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NASA NTRSDate not supplied

Maintenance Modernization in the Era of Artemis An RCM Journey

The NASA Artemis Program mission to return humans to the moon requires it ground test facilities to meet today’s performance and throughput demands. The NASA Ames Arc Jet Complex (AJC) at Ames Research Center is the Agency’s sole ground test facility supporting reentry thermal protection system testing and was developed to support Apollo era spacecraft. The research and development required to meet current and future mission demands continually grows, but the maintenance upkeep to ensure safe and reliable operations holds stagnant. This paper presents the journey and subsequent industry relatable story that includes the challenges, lessons learned, and testimonials to the AJC’s approach to the modernization of maintenance using RCM at NASA.

Reliability Centered Maintenance↗
NASA NTRS2016

The International Space Station (ISS) Solar Alpha Rotary Joint (SARJ): Materials & Processes (M&P) Lessons Learned for a Large, Spacecraft Rotating Mechanism

The ISS utilizes two large rotating mechanisms, the SARJ, as part of the solar arrays alignment system for more efficient power generation. The SARJ is a 10.3m circumference, nitrided 15-5PH steel race ring of triangular cross-section, with 12 sets of trundle bearing assemblies transferring load across the rolling joint. The SARJ mechanism rotates continuously and slowly - once every orbit, or every 90 minutes. In 2008, the starboard SARJ suffered a lubrication failure, resulting in severe damage (spalling) of one of the race ring surfaces. Extensive effort was conducted to prevent the port SARJ from suffering the same failure, and fortunately was ultimately successful in recovering the functionality of the starboard SARJ. The M&P function was key in determining the cause of failure and the means for mechanism recovery. From a M&P lessons-learned perspective, observations are made concerning the original SARJ design parameters (boundary conditions), the perceived need for nitriding the race ring, the test conditions employed during qualification, the environmental controls used for the hardware preflight, and the lubrication robustness necessary for complex kinematic mechanisms expecting high-reliability and long-life.

