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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Abstract Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

25 ENERGY STORAGE↗

Mapping Rare Earths and Toxics in E-Waste via Hyperspectral Imaging and Machine Learning

Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.

Circular economy↗

MILSTAR's flexible substrate solar array: Lessons learned, addendum

MILSTAR's Flexible Substrate Solar Array (FSSA) is an evolutionary development of the lightweight, flexible substrate design pioneered at Lockheed during the seventies. Many of the features of the design are related to the Solar Array Flight Experiment (SAFE), flown on STS-41D in 1984. FSSA development has created a substantial technology base for future flexible substrate solar arrays such as the array for the Space Station Freedom. Lessons learned during the development of the FSSA can and should be applied to the Freedom array and other future flexible substrate designs.

Gibb, John↗

Overview of Pre-Flight Physical Training, In-Flight Exercise Countermeasures and the Post-Flight Reconditioning Program for International Space Station Astronauts

International Space Station (ISS) astronauts receive supervised physical training pre-flight, utilize exercise countermeasures in-flight, and participate in a structured reconditioning program post-flight. Despite recent advances in exercise hardware and prescribed exercise countermeasures, ISS crewmembers are still found to have variable levels of deconditioning post-flight. This presentation provides an overview of the astronaut medical certification requirements, pre-flight physical training, in-flight exercise countermeasures, and the post-flight reconditioning program. Astronauts must meet medical certification requirements on selection, annually, and prior to ISS missions. In addition, extensive physical fitness testing and standardized medical assessments are performed on long duration crewmembers pre-flight. Limited physical fitness assessments and medical examinations are performed in-flight to develop exercise countermeasure prescriptions, ensure that the crewmembers are physically capable of performing mission tasks, and monitor astronaut health. Upon mission completion, long duration astronauts must re-adapt to the 1 G environment, and be certified as fit to return to space flight training and active duty. A structured, supervised postflight reconditioning program has been developed to prevent injuries, facilitate re-adaptation to the 1 G environment, and subsequently return astronauts to training and space flight. The NASA reconditioning program is implemented by the Astronaut Strength, Conditioning, and Rehabilitation (ASCR) team and supervised by NASA flight surgeons. This program has evolved over the past 10 years of the International Space Station (ISS) program and has been successful in ensuring that long duration astronauts safely re-adapt to the 1 g environment and return to active duty. Lessons learned from this approach to managing deconditioning can be applied to terrestrial medicine and future exploration space flight missions.

Kerstman, Eric↗

Replacement of HCFC-225 Solvent for Cleaning NASA Propulsion Oxygen Systems

Since the 1990's, when the Class I Ozone Depleting Substance (ODS) chlorofluorocarbon‐113 (CFC‐113) was banned, NASA's propulsion test facilities at Marshall Space Flight Center (MSFC) and Stennis Space Center (SSC) have relied upon hydrochlorofluorocarbon‐225 (HCFC‐225) to safely clean and verify the cleanliness of large scale propulsion oxygen systems. Effective January 1, 2015, the production, import, export, and new use of HCFC‐225, a Class II ODS, was prohibited by the Clean Air Act. In 2012 through 2014, leveraging resources from both NASA and the Defense Logistics Agency ‐ Aviation Hazardous Minimization and Green Products Branch, test labs at MSFC, SSC, and Johnson Space Center's White Sands Test Facility (WSTF) collaborated to seek out, test, and qualify a replacement for HCFC‐225 that is both an effective cleaner and safe for use with oxygen systems. This presentation summarizes the tests performed, results, and lessons learned. It also demonstrates the benefits of cross‐agency collaboration in a time of limited resources.

Lowrey, Nikki M.↗

Results of the Test Program for Replacement of AK-225G Solvent for Cleaning NASA Propulsion Oxygen Systems

Since the 1990's, when the Class I Ozone Depleting Substance chlorofluorocarbon-113 was banned, NASA's propulsion test facilities at Marshall Space Flight Center and Stennis Space Center have relied upon the solvent Asahiklin AK-225 (hydrochlorofluorocarbon-225ca/cb or HCFC-225ca/cb) and, more recently AK-225G (the single isomer form, HCFC-225cb) to safely clean and verify the cleanliness of large scale propulsion oxygen systems. Effective January 1, 2015, the production, import, export, and new use of Class II Ozone Depleting Substances, including AK-225G, was prohibited in the United States by the Clean Air Act. In 2012 through 2014, NASA test labs at MSFC, SSC, and Johnson Space Center's White Sands Test Facility collaborated to seek out, test, and qualify a solvent replacement for AK-225G that is both an effective cleaner and safe for use with oxygen systems. This paper summarizes the tests performed, results, and lessons learned.

