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NASA NTRSDate not supplied

Wavelength Calibration of the Full-sun Ultraviolet Rocket SpecTrograph (FURST)

The Sun has a well-known periodicity in sunspot number and magnetic field variation. The underlying cause of this 11-year cycle is not fully understood and has yet to be connected with those processes in other stellar objects. The Full-sun Ultraviolet Rocket SpecTrograph (FURST) is a sounding rocket payload being developed by Montana State University (MSU) alongside the Marshall Space Flight Center(MSFC) solar physics group. Scheduled to launch from White Sands Missile Range (WSMR) in 2022, this instrument is unique in that it will provide the connection between stellar observatories with measurements of our Sun. It will achieve this through measuring high-resolution full-disk spectral irradiance. We aim to obtain a wavelength resolution R>10,000 in the 120 - 181 nm UltraViolet (UV) range, on par with that of the Hubble (HST) Space Telescope Imaging Spectrograph (STIS). This resolution goal will allow us to study the relatively low-temperature plasma in the chromosphere and lower corona with spectral accuracy down to 0.1 Å (a Doppler-shift of about±30 km/s). In addition, the Lyman Alpha(121 nm) line is known to saturate most CCD electronics. These factors illustrate the particular challenge of precise wavelength calibration for this spectral range. We are building a collimator in order to calibrate the FURST instrument under these strict spectral requirements. This paper will present the results of our simulation of the diagnostic lamp signal to be used for wavelength calibration. The simulation allows us to begin to account for photon noise, electronic readout noise, and statistical error. These in turn lead to the development of our pre- and post-launch calibration plans. Future work includes absolute radiometric and wavelength calibration with this new collimator. In addition, the ability of FURST to measure small Doppler-shifts will provide capabilities for planetary atmospheric scientists. This impact is coupled with the diverse international partnership created by the closely-knit Sounding Rocket teams around the globe. Sounding Rockets like FURST have an even broader impact, as they encourage future satellite missions under the prospect of long-term observations

FURST↗
NASA NTRSDate not supplied

Wavelength Calibration of the Full-sun Ultraviolet Rocket SpecTrograph: (FURST)

The Sun has a well-known periodicity in sunspot number and magnetic field variation. The underlying cause of this 11-year cycle is not fully understood and has yet to be connected with those processes in other stellar objects. The Full-sun Ultraviolet Rocket SpecTrograph (FURST) is a sounding rocket payload being developed by Montana State University (MSU) alongside the Marshall Space Flight Center (MSFC) solar physics group. Scheduled to launch from White Sands Missile Range (WSMR) in 2022, this instrument is unique in that it will provide the connection between stellar observatories with measurements of our Sun. It will achieve this through extremely high-resolution full-disk spectroscopy in EUV. We aim to obtain a wavelength resolution R > 10,000 in the 115 - 181 nm range, on par with that of the Hubble (HST) Space Telescope Imaging Spectrograph (STIS). The Lyman Alpha line (121 nm) is known to oversaturate most CCD electronics and is of particular challenge for this spectral range. In addition, this resolution goal will allow us to study the 3 km/s motion (a Doppler-shift of about 0.01 Angstroms) of the relatively low-temperature plasma in the chromosphere and lower corona. This paper will present the results of our simulation of the diagnostic lamp signal to be used in this wavelength calibration. To test the viability of this precise of a device, we are building a collimator capable of calibrating the FURST instrument under these strict radiometric requirements. By way of a diagnostic lamp simulation, we will account for photon noise, CCD electronic readout noise, and statistical error. These will lead to the development of our pre- and post-launch calibration plan. Future work includes absolute radiometric and wavelength calibration with this new collimator. In addition, the ability of FURST to measure extremely small Doppler-shifts will provide capabilities for planetary atmospheric scientists. This impact is coupled with the diverse international partnership created by the closely-knit Sounding Rocket teams across the globe. These Sounding Rockets have an even broader impact, as they encourage future satellite missions under the prospect of long-term observations.

