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Multi-Agent Swarm State of the Art Report

The Next-Generation Multi-Agent Swarm (NGS) Study conducted by NASA’s Ames Research Center for NASA’s Space Technology Mission Directorate (STMD) will develop a comprehensive understanding of emerging multi-agent swarm capabilities. The study aims to identify existing swarm capabilities and asses their potential for persistent lunar space situational awareness, surface monitoring, and distributed autonomy demonstrations. A key objective is to inform the design of a next-generation multi-agent swarm that can perform autonomous distributed remote sensing, position, navigation, and timing (PNT) services, automated deployment that leverages autonomy, edge computing, and interoperable networking to enable cooperative operations without the need for immediate human operation. This study will address specific shortfalls identified by STMD, including intelligent multi-agent constellations, autonomy, edge computation, position, navigation, and timing for small spacecraft, small spacecraft propulsion, and space situational awareness (1625, 1438, 1433, 1557, 1431, 1430, 1589). The NASA Ames Mission Design Center (MDC) will provide subject matter expertise to support systems engineering trades, while experts in autonomy and spacecraft swarms in NASA’s Intelligent Systems Division will lead the study and focus on identifying emerging next-generation swarm capabilities. The study objectives include: capturing the current state-of-the-art for multi-agent swarm capabilities, evaluating technologies and creating technology roadmaps, and developing at least one new technology demonstration mission concept. This initial NGS study report surveys the current state of the art in technology areas relevant for the next-generation multi-agent swarm design. Our primary focus is on surveying relevant deployed space systems1, supplemented with selective analysis of relevant proposed missions and technology developments that have yet to fly.

agent

Mercury Corrosion Loop Testing Program

The Mercury Corrosion Loop Testing Program continued through the second quarter. Materials to be tested in the 10 forced-convection test loops were selected; the materials are: Haynes 25, 9 Cr-1Mo alloy clad with Type 316 stainless steel, AM 350 stainless steel, and columbium (niobium) clad with Type 316 stainless steel. The eight-test-cell facility was completed and all utilities were installed. Pneumatic and electronic instrumentation for all test cells was installed and calibrated. Fabrication was initiated on the first four Haynes 25 loops. The first loop was 85% assembled at the end of the quarter. A centrifugal pump made from Type 316 stainless steel was tested; bearing modifications were made to improve operation.

Corrosion

NASA Agile Community of Practice 2024-2026 Report

This 2024-2026 report provides a summary of the products and activities executed by the NASA Agile Community of Practice (CoP) during its second and third years. Building on the foundation established in its inaugural year, the CoP continued to advance Agile values and principles across NASA centers. The report highlights key initiatives, including specialized framework training, AI integration in Agile toolkits, and active participation in agency-wide project management and systems engineering workshops.

Agile

Astrophysical science with a spaceborne photometric telescope

The FRESIP Project (FRequency of Earth-Sized Inner Planets) is currently under study at NASA Ames Research Center. The goal of FRESIP is the measurement of the frequency of Earth-sized extra-solar planets in inner orbits via the photometric signature of a transit event. This will be accomplished with a spaceborne telescope/photometer capable of photometric precision of two parts in 100,000 at a magnitude of m(sub v) = 12.5. To achieve the maximum scientific value from the FRESIP mission, an astrophysical science workshop was held at the SETI Institute in Mountain View, California, November 11-12, 1993. Workshop participants were invited as experts in their field of astrophysical research and discussed the astrophysical science that can be achieved within the context of the FRESIP mission.

Granados, Arno F.

Improving Adhesive Bondline Time of Flight Predictions During Autoclave Cure Utilizing Machine Learning