Lowrey, Nikki M.↗

Results of the Test Program for Replacement of AK-225G Solvent for Cleaning NASA Propulsion Oxygen Systems

Since the 1990's, when the Class I Ozone Depleting Substance (ODS) chlorofluorocarbon-113 (CFC-113) was banned, NASA's propulsion test facilities at Marshall Space Flight Center (MSFC) and Stennis Space Center (SSC) have relied upon the solvent AsahiKlin AK-225 (hydrochlorofluorocarbon-225ca/cb or HCFC-225ca/cb) and, more recently AK-225G (the single isomer form, HCFC-225cb) to safely clean and verify the cleanliness of large scale propulsion oxygen systems. Effective January 1, 2015, the production, import, export, and new use of Class II Ozone Depleting Substances, including AK-225G, was prohibited in the United States by the Clean Air Act. In 2012 through 2014, NASA test labs at MSFC, SSC, and Johnson Space Center's White Sands Test Facility (WSTF) collaborated to seek out, test, and qualify a solvent replacement for AK-225G that is both an effective cleaner and safe for use with oxygen systems. This paper summarizes the tests performed, results, and lessons learned.

Lowrey, Nikki M.↗

The Soil Moisture Acttive Passive Mission: Fault Protection Performance and Lessons Learned

Fault protection as a discipline involves a collection of flight software logic and operational processes for detecting unacceptable anomalous behavior, responding prior to reaching criticality, restricting the propagation of a failure beyond a fault containment region, and recovering the vehicle back to full or degraded functionality if possible. The System Fault Protection (SFP) design for the SMAP Earth orbiter was put to the test during its 90-day vehicle commissioning activities. During this time, the SFP software autonomously protected the vehicle from multiple faults to critical hardware, and the operations team successfully returned the observatory to its science state. The SFP also performed well in the presence of anomalous behavior below true safety limits by not taking unnecessary response actions, instead allowing the operations team time to monitor the behavior. Certain aspects of the SFP design were modified during operations via both parameter updates and a full flight software update in order to better match the vehicle behavior in the flight environment. An evaluation of the SMAP SFP performance during vehicle Commissioning will be provided in this paper, as well as a set of lessons learned largely focused on visibility, SFP mutability in operations, responses to peripheral device faults, and Safe Mode recovery and design. By capturing some of the knowledge gained during SMAP Commissioning, it is intended that this paper provide guidance for making future System Fault Protection designs more robust and supportive of operations.

Clark, Jessica↗

Atmospheric Ionizing Radiation (AIR): Analysis, Results, and Lessons Learned From the June 1997 ER-2 Campaign

The United States initiated a program to assess the technology required for an environmentally safe and operationally efficient High Speed Civil Transport (HSCT) for entrance on the world market after the turn of the century. Due to the changing regulations on radiation exposures and the growing concerns over uncertainty in our knowledge of atmospheric radiations, the NASA High Speed Research Project Office (HSRPO) commissioned a review of "Radiation Exposure and High-Altitude Flight" by the National Council on Radiation Protection and Measurements (NCRP). On the basis of the NCRP recommendations, the HSRPO funded a flight experiment to resolve the environmental uncertainty in the atmospheric ionizing radiation levels as a step in developing an approach to minimize the radiation impact on HSCT operations. To minimize costs in this project, an international investigator approach was taken to assure coverage with instrument sensitivity across the range of particle types and energies to allow unique characterization of the diverse radiation components. The present workshop is a result of the flight measurements made at the maximum intensity of the solar cycle modulated background radiation levels during the month of June 1997.

Wilson, J. W.↗

MLAS and LAS: Project Comparison of Lessons Learned

The development of new and safer manned space vehicles is a top priority at NASA. Recently two different approaches of how to accomplish this mission of keeping astronauts safe was successfully demonstrated. With work already underway on an Apollo-like launch abort system for the Orion Crew Exploration Vehicle (CEV), an alternative design concept named the Max Launch Abort System, or MLAS, was developed as a parallel effort. The Orion system, managed by the Constellation office, is based on the design of a single solid launch abort motor in a tower positioned above the capsule. The MLAS design takes a different approach placing the solid launch abort motor underneath the capsule. This effort was led by the NASA Engineering and Safety Center (NESC). Both escape systems were designed with the Ares I Rocket as the launch vehicle and had the same primary requirement to safely propel a crew module away from any emergency event either on the launch pad or during accent. Beyond these two parameters, there was little else in common between the two projects, except that they both concluded in successful launches that will further promote the development of crew launch abort systems. This presentation will compare both projects from the standpoint of technical requirements; program management and flight test objectives and highlight the synergistic lessons learned by two engineers who worked on each program.