Golden, Johnny L.↗
NASA NTRSDate not supplied

22 N HPGP Thruster Life Testing

In the ever-changing paradigm of efficient and capable spacecraft design, scientific missions continue pushing the envelope enabling spacecraft subsystems to deliver effective solutions to meet challenging new mission/spacecraft needs. From an in-space storable liquid chemical propulsion perspective, monopropellant hydrazine has been, and continues to be, a dependable propellant with considerable flight heritage, a variety of engine thrust classes available from multiple vendors, with repeatable and reliable performance. Additionally, the space propulsion industry has learned to successfully handle hydrazine, its regulations, the safety protocols, the personnel protective equipment, and the unique training standards–all requisite for loading spacecraft propulsion systems with toxic hypergolic hydrazine. The question now arises as to “what is next for in-space chemical propulsion?” Further, with the evolution and concrete advancements in innovative in-space green propellant technologies, capable of providing realizable benefits to scientific missions, concern over the reliability and availability of this higher performing and safer to handle class of propellants is waning. As science missions move forward with the potential flight in fusion of High Performance Green Propulsion (HPGP), NASA and its industry partners are working to address any gaps in system reliability, performance, or unique operational considerations. Propellant technology that offers both higher performance and significant reduction in personnel hazards compared to hydrazine presents an attractive propulsion subsystem design opportunity. Increased propulsion subsystem performance can result in lower spacecraft launch mass, larger scientific payloads, or extended on-orbit lifetimes. Mission trades using green propulsion technologies have been documented on multiple NASA Goddard Space Flight Center (GSFC) mission classes, examining various parameters and requirements to support mission architectures in Low Earth Orbit (LEO), High Earth Orbit (HEO), geostationary, lunar, planetary, and Quasi-halo orbit around Sun-Earth Lagrange point (L2). The results of these trade studies show promising, attainable benefits. The perceived programmatic risk of flying a newer propulsion technology has, unfortunately, not outweighed the benefits to date. To take advantage of the improved performance and mitigate programmatic risk, HPGP engines must demonstrate life testing at higher propellant throughputs than have currently been demonstrated. In an effort to proactively address the challenges with technology infusion into a risk-averse community, NASA and the Swedish National Space Agency (SNSA) outlined a collaborative Implementing Arrangement (IA) for the respective agencies to pursue increased HPGP technology maturation. This initial IA effort began in 2013, fresh off the heels of the successful PRISMA HPGP technology demonstration mission. The IA targeted objective is to reduce risk to potential future HPGP missions and fully characterize the LMP-103S propellant and associated engine performance. Over the past eight years, HPGP has flown in propulsion systems on twenty-five(25) spacecraft from seven(7) different Launch Ranges around the globe and on seven (7) different Launch Vehicles. Six(6) of these launches involved multiple loading operations for multiple spacecraft. For U.S. Range operations, nine (9) HPGP systems have been processed at Vandenberg Space Force Base(VSFB):six(6) in 2017, and three (3) in 2018. Six (6) more have been processed at Cape Canaveral Air Force Station (CCAFS)in May 2020, with three (3) systems launched in June 2020 and the remaining three (3) system were left loaded and ready until their launch in August of 2020. Three (3) more systems have been processed at Wallops Flight Facility(WFF)and launched in June 2021. In addition, these propulsion subsystems employed heritage propulsion subsystem component such as valves, filters, and pressure transducers, and have further demonstrated nominal functionality in both diaphragm and Propellant Management Device (PDM) propellant tanks. Based on these successes, HPGP technology continues to be considered for NASA Science Mission Directorate missions at GSFC. The work presented herein represents many years of development and collaborative efforts to successfully align higher performance, low toxicity hydrazine alternatives into scientific missions. NASA GSFC Propulsion Engineering, in collaboration with Bradford ECAPS, has developed mission specific thruster design and testing requirements to establish GSFC’s desired test conditions and firing sequences.In2017, the first flight-like 22N HPGP thruster Engineering Qualification Model (EQM-1)was designed and built by Bradford ECAPS to prove out the thruster design, materials, build process, and test campaign with respect to NASA GSFC critical component and mission requirements. This test program was developed to comprehensively test the thruster, the technology, and ultimately increase the 22N HPGP Technology Readiness Level(TRL). EQM-1was tested to environmental qualification levels prior to hot fire performance testing to represent the relevant end-to-end environment (launch to on-orbit operation)with required margin. This thruster demonstrated steady-state and pulse mode operational capability with propellant thruster throughput up to~53kg.At this throughput level, the EQM-1 engine began to present off-nominal performance and the test campaign was halted to allow for non-destructive testing and identify the root cause for the an omalous performance. Capitalizing on the successful elements of the EQM-1 campaign, an upgraded 22N HPGP EQM-2 has been manufactured by Bradford ECAPS to meet the complete GSFC requirements. The EQM-2 thruster’s test campaign has further demonstrated the robustness of the HPGP propulsion technology and increased the Technology Readiness Level (TRL) by undergoing a full acceptance test program, then proceeding into qualification, including environmental testing (vibration and shock to qualification levels),as well as hot-fire life testing, operating at steady-state and pulse modes with increased propellant thruster throughput to~150kg. The HPGP thruster performance testing enables effective HPGP thruster readiness evaluation to meet NASA candidate mission requirements in the future.