FURST↗
NASA NTRS2006

A comparison of global estimates of marine primary production from ocean color

The third primary production algorithm round robin (PPARR3) compares output from 24 models that estimate depth-integrated primary production from satellite measurements of ocean color, as well as seven general circulation models (GCMs) coupled with ecosystem or biogeochemical models. Here we compare the global primary production fields corresponding to eight months of 1998 and 1999 as estimated from common input fields of photosynthetically-available radiation (PAR), sea-surface temperature (SST), mixed-layer depth, and chlorophyll concentration. We also quantify the sensitivity of the ocean-color-based models to perturbations in their input variables. The pair-wise correlation between ocean-color models was used to cluster them into groups or related output, which reflect the regions and environmental conditions under which they respond differently. The groups do not follow model complexity with regards to wavelength or depth dependence, though they are related to the manner in which temperature is used to parameterize photosynthesis. Global average PP varies by a factor of two between models. The models diverged the most for the Southern Ocean, SST under 10 C, and chlorophyll concentration exceeding 1mg Chlm-3. Based on the conditions under which the model results diverge most, we conclude that current ocean-color-based models are challenged by high-nutrient low-chlorophyll conditions, and extreme temperatures or chlorophyll concentrations. The GCM-based models predict comparable primary production to those based on ocean color: they estimate higher values in the Southern Ocean, at low SST, and in the equatorial band, while they estimate lower values in eutrophic regions (probably because the area of high chlorophyll concentrations is smaller in the GCMs). Further progress in primary production modeling requires improved understanding of the effect of temperature on photosynthesis and better parameterization of the maximum photosynthetic rate.

Yamanaka, Yasuhiro↗
NASA NTRS2017

The Sun Radio Imaging Space Experiment (SunRISE)

Radio emission from coronal mass ejections (CMEs) is a di- rect tracer of particle acceleration in the inner heliosphere and potential magnetic connections from the lower solar corona to the larger heliosphere. Energized electrons excite Langmuir waves, which convert into radio emission at the local plasma frequency, with the most intense acceleration thought to occur within 20 RS, but many aspects of this par- ticle acceleration and transport remain poorly constrained. Ground-based arrays would be quite capable of tracking the radio emission associated with CMEs, but absorption by the Earth’s ionosphere limits the frequency coverage of ground-based arrays (n > 15 MHz), which in turn limits the altitudes over which they can track the radio emission ( < 3RS). The state-of-the-art for tracking such emission from space is defined by single antennas (Wind/WAVES, Stereo/SWAVES), in which the tracking is accomplished by assuming a frequency-to-density mapping; there has been some success in triangulating the emission between the spacecraft, but considerable uncertainties remain. We describe the Sun Radio Imaging Space Experiment (Sun- RISE) mission concept: A constellation of small spacecraft in a geostationary graveyard orbit designed to localize and track radio emissions in the inner heliosphere. Each space- craft would carry a receiving system for observations be- low 25 MHz, and SunRISE would image CMEs more than a few solar radii from the Sun.

Zaslavsky, Arnaud↗
NASA NTRSDate not supplied

Black-silicon as a Wideband Infrared Absorber for the Space Instruments: Fabrication, Modeling, and Characterization

The uniform black silicon (BSI) cryogenically etched has been demonstrated as a superior absorber in par with other ultra-absorbers such as carbon nano tubes in the visible and near-infrared spectrum. In this work, we discuss the fabrication, modeling, and characterization of the BSI aiming at the 2.5-5 microns range. We investigated a series of cryogenic parameters such as temperature, pressure, oxygen flowrate, power, and etching duration and fabricated a series of uniformly etched wafers. Additionally, we established at hree-dimensional mathematical model of a unit cell and manipulated the silicon needle geometry and shape. Our preliminary fabrication results show silicon needles of 8 micron height have five orders of magnitude specular reflectance in the infrared region. The modeling efforts how longer pillar heights could be better absorbers as the wavelength increases.

Wideband absorber↗
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 NTRS2020

Optical Navigation for Autonomous Approach of Small Unknown Bodies

State of the practice in navigation around small celestial bodies heavily relies on ground sup- port and human skill, in particular, for perception-based operations such as optical navigation and mapping. This leads to longer duration and more complex mission operations and sub- sequently higher cost. Furthermore, it imposes limitations for certain missions such as fast fly-bys or multi-agent operations. In this work, we present an autonomous navigation strat- egy suitable for approaching small unexplored bodies. During the approach, we estimate the body’s physical properties as well as the spacecraft’s relative trajectory and associated un- certainties. The autonomous navigation strategy, which is solely based on optical measure- ments, begins as soon as the body becomes resolved in the navigation camera and terminates at the start of proximity operations, when the spacecraft makes its first trajectory correction to stay in the vicinity of the body. Our strategy uses multiple image-processing algorithms: light-curve analysis for estimating the target body’s rotation rate, Shape-from-Silhouette for reconstructing the 3D shape and estimating its rotation pole, and feature tracking tailored to Small-Body images for estimating relative navigation parameters. We used the Mission Analysis, Operations, and Navigation Toolkit Environment (MONTE) developed by the Jet Propulsion Laboratory to evaluate the feasibility of this multi-phase navigation strategy using simulated images of an approach trajectory. We used the Rosetta mission data to generate photorealistic images to characterise the performance of this approach. This work is based on the assumptions that the spacecraft attitude is known, the body is a principal-axis rotator, a-priori estimates of ephemerides and scale are available, and the body is observed from a zero sun phase only during initial approach. Preliminary results show orbit determination performance that is on par with the human navigation from the Rosetta mission; albeit with a 1% bias in spacecraft-target radial distance estimate. The bias error is likely due to the robustness and accuracy of the visual tracking under dynamic lighting conditions and per- spective changes, which decrease accuracy.

