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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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NASA NTRS2026

Remote Sensing-Driven Hydrodynamic Modeling in Data-Scarce Regions: Integrating ICESat-2, Sentinel-2, SWOT and Re-analysis Models for Coastal Monitoring

Hydrodynamic models in coastal and estuarine systems are typically constrained by sparse bathymetry, boundary, and validation data, especially in regions where field campaigns are costly or impractical. Here we develop and test a fully satellite-driven framework for hydrodynamic modeling in South Africa’s Langebaan Lagoon without using any local in situ measurements. Bathymetry is derived by training multispectral Sentinel-2 reflectance against ICESat-2 ATL24 photon-derived depths using an XGBoost model optimized with Bayesian search. The final satellite-derived bathymetry reproduces independent ATL24 points with RMSE = 0.45 m and R 2 = 0.97. This bathymetry was used in a depth-averaged Delft3D Flexible Mesh model driven at the open boundary by TPXO tidal harmonics and by ERA5 winds. We validate modeled water surface elevation against 16 SWOT low-rate (250 m, unsmoothed) passes in 2023. SWOT–model comparisons yield an overall RMSE of 0.11 m and R 2 = 0.61, with typical point differences <0.10 m (∼7% of the 1.5 m tidal range), and showed consistent spatial gradients in water level from the offshore boundary, through Saldanha Bay, and into the lagoon. At the offshore boundary, TPXO and SWOT sea surface heights agree closely (R 2 = 0.86). A simple phase adjustment of ∼26,min between TPXO and SWOT lowers the RMSE from 0.18,m to 0.11,m, showing that phase offset accounts for some of the discrepancy, with additional errors likely linked to non-tidal signals. Our results demonstrate that combining passive optical, photon-counting LiDAR, radar interferometry, and global tidal/atmospheric models enables robust, transferrable hydrodynamic modeling in data-scarce coastal systems, offering a cost-effective pathway for monitoring.

ICESat-2↗
NASA NTRS1990

Development of nickel hydrogen battery expert system

The Hubble Telescope Battery Testbed employs the nickel-cadmium battery expert system (NICBES-2) which supports the evaluation of performances of Hubble Telescope spacecraft batteries and provides alarm diagnosis and action advice. NICBES-2 also provides a reasoning system along with a battery domain knowledge base to achieve this battery health management function. An effort to modify NICBES-2 to accommodate nickel-hydrogen battery environment in testbed is described.

Shiva, Sajjan G.↗
NASA NTRS2017

Modeling for Battery Prognostics

For any battery-powered vehicles (be it unmanned aerial vehicles, small passenger aircraft, or assets in exoplanetary operations) to operate at maximum efficiency and reliability, it is critical to monitor battery health as well performance and to predict end of discharge (EOD) and end of useful life (EOL). To fulfil these needs, it is important to capture the battery's inherent characteristics as well as operational knowledge in the form of models that can be used by monitoring, diagnostic, and prognostic algorithms. Several battery modeling methodologies have been developed in last few years as the understanding of underlying electrochemical mechanics has been advancing. The models can generally be classified as empirical models, electrochemical engineering models, multi-physics models, and molecular/atomist. Empirical models are based on fitting certain functions to past experimental data, without making use of any physicochemical principles. Electrical circuit equivalent models are an example of such empirical models. Electrochemical engineering models are typically continuum models that include electrochemical kinetics and transport phenomena. Each model has its advantages and disadvantages. The former type of model has the advantage of being computationally efficient, but has limited accuracy and robustness, due to the approximations used in developed model, and as a result of such approximations, cannot represent aging well. The latter type of model has the advantage of being very accurate, but is often computationally inefficient, having to solve complex sets of partial differential equations, and thus not suited well for online prognostic applications. In addition both multi-physics and atomist models are computationally expensive hence are even less suited to online application An electrochemistry-based model of Li-ion batteries has been developed, that captures crucial electrochemical processes, captures effects of aging, is computationally efficient, and is of suitable accuracy for reliable EOD prediction in a variety of operational profiles. The model can be considered an electrochemical engineering model, but unlike most such models found in the literature, certain approximations are done that allow to retain computational efficiency for online implementation of the model. Although the focus here is on Li-ion batteries, the model is quite general and can be applied to different chemistries through a change of model parameter values. Progress on model development, providing model validation results and EOD prediction results is being presented.

