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NASA NTRS2024

Development of a Data Fusion Methodology for Lineload Aerodynamic Databases for a Launch Vehicle during Liftoff and Transition

The need for databases for the distributed loading on launch vehicles during the early portion of flight necessitates the use of expensive computational flows in regimes where wake effects dominate. While also being expensive, this is a regime that computational tools tend to historically have problems simulating accurately. To help tackle this problem, a method of data fusion to combine computational results to wind tunnel derived force and moment data is developed. Using this method, significant reduction in computational costs and increases in confidence of the final product is possible and has been used to generate several databases for the Space Launch System (SLS) at NASA. While the full details of database generation are not part of this work, the crucial method at its core is developed here. Two SLS geometries are used throughout the work to demonstrate the techniques. These are two of the larger geometries and represent both planned crewed missions to the Moon as well as potential cargo missions to deep space. The method uses principal component analysis (PCA) to generate a reduced ordered model (ROM) to help fill in the full parameter space. Other similar techniques are explored, but were not found to have a significant result on the predictions of the ROM. Because the full number of components are kept to generate the model, this lack of difference is expected. This method is then extended to ensure that predicted surfaces match trusted force and moment data derived from wind tunnel testing. This extension is done by setting up a constrained optimization problem in order to minimize the deviation from the surface resolved computational data while still integrating to the desired values. When generating the constrained optimization problem, a weighting factor to balance these competing needs is introduced. The work compares previously introduced weighting terms from similar work to the proposed terms and shows that the previously used terms do not have as desirable behavior in this flow regime. This method is then expanded by developing a technique to incorporate uncertainty quantification into the developed data fusion methodology. This expansion takes a two pronged approach. One examines transferring the uncertainties in the force and moment database and characterizes how those adjustments change the predicted lineloads. The second looks at model form error and looks how rebuilding the model using slightly different data changes the predictions. These two terms are then combined in order to create an uncertainty model that takes both effects into account. The limitations of the proposed methods is then discussed as well as possible techniques to address these shortcomings.

Launch Vehicles↗

NASA NTRSDate not supplied

Seeing the Future of Spaceflight: Applications of Extended Reality (XR) Technologies

INTRODUCTION Advances in Earth Independent Medical Operations (EIMO) focused capabilities will be crucial for completing successful beyond low-earth-orbit (LEO) missions, where traditional Earth-based medical support and near real-time communication are limited, intermittent, or absent. In such restricted or limited communication environments, emerging extended reality (ER or XR) technologies may play a vital role in ensuring crew health and safety. Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) are common terms used to describe technologies that "generate or modify" while “extended reality” (ER or XR) is often used as an umbrella term for these technologies. This review explores how such cutting-edge “extended reality” information delivery systems are currently applied across various disciplines within NASA. The review focuses on their capabilities, limitations, and prospective future applications as they pertain to Earth-Independent Medical Operations and its components of pre-mission planning, medical decision making, resource management, and task load management. METHODS/DESIGN The review began with a literature search to identify relevant disciplines (e.g., medical education, austere environments) where XR technologies have been studied to highlight areas where evidence is lacking. Previous and current NASA XR projects and applications, as well as commercial partnerships, such as those with Small Business Innovation Research (SBIR), were identified. Discussions with technical experts involved in the design, development, and application of these technologies allowed for classification based on their respective spaceflight applications. RESULTS AND CONCLUSION This review highlights current and emerging XR technologies that have been developed, tested, and implemented both terrestrially and in spaceflight by NASA, its international partners, the U.S. Armed Forces, and various relevant commercial entities. While these novel information delivery systems vary in their respective technology readiness levels (TRL), they have the potential to be implemented on Earth, in LEO, and during deep space exploration. These systems have the potential for impacting and optimizing many aspects of EIMO.

Yevgeniy Zhivotovskiy↗

NASA NTRS1982

Selection of frequencies for deep-space telecommunications

The procedures used by the Jet Propulsion Laboratory to select and recommend frequencies to be used for deep space telecommunications are explained. The frequency selection process described deals only with the potential for radio frequency interference between deep space telecommunication links. Channel plans based on bandwidth, hardware implementation, and frequency ratio considered are used. The channel selection process is based on calculations and analysis of interference-to-signal power ratios as a function of time for each possible pair of missions. The modes of interference and the interference protection ratio are explained, and the interference analysis procedure described. Equations used in calculating the downlink and uplink interference analyses are presented.

Degroot, N. F.↗

NASA NTRS1990

Microwave analog fiber-optic link for use in the deep space network

A novel fiber-optic system with dynamic range of up to 150 dB-Hz for transmission of microwave analog signals is described. The design, analysis, and laboratory evaluations of this system are reported, and potential applications in the NASA/JPL Deep Space Network are discussed.

