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At least 415 records · Page 23

Non-Nuclear Testing of Compact Reactor Technologies at NASA MSFC

Safe, reliable, compact, autonomous, long-life fission systems have numerous potential applications, both terrestrially and in space. Technologies and facilities developed in support of these systems could be useful to a variety of concepts. At moderate power levels, fission systems can be designed to operate for decades without the need for refueling. In addition, fast neutron damage to cladding and structural materials can be maintained at an acceptable level. Nuclear design codes have advanced to the stage where high confidence in the behavior and performance of a system can be achieved prior to initial testing. To help ensure reactor affordability, an optimal strategy must be devised for development and qualification. That strategy typically involves a combination of non-nuclear and nuclear testing. Non-nuclear testing is particularly useful for concepts in which nuclear operating characteristics are well understood and nuclear effects such as burnup and radiation damage are not likely to be significant. To be mass efficient, a SFPS must operate at higher coolant temperatures and use different types of power conversion than typical terrestrial reactors. The primary reason is the difficulty in rejecting excess heat to space. Although many options exist, NASA s current reference SFPS uses a fast spectrum, pumped-NaK cooled reactor coupled to a Stirling power conversion subsystem. The reference system uses technology with significant terrestrial heritage while still providing excellent performance. In addition, technologies from the SFPS system could be applicable to compact terrestrial systems. Recent non-nuclear testing at NASA s Early Flight Fission Test Facility (EFF-TF) has helped assess the viability of the reference SFPS and evaluate methods for system integration. In July, 2011 an Annular Linear Induction Pump (ALIP) provided by Idaho National Laboratory was tested at the EFF-TF to assess performance and verify suitability for use in a10 kWe technology demonstration unit (TDU). In November, 2011 testing of a 37-pin core simulator (designed in conjunction with Los Alamos National Laboratory) for use with the TDU will occur. Previous testing at the EFFTF has included the thermal and mechanical coupling of a pumped NaK loop to Stirling engines (provided by GRC). Testing related to heat pipe cooled systems, gas cooled systems, heat exchangers, and other technologies has also been performed. Integrated TDU testing will begin at GRC in 2013. Thermal simulators developed at the EFF-TF are capable of operating over the temperature and power range typically of interest to compact reactors. Small and large diameter simulators have been developed, and simulators (coupled with the facility) are able to closely match the axial and radial power profile of all potential systems of interest. A photograph of the TDU core simulator during assembly is provided in Figure 2.

Houts, Michael G.↗

Evaluation of Technology Concepts for Traffic Data Management and Relevant Audio for Datalink in Commercial Airline Flight Decks

Datalink is currently operational for departure clearances and in oceanic environments and is currently being tested in high altitude domestic enroute airspace. Interaction with even simple datalink clearances may create more workload for flight crews than the voice system they replace if not carefully designed. Datalink may also introduce additional complexity for flight crews with hundreds of uplink messages now defined for use. Finally, flight crews may lose airspace awareness and operationally relevant information that they normally pickup from Air Traffic Control (ATC) voice communications with other aircraft (i.e., “party-line” transmissions). Once again, automation may be poised to increase workload on the flight deck for incremental benefit. Datalink implementation to support future air traffic management concepts needs to be carefully considered, understanding human communication norms and especially, the change from voice- to text-based communications modality and its effect on pilot workload and situation awareness. Increasingly autonomous systems, where autonomy is designed to support human-autonomy teaming, may be suited to solve these issues. NASA is conducting research and development of increasingly autonomous systems, utilizing machine-learning algorithms seamlessly integrated with humans whereby task performance of the combined system is significantly greater than the individual components. Increasingly autonomous systems offer the potential for significantly improved levels of performance and safety that are superior to either human or automation alone. Two increasingly autonomous systems concepts - a traffic data manager and a conversational co-pilot - were developed to intelligently address the datalink issues in a complex, future state environment with significant levels of traffic. The system was tested for suitability of datalink usage for terminal airspace. The traffic data manager allowed for automated declutter of the Automatic Dependent Surveillance-Broadcast (ADS-B) display. The system determined relevant traffic for display based on machine learning algorithms trained by experienced human pilot behaviors. The conversational co-pilot provided relevant audio air traffic control messages based on context and proximity to ownship. Both systems made use of the connected aircraft concepts to provide intelligent context to determine relevancy above and beyond proximity to ownship. A human-in-the-loop test was conducted in NASA Langley Research Center’s Integration Flight Deck B-737-800 simulator to evaluate the traffic data manager and the conversational co-pilot. Twelve airline crews flew various normal and non-normal procedures and their actions and performance were recorded in response to the procedural events. This paper details the flight crew performance and evaluation during the events.