Villa, Jacopo↗
NASA NTRS2021

Exoplanets and Radio Astronomy Beyond Earth

Jupiter’s radio emission has been linked to its planetary- scale magnetic field, and spacecraft investigations have re- vealed that most planets, and some moons, have or had a global magnetic field. Generated by internal dynamos, magnetic fields are one of the few remote sensing means of constraining the properties of planetary interiors. For the Earth, its magnetic field has been speculated to be par- tially responsible for its habitability, and knowledge of an extrasolar planet’s magnetic field may be necessary to as- sess its habitability. The radio emission from Jupiter and other solar system planets is produced by an electron cy- clotron maser, and detections of extrasolar planetary elec- tron cyclotron masers will enable measurements of extraso- lar planetary magnetic fields. Key advances would include the ground-based detection of the radio emission from Jovian-mass planets and laying the technological founda- tions for space-based detections of the radio emissions from lower-mass planets.

Lazio, Joseph↗
NASA NTRS2021

Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

Lianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated trees. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition, and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (−30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana-specific traits were also responsible for a significant reduction of tree (−19%) and ecosystem (−7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of lianas on forest functioning, and paves the way for the evaluation of the large-scale impacts of lianas on forest biogeochemical cycles.

Félicien Meunier↗
NASA NTRSDate not supplied

Status and Operations at the Granite Island, Michigan (GIM) BSRN Station

In June 2018, a new surface radiation site was established on Granite Island, Michigan (GIM), located in Lake Superior. GIM is a 0.1 square km (2.5 acre) rock island. GIM moved from candidate to active and became a fully-fledged member of the Baseline Surface Radiation Network (BSRN) in July 2020 (BSRN Label: GIM). The installation is solar powered, and autonomous instrument functions are accessed remotely from Hampton, Virginia, USA. The original motivation to establishing surface radiation measurements at GIM was to combine high quality downwelling shortwave and longwave measurements with existing evaporation measurements that could lead to improved understanding of the Earth’s energy budget. Other scientific benefits are the addition of a new "water" site to the BSRN network (Water sites are rare in the BSRN network) and a surface validation site for satellite measurements such as the Clouds and the Earth’s Radiant Energy System (CERES). In this poster, we present the status of GIM. GIM is equipped with standard downwelling flux at visible and infrared wavelengths as required by the BSRN network. Basic meteorological parameters are being monitored, as well as other data collections for aerosol and total column water vapor, PAR, and lake skin temperature. Lastly, the capabilities to clean shortwave optics remotely will be discussed and a current list of instruments and pictures will be displayed.

Bryan Fabbri↗
NASA NTRS2022

The Contribution of Planetary Period Oscillations Toward Circulation and Mass Loss in Saturn's Magnetosphere

Magnetic reconnection is a process during which magnetic energy is released as kinetic energy. It is considered a crucial driver of energy transport and mass loss within Saturn's magnetosphere. On longterm timescales, is thought to be predominantly driven by the rapid rotation of equatorially mass-loaded flux tubes (i.e., the Vasyliunas cycle), but there is some non-negligible driving from the solar wind as well (i.e., the Dungey cycle). In this study, we investigate an atmospheric driven phenomenon that modulates Saturn's magnetosphere every ∼10.6–10.8 hr, known as planetary period oscillations (PPOs), as an additional driver of magnetic reconnection at Saturn. Using an empirical model of PPO dynamics and Cassini magnetic field and plasma measurements, we find that PPO-driven magnetic reconnection is likely to occur in Saturn's magnetosphere, however, the occurrence of the phenomenon depends on temporally variable characteristics of the PPO systems and spatial asymmetries within Saturn's equatorial magnetosphere. Thus, it is not expected to be an on-going process. On year-long timescales, we find that PPOs are expected to be on par with the Dungey Cycle in driving circulation within Saturn's magnetosphere. However, on ∼1–2 weeks-long timescales, under specific conditions where PPO-driven reconnection is expected to be active, this phenomenon can become more significant than the Vasyliunas cycle, and thus dominate circulation within Saturn's magnetosphere. On year-long timescales, this process is estimated to remove upwards of ∼20% of the mass loaded into the magnetosphere by Enceladus.