High↗
NASA NTRS2018

Stirling Convertor Controller Development at NASA Glenn Research Center

Over the past decade, NASA Glenn Research Center (GRC) has been supporting the development of Radioisotope Power Systems (RPS). NASA desires higher conversion efficiency RPS options that are reliable and robust with long life design. Dynamic conversion, such as Stirling and Brayton, offer the potential for higher conversion efficiencies but have yet to be demonstrated in a flight application. The RPS program sent out a solicitation to investigate options for dynamic conversion technologies. As a result of this solicitation, four dynamic power convertor (DPC) technologies were selected for design and fabrication of a prototype dynamic convertor. One lesson learned from the Advanced Stirling Radioisotope Generator (ASRG) project is that controller development should start early in the development of a dynamic convertor. As a result of this, NASA GRC has been utilizing hardware from past Stirling convertor projects including that of the ASRG to support controller development for the DPC's. NASA GRC has developed a strong knowledge base on both analog and digital Stirling dynamic power convertor controllers and will continue to expand and apply that knowledge to the four DPC's. Over the past 15 years, controllers were developed in-house at GRC, at Lockheed Martin Coherent Technologies (LMCT) and by the Johns Hopkins University/Applied Physics Laboratory (JHU/APL). Various generations of the controllers, have been developed as lessons were learned through various component and system level tests. Some of the tests performed were fault tolerance, qualification vibration level, electromagnetic interference, Radioisotope Power System Systems Integration (RSIL) tests, and extended operation. The fault tolerance test characterized the controller's ability to handle various fault conditions, including high or low bus power consumptions, total open load or short circuit, and replacing a failed controller card while the backup maintains control of the ASC. The vibration test confirms the controller's ability to control an ASC during launch. The EMI test characterized the AC and DC magnetic and electric fields emitted by the single ASC and if the controller has an impact on the radiated EMI. RSIL testing provided insight into the electrical interactions between the representative RPS, its associated control schemes, and realistic electric system loads. The extended operation test allows data to be collected over a period of thousands of hours to obtain long term performance data of the system. This paper describes the history of controller development at NASA GRC, tests performed on these controllers, and lessons learned.

Dugala, Gina M.↗
NASA NTRS2000

Human orientation and movement control in weightless and artificial gravity environments

Our goal is to summarize what has been learned from studies of human movement and orientation control in weightless conditions. An understanding of the physics of weightlessness is essential to appreciate the dramatic consequences of the absence of continuous contact forces on orientation and posture. Eye, head, arm, leg, and whole body movements are discussed, but only experiments whose results seem relatively incontrovertible are included. Emphasis is placed on distinguishing between virtually immediate adaptive compensations to weightlessness and those with longer time courses. The limitations and difficulties of performing experiments in weightless conditions are highlighted. We stress that when astronauts and cosmonauts return from extended space flight they do so with both physical "plant" and neural "controller" structurally and functionally altered. Recent developments in adapting humans to artificial gravity conditions are discussed as a way of maintaining sensory-motor and structural integrity in extended missions involving transitions between different force environments.

NASA Discipline Neuroscience↗
NASA NTRS2009

Integrated Risk and Knowledge Management Program -- IRKM-P

The NASA Exploration Systems Mission Directorate (ESMD) IRKM-P tightly couples risk management and knowledge management processes and tools to produce an effective "modern" work environment. IRKM-P objectives include: (1) to learn lessons from past and current programs (Apollo, Space Shuttle, and the International Space Station); (2) to generate and share new engineering design, operations, and management best practices through preexisting Continuous Risk Management (CRM) procedures and knowledge-management practices; and (3) to infuse those lessons and best practices into current activities. The conceptual framework of the IRKM-P is based on the assumption that risks highlight potential knowledge gaps that might be mitigated through one or more knowledge management practices or artifacts. These same risks also serve as cues for collection of knowledge particularly, knowledge of technical or programmatic challenges that might recur.

Lengyel, David M.↗
NASA NTRSDate not supplied

Datascope to Enable Earth Independent Medical Operations (EIMO)

BACKGROUND: NASA has amassed sixty years of knowledge and experience relevant to maintenance of crew health and performance in low earth orbit. The Apollo Program introduced the importance of ensuring progressively autonomous operational capability. Earth Independent Medical Operations (EIMO) will require a gradual shift in the balance of medical responsibility, management, and authority from terrestrial to space-based assets. Terrestrial assets will continue to be essential for pre-mission screening and planning in addition to maintenance of crew health and performance. However, new capabilities are needed to enable EIMO and the amount of data required to support these systems, and mitigate the impacts of data transmission delays and reduced bandwidth coupled with lack of cloud-like resources and on-board computing capacity that is currently unclear or operationally insufficient. OVERVIEW: The overall goal of EIMO is to develop artificial intelligence (AI)-based solutions to analyze crew health and performance data utilizing a clinical decision support system (CDSS) to provide crew medical officers (CMO) with the equivalent of real-time, on-board medical consults. The EIMO ecosystem is envisioned as a “system of systems” where embedded reference databases and real-time data streams from multiple input vectors continuously and seamlessly assess crew health and performance. EIMO will be designed to make recommendations to the CMO using multi-modal AI-based natural language processing and machine learning methods with interoperability to push/pull data within and between multiple vehicle and habitat architectures. DISCUSSION: Data flows and storage/retrieval capacity are severely constrained during space missions and the challenges will become even greater during exploration missions. Just as each past program from Mercury to the International Space Station (ISS) required rethinking the interaction between ground-based controllers and space-based crew, so too will future missions to the Moon and Mars. While the NASA High-Performance Spaceflight Computing Processor project aims to increase computational capacity by 100 times over current spaceflight computers, the projected deliverable still lags considerably behind what will be needed to enable an AI-driven CDSS. Restrictions in processing speed and data storage capacity, coupled with transmission bottlenecks and delays, necessitate definition and optimization of an integrated data architecture to enable a progressively autonomous medical capability.