Composite materials are increasingly being used in aerospace applications due to their superior strength-to-weight ratio compared to commonly used metals. A current limitation to widespread adoption is the certification of adhesively bonded joints. One approach to improving adhesive bonding in composites is accurately measuring the thickness of adhesive bondlines in composite laminates. Precise bondline thickness control is essential for aerospace applications where adhesive layer thickness directly affects joint fracture properties and structural performance. This study focused on implementing machine learning techniques to determine the ultrasonic time of flight (directly correlated to thickness) in adhesive bondlines throughout autoclave cure cycles. A high-temperature (use up to 180°C) ultrasonic scanning system was deployed in an autoclave to provide time of flight data through composite panels. Three experiments were conducted on the curing of 305 mm × 305 mm unidirectional composite panels. In the first experiment, a piecewise function was fit for the temperature correction factor to account for changing autoclave temperatures. Due to deficiencies in the first calibration experiment, a second experiment was run, and the results were used to train a machine learning model. The revised experiment, in combination with the machine learning model, significantly increased the accuracy of the bondline time of flight predictions (~14% error reduced to <1%). Data was processed using the Regression Learner Application in MATLAB®, with a Support Vector Machine selected for the model. The result was a machine learning algorithm capable of reliably quantifying ultrasonic time of flight through adhesive bondlines. The third experiment provided independent test data for the machine learning model, demonstrating that the model produces accurate predictions from data beyond its training set.

Machine Learning

Americium-Fuelled Radioisotope Stirling Generator Elegant Breadboard

High power solutions for spacecraft are becoming increasingly sought after in the new era of space exploration, with energy intensive in-situ lunar surface exploration becoming a key part of the roadmap for major space agencies, as well as commercial companies. Nuclear power provides the most feasible solution to achieve continued operation through the long lunar night, and in extended periods in Permanently Shadowed Regions (PSRs). Americium-241 (241Am) is used as the fuel of choice in European Radioisotope Power Systems (RPS). The University of Leicester have developed a number of systems using this fuel type, including a 200 Wth heat source. Building on existing international partnerships, the University of Leicester is continuing to work with NASA Glenn Research Center (GRC) to maximise the electrical output that can be generated from the heat source by progressing the radioisotope-Stirling generator concept to elegant breadboard level, building on the long heritage of dynamic power conversion developments at NASA Glenn. Presented in the following conference paper is the next iteration of the Americium-fuelled Radioisotope Stirling Generator (Am-RSG) Elegant Breadboard, following on from a successful technology demonstration design conducted at NASA Glenn in early 2025. The design laid-out in this paper is a more flight-like version of the technology demonstrator, and is intended to pair with the next generation of Stirling convertor technology, the SunPower Robust Stirling Converter (SRSC). The SRSC is able to operate more efficiently at lower thermal power levels than the previous Advanced Stirling Convertor (ASC), making it more suitable for the 241Am system.

Lunar

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen

Design Criteria for Integrated Flight/Propulsion Control Systems for STOVL Fighter Aircraft

As part of NASA's program to develop technology for short takeoff and vertical landing (STOVL) fighter aircraft, control system designs have been developed for a conceptual STOVL aircraft. This aircraft is representative of the class of mixed-flow remote-lift concepts that was identified as the preferred design approach by the US/UK STOVL Joint Assessment and Ranking Team. The control system designs have been evaluated throughout the powered-lift flight envelope on Ames Research Center's Vertical Motion Simulator. Items assessed in the control system evaluation were: maximum control power used in transition and vertical flight, control system dynamic response associated with thrust transfer for attitude control, thrust margin in the presence of ground effect and hot gas ingestion, and dynamic thrust response for the engine core. Effects of wind, turbulence, and ship airwake disturbances are incorporated in the evaluation. Results provide the basis for a reassessment of existing flying qualities design criteria applied to STOVL aircraft.

James A Franklin

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra

Deployment and retrieval mechanism redesigned for Spartan spacecraft on the STS

The Spartan Release Engage Mechanism (REM) is a system designed to restrain the Spartan spacecraft during Space Transportation System (STS) launch and landing. The mechanism is designed to allow deployment and retrieval of the Spartan free flyer spacecraft from the shuttle payload bay. Because current Spartan spacecraft payloads are much heavier than payloads intended for the original REM, an extensive redesign, analysis, and test program was necessary. Also, increased emphasis on safety in the post-Challenger era prompted a reevaluation of possible failures. Much of the design effort focused on improving the latch mechanism gearbox. Key concerns were effective gear lubrication, thermal gradients at the gearbox mounts, operation at thermal extremes, and gear-train failure contingencies. Increased concern for reliability led to the design of an Extra Vehicular Activity (EVA) backup latch system.