O. Agiwal↗
NASA NTRSDate not supplied

Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS): Robotically Assembled Sustainable Lunar Infrastructure

Introduction: The Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) project at NASA Ames Research Center is developing autonomous infrastructure, instrumentation, and spacecraft assembly and manufacturing capabilities for next generation exploration and science missions, with a goal to change the cost scaling of these missions relative to mission size and duration. Using a building-block approach with a 'kit of parts' composed of ultra-light, high-performance mechanical metamaterials, simplified robots leverage the period environment to achieve high levels of autonomy and reliability for in-space and surface assembly of large-scale apertures, solar-arrays, towers, habitats, and other infrastructure. Robots and structure break down into a compact form factor for launch. By leveraging economies of scale and achieving high-packing ratios, ARMADAS technology can revolutionize possible space missions by breaking the tyranny of the launch shroud, decreasing development times, decreasing mission costs for transformative science capability, and providing a scalable and versatile space infrastructure strategy. Capabilities: To date, the ARMADAS project has demonstrated autonomous assembly of large numbers of structural modules into a meters-scale structure in an earth gravity environment. The mechanical performance is on par with conventional space structures, at a fraction of the cost. By using highly repeatable manufacturing processes (injection molding carbon fiber reinforced space-rated polymers), the structures are both cost effective and highly precise. The ARAMADAS system carefully designs parts for high-precision assembly–the simple and cost-effective robots build structures much larger and more precise than themselves. While maintaining structural efficiency, the ARMADAS system encompasses many functional module types, including for solar power, comm/power routing, etc., that will enable entire infrastructure systems to be engineered and autonomously assembled with modular parts. With well-defined interfaces, custom instrumentation modules can easily be added to fit many mission objectives. Lunar Infrastructure Vision: At the LSIC spring meeting, ARMADAS will present a vision for a general-purpose lunar construction kit capable of meeting a wide variety of lunar surface infrastructure needs (Figure 1). Investing in an ecosystem of reconfigurable, discretely repairable parts and robots enables systems than can expand their capability, reconfigure to meet emergency or unforeseen needs, self-repair and reduce spare-part needs. With a small set of parts (module types), a wide variety of infrastructure needs can be met. Footing modules will allow infrastructure construction at locations with no surface preparation. Footer, primary structure, and rail modules can create reconfigurable rail systems. These rail systems can be used to reduce cost-of-transport between frequently accessed sites, provide dust mitigation, and convey power and communications. High-performance structure modules combined with power-routing and solar modules can create tall towers for power and communication. Concepts for habitats and garages encompass a structure to support regolith cover for a pre-integrated module to an entirely ARMADAS system-based structure. This system can be leveraged by many in-situ resource utilization (ISRU) technologies to simplify processes and augment capabilities.

C. E. Gregg↗
NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗
NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗
NASA NTRS2024

Ultra-Light, Strong, and Self-Reprogrammable Mechanical Metamaterials

Versatile programmable materials have long been envisioned that can reconfigure themselves to adapt to changing use cases in adaptive infrastructure, space exploration, disaster response, and more. We introduce a robotic structural system as an implementation of programmable matter, with mechanical performance and scale on par with conventional high-performance materials and truss systems. Fiber reinforced composite truss-like building-blocks form strong, stiff, and lightweight lattice structures as mechanical metamaterials. Two types of mobile robots operate over the exterior surface and through the interior of the system, performing transport, placement, and reversible fastening using the intrinsic lattice periodicity for indexing and metrology. Leveraging programmable matter algorithms to achieve scalability in size and complexity, this system design enables robust collective automated assembly and reconfiguration of large structures with simple robots. We describe the system design and experimental results from a 256-unit cell assembly demonstration and lattice mechanical testing, as well as demonstration of disassembly and reconfiguration. The assembled structural lattice material exhibits ultra-light mass density (0.0103 g/cc) with high strength and stiffness for its weight (0.01117 MPa and 1.1129 MPa, respectively), a material performance realm appropriate for applications like space structures. With simple robots and structure, high mass-specific structural performance, and competitive throughput, this system demonstrates potential for self-reconfiguring autonomous metamaterials for diverse applications.