Dittemore, Gary↗

Joint Communication Resource Allocation and Velocity Selection in Urban Air Mobility via Multi-agent Reinforcement Learning

With traffic congestion problems becoming more severe in urban areas, the National Aeronautics and Space Administration promotes the Urban Air Mobility (UAM) concept, which envisages a safe and efficient air transportation system. However, the increased communication demands in UAM can exacerbate the spectrum scarcity. Therefore, a new communication resource allocation solution is necessary. In this paper, we focus on uplink UAM communications, where multiple aerial vehicles (AV) perform cargo/passenger delivery tasks. With predefined flight paths, AVs make decisions on communication resource allocation and velocity selection to complete their missions under safety constraints. Accordingly, we formulate a joint optimization problem to minimize the weighted sum of the total travel time and communication outage time. We first model the optimization problem as a Markov game and propose a multi-agent reinforcement learning based solution. Simulation results corroborate the effectiveness of the proposed solution.

Ruixuan Han↗

Automated Planning and Scheduling for Orbital Express (151)

The challenging timeline for DARPA's Orbital Express mission demanded a flexible, responsive, and (above all) safe approach to mission planning. Because the mission was a technology demonstration, pertinent planning information was learned during actual mission execution. This information led to amendments to procedures, which led to changes in the mission plan. In general, we used the ASPEN planner scheduler to generate and validate the mission plans. We enhanced ASPEN to enable it to reason about uncertainty. We also developed a model generator that would read the text of a procedure and translate it into an ASPEN model. These technologies had a significant impact on the success of the Orbital Express mission.

autonomy↗

Development of a Route Crossing Tool for Shared Airspace Environments

In current-day Terminal Radar Approach Control (TRACON) operations, departure and arrival controllers maintain separate and dedicated airspace for their respective traffic flows. Although this practice has obvious safety features, it also leads to inefficiencies; for example, departure aircraft may be routinely capped beneath arrival airspace. With the right decision-support and coordination tools, departures could continue to climb through arrival airspace when sufficient gaps exist. Previous studies of ‘shared airspace’ have examined pre-arranged coordination procedures, as well as tools that gave feedback to the controllers on where gaps between arrivals were located and whether the departure aircraft could be scheduled to fly through those gaps [1, 2, 3, 4]. Since then, the Route Crossing Tool (RCT) has been developed to allow controllers to assess multiple pre-defined route options at points where the arrivals and departures cross, thereby increasing the possibility of climbing a departure through an arrival gap.The RCT aids in ensuring lateral separation between departure and arrival aircraft that pass through the same altitude. Since the RCT can be applied tactically, it can enable aircraft to fly through arrival flows even if these aircraft depart outside scheduled times. The RCT makes use of a set of predefined parallel departure routes crossing the arrival flow at equidistant intersecting points on the arrival route. The RCT uses the Estimated Time of Arrival (ETA) of the departure aircraft at each intersecting point to calculate the lateral separation with the neighboring arrivals when it crosses that point; this information is graphically displayed to the controller. Additionally, the RCT incorporates forecast winds in its ETA predictions.Multiple prototypes of the RCT have been iteratively developed with feedback from Subject Matter Experts (SMEs). This paper presents the final design, the design process, and lessons learned. Initial results from a simulation suggest that the tool was successful in helping controllers to safely climb more aircraft. Controller feedback on the tool was also positive.

shared airspace↗

WM26 Paper Multi-Robot Collaboration for Hazardous Environments

Hazardous nuclear and industrial facilities are rarely designed for robots. Work in these domains demand precise manipulation and robust mobility in cluttered, constrained spaces where off-the-shelf platforms struggle and “one-size-fits-all” machines become costly and complex. Idaho National Laboratory (INL) is developing an autonomous, multi-robot inspection system that coordinates task-specific platforms rather than relying on a single omni-tool robot. An electric truck serves as a power and compute hub for a custom manipulator co-developed with Florida International University (FIU), a commercial mini crawler, a pan–tilt–zoom camera, and a Nexxis Argus LiDAR mapping system. Working in concert, these robots generate spatial, radiation, and temperature maps of the pit environments at the Hanford Waste Tank Farms. These systems will capture visual records and environmental telemetry to allow for analysis post inspection. The system architecture uses Robot Operating System 2 (ROS 2) for publish/subscribe integration, NVIDIA Isaac Sim and Unity for simulation and visualization, and algorithms such as NVBlox to fuse data into unified 3D overlays. This robot-agnostic approach reduces operator burden by enabling autonomy across heterogeneous platforms and lets each robot be used where it is strongest. Having autonomous functions means operators don’t have to fully control multiple different components. The ease of use could allow for more widespread adoption of advanced robotics at waste management sites that see continued use. By coordinating simpler, purpose-built mechanisms, the approach lowers design and manufacturing complexity, reduces capital risk in contaminated settings, and improves controllability for complex inspection and manipulation tasks. We present the architecture, early results, and lessons learned from building and deploying this coordinated multi-robot system, with the goal of accelerating safe, cost-effective adoption of advanced robotics at waste-management sites.