Prognostics↗
NASA NTRS2026

The Operational NASA Anomaly Gas Analyzer and Exploration Follow-Ons

The NASA Anomaly Gas Analyzer (AGA) is the culmination of nearly ten years of advancement of core technology originally developed by Vista Photonics through the Small Business Innovation Research (SBIR) program and expanded using NASA program funding. The AGA is a portable, battery operated, optical gas detection instrument for continuous monitoring of H 2 O, CO 2 , O 2 , CO, NH 3 , HCN, HF, HCl in the spacecraft environment. Real time, in-situ monitored, gas concentrations are communicated at 1 Hz through a built-in display on-orbit and additionally through a serial interface on the ground. The instruments function over the typical range of temperatures and pressures required for the spacecraft environment. A ten-year operational life is targeted. Seven AGA instruments are currently deployed on the International Space Station and two Orion-specific units are manifested for Artemis 2. A variation of the operational AGA is under development for lunar Human Landing Systems. A wider calibrated operating pressure envelope is required in this application. Otherwise, the instruments straightforwardly address lessons learned from the original AGA by improving aspects of thermal control, ruggedness, and ease-of-use. A subset of AGA-developed sensed gases (H 2 O, CO 2 , O 2 ) is the subject of the Primary Constituent Monitor (PCM) development for the Habitation and Logistics Outpost (HALO) destined for lunar orbit. Two PCMs will be installed in HALO as part of the environmental control loop where they will interface with vehicle power and communications. While the basic sensor techniques are unchanged from the AGA, these instruments have been radiation hardened to survive for many years in the harsh target environment. Current development status for these instruments will be presented along with recent developments of a naval submarine variant.

Air Monitoring↗
NASA NTRS2024

Performance of Two Battery Prognostic Applications used by Two Octocopters for Safe Low Altitude Autonomous Flight Operations

This paper addresses the problem of building trust in online predictions of the remaining available flying time for two different electric Unmanned Aerial Vehicles (eUAVs) powered by lithium-ion-polymer batteries. Flight tests for various automation research missions for the two vehicles were monitored using two on-board battery health management applications to make predictions of the remaining flying time (RFT) for each eUAV and to predict the state of the battery. Playback of the voltage, current and temperature profiles of the battery discharge were used to assess the accuracy of the estimation of the voltage and the charge states of the models as well as the estimate of the RFT. The reference ground truth values were the observed landing time and the measured battery pack resting pack voltage 20 minutes after the flight. The predicted RFT, state of charge (SoC), and state of energy (SoE) were compared with the observed results. Noise values of one standard deviation from the mean values of the internal charge states of the battery model during a reference run were used to vary the states during simulation. One application used an equivalent circuit model of the electrical dynamics of the battery pack, and the other application used a reduced-order electrochemistry model. The variation of the model state components was compared to the variation in the estimate of the RFT and the variation in the SoE to estimate a confidence factor. Variation in the estimates caused by factors affecting the off-line laboratory parameter identification experiments is considered. Variation in the estimates due to environmental factors are discussed.

Assurance↗
NASA NTRS2006

Data Mining for ISHM of Liquid Rocket Propulsion Status Update

This document consists of presentation slides that review the current status of data mining to support the work with the Integrated Systems Health Management (ISHM) for the systems associated with Liquid Rocket Propulsion. The aim of this project is to have test stand data from Rocketdyne to design algorithms that will aid in the early detection of impending failures during operation. These methods will be extended and improved for future platforms (i.e., CEV/CLV).