Logan, R. T., Jr.↗

NASA NTRS2004

Europa's Icy Shell: A Bridge Between Its Surface and Ocean

Europa, a Moon-sized, ice-covered satellite of Jupiter, is second only to Mars in its astrobiological potential. Beneath the icy surface, an ocean up to 150 km deep is thought to exist, providing a potential habitat for life,and a tempting target for future space missions. The Galileo mission to the Jovian system recently ended, but there are already long-range plans to send much more capable spacecraft,such as the proposed Jupiter Icy Moons Orbiter (JIMO), to take a closer look at Europa and her siblings, Ganymede and Callisto, some time in the next two decades. Europak outer icy shell is the only interface between this putative ocean and the surface, but many aspects of this shell are presently poorly understood; in particular, its composition, thickness, deformational history, and mechanical properties. To discuss the ice shell and our current understanding of it, 78 scientists from the terrestrial and planetary science communities in the United States and Europe gathered for a 3-day workshop hosted by the Lunar and Planetary Institute in Houston in February. A key goal was to bring researchers from disparate disciplines together to discuss the importance and limitations of available data on Europa with a post-Galileo perspective. The workshop featured 2 days of reviews and contributed talks on the composition, physical properties, stratigraphy, tectonics, and future exploration of the ice shell and underlying ocean. The final morning included an extended discussion period, moderated by a panel of noted experts, highlighting outstanding questions and areas requiring future research.

Schenk, Paul↗

DOE OSTI2025

Structure–Property Linkage in Alloys Using Graph Neural Network and Explainable Artificial Intelligence

Deep learning tools have recently shown significant potential for accelerating the prediction of microstructure–property linkage in materials. While deep neural networks like convolution neural networks (CNNs) can extract physics information from 3D microstructure images, they often require a large network architecture and substantial training time. In this research, we trained a graph neural network (GNN) using phase field generated microstructures of Ni-Al alloys to predict the evolution of mechanical properties. We found that a single GNN is capable of accurately predicting the strengthening of Ni-Al alloys with microstructures of varying sizes and dimensions, which cannot otherwise be done with a CNN. Additionally, GNN requires significantly less GPU utilization than CNN and offers more interpretable explanation of predictions using saliency analysis as features are manually defined in the graph. We also utilize explainable artificial intelligence tool Bayesian Inference to determine the coefficients in the power law equation that governs coarsening of precipitates. Overall, our work demonstrates the ability of the GNN to accurately and efficiently extract relevant information from material microstructures without having restrictions on microstructure size or dimension and offers an interpretable explanation.

Chemistry↗

NASA NTRSDate not supplied

Segmentation of Lightweight Ablator Micro-Tomography Using Deep Learning

Ablative thermal protection systems (TPS) are essential for high speed entry of planetary atmospheres, such as those of Earth and Mars. Upon entry the kinetic energy of the spacecraft is converted into thermal energy, leading to high heat fluxes at the wall of the craft. Because of this extreme heating, a robust ablative TPS material must be selected. A common material selection today is phenolic-impregnated carbon ablator (PICA), which is a low-density carbon material known for producing dust that is not suitable to a cleanroom environment. To mitigate dust created by a PICA heatshield, a silicone-based spray called NuSil is applied to the surface of the TPS, creating PICA-NuSil (PICA-N). PICA-N has been observed to have a different material response from regular PICA during high enthalpy flow testing, producing surface temperatures up to 200K less than those seen for PICA [1]. To better understand this phenomenon, it is critical that robust methods of PICA-N material characterization are developed. The purpose of this project is to investigate Object Research Systems’ (ORS) Dragonfly deep learning tools as a means of accurately segmenting and characterizing PICA-N. Systematic testing of this software has shown that Dragonfly deep learning tools have strong potential for accurate segmentation/ characterization of PICA-N and other TPS materials.