Medical operations↗
NASA NTRSDate not supplied

ISS EVA 23 Lessons Learned

Roughly 44 minutes into EVA 23, ESA Astronaut Luca Parmitano reported water inside his helmet on the back of his head. The EVA ground team, Luca and his spacewalking partner Chris Cassidy were unable to identify the water’s source. As they continued to work, the amount of water in Luca’s helmet increased and eventually migrated from the back of his head onto his face. EVA 23 was terminated early and the crew safely returned to the Space Station. What started as a normal EVA day on ISS became one of the most serious mishaps in the history of Spacewalking.

Chris Hansen↗
DOE OSTI2025

Phase transitions and dimensional cross-over in layered confined solids

The nature of solid phases and cross-over of order–disorder phase transitions from two-dimensional (2D) layers to three-dimensional (3D) bulk in confined atomic systems remain largely unexplained. To this end, we consider noble gases and aluminum confined between graphene sheets at different pressures and temperatures. Using crystal structure search methods and molecular dynamics based on machine-learned potentials with quantum-mechanical accuracy, we identify structures of multilayer confined solids that deviate from simple close packing. Upon heating, we find that confined 2D monolayers melt according to the two-step continuous Kosterlitz–Thouless–Halperin–Nelson–Young theory. However, multilayer solids transition continuously into an intermediate layered-hexatic phase before melting discontinuously into an isotropic liquid. This intermediate phase persists at least up to 12 layers studied here. This change can be qualitatively understood based on the cross-over from 2D topological defects toward 3D ones during melting as the number of layers increases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning

Reconstructing 3D granular microstructures within volumes of arbitrary geometries from limited 2D image data is crucial for predicting the material properties, as well as performances of structural components accounting for material microstructural effects. We present a novel generative learning framework that enables exascale reconstruction of granular microstructures within complex 3D geometric volumes. Building upon existing transfer learning techniques using pre-trained convolutional neural networks (CNN), we introduce several key innovations to overcome the difficulties inherent in arbitrary geometries. Our framework incorporates periodic boundary conditions using circular padding techniques, ensuring continuity and representativeness of the reconstructed microstructures. We also introduce a novel seamless transition reconstruction (STR) method that creates statistically equivalent transition zones to integrate multiple pre-existing 3D microstructure volumes. Based on STR, we propose a cost-effective strategy for reconstructing microstructures within complex geometric volumes, minimizing computational waste. Validation through numerical experiments using kinetic Monte Carlo simulations demonstrates accurate reproduction of grain statistics, including grain size distributions and morphology. A case study involving the reconstruction of a 4-blade propeller microstructure illustrates the method’s capability to efficiently handle complex geometries. In conclusion, the proposed framework significantly reduces computational demands while maintaining high reconstruction quality, paving the way for scalable microstructure reconstruction in materials design and analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

DASEventNet: AI‐Based Microseismic Detection on Distributed Acoustic Sensing Data From the Utah FORGE Well 16A (78)‐32 Hydraulic Stimulation

Abstract Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)‐32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision‐making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short‐Term Average/Long‐Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long‐term, real‐time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.