42 - ENGINEERING↗

Two gimbal bearing case studies: Some lessons learned

Two troublesome, torque related problems associated with gimbal actuators are discussed. Large, thin section angular contact bearings can have a surprisingly high torque sensitivity to radial thermal gradients. A predictive thermal-mechanical bearing analysis, as described, was helpful in establishing a safe temperature operating envelope. In the second example, end-of-travel torque limits of an oscillatory gimbal bearing appoached motor stall during limit cycling life tests. Bearing modifications required to restore acceptable torque performance are described. The lessons learned from these case studies should benefit designers of precision gimbals where singular bearing torque related problems are not uncommon.

Loewenthal, Stuart H.↗

Lessons Learned from the Space Shuttle Engine Hydrogen Flow Control Valve Poppet Breakage

The Main Propulsion System (MPS) uses three Flow Control Valves (FCV) to modulate the flow of pressurant hydrogen gas from the Space Shuttle Main Engines (SSME) to the hydrogen External Tank (ET). This maintains pressure in the ullage volume as the liquid level drops, preserving ET structural integrity and assuring the engines receive a sufficient amount of head pressure. On Space Transportation System (STS)-126 (2009), with only a handful of International Space Station (ISS) assembly flights from the end of the Shuttle program, a portion of a single FCV?s poppet head broke off at about a minute and a half after liftoff. The risk of the poppet head failure is that the increased flow area through the FCV could result in excessive gaseous hydrogen flow back to the external tank, which could result in overboard venting of hydrogen ullage pressure. If the hydrogen venting were to occur in first stage (i.e., lower atmosphere), a flammability hazard exists that could lead to catastrophic loss of crew and vehicle. Other failure risks included particle impact damage to MPS downstream hardware. Although the FCV design had been plagued by contamination-related sluggish valve response problems prior to a redesign at STS-80 (1996), contamination was ruled out as the cause of the STS-126 failure. Employing a combination of enhanced hardware inspection and a better understanding of the consequences of a poppet failure, safe flight rationale for subsequent flights (STS-119 and later) was achieved. This paper deals with the technical lessons learned during the investigation and mitigation of this problem at a time when assembly flights were each in the critical path to Space Station success.

Martinez, Hugo E.↗

Constellation Operations: Lessons Learned For Future Exploration

The Earth science community has long advocated placing numerous instruments in space to study the Earth and its environment. Space agencies from many countries have responded to this call with a wide range of orbiting satellites. Scientists also envisioned placing some satellites in constellations, to enable diverse remote sensing instruments to observe the same part of the Earth (or its atmosphere) at about the same time, thereby increasing the opportunities for coincident science observations. The Earth Science Afternoon Constellation is answering this call, but there have been unique challenges on the way to its deployment. Currently, the Afternoon Constellation is to comprise six satellites. Three are currently on orbit: NASA's Earth Observing System (EOS)-Aqua (2002) and EOS-Aura (2004), and CNES's Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) (2004). Two more satellites, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Cloudsat, are to be jointly launched in late 2005, followed by the Orbiting Carbon Observatory (OCO) in 2008. The Afternoon Constellation is unlike most satellite constellations in that: 1) It is not a homogenous mix of identical satellites; rather it comprises several satellites with complementary observational capabilities; 2) The satellites are not spaced around the Earth to provide instantaneous, global coverage (as for a communications satellite constellation); rather they orbit in close proximity so observations occur at about the same time over approximately the same region; and 3) Lastly, the satellites are not managed and controlled by one organization; rather the list of organizations is diverse: CNES in France, NASA Centers at Goddard, Langley, and the Jet Propulsion Laboratory, and the US Air Force facility in New Mexico. The PARASOL launch and early orbit (L&EO) phase proved to be a learning experience for constellation members (including Constellation management). Prior to launch, all members signed an operations coordination document that spelled out basic requirements for keeping the constellation safe and resolving non-nominal events. Once PARASOL reached orbit and the mission teams gained experience using the newly-developed constellation monitoring tools, it became clear that some of the guidelines in the signed agreements had to be adjusted. This paper presents the L&EO lessons learned and how they were used to prepare for the next phase - the period following the CloudSat/CALIPSO launch.

Kelly, Angelita C.↗

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Safe and efficient surface operations↗