Greg Galloway

High-Resolution Global Ocean Simulation in the SWOT ERA: Introducing ECCO Llc4320v2

The Surface Water and Ocean Topography (SWOT) mission is opening up new opportunities to evaluate and drive improvement in km-scale global ocean and atmosphere-ocean models via its simultaneous swath observations of sea-surface height, wind speed, and significant wave height down to 2 km scales globally. Motivated by this, we have initiated a collaborative effort at NASA Ames to run a new global ocean-sea-ice simulation in MITgcm at a nominal 1/48-deg resolution of the year 2023 that enables direct comparisons with SWOT observations. New features relative to a prior simulation at a similar resolution of the year 2012 include much more realistic tidal forcing, thermodynamic coupling in ice-shelf cavities, high-frequency hourly atmospheric forcing, much improved river runoff, double the vertical resolution in the deep ocean (173 vs 90 levels), updated bathymetry including improvements in finescale bathymetry derived from SWOT, and updated vertical mixing scheme parameters that improve the equatorial Pacific circulation.

ECCO

Piloting Vertical Flight Aircraft: A Conference on Flying Qualities and Human Factors

This document contains papers from a specialists' meeting entitled "Piloting Vertical Flight Aircraft. A Conference on Flying Qualities and Human Factors." The conference was co-sponsored by the American Helicopter Society--San Francisco Bay Chapter, and the NASA Ames Research Center. It was held January 20-22, 1993 at the Sheraton Hotel Fisherman's Wharf, San Francisco, California. Vertical flight aircraft, including helicopters and a variety of Vertical Takeoff and Landing (VTOL) concepts, place unique requirements on human perception, control, and performance for the conduct of their design missions. The intent of this conference was to examine, for these vehicles, advances in: (1) design of flight control systems for ADS-33C standards; (2) assessment of human factors influences of cockpit displays and operational procedures; (3) development of VTOL design and operational criteria; and (4) development of theoretical methods or models for predicting pilot-vehicle performance and mission suitability. Recognizing that human capabilities and limitations form an integral aspect of the operations for these classes of vehicles, a secondary goal of the conference was to provide an initial venue for enhanced interaction between human factors and handling qualities specialists.

Helicopter

NASA Systems Autonomy Demonstration Project: Advanced Automation Demonstration of Space Station Freedom Thermal Control System

The NASA Systems Autonomy Demonstration Project (SADP) was initiated in response to Congressional interest in Space station automation technology demonstration. The SADP is a joint cooperative effort between Ames Research Center (ARC) and Johnson Space Center (JSC) to demonstrate advanced automation technology feasibility using the Space Station Freedom Thermal Control System (TCS) test bed. A model-based expert system and its operator interface were developed by knowledge engineers, AI researchers, and human factors researchers at ARC working with the domain experts and system integration engineers at JSC. Its target application is a prototype heat acquisition and transport subsystem of a space station TCS. The demonstration is scheduled to be conducted at JSC in August, 1989. The demonstration will consist of a detailed test of the ability of the Thermal Expert System to conduct real time normal operations (start-up, set point changes, shut-down) and to conduct fault detection, isolation, and recovery (FDIR) on the test article. The FDIR will be conducted by injecting ten component level failures that will manifest themselves as seven different system level faults. Here, the SADP goals, are described as well as the Thermal Control Expert System that has been developed for demonstration.

Jeffrey Dominick

A Review of Direct Ink Writing of Polymer Derived Ceramics

With the growing demand for materials capable of withstanding extreme temperatures and pressures, ceramic components with exceptional corrosion resistance and reliable mechanical properties have experienced a significant surge in demand. However, traditional ceramic forming methods involve high-temperatures and energy-intensive processes that often struggle to produce complex parts or composites efficiently. Polymer-Derived Ceramics (PDCs) offer a transformative solution by using polymeric precursors that can be converted into a wide variety of silicon-based and non-silicon-based ceramics through heat treatment. The polymeric nature of PDC precursors enables the fabrication of geometrically intricate components using conventional polymer-forming techniques at significantly lower processing temperatures. Furthermore, PDCs are well-suited for additive manufacturing (AM), allowing the production of complex structural and functional components through cost-effective, low-temperature processes. By leveraging the diverse properties of PDC materials – each with unique advantages and limitations – manufacturers can optimise performance for specific applications. This review provides an overview of the types of PDCs developed to date and their broad range of applications. Specifically, it delves into the Direct Ink Writing (DIW) process, exploring its rheological requirements and the critical role of fillers in tailoring the rheological properties of polymeric precursors to meet the specific demands of DIW.