Micro-tomography↗

NASA NTRS2003

Energy Requirements of Hydrogen-utilizing Microbes: A Boundary Condition for Subsurface Life

Microbial ecosystems based on the energy supplied by water-rock chemistry carry particular significance in the context of geo- and astrobiology. With no direct dependence on solar energy, lithotrophic microbes could conceivably penetrate a planetary crust to a depth limited only by temperature or pressure constraints (several kilometers or more). The deep lithospheric habitat is thereby potentially much greater in volume than its surface counterpart, and in addition offers a stable refuge against inhospitable surface conditions related to climatic or atmospheric evolution (e.g., Mars) or even high-energy impacts (e.g., early in Earth's history). The possibilities for a deep microbial biosphere are, however, greatly constrained by life s need to obtain energy at a certain minimum rate (the maintenance energy requirement) and of a certain minimum magnitude (the energy quantum requirement). The mere existence of these requirements implies that a significant fraction of the chemical free energy available in the subsurface environment cannot be exploited by life. Similar limits may also apply to the usefulness of light energy at very low intensities or long wavelengths. Quantification of these minimum energy requirements in terrestrial microbial ecosystems will help to establish a criterion of energetic habitability that can significantly constrain the prospects for life in Earth's subsurface, or on other bodies in the solar system. Our early work has focused on quantifying the biological energy quantum requirement for methanogenic archaea, as representatives of a plausible subsurface metabolism, in anoxic sediments (where energy availability is among the most limiting factors in microbial population growth). In both field and laboratory experiments utilizing these sediments, methanogens retain a remarkably consistent free energy intake, in the face of fluctuating environmental conditions that affect energy availability. The energy yields apparently required by methanogens in these sediment systems for sustained metabolism are about half that previously thought necessary. Lowered energy requirements would imply that a correspondingly greater proportion of the planetary subsurface could represent viable habitat for microorganisms.

Hoehler, Tori M.↗

DOE OSTI2024

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2009

Mud Volcanoes in the Martian Lowlands: Potential Windows to Fluid-Rich Samples from Depth

The regional setting of the Chryse-Acidalia area augurs well for a fluid-rich subsurface, accumulation of diverse rock types reflecting the wide catchment area, astrobiological prospectivity, and mud volcanism. This latter provides a mechanism for transporting samples from relatively great depth to the surface. Since mud volcanoes are not associated with extreme heat or shock pressures, materials they transport to the surface are likely to be relatively unaltered; thus such materials could contain interpretable remnants of potential martian life (e.g., organic chemical biomarkers, mineral biosignatures, or structural remains) as well as unmetamorphosed rock samples. None of the previous landings on Mars was located in an area with features identified as potential mud volcanoes (Fig. 3), but some of these features may offer targets for future missions aimed at sampling deep fluid-rich strata with potential habitable zones.

Oehler, Dorothy Z.↗

NASA NTRS2016

The Auto-Gopher: A Wireline Rotary-Percussive Deep Sampler

Accessing regions on planetary bodies that potentially preserved biosignatures or are presently habitable is vital to meeting NASA solar system "Search for Life" exploration objectives. To address these objectives, a wireline deep rotary-percussive corer called Auto-Gopher was developed. The percussive action provides effective material fracturing and the rotation provides effective cuttings removal. To increase the drill's penetration rate, the percussive and rotary motions are operated simultaneously. Initially, the corer was designed as a percussive mechanism for sampling ice and was demonstrated in 2005 in Antarctica reaching about 2 m deep. The lessons learned suggested the need to use a combination of rotation and hammering to maximize the penetration rate. This lesson was implemented into the Auto-Gopher-I deep drill which was demonstrated to reach 3-meter deep in gypsum. The average drilling power that was used has been in the range of 100-150 Watt, while the penetration rate was approximately 2.4 m/hr. Recently, a task has started with the goal to develop Auto-Gopher-II that is equipped to execute all the necessary functions in a single drilling unit. These functions also include core breaking, retention and ejection in addition drilling. In this manuscript, the Auto-Gopher-II, its predecessors and their capability are described and discussed.

drilling↗

DOE OSTI2026

Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra

Identifying molecular structure based on spectroscopic readings is a key task in a variety of chemical and biological applications. Common spectroscopy techniques, such as Infrared (IR) Spectroscopy and Mass Spectrometry (MS), provide detailed information on the structure of molecular compounds but nonetheless require expert-level knowledge to decode. Machine learning has emerged as a potential solution for automating structure prediction from chemical spectra; however, current approaches generally focus on single sensor modalities, neglecting to leverage the complementary information contained within differing spectra. In this paper, we introduce Peak2Patch, a novel approach to fusion-enhanced prediction of functional groups from IR and mass spectra. First, we perform a detailed comparison of backbone networks for encoding both sparse mass spectra and dense IR spectra and demonstrate the superior performance of transformer neural networks over current state-of-the-art convolutional neural networks. Second, we evaluate three broad categories of fusion: early (raw feature), middle (deep feature), and late (decision) fusion, demonstrating the potential of a deep feature fusion-based approach. Lastly, we present Peak2Patch, our attention-based fusion scheme, which leverages cross-attention to mix features between encoded tokens of the two modalities. We validate our approach on a publicly available multimodal spectroscopic data set of 790k simulated molecules, demonstrating a large improvement in functional group prediction over both the previous state-of-the-art and our own strong single-modal baselines.