Etherington, Timothy↗

Development of Biomorphic Flyers

Biomorphic flyers have recently been demonstrated that utilize the approach described earlier in "Bio-Inspired Engineering of Exploration Systems" (NPO-21142), NASA Tech Briefs, Vol. 27, No. 5 (May 2003), page 54, to distill the principles found in successful, nature-tested mechanisms of flight control. Two types of flyers are being built, corresponding to the imaging and shepherding flyers for a biomorphic mission described earlier in "Cooperative Lander- Surface/Aerial Microflyer Missions for Mars Exploration" (NPO-30286), NASA Tech Briefs, Vol. 28, No. 5 (May 2004), page 36. The common features of these two types of flyers are that both are delta-wing airplanes incorporating bio-inspired capabilities of control, navigation, and visual search for exploration. The delta-wing design is robust to approx.40 G axial load and offers ease of stowing and packaging. The prototype that we have built recently is shown in the figure. Such levels of miniaturization and autonomous navigation are essential to enable biomorphic microflyers (<1 kg) that can be deployed in large numbers for distributed measurements and exploration of difficult terrain while avoiding hazards. Individual bio-inspired sensors that will be incorporated in a biomorphic flyer have been demonstrated recently. These sensors include a robust, lightweight (~6 g), and low-power (~40 mW) horizon sensor for flight stabilization. It integrates successfully the principles of the dragonfly ocelli. The ocelli are small eyes on the dorsal and forward regions of the heads of many insects. The ocelli are distinct from the compound eyes that are most commonly associated with insect vision. In many insects, the ocelli are little more than single-point detectors of short-wavelength light and behavioral responses to ocelli stimuli are hard to observe. The notable exception is found in dragonflies, where flight control is notably degraded by any interference with the ocellar system. Our team has discovered recently that the ocelli are a dedicated horizon sensor, with substantial optical processing and multiple spectral sensitivity. To our knowledge, this is the world s first demonstrated use of a "biomorphic ocellus" as a flight-stabilization system. The advantage of the ocelli over a similarly sized system of rate gyroscopes is that both attitude control and rate damping can be realized in one device. A full inertial unit and significant processing would otherwise be required to achieve the same effect. As a prelude to full autonomy, substantial stability augmentation is provided to the pilot at very low cost in terms of space, power, and mass. The sensor is about 40 times lighter than a comparable inertial attitude reference system. Other significant features of the biomorphic flyer shown in the figure include its ability to fly at high angles of attack ~30 and a deep wing chord which allows scaling to small size and low Reynold s number situations. Furthermore, the placement of the propulsion system near the center of gravity allows continued control authority at low speeds. These attributes make such biomorphic flyers uniquely suited to planetary and terrestrial exploration where small size and autonomous airborne operation are required.

Thakoor, Sarita↗

Autonomous Guidance of Agile Small-scale Rotorcraft

This report describes a guidance system for agile vehicles based on a hybrid closed-loop model of the vehicle dynamics. The hybrid model represents the vehicle dynamics through a combination of linear-time-invariant control modes and pre-programmed, finite-duration maneuvers. This particular hybrid structure can be realized through a control system that combines trim controllers and a maneuvering control logic. The former enable precise trajectory tracking, and the latter enables trajectories at the edge of the vehicle capabilities. The closed-loop model is much simpler than the full vehicle equations of motion, yet it can capture a broad range of dynamic behaviors. It also supports a consistent link between the physical layer and the decision-making layer. The trajectory generation was formulated as an optimization problem using mixed-integer-linear-programming. The optimization is solved in a receding horizon fashion. Several techniques to improve the computational tractability were investigate. Simulation experiments using NASA Ames 'R-50 model show that this approach fully exploits the vehicle's agility.

Mettler, Bernard↗

DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]

DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.

Gasper, Paul↗

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

36 MATERIALS SCIENCE↗

Task-level control for autonomous robots

Task-level control refers to the integration and coordination of planning, perception, and real-time control to achieve given high-level goals. Autonomous mobile robots need task-level control to effectively achieve complex tasks in uncertain, dynamic environments. This paper describes the Task Control Architecture (TCA), an implemented system that provides commonly needed constructs for task-level control. Facilities provided by TCA include distributed communication, task decomposition and sequencing, resource management, monitoring and exception handling. TCA supports a design methodology in which robot systems are developed incrementally, starting first with deliberative plans that work in nominal situations, and then layering them with reactive behaviors that monitor plan execution and handle exceptions. To further support this approach, design and analysis tools are under development to provide ways of graphically viewing the system and validating its behavior.