15 GEOTHERMAL ENERGY↗
NASA NTRS2011

International Space Station (ISS) Risk Reduction Activities

As the assembly of the ISS nears completion, it is worthwhile to step back and review some of the actions pursued by the Program in recent years to reduce risk and enhance the safety and health of ISS crewmembers, visitors, and space flight participants. While the ISS requirements and initial design were intended to provide the best practicable levels of safety, it is always possible to reduce risk -- given the determination and commitment to do so. The following is a summary of some of the steps taken by the ISS Program Manager, by our International Partners, by hardware and software designers, by operational specialists, and by safety personnel to continuously enhance the safety of the ISS. While decades of work went into developing the ISS requirements, there are many things in a Program like the ISS that can only be learned through actual operational experience. These risk reduction activities can be divided into roughly three categories: (1) Areas that were initially noncompliant which have subsequently been brought into compliance or near compliance (i.e., Micrometeoroid and Orbital Debris [MMOD] protection, acoustics) (2) Areas where initial design requirements were eventually considered inadequate and were subsequently augmented (i.e., Toxicity Level 4 materials, emergency hardware and procedures) (3) Areas where risks were initially underestimated, and have subsequently been addressed through additional mitigation (i.e., Extravehicular Activity [EVA] sharp edges, plasma shock hazards) Due to the hard work and cooperation of many parties working together across the span of nearly a decade, the ISS is now a safer and healthier environment for our crew, in many cases exceeding the risk reduction targets inherent in the intent of the original design. It will provide a safe and stable platform for utilization and discovery.

Fodroci, Michael↗
NASA NTRS2011

Next Steps in the Evolution of Human Spaceflight Training

Train before you fly has always been a watchword at NASA, and consequently, NASA has been conducting training for human spaceflight missions for longer than it has been involved in the actual conduct of human spaceflight missions. Throughout that time, NASA s approach to human spaceflight training has continuously evolved to keep pace with the technology of the modern world, but the approach to training itself has not changed significantly. Today, there are more tools and technologies that enable learning than ever before. This paper intends to review the challenges of human spaceflight training and how modern technology and an updated approach could improve that training. The Spaceflight Training Management Office (DA7) within the Mission Operations Directorate (MOD) has been investigating the current training of instructors, flight controllers and astronauts in order to identify where a new approach to training and training management may be necessary to improve the efficacy of the training provided. Through this investigation, the DA7 team has identified potential areas of improvement within International Space Station (ISS) training in a wide range of areas, including the delivery of training, the structure of the training program, the concept of what is considered training, and the management of that training. The ISS is an operational program with an established training paradigm. As such, the implementation of these concepts will be met with several challenges that may prevent or preclude them from being adopted. These challenges include demonstrating return-on-investment (ROI) and overcoming cultural or technological obstacles. This report will delve into the possible improvement areas for training, the future training concepts that are being considered, and the challenges associated with implementation. The paper will include concepts for utilization of Web 2.0 technologies, electronic learning, digital media, and other technologies in the development, management, and conduct of human spaceflight training.

Balmain, Clint↗
NASA NTRS2023

Information Systems Technology for NASA Earth Systems Digital Twins (ESDT)

The term “Digital Twin” was first used in 2002 for product lifecycle management. Since then, Digital Twin concepts have been proposed in various domains until very recently for Earth Science. For NASA’s Advanced Information Systems Technology (AIST) Program, an Earth System Digital Twin (ESDT) is defined as composed of three components: 1. A Digital Replica, i.e., an integrated picture of the past and current states of Earth systems 2. Forecasting capabilities, providing an integrated picture of how Earth systems will evolve in the future from the current state 3. Impact Assessment capabilities, providing an integrated picture of how Earth systems could evolve under different hypothetical what-if scenarios. Developing such a vision will require technologies related to: integrating continuous observations from various disparate sources; developing frameworks that builds on inter-connected models; improving the speed and accuracy of integrated prediction, analysis and visualization capabilities (e.g., by using machine learning); and utilizing causality and uncertainty quantification to improve our understanding of the evolution of Earth Science systems as a function of their interactions with other Earth and human systems. In addition, AIST is also investigating interoperability standards to federate multiple Digital Twins, as well as computational resources required by those systems.