Jacobson, Philip [Sandia National Laboratories (SN↗

NASA NTRS2016

NASA's Space Launch System: A Transformative Capability for Exploration

Currently making rapid progress toward first launch in 2018, NASA's exploration-class Space Launch System (SLS) represents a game-changing new spaceflight capability, enabling mission profiles that are currently impossible. Designed to launch human deep-space missions farther into space than ever before, the initial configuration of SLS will be able to deliver more than 70 metric tons of payload to low Earth orbit (LEO), and will send NASA's new Orion crew vehicle into lunar orbit. Plans call for the rocket to evolve on its second flight, via a new upper stage, to a more powerful configuration capable of lofting 105 t to LEO or comanifesting additional systems with Orion on launches to the lunar vicinity. Ultimately, SLS will evolve to a configuration capable of delivering more than 130 t to LEO. SLS is a foundational asset for NASA's Journey to Mars, and has been recognized by the International Space Exploration Coordination Group as a key element for cooperative missions beyond LEO. In order to enable human deep-space exploration, SLS provides unrivaled mass, volume, and departure energy for payloads, offering numerous benefits for a variety of other missions. For robotic science probes to the outer solar system, for example, SLS can cut transit times to less than half that of currently available vehicles, producing earlier data return, enhancing iterative exploration, and reducing mission cost and risk. In the field of astrophysics, SLS' high payload volume, in the form of payload fairings with a diameter of up to 10 meters, creates the opportunity for launch of large-aperture telescopes providing an unprecedented look at our universe, and offers the ability to conduct crewed servicing missions to observatories stationed at locations beyond low Earth orbit. At the other end of the spectrum, SLS opens access to deep space for low-cost missions in the form of smallsats. The first launch of SLS will deliver beyond LEO 13 6U smallsat payloads, representing multiple disciplines, including three spacecraft competitively chosen through NASA's Centennial Challenges competition. Private organizations have also identified benefits of SLS for unique public-private partnerships. This paper will give an overview of SLS' capabilities and its current status, and discuss the vehicle's potential for human exploration of deep space and other game-changing utilization opportunities.

Robinson, Kimberly F.↗

NASA NTRS2016

NASA's Space Launch System: A Transformative Capability for Exploration

Currently making rapid progress toward first launch in 2018, NASA's exploration-class Space Launch System (SLS) represents a game-changing new spaceflight capability, enabling mission profiles that are currently impossible. Designed to launch human deep-space missions farther into space than ever before, the initial configuration of SLS will be able to deliver more than 70 metric tons of payload to low Earth orbit (LEO), and will send NASA's new Orion crew vehicle into lunar orbit. Plans call for the rocket to evolve on its second flight, via a new upper stage, to a more powerful configuration capable of lofting 105 tons to LEO or co-manifesting additional systems with Orion on launches to the lunar vicinity. Ultimately, SLS will evolve to a configuration capable of delivering more than 130 tons to LEO. SLS is a foundational asset for NASA's Journey to Mars, and has been recognized by the International Space Exploration Coordination Group as a key element for cooperative missions beyond LEO. In order to enable human deep-space exploration, SLS provides unrivaled mass, volume, and departure energy for payloads, offering numerous benefits for a variety of other missions. For robotic science probes to the outer solar system, for example, SLS can cut transit times to less than half that of currently available vehicles, producing earlier data return, enhancing iterative exploration, and reducing mission cost and risk. In the field of astrophysics, SLS' high payload volume, in the form of payload fairings with a diameter of up to 10 meters, creates the opportunity for launch of large-aperture telescopes providing an unprecedented look at our universe, and offers the ability to conduct crewed servicing missions to observatories stationed at locations beyond low Earth orbit. At the other end of the spectrum, SLS opens access to deep space for low-cost missions in the form of smallsats. The first launch of SLS will deliver beyond LEO 13 6-unit smallsat payloads, representing multiple disciplines, including three spacecraft competitively chosen through NASA's Centennial Challenges competition. Private organizations have also identified benefits of SLS for unique public-private partnerships. This paper will give an overview of SLS' capabilities and its current status, and discuss the vehicle's potential for human exploration of deep space and other game-changing utilization opportunities.

