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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

MONTE Python for Deep Space Navigation

The Mission Analysis, Operations, and Navigation Toolkit Environment (MONTE) is the Jet Propulsion Laboratory’s (JPL) signature astrodynamic computing platform. It was built to support JPL’s deep space exploration program, and has been used to fly robotic spacecraft to Mars, Jupiter, Saturn, Ceres, and many solar system small bodies. At its core, MONTE consists of low-level astrodynamic libraries that are written in C++ and presented to the end user as an importable Python language module. These libraries form the basis on which Python-language applications are built for specific astrodynamic applications, such as trajectory design and optimization, orbit determination, flight path control, and more. The first half of this paper gives context to the MONTE project by outlining its history, the field of deep space navigation and where MONTE fits into the current Python landscape. The second half gives an overview of the main MONTE libraries and provides a narrative example of how it can be used for astrodynamic analysis.

aerospace↗
NASA NTRS1964

Unmanned Spacecraft of the United States

In 1957 the first earth satellite ushered in the age of space flight. Since that historic event, space exploration has become a major national objective of both the United States and the Soviet Union. These two nations have attempted a total of well over 200 space flight missions. Other nations are also participating in various degrees in what will continue to grow as a cooperative world effort. In the years since 1957, man has successfully flown in earth orbit. He has initiated programs to land on the moon and return. He has made dramatic applications of earth satellites in meteorology, communications, navigation, and geodesy. A host of scientific satellites.continue to advance understanding of the earth's environment, the sun, and the stars. Automated spacecraft are being flown to the moon, deep into interplanetary space, and to the near planets, Mars and Venus. One of the most exciting technological aspects of space exploration has been the development of automated spacecraft. Most of the scientific exploration of space and the useful applications of space flight thus far have been made possible by automated spacecraft. Development of these spacecraft and their many complex subsystems is setting the pace today for many branches of science and technology. Guidance, computer, attitude control, power, telecommunication, instrumentation, and structural subsystems are being subjected to new standards of light weight, high efficiency, extreme accuracy, and unsurpassed reliability and quality. This publication reviews the automated spacecraft which have been developed and flown, or which are under active development in the United States by the National Aeronautics and Space Administration. From the facts and statistics contained herein, certain observations can be made and certain conclusions drawn.

Cortright, Edgar M.↗
NASA NTRS2016

Visual Odometry for Autonomous Deep-Space Navigation Project

Autonomous rendezvous and docking (AR&D) is a critical need for manned spaceflight, especially in deep space where communication delays essentially leave crews on their own for critical operations like docking. Previously developed AR&D sensors have been large, heavy, power-hungry, and may still require further development (e.g. Flash LiDAR). Other approaches to vision-based navigation are not computationally efficient enough to operate quickly on slower, flight-like computers. The key technical challenge for visual odometry is to adapt it from the current terrestrial applications it was designed for to function in the harsh lighting conditions of space. This effort leveraged Draper Laboratory’s considerable prior development and expertise, benefitting both parties. The algorithm Draper has created is unique from other pose estimation efforts as it has a comparatively small computational footprint (suitable for use onboard a spacecraft, unlike alternatives) and potentially offers accuracy and precision needed for docking. This presents a solution to the AR&D problem that only requires a camera, which is much smaller, lighter, and requires far less power than competing AR&D sensors. We have demonstrated the algorithm’s performance and ability to process ‘flight-like’ imagery formats with a ‘flight-like’ trajectory, positioning ourselves to easily process flight data from the upcoming ‘ISS Selfie’ activity and then compare the algorithm’s quantified performance to the simulated imagery. This will bring visual odometry beyond TRL 5, proving its readiness to be demonstrated as part of an integrated system.Once beyond TRL 5, visual odometry will be poised to be demonstrated as part of a system in an in-space demo where relative pose is critical, like Orion AR&D, ISS robotic operations, asteroid proximity operations, and more.

Robinson, Shane↗
NASA NTRS2016

Visual Odometry for Autonomous Deep-Space Navigation Project

Autonomous rendezvous and docking (AR&D) is a critical need for manned spaceflight, especially in deep space where communication delays essentially leave crews on their own for critical operations like docking. Previously developed AR&D sensors have been large, heavy, power-hungry, and may still require further development (e.g. Flash LiDAR). Other approaches to vision-based navigation are not computationally efficient enough to operate quickly on slower, flight-like computers. The key technical challenge for visual odometry is to adapt it from the current terrestrial applications it was designed for to function in the harsh lighting conditions of space. This effort leveraged Draper Laboratory's considerable prior development and expertise, benefitting both parties. The algorithm Draper has created is unique from other pose estimation efforts as it has a comparatively small computational footprint (suitable for use onboard a spacecraft, unlike alternatives) and potentially offers accuracy and precision needed for docking. This presents a solution to the AR&D problem that only requires a camera, which is much smaller, lighter, and requires far less power than competing AR&D sensors. We have demonstrated the algorithm's performance and ability to process 'flight-like' imagery formats with a 'flight-like' trajectory, positioning ourselves to easily process flight data from the upcoming 'ISS Selfie' activity and then compare the algorithm's quantified performance to the simulated imagery. This will bring visual odometry beyond TRL 5, proving its readiness to be demonstrated as part of an integrated system. Once beyond TRL 5, visual odometry will be poised to be demonstrated as part of a system in an in-space demo where relative pose is critical, like Orion AR&D, ISS robotic operations, asteroid proximity operations, and more.