Earth Science Remote Sensing; Information Systems↗
NASA NTRS2012

Antarctic Exploration Parallels for Future Human Planetary Exploration: Science Operations Lessons Learned, Planning, and Equipment Capabilities for Long Range, Long Duration Traverses

The purpose for this workshop can be summed up by the question: Are there relevant analogs to planetary (meaning the Moon and Mars) to be found in polar exploration on Earth? The answer in my opinion is yes or else there would be no reason for this workshop. However, I think some background information would be useful to provide a context for my opinion on this matter. As all of you are probably aware, NASA has been set on a path that, in its current form, will eventually lead to putting human crews on the surface of the Moon and Mars for extended (months to years) in duration. For the past 50 V 60 years, starting not long after the end of World War II, exploration of the Antarctic has accumulated a significant body of experience that is highly analogous to our anticipated activities on the Moon and Mars. This relevant experience base includes: h Long duration (1 year and 2 year) continuous deployments by single crews, h Established a substantial outpost with a single deployment event to support these crews, h Carried out long distance (100 to 1000 kilometer) traverses, with and without intermediate support h Equipment and processes evolved based on lessons learned h International cooperative missions This is not a new or original thought; many people within NASA, including the most recent two NASA Administrators, have commented on the recognizable parallels between exploration in the Antarctic and on the Moon or Mars. But given that level of recognition, relatively little has been done, that I am aware of, to encourage these two exploration communities to collaborate in a significant way. [Slide 4] I will return to NASA s plans and the parallels with Antarctic traverses in a moment, but I want to spend a moment to explain the objective of this workshop and the anticipated products. We have two full days set aside for this workshop. This first day will be taken up with a series of presentations prepared by individuals with experience that extends back as far as the late 1940s and includes contemporary experience. The people presenting bring a variety of points of view, including not only U.S. but international, although most, if not all, have collaborated on international teams. The second day will consist of a series of small focused group interactions centered on those elements likely to be needed for traverse missions, such as mobility, habitation, and extravehicular activity (EVA, aka space suits). Our invited participants will be talking with people that specialize in these elements so that we can foster more direct interaction and exchange of experiences between these two exploration communities. After the workshop we will be preparing a report documenting these presentations and the essence of the focused interactions.

Hoffman, Stephen J.↗
NASA NTRS2021

Machine Learning Based Path Planning for Improved Rover Navigation

Enhanced AutoNav (ENav), the baseline surface navigation software for NASA’s Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software.

Yue, Yisong↗
NASA NTRSDate not supplied

Dragonfly Rotor Optimization using Machine Learning Applied to an OVERFLOW Generated Airfoil Database

NASA’s 4th New Frontiers Mission is the Titan Dragonfly relocatable lander. This coaxial quadrotor vehicle will be launched on a rocket to Titan in 2028. Following a gravity assisted Earth flyby and an approximate 6-year transit, Dragonfly will enter the Titan atmosphere around 2034 with the goal of exploring Titan’s pre-biotic chemistry and habitability. The multirotor design for this unique application has continually evolved since 2016 with constraints such as Titan’s cryogenic atmosphere at 95 Kelvin (-288 F), gravity 14% that of Earth’s, atmospheric density 440% of standard sea-level air, and the inability to test the entire system together under all these conditions until the first flight on Titan. This paper focuses on rotor design aspects of the Dragonfly lander and introduces a novel framework for multirotor design optimization considering multiple flight conditions. The methodology leverages machine learning methods and is demonstrated in the context of Dragonfly. A new OVERFLOW Machine Learning Airfoil Performance (PALMO) database is first presented. PALMO is then wrapped inside a Bayesian optimization framework and applied to a 4-rotor system (one side of the Dragonfly lander). Training data is generated on each iteration of the optimization using the CAMRAD-II comprehensive analysis software to evaluate successive rotor designs in multiple relevant flight conditions. An optimal design for the 4-rotor system was found with approximately 900 rotor designs analyzed in CAMRAD-II, which required 9 million queries of the PALMO surrogate models. This demonstration case evaluated 10,000,000 potential candidate rotor designs in 5.5 hours on 114 CPU cores using uniform inflow, and in 27.8 hours using the prescribed wake model. This work thus enables mid-fidelity rotor design optimization without requiring access to high-performance computing.

Dragonfly↗
DOE OSTI2026

A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications

Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information

Silber, Israel↗