Simmons, Reid↗

Software Design for the Supervised Autonomous Assembly of a Tall Lunar Tower

Tall towers enable a wide-ranging set of capabilities on the lunar surface including communication, navigation, surveillance, power generation, and more. The Tall Lunar Tower project at NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of an engineering development unit to assemble a tall tower through supervised autonomous operations. In this paper, the software design for the supervised autonomous assembly of a tall lunar tower is presented. The paper includes a high-level description of the concept of operations, the agents, and an overview of the software architecture.

Moon↗

Technology Development to Support Human Health and Performance in Exploration Beyond Low Earth Orbit

In the course of defining the level of risks and mitigating the risks for exploration missions beyond low Earth orbit, NASA s Human Research Program (HRP) has identified the need for technology development in several areas. Long duration missions increase the risk of serious medical conditions due to limited options for return to Earth; no resupply; highly limited mass, power, volume; and communication delays. New space flight compatible medical capabilities required include: diagnostic imaging, oxygen concentrator, ventilator, laboratory analysis (saliva, blood, urine), kidney stone diagnosis & treatment, IV solution preparation and delivery. Maintenance of behavioral health in such an isolated, confined and extreme environment requires new sensory stimulation (e.g., virtual reality) technology. Unobtrusive monitoring of behavioral health and treatment methods are also required. Prolonged exposure to weightlessness deconditions bone, muscle, and the cardiovascular system. Novel exercise equipment or artificial gravity are necessary to prevent deconditioning. Monitoring of the degree of deconditioning is required to ensure that countermeasures are effective. New technologies are required in all the habitable volumes (e.g., suit, capsule, habitat, exploration vehicle, lander) to provide an adequate food system, and to meet human environmental standards for air, water, and surface contamination. Communication delays require the crew to be more autonomous. Onboard decision support tools that assist crew with real-time detection and diagnosis of vehicle and habitat operational anomalies will enable greater autonomy. Multi-use shield systems are required to provide shielding from solar particle events. The HRP is pursuing the development of these technologies in laboratories, flight analog environments and the ISS so that the human health and performance risks will be acceptable with the available resources.

Kundrot, C.E.↗

Advances in Medical Analytics Solutions for Autonomous Medical Operations on Long-Duration Missions

A review will be presented on the progress made under STMDGame Changing Development Program Funding towards the development of a Medical Decision Support System for augmenting crew capabilities during long-duration missions, such as Mars Transit. To create an MDSS, initial work requires acquiring images and developing models that analyze and assess the features in such medical biosensor images that support medical assessment of pathologies. For FY17, the project has focused on ultrasound images towards cardiac pathologies: namely, evaluation and assessment of pericardial effusion identification and discrimination from related pneumothorax and even bladder-induced infections that cause inflammation around the heart. This identification is substantially changed due to uncertainty due to conditions of fluid behavior under space-microgravity. This talk will present and discuss the work-to-date in this Project, recognizing conditions under which various machine learning technologies, deep-learning via convolutional neural nets, and statistical learning methods for feature identification and classification can be employed and conditioned to graphical format in preparation for attachment to an inference engine that eventually creates decision support recommendations to remote crew in a triage setting.

Medical Decision Support Systems↗

Psychophysiological Monitoring of Aerospace Crew State

As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate crewmember interactions with these systems. The success of present and future autonomous technology in exploration spaceflight is ultimately dependent upon safe and efficient interaction with the human operator. Optimal interaction is particularly important for surface missions during highly coordinated extravehicular activity (EVA), which consists of high physical and cognitive demands with limited ground support. Crew functional state may be affected by a number of variables including workload, stress, and motivation. Real-time assessments of crew state that do not require a crewmember’s time and attention to complete will be especially important to assess operational performance and behavioral health during flight. In response to the need for objective, passive assessment of crew state, the aim of this work is to develop an accurate and precise prediction model of human functional state for surface EVA using multi-modal psychophysiological sensing. The psychophysiological monitoring approach relies on extracting a set of features from physiological signals and using these features to classify an operator’s cognitive state. This work aims to compile a non-invasive sensor suite to collect physiological data in real-time. Training data during cognitive and more complex functional tasks will be used to develop a classifier to discriminate high and low cognitive workload crew states. The classifier will then be tested in an operationally relevant EVA simulation to predict cognitive workload over time. Once a crew state is determined, further research into specific countermeasures, such as decision support systems, would be necessary to optimize the automation and improve crew state and operational performance.

