formation of primitive planetesimals and to the meteorite record. The model follows, and corrects, calculations of the primary planetesimal Initial Mass Function (IMF) by Cuzzi et al. (2010), in which an incorrect cascade model was used. Here we use the model of Hartlep et al. (2017), which has been validated against several published numerical simulations of particle concentration in turbulence. We find that, for a range of nebula and particle properties, planetesimals may be “born big," formed as sandpiles with diameters in the 10 - 100km range, directly from freely floating particles. The IMFs have a modal nature, with a well-defined peak rather than a powerlaw size dependence. Predictions for the inner and outer nebula behave similarly in these regards, and observations of inner and outer nebula primitive bodies support such modal IMFs. Also, we present predictions of local particle concentrations on several lengthscales in which particles “commonly" find themselves, which have significance for meteoritical observations of the redox state and isotopic fractionation in regions of chondrule formation. An important difference between these results, and those of Cuzzi et al. (2010), is that particle growth-by-sticking must proceed to at least the 1-few cm radius range for the IMF and meteoritical properties to be most plausibly satisfied. That is, as far as the inner nebula goes, the predominant “particles" must be aggregates of chondrules (or chondrule-size precursors) rather than individual chondrules themselves.

Planetesimals↗
NASA NTRS2023

Can the Streaming Instability Form the First Planetesimals in Globally Turbulent Protoplanetary Disks?

Understanding the formation of the first planetesimals remains key to deciphering the history of planet formation within our own solar system and beyond. Evidence from the vast meteorite record [e.g., 1] as well as observations [e.g., 2] strongly suggest that the first planetesimals, and perhaps giant planet core accretion occurred well within the first million years of disk evolution. Moreover, chemical and lithological mixing as well as observations of line-broadening in protoplanetary disks (PPDs) [e.g., 3-6] suggest that the solar system nebula in this epoch was weakly-to-moderately turbulent in the regions where particle growth is of the greatest interest [7-8]. It is a well-known though that global hydrodynamic turbulence complicates particle growth due to a slew of barriers that can slow or even stall particle or aggregate growth at pebble sizes (with corresponding small particle Stokes numbers St) that can lead to loss to the central star via radial drift before planetesimals can ever form [9,10], requiring that some other mechanism come into play that collects growth-frustrated pebbles into gravitationally bound multi-km bodies – objects that are “born big” [11]. The current leading candidate for such a “leap-frog” mechanism is the so-called Streaming Instability (SI), a gas-drag mediated momentum exchange resonance in which the relative velocity between the particle component and a rotating gaseous fluid can lead to high densities in the particle field [12], which has been invoked in a number of recent PPD models that include a turbulent intensity α [e.g., 13-14] as the defacto mechanism for planetesimal formation if conditions for the SI (depending on particle St and the solids-to-gas mass ratio) are satisfied. However, these previous works use conditions established from occurrence studies for the onset of SI in laminar disks [15-17] in which the only source of turbulence is that self-generated by the settling particle layer, and not externally driven global turbulence. Recent analytical theories of the SI subject to global turbulence predict much more stringent conditions for the effectiveness of the SI than the laminar case [18-19]. Thus, whether the efficient operation of the SI can be attained in realistic models of the solar nebula have yet to be established. In this work, we ask whether the conditions under which the SI can produce gravitationally bound particle overdensities can actually be met in the first million years of evolution in globally turbulent PPDs.

P R Estrada↗
NASA NTRS2023

Mineralogical, Elemental, and Tomographic Reconnaissance Investigation for CLPS (METRIC)

METRIC is a robotic science laboratory that can determine the mineralogy, elemental chemistry, micromorphology, and thermophysical properties of planetary regolith. The METRIC suite comprises METRIC XRD/F, an X-ray diffraction/X-ray fluorescence instrument that can determine the mineralogy and elemental chemistry of regolith samples; METRIC XCT, a micro X-ray computed tomography instrument that can be used to evaluate grain/crystallite sizes and textures; METRIC IRS, an imaging spectrometer mounted on a rover that can determine mineralogy and thermophysical properties at the landing site; and a pneumatic sample collection, processing, distribution system developed by Honeybee Robotics. The payload elements could be deployed on a static lander or a rover. Data returned from the METRIC payload would inform origin, formation, and evolution of rocky planetary bodies. METRIC XRD/F draws on heritage from the CheMin instrument on the Mars Science Laboratory (MSL) Curiosity rover [1], with a few important improvements. Like CheMin, METRIC XRD/F operates in transmission geometry and uses piezoelectric actuators on sample cells in a tuning fork geometry to induce convective grain motion of the regolith to create a randomly oriented powder. MSL CheMin uses an energy-sensitive CCD to collect XRD patterns and XRF spectra simultaneously from the same sample cell, resulting in qualitative XRF data. METRIC XRD/F uses two different sample cells, one optimized for XRD and one optimized for XRF, and a silicon drift detector to detect fluoresced X-rays. This improvement to the XRF capabilities provides quantitative geochemical data of major elements down to Z = 11 and allows for the detection of minor and trace elements that are critical for evaluating geologic evolution of the Moon (e.g., P and Th). Modest improvements to the XRD geometry and hardware allow for better angular resolution and the ability to distinguish between members of the pyroxene group. METRIC XCT uses the same geometry and much of the same hardware as METRIC XRD/F, where a CCD would capture images of a regolith sample in a 3 mm diameter sample tube that is rotated 360° in steps <1°. Image brightness can be used to infer compositional data, where brighter materials indicate a higher Z, much like scanning electron microscopy. Data from METRIC XCT complement those from METRIC XRD/F. Particle size, shape, and texture can provide petrologic and provenance information, whereas vesicle size and morphology in volcanic or impact melt lithologies can inform cooling rates. METRIC IRS is a hyperspectral thermal imager that can be mounted to a lander or rover to provide mineralogical data from the broader landing site and help determine whether the samples analyzed by METRIC XRD/F and XCT are representative. The METRIC IRS spectral range (8–14 μm) and resolution (10.8 cm-1) allow for quantitative mineralogy from modelling Reststrahlen bands of major rock-forming minerals (e.g., silicates, phosphates). Radiance cubes can be processed and modelled with an onboard high-performance computer to determine mineral abundances of plagioclase, high-Ca pyroxene, pigeonite, orthopyroxene, olivine, and glass. Regolith samples can be acquired, processed, and delivered to the X-ray instruments via multiple sample handling systems, but the pneumatic sampling systems developed by Honeybee Robotics [e.g., 2] are best suited for relatively low-cost missions that are being competed for the Moon (e.g., NASA’s Payloads and Research Investigations for the Surface of the Moon program). There are pneumatic sampling systems that collect surface material and other systems that pneumatically drill up to ~1 m below the surface, providing material that has not been space weathered and has not been affected by the lander’s exhaust. [1] Blake, D. F., Vaniman, D., Achilles, C., Anderson, R., Bish, D., et al. (2012). Space Sci. Rev. 170, 341-478. https://doi.org/10.1007/s11214-012-9905-1. [2] Zacny, K., Betts, B., Hedlund, M., Long, P., Gramlich, M., Tura, K., Chu, P., Jacob, A., Garcia, A. (2014). IEEE Aerospace Conference, 3-7 March 2014, Big Sky, MT, U.S.A.

X-ray diffraction↗