Ashok Srivastava↗
NASA NTRS2010

Ares I-X Ground Diagnostic Prototype

Automating prelaunch diagnostics for launch vehicles offers three potential benefits. First, it potentially improves safety by detecting faults that might otherwise have been missed so that they can be corrected before launch. Second, it potentially reduces launch delays by more quickly diagnosing the cause of anomalies that occur during prelaunch processing. Reducing launch delays will be critical to the success of NASA's planned future missions that require in-orbit rendezvous. Third, it potentially reduces costs by reducing both launch delays and the number of people needed to monitor the prelaunch process. NASA is currently developing the Ares I launch vehicle to bring the Orion capsule and its crew of four astronauts to low-earth orbit on their way to the moon. Ares I-X will be the first unmanned test flight of Ares I. It is scheduled to launch on October 27, 2009. The Ares I-X Ground Diagnostic Prototype is a prototype ground diagnostic system that will provide anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage thrust vector control (TVC) and for the associated ground hydraulics while it is in the Vehicle Assembly Building (VAB) at John F. Kennedy Space Center (KSC) and on the launch pad. It will serve as a prototype for a future operational ground diagnostic system for Ares I. The prototype combines three existing diagnostic tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool that is commercially produced by Qualtech Systems, Inc. It uses a qualitative model of failure propagation to perform fault isolation and diagnostics. We adapted an existing TEAMS model of the TVC to use for diagnostics and developed a TEAMS model of the ground hydraulics. The second tool, Spacecraft Health Inference Engine (SHINE), is a rule-based expert system developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification. The prototype uses the outputs of SHINE as inputs to TEAMS. The third tool, the Inductive Monitoring System (IMS), is an anomaly detection tool developed at NASA Ames Research Center and is currently used to monitor the International Space Station Control Moment Gyroscopes. IMS automatically "learns" a model of historical nominal data in the form of a set of clusters and signals an alarm when new data fails to match this model. IMS offers the potential to detect faults that have not been modeled. The three tools have been integrated and deployed to Hangar AE at KSC where they interface with live data from the Ares I-X vehicle and from the ground hydraulics. The outputs of the tools are displayed on a console in Hangar AE, one of the locations from which the Ares I-X launch will be monitored. In a previous publication, we discussed how we selected the three tools based primarily on their ability to be certified for human spaceflight and described our plans for the prototype. This abstract is due October 23, 2009, and the Ares I-X launch is currently scheduled for October 27, 2009. If this abstract is accepted, then the full paper will describe how the prototype performed before the launch. It will include an analysis of the prototype's accuracy, including false-positive rates, false-negative rates, and receiver operating characteristics (ROC) curves. It will also include a description of the prototype's computational requirements, including CPU usage, main memory usage, and disk usage. If the prototype detects any faults during the prelaunch period then the paper will include a description of those faults. Similarly, if the prototype has any false alarms then the paper will describe them and will attempt to explain their causes. Also, the paper will describe the three tools and how they are used in the prototype. It will include a description of the TEAMS models of the Ares I-X first-stage TVC and associated ground hydraulics and how we adapted the TVC model for use in real-time diagnostics. It will describe the SHINE rules used for fault detection and mode identification and the software architecture that interfaces the various pieces of existing software that are part of the prototype to one another. It will describe how we selected the sensor values and commands that were used to train the IMS model and how we optimized the number of clusters in the IMS model. It will include screen shots of the graphical display that we developed in Java to display the outputs of the three tools. Because Ares I-X data was not yet available to us while we were developing the prototype, we used historical data from the Space Shuttle's Solid Rocket Booster (SRB) TVCs and the associated ground hydraulics to train IMS and to test the entire prototype. Because most of the failure modes that we modeled have never occurred in the Shuttle we inserted simulated failures into the Shuttle data. The Ares I-X first-stage TVC is very similar to the SRB TVC and we expect the data will be very similar. After the launch, we will determine how similar the data actually is and report how any differences in the data affected the diagnostic accuracy of the prototype. Finally, although we did not get the prototype certified, we designed it in a way that it could be certified and wrote a preliminary certification plan. The paper will include a brief summary of how we considered the need for certification in the design of the prototype, how we tested the prototype before deploying it to Hangar AE, and how we would propose to get it certified if it were deployed as an operational system. The paper will conclude with a description of some of the challenges we faced and some of the lessons learned in developing and deploying the prototype.

International Space Station↗
NASA NTRSDate not supplied

NASA Quarantine Program

This presentation describes the historical context and contemporary implementation of the NASA pre-launch quarantine program known as the Health Stabilization Program (HSP).

Quarantine↗
NASA NTRS2026

Using Satellite Surface Temperature Data to Monitor Urban Heat Island

Exposure to heat is a growing concern nationwide. Temperatures can be elevated in cities compared to surrounding rural areas, referred to as an “urban heat island” (UHI) effect, which is intensified during heat waves. The lack of dense networks of air temperature measurements results in few studies on urban heat. Now, a vast amount of high spatial and temporal satellite data on land surface temperature is available. We identified satellite datasets with the longest surface temperature records but different spatial and temporal resolutions: Landsat (1985-current, biweekly at 60 m and 100 m spatial resolution) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellites (2000-current, daily at 1 km spatial resolution). We investigated how satellites with different spatial and temporal resolutions detect UHI effects differently. We hypothesized that 1) a dataset’s spatial resolution impacts the precision of detected UHIs spatially, since high spatial resolution Landsat data better captures spatial variability in temperature, and 2) daily surface temperature data can detect temporal patterns of UHIs and heatwave frequencies. We analyzed Landsat and MODIS satellite data in the Washington, D.C. and Baltimore region. We found that Landsat describes higher spatial variability of the UHI effect than MODIS data. However, MODIS data shows more consistent seasonal surface temperature patterns than Landsat when compared to in situ air temperature measurements. MODIS data was also able to consistently measure the frequency of heat waves. This study demonstrates the value of NASA satellite data for urban heat and climate change studies.