Wusk, Grace C.↗

Principles for Architecting Autonomous Systems

This paper distills principles for developing autonomous systems based on experience and lessons learned from past efforts. The purpose of these principles is to establish a common understanding and knowledge of architectural elements to guide the development of next-generation multi-mission autonomous systems and ensure the safe and productive operation of space assets. An attempt has been made to ground these principles in fundamentals that should withstand the test of time while allowing for and enabling the advancement of technologies. They are not intended to prescribe a design nor a software representation. There may be multiple designs that can honor these principles. These principles are focused on autonomy for robotic assets. As such, they do not address autonomy for crewed assets nor autonomy that can collectively generate intelligent behavior without top-level system cognizance (e.g., intelligent swarm behavior). These areas would be a subject of future efforts.

Day, John↗

Issues in human/computer control of dexterous remote hands

Much research on dexterous robot hands has been aimed at the design and control problems associated with their autonomous operation, while relatively little research has addressed the problem of direct human control. It is likely that these two modes can be combined in a complementary manner yielding more capability than either alone could provide. While many of the issues in mixed computer/human control of dexterous hands parallel those found in supervisory control of traditional remote manipulators, the unique geometry and capabilities of dexterous hands pose many new problems. Among these are the control of redundant degrees of freedom, grasp stabilization and specification of non-anthropomorphic behavior. An overview is given of progress made at the MIT AI Laboratory in control of the Salisbury 3 finger hand, including experiments in grasp planning and manipulation via controlled slip. It is also suggested how we might introduce human control into the process at a variety of functional levels.

Salisbury, K.↗

Fluid Phase Separation (FPS) experiment for flight on a space shuttle Get Away Special (GAS) canister

The separation of fluid phases in microgravity environments is of importance to environmental control and life support systems (ECLSS) and materials processing in space. A successful fluid phase separation experiment will demonstrate a proof of concept for the separation technique and add to the knowledge base of material behavior. The phase separation experiment will contain a premixed fluid which will be exposed to a microgravity environment. After the phase separation of the compound has occurred, small samples of each of the species will be taken for analysis on the Earth. By correlating the time of separation and the temperature history of the fluid, it will be possible to characterize the process. The experiment has been integrated into space available on a manifested Get Away Special (GAS) experiment, CONCAP 2, part of the Consortium for Materials Complex Autonomous Payload (CAP) Program, scheduled for STS-42. The design and the production of a fluid phase separation experiment for rapid implementation at low cost is presented.

Peters, Bruce↗

Autonomous Mobile Platform for Research in Cooperative Robotics

This paper describes the design and development of a platform for research in cooperative mobile robotics. The structure and mechanics of the vehicles are based on R/C cars. The vehicle is rendered mobile by a DC motor and servo motor. The perception of the robot's environment is achieved using IR sensors and a central vision system. A laptop computer processes images from a CCD camera located above the testing area to determine the position of objects in sight. This information is sent to each robot via RF modem. Each robot is operated by a Motorola 68HC11E micro-controller, and all actions of the robots are realized through the connections of IR sensors, modem, and motors. The intelligent behavior of each robot is based on a hierarchical fuzzy-rule based approach.

Daemi, Ali↗

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Abstract Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

36 MATERIALS SCIENCE↗

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗

Accelerating Structure–Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy

Microscopy combined with local spectroscopy is widely used to correlate nanoscale structure with functional properties in materials, but conventional measurements rely heavily on human-selected sampling locations and predefined targets, limiting data set diversity and the potential for discovery. Here, we present a framework that integrates autonomous microscopy with dual-novelty deep kernel learning (DN-DKL) for adaptive data acquisition and a dual variational autoencoder (VAE) for representation learning. DN-DKL actively guides the microscopy toward structurally and spectroscopically novel regions, enabling efficient collection of large spectral data sets. Dual-VAE embeds local structures and spectroscopic responses into a shared latent manifold that serves as a structure–property relationship map. We applied this framework for the investigation of halide perovskite films by using conductive atomic force microscopy. The results reveal distinct hysteresis behaviors that are linked to specific nanoscale structural motifs, including grain boundary junction points that show hysteresis under different bias conditions and asymmetric grain boundaries that suppress the charge transport. This framework establishes a general strategy that leverages the complementary strengths of self-driving microscopy, machine learning, and human expertise to accelerate scientific discovery in functional materials.

atomic force microscopy↗