Victoria Bishop

Application of System-Theoretic Process Analysis (STPA) to a NASA Concept Electric Aircraft Battery System

System-Theoretic Process Analysis (STPA) is a systems-based hazard analysis method that identifies unsafe interactions and control deficiencies in complex systems but has been rarely used for NASA programs in favor of more well-established hazard analyses. To evaluate its applicability, a NASA Safety and Mission Assurance (SMA) team applied the STPA method to an early-stage hybrid electrified aircraft concept, focusing on the energy storage system and electric powertrain. Objectives include assessing STPA’s value relative to traditional methods and its suitability for early design phases. Activities include team training, system review, detailed STPA execution, and comparison with traditional analyses. Findings show STPA provides a structured, comprehensive hazard evaluation and can identify additional risks by expanding analysis boundaries. However, traditional methods can yield similar results when applied rigorously, though they typically require more mature designs. Overall, STPA is a valuable addition, particularly for early development, informing safety requirements and supporting preliminary hazard analyses. Further pilot applications are recommended.

Electric Aviation

Shuttle-Mir [Мир-Шаттл] Supplement

This is a companion to an illustrated history book with the same title. This set of electronic files includes the full text and images in the book, as well as additional material. Interviews, photographs, and biographies of the U.S. Astronauts, cosmonauts, and team members for the Shuttle-Mir Program are available. STS Mission Summaries for each mission involved can be viewed, including launch and landing details, crew lists, and mission highlights. Photographs and videos from each mission are included, as well as diagrams of different spacecraft, and computer-generated animations of the Mir deorbit, collision, and flyaround. Additional documents include mission status reports, published documents, news releases, personal letters, and oral histories. The experiments carried out on Mir are described, highlighting combustion and fluid physics research, life in microgravity, and research of the development of the solar system. The focus on improving space technology and planning for the International Space Station is explained. The main features of the book itself include: (1) Training and Operations; (2) Long Duration Psychology; (3) Bilingual Blues; and (4) Earth Observations.

Astronautics

Trajectory Engineering with Modular Patched Conics for Entry Systems and TPS (TEMPEST)