Robotics↗
NASA NTRSDate not supplied

Evaluation of Composite Airframe Dynamic Impact Modeling Using Hawker 4000 Fuselage Drop Test Data

Two drop tests of partial Hawker 4000 fuselage sections were conducted at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) to characterize the response of representative composite aerospace structure to dynamic impact loads. Test conditions were selected to induce damage into the composite structure in order to study material failure within a composite fuselage and evaluate the capability of finite element (FE) model analysis to predict that failure. The tests were simulated using FE models which were generated to isolate the effect of developmental data availability on predictive capability. FE Models of the tested fuselage sections were generated using two limited data sets. The first model configuration was reverse engineered from the test article with no information related to design or fabrication details which would be known only by the manufacturer. The second model was generated from data provided by the manufacturer but without additional material characterization test data. Models were developed using these methodologies for both fuselage sections tested. Correlation of each model to the tests conducted was evaluated in terms of damage, deformation, and cabin acceleration measurements. Correlation between the developed models and the tested fuselage sections showed that the reverse engineered model predicted the composite damage and cabin acceleration measured during test though it was limited due to lack of detail in the composite layup changes through the structure. The model developed using manufacturer specifications did not predict damage, due to limited material and component model characterization data, but it did predict acceleration on par with the reverse engineered model. Model capability and limitation sources identified were verified through correlation of a final model which was developed by combining the individual data sets. The combined model demonstrated that the addition of calibrated composite material models to accurate composite layup definitions and detailed geometry led to improved correlation of damage and acceleration response within the composite fuselage structures.

Crashworthiness↗
NASA NTRS2022

Deep Space Atomic Clock Technology Demonstration Mission Results

The Deep Space Atomic Clock (DSAC), a NASA Technology Demonstration Mission, was launched into low-Earth orbit on June 25, 2019 as a hosted payload aboard General Atomics’ Orbital Test Bed (OTB) spacecraft. The DSAC mission has been conducting a two-year demonstration of a mercury ion atomic clock to characterize its space-based performance and to validate its utility for deep space navigation and radio science. Analysis of the collected data using JPL’s GIPSY-OASIS software has shown DSAC’s AD at one-day to be near 310-15; much better than required AD of 210-14. Such low spacecraft clock errors will enable one-way radiometric tracking data with precision equivalent to or better than current-day two way tracking data, allowing a shift to a more efficient and flexible one-way deep space navigation architecture. To verify this, an analog deep space navigation experiment was performed using JPL’s operational navigation software (Monte). The experiment recovered orbit solutions with reduced data sets and geometric variations that are more representative of deep space missions, and showed that orbit determination using DSAC derived data is on par with more traditional two-way datatypes. As a technology demonstrator, DSAC’s development focus has been on maturing the mercury ion trap clock technology rather than achieving the smallest size, weight, and power (SWaP). Over the course of DSAC’s development the project has identified numerous improvements that could be made to significantly reduce SWaP for DSAC’s next version. Indeed, DSAC-2 was recently selected by NASA for further demonstration on the VERITAS mission to Venus. This work will review the DSAC technology, mission, and results from its two-year mission.

Wang, Rabi↗
NASA NTRS2023

Impacts of Marine Surface Pressure Observations From A Spaceborne Differential Absorption Radar Investigated With an Observing System Simulation Experiment

A new instrument has been proposed for measuring surface air pressure over the marine surface with a combined active/passive scanning multi-channel differential absorption radar (DAR) to provide an estimate of the total atmospheric column oxygen content. A demonstrator instrument, the Microwave Barometric Radar and Sounder (MBARS), has been funded by the National Aeronautics and Space Administration (NASA) for airborne test missions. Here, a proof-of-concept study to evaluate the potential impact of spaceborne surface pressure data on numerical weather prediction is performed using the Goddard Modeling and Assimilation Office global observing system simulation experiment (OSSE) framework. This OSSE framework employs the Goddard Earth Observing System model and the hybrid 4D ensemble variational Gridpoint Statistical Interpolation data assimilation system. Multiple flight and scanning configurations of potential spaceborne orbits are examined. Swath width and observation spacing for the surface pressure data are varied to explore a range of sampling strategies. For wider swaths, the addition of surface pressures reduces the root mean square surface pressure analysis error by as much as 20% over some ocean regions. The forecast sensitivity observation impact tool estimates impacts on the Pacific Ocean basin boundary layer 24-hour forecast temperatures for spaceborne surface pressures on par with rawinsondes and aircraft, and greater impacts than the current network of ships and buoys. The largest forecast impacts are found in the southern hemisphere extratropics.

N.C. Privé↗