landsat↗
NASA NTRSDate not supplied

Numerical Investigation of Heat Transfer and Fluid Flow within Electrochemical Hydrogen Peroxide Generation Unit

Long-term manned space missions require the onboard production of disinfectants essential for maintaining crew health and supporting life systems. Currently, disinfection aboard the International Space Station (ISS) relies on disposable wetted wipes, which are regularly resupplied from Earth. This approach imposes a significant burden on resupply logistics, storage, and waste management. To address these challenges and support future missions, efforts are underway to develop an in-situ solution that electrochemically generates hydrogen peroxide disinfectant using onboard resources. In collaboration with NASA, Faraday Technology, Inc. has advanced this concept through a series of Small Business Innovation Research (SBIR) projects, resulting in the development of a Peroxide Generation Unit (PGU). The PGU can produce up to 3 wt.% hydrogen peroxide on-demand at a rate of 1 liter per day, providing a sustainable alternative to Earth-dependent supplies. The resulting aqueous hydrogen peroxide (H₂O₂) is an effective disinfectant, safe for crew use, compatible with spacecraft systems, and free from volatiles, off-gassing, or residues. This innovation offers a reliable, efficient solution for onboard disinfection, reducing dependence on Earth-based resupply while ensuring the health and safety of space crews. Generating hydrogen peroxide at the required rate needs high voltages and currents, exceeding 20V and 2A respectively, which leads to significant heat generation from Joule heating. This temperature rise poses a risk to sensitive system components, especially critical and expensive membranes that can degrade under thermal stress. To mitigate this risk, the thermal, fluid, and electrical flows within the system are modeled computationally using the commercial software COMSOL. The numerical simulations are validated against experimental data from both sub-scale and alpha-scale systems. Once verified, the model is employed to identify thermal hotspots, investigate their underlying causes, and explore solutions to prevent them.

Life Support Systems (LSS)↗
NASA NTRS1978

Microprocessor controlled advanced battery management systems

The advanced battery management system described uses the capabilities of an on-board microprocessor to: (1) monitor the state of the battery on a cell by cell basis; (2) compute the state of charge of each cell; (3) protect each cell from reversal; (4) prevent overcharge on each individual cell; and (5) control dual rate reconditioning to zero volts per cell.

W. T. Payne↗
NASA NTRS1990

Battery test expert systems

The characteristics of NIHBES (nickel-hydrogen battery expert system) are described, with attention also given to NICBES-2 (nickel-cadmium battery expert system-2). The nickel-hydrogen battery testbed is set up almost identically to the nickel-cadmium battery testbed, with the exceptions of no battery protection and reconditioning circuits (BPRCs) and the frequency of transmission of data. The Ni-H2 testbed has no BPRCs and the data are transmitted every 30 s instead of every minute. An expert system shell was chosen to develop this particular expert system. The GoldWorks expert system shell from Gold Hill Computers was chosen for the task. NIHBES will extract the desired data and return fault diagnosis, status and advice, and decision support. Expert systems have been proven to be viable tools in the control and monitoring of space power systems. Presently, the DDAS (digital data acquisition system) monitors and controls the orbit time, and is responsible for limit checking, data acquisition, and data summaries. It is concluded that in the future control of the Hubble Space Telescope breadboard will be passed to NIHBES. NIHBES will be more beneficial to the testbed than the DDAS alone due to the limitations of the DDAS. The DDAS cannot provide long-term trend analysis, plotting capability, fault diagnosis, or advice.

Johnson, Yvette B.↗
NASA NTRS2025

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↗
NASA NTRS1991

Nickel hydrogen battery expert system

The Hubble Telescope Battery Testbed at MSFC uses the Nickel Cadmium (NiCd) Battery Expert System (NICBES-2) which supports the evaluation of performance of Hubble Telescope spacecraft batteries and provides alarm diagnosis and action advice. NICBES-2 provides a reasoning system along with a battery domain knowledge base to achieve this battery health management function. An effort is summarized which was used to modify NICBES-2 to accommodate Nickel Hydrogen (NiH2) battery environment now in MSFC testbed. The NICBES-2 is implemented on a Sun Microsystem and is written in SunOS C and Quintus Prolog. The system now operates in a multitasking environment. NICBES-2 spawns three processes: serial port process (SPP); data handler process (DHP); and the expert system process (ESP) in order to process the telemetry data and provide the status and action advice. NICBES-2 performs orbit data gathering, data evaluation, alarm diagnosis and action advice and status and history display functions. The adaptation of NICBES-2 to work with NiH2 battery environment required modification to all of the three component processes.

Shiva, Sajjan G.↗