Brief Presenter Biography (35 word limit): Bohdan Wesely is an Aerospace Engineer in the Entry Systems and Technology Division at Ames. He has worked on a variety of projects for NASA including integrated TPS (thermal protection system) flight hardware deliveries and testing services for commercial partners. Introduction: TEMPEST is a new trajectory analysis framework that is designed to fill the gap between dedicated flight mechanics tools and aerothermal and TPS sizing tools. The project started as an SJSU master’s thesis and has since evolved into a general conceptual design tool capable of studying a wide variety of entry problems. Development is ongoing in the Entry Systems and Technology Division at NASA ARC. Why TEMPEST: Space missions involving entry into a planetary atmosphere involve a series of unique requirements across multiple disciplines. Whether it is traditional entry descent and landing (EDL), or aerocapture, the vehicle must navigate to its target landing location or orbit state, and the TPS must protect the payload during entry. The design process typically involves iterative handoffs between various flight mechanics, flow solver, and material response level tools. During the early conceptual phase, a wide variety of feasible trajectories are simulated in a Monte Carlo scenario which broadly satisfy the mission or landing requirements. Next, computational fluid dynamics (CFD), direct simulation Monte Carlo (DSMC), and other flow solver analyses are performed at various key trajectory points to generate an aero-database, heating and TPS design requirements also emerge at this stage. At this point, with updated aerodynamics from the various flow solvers, trajectories can be re-run, this in turn can change the required freestream conditions for the CFD tools, and as a project progresses, these analyses converge, and uncertainty is reduced. However, there is always a “hand-off” occurring between two inherently coupled phenomena. Analysis Description: One of the goals with TEMPEST is to use a variety of first principles estimation methods coupled with an atmosphere model to predict vehicle aerothermodynamics across the entire flight regime while propagating a 3 or 6 degree of freedom (DoF) trajectory. Aerodynamics methods include modified Newtonian, Maxwell and Cercignani- Lampis-Lord (CLL) for continuum, transitional, and free molecular flow regimes. Aerothermodynamics include boundary layer and reference enthalpy methods, and Mutation++ for non-equilibrium chemistry modeling. TEMPEST is also capable of stitching multiple trajectory segments together to study mission scenarios like multi-pass aerocapture and aero-gravity assists. Most of the program is implemented in MATLAB using modern system objects, it relies on several C++ shared libraries for supporting tools like Gmsh, the Global Reference Atmospheric Model (GRAM), and Mutation++. The various first principles aerothermal estimation methods are discretized across either a structured axisymmetric panel mesh or an unstructured tri-mesh generated from an open-source tool such as Gmsh, this allows solutions on the same mesh to be compared across tools such as CB-Aero. CFD Coupling. A physics-aware, gaussian process CFD anchoring scheme is proposed to adjust the various first principles methods as a CFD database is populated. One goal for this anchoring module is to inform the project where CFD should be run. Full knowledge of the entire trajectory, atmosphere, and aerothermodynamics allows for easier identification of high sensitivity areas and uncertainty quantification. While the first principles effects are well known and proven accurate in existing tools such as CB- Aero and Cart3D, a physics aware CFD anchoring scheme increases tool credibility across a project lifecycle. Material Response Modeling. Correct TPS sizing is critical for optimizing mass for science payloads and ensuring mission success. The process typically involves a thermal analysis along the trajectory with surface heating environments as a boundary condition. Several design constraints are maximum bondline temperature and maximum recession with various margining techniques. The material response tool FIAT, developed out of NASA Ames, is currently being integrated into the TEMPEST environment. TPS recession, shape change, mass loss, and mass property alteration are all factors that can perturb an entry trajectory. For missions like Mars 2020, recession was minimal and was safely handled separately as a post process. For missions such as Jupiter Galileo with a high TPS mass fraction or asteroid entries, recession plays a major role. The proposed fully coupled scheme is to use an epoch-based approach where the trajectory integration is halted after a recession threshold, the energy balance and FIAT are solved at each panel, the mesh, aerodynamics, and mass properties are updated, and the trajectory continues. Several computational tradeoffs have been made during the development of TEMPEST to limit the cost of a single trajectory and preserve its utility as a conceptual, rapid iteration tool. Conclusion: Development of TEMPEST is ongoing and the project is still in its infancy. This talk aims to showcase its unique capabilities to support future NASA entry systems missions.

Bohdan O Wesely

Three Dimensionally Woven Mid-Density Carbon Phenolic (3MDCP) is a Novel Single Piece Ablative TPS for Extreme Entry Environments

Three Dimensionally Woven Mid-Density Carbon Phenolic (3MDCP) is robust, single piece, carbon phenolic ablative thermal protection system under development at NASA Ames Research Center initially for the Mars Sample Return Earth Entry System (MSR EES). The MSR EES requirements drove the need for TPS with no seams, capable of surviving the highest entry conditions for any NASA Earth return capsule with heat fluxes >2000 W/cm2 and pressures >1.5 atmospheres. To produce 3MDCP required development of new weaving infrastructure to enable weaving of preforms large enough to form into a single piece heatshield. It required development of forming techniques to transform a flat woven panel into a sphere cone shape and enhanced infusion processes to support larger scale infusion of resin into the formed preforms. A rigorous performance testing campaign was conducted to develop and validate the materials thermal response model used to determine the required material thickness and to demonstrate the material can survive the extreme entry conditions. The end of the development effort (end of FY26) will result in a TPS at Technical Readiness Level (TRL) 6 and Manufacturing Readiness Level (MRL) 6+ for the MSR EES mission and a mature system ready to support other missions. This poster will provide a snapshot of where 3MDCP is in its development phase.

Ablator