Robinson, Kimberly F.↗

DOE OSTI2025

Reproducibility of Radiokrypton in Deep Desert Aquifers: Insights from a Decade of Research

Great technical advances have been achieved since the first atom-trap trace analysis (ATTA) -based radiokrypton application in Egypt, where 1 Myr old groundwater was discovered. Beyond advances in ATTA measurement capabilities, including reduction in sample size, analysis duration, and analytical uncertainty, major progress has been achieved over the past two decades in the sample collection and preparation techniques. These advances paved the expansion of ATTA-based noble gas applications to many other aquifers worldwide, illuminating the nature and flow pattern of deep groundwater systems. While the potential of this new analytical technique for old groundwater dating is well recognized, another important aspect yet to be examined is the reproducibility of radiokrypton in aquifers over time, i.e., how representative is a discrete groundwater sample, collected at a specific time and location, for the natural groundwater system? The likelihood of a negative answer is increased by flow-field disturbance in aquifers following massive groundwater abstraction. Here, in this work, we present repeated 81 Kr sampling and measurements in twenty-one sites over Israel, mostly of deep (up to 1 km) wells tapping confined aquifers in the arid to hyperarid Negev desert. The results demonstrate that radiokrypton measurements are indeed reproducible, even in cases where samples were collected as long as nine years apart and from highly productive (∼1 Mm 3 /yr order) pumping wells. Furthermore, many of the repeated measurements in this study (17 out of the 21 sites) were conducted with different ATTA Instruments in two different laboratories using slightly different sampling, preparation, and analysis techniques, yet with an overall good agreement. The consistency in the ATTA-based 81 Kr-dating results over time highlights the robustness of this state-of-the-art technique as a tool to unravel groundwater flow patterns and encourages further applications to many other yet-to-be-explored deep aquifers.

atom-trap trace analysis↗

NASA NTRS2017

NASA's Space Launch System: A Transformative Capability for Deep Space Missions

Already making substantial progress toward its first launches, NASA’s Space Launch System (SLS) exploration-class launch vehicle presents game-changing new opportunities in spaceflight, enabling human exploration of deep space, as well as a variety of missions and mission profiles that are currently impossible. Today, the initial configuration of SLS, able to deliver more than 70 metric tons of payload to low Earth orbit (LEO), is well into final production and testing ahead of its planned first flight, which will send NASA’s new Orion crew vehicle around the moon and will deploy 13 CubeSats, representing multiple disciplines, into deep space. At the same time, production work is already underway toward the more-capable Block 1B configuration, planned to debut on the second flight of SLS, and capable of lofting 105 tons to LEO or of co-manifesting large exploration systems with Orion on launches to the lunar vicinity. Progress being made on the vehicle for that second flight includes initial welding of its core stage and testing of one of its engines, as well as development of new elements such as the powerful Exploration Upper Stage and the Universal Stage Adapter “payload bay.” Ultimately, SLS will evolve to a configuration capable of delivering more than 130 tons to LEO to support humans missions to Mars. In order to enable human deep-space exploration, SLS provides unrivaled mass, volume, and departure energy for payloads, offering numerous benefits for a variety of other missions. For robotic science probes to the outer solar system, for example, SLS can cut transit times to less than half that of currently available vehicles or substantially increased spacecraft mass. In the field of astrophysics, SLS’ high payload volume, in the form of payload fairings with a diameter of up to 10 meters, creates the opportunity for launch of large-aperture telescopes providing an unprecedented look at our universe. This presentation will give an overview of SLS’ capabilities and its current status, and discuss the vehicle’s potential for human exploration of deep space and other game-changing utilization opportunities.

Creech, Stephen D.↗

NASA NTRS2018

A Performance-Based Comparison of Deep-Space Navigation using Optical-Communication and Conventional Navigation Techniques: Small Body Missions

Optical communications may be used in future NASA deep-space missions, resulting in much higher data transfer rates. Those optical communication links could also be used for navigation purposes. The performance of deep-space navigation for an asteroid flyby mission using ground-based optical tracking and conventional navigation techniques was investigated in this work. We present the results of variety of asteroid flyby scenarios including low phase and high phase approach angle flybys, one slow flyby in a Trojan tour mission, and also one slow flyby in a Psyche mission. In this task, four different types of observables were simulated, namely ground-based radiometric, spacecraft on-board optical, ground-based optical tracking of spacecraft (astrometry and 2-way range magnitude), and ground-based asteroid astrometry. Different combinations of these four types of observables were compared with currently in-practice ground-based radiometric/on-board optical measurements. The results showed that the ground-based optical tracking is promising and could be a potential candidate for future deep-space navigation. Precise astrometry is not possible for active comets.

McCandless, Sara Elizabeth↗

NASA NTRS2018

A Performance-Based Comparison of Deep-Space Navigation using Optical-Communication and Conventional Navigation Techniques: Small Body Missions

McCandless, Sarah Elizabeth↗