Robinson, Shane↗
NASA NTRS2010

Adaptive Fault Tolerance for Many-Core Based Space-Borne Computing

This paper describes an approach to providing software fault tolerance for future deep-space robotic NASA missions, which will require a high degree of autonomy supported by an enhanced on-board computational capability. Such systems have become possible as a result of the emerging many-core technology, which is expected to offer 1024-core chips by 2015. We discuss the challenges and opportunities of this new technology, focusing on introspection-based adaptive fault tolerance that takes into account the specific requirements of applications, guided by a fault model. Introspection supports runtime monitoring of the program execution with the goal of identifying, locating, and analyzing errors. Fault tolerance assertions for the introspection system can be provided by the user, domain-specific knowledge, or via the results of static or dynamic program analysis. This work is part of an on-going project at the Jet Propulsion Laboratory in Pasadena, California.

fault tolerance↗
NASA NTRS1993

The analysis of convolutional codes via the extended Smith algorithm

Convolutional codes have been the central part of most error-control systems in deep-space communication for many years. Almost all such applications, however, have used the restricted class of (n,1), also known as 'rate 1/n,' convolutional codes. The more general class of (n,k) convolutional codes contains many potentially useful codes, but their algebraic theory is difficult and has proved to be a stumbling block in the evolution of convolutional coding systems. In this article, the situation is improved by describing a set of practical algorithms for computing certain basic things about a convolutional code (among them the degree, the Forney indices, a minimal generator matrix, and a parity-check matrix), which are usually needed before a system using the code can be built. The approach is based on the classic Forney theory for convolutional codes, together with the extended Smith algorithm for polynomial matrices, which is introduced in this article.

Mceliece, R. J.↗
DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗
NASA NTRS1999

Progress of Cometary Science in the Past 100 Years

Enormous strides made by cometary science during the 20th century defy any meaningful comparison of its state 100 years ago and now. The great majority of the subfields enjoying much attention nowadays did not exist in the year 1900. Dramatic developments, especially in the past 30-50 years, have equally affected observational and theoretical studies of comets. The profound diversification of observing techniques has been documented by the ever widening limits on the electromagnetic spectrum covered. While the time around 1900 marked an early period of slow and painful experimentation with photographic methods in cometary studies, observations of comets from the x-ray region to the radio waves have by now become routine. Many of the new techniques, and all those involved with the wavelengths shorter than about 300 nm, were made possible by another major breakthrough of this century - observing from space. Experiments on dedicated Earth-orbiting satellites as well as several deep-space probes have provided fascinating new information on the nature and makeup of comets. In broader terms, much of the progress has been achieved thanks to fundamental discoveries and major advances in electronics, whose applications resulted in qualitatively new instruments (e.g. radiotelescopes) and sensors or detectors (e.g. CCD arrays). The most universal effect on the entire cometary science, from observing to data handling to quantitative interpretations, has been, as in any other branch of science, due to the introduction of electronic computers, with their processing capabilities not only unheard of, but literally unimaginable, in the age of classical desk calculators. As if all this should not be enough, the today's generations of comet scientists have, in addition, been blessed with nature's highly appreciated cooperation. Indeed, in the span of a dozen years, between 1985 and 1997, we were privileged to witness four remarkable cometary events: (i) a return of Halley's celebrated comet; (ii) the impact of an extensively fragmented comet Shoemaker-Levy 9 into Jupiter - a once-in-a-millenium episode; (iii) a closeup, appearance of a small but very active Earth-approaching comet Hyakutake; and (iv) an unforgettable celestial show of comet Hale-Bopp, one of the brightest and the most massive comets ever observed. We have been - and for many years to come will be - harvesting scientifically lucrative findings based on the flood of data accumulated during observational campaigns organized for these and other recent objects. Our understanding of cometary phenomena will continue to grow as observing techniques are further being improved and as more sophisticated theories are being developed. Recent accomplishments achieved in the rapidly expanding field of comets are certain to stimulate the members of this astronomical community in their quest for new discoveries in the 21st century!

Sekanina, Zdenek↗
NASA NTRSDate not supplied

A generalizable machine learning approach to predict land surface temperature

Monitoring of land surface and atmospheric states is highly reliant on satellite data. Traditionally, data products are generated using carefully tuned and validated algorithms for low-earth orbit (LEO) sensors. However, the emerging constellation of geostationary (GEO) sensors contributes global, high temporal resolution observations which can better capture the diurnal variability of key observables like land surface temperature (LST). Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from LEO and GEO satellites to develop a deep learning-based method for sensor-to-sensor algorithm emulation. Our model is trained on GOES-16 thermal bands to predict MODIS Terra LST and achieves a validation error <2K. Further, application of the model to unseen times of day and a second GEO sensor observing an unseen spatial domain demonstrate the generalization of the deep learning model across space, time and spectra. We anticipate that the synergies between a variety of active orbit configurations can be used to accelerate application of existing algorithms to new datasets.

Kate Marie Duffy↗
DOE OSTI2025

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

In materials science, plant biology, agriculture, and environmental research, the automated analysis of high-magnification, complex microscopy images, such as those generated by freeze-fracture electron microscopy (FF-TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high-throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF-TEM images. The pipeline integrates preprocessing, detection, human-in-the-loop verification, and semantic segmentation to quantify features such as rosette diameter and inter-lobe spacing. The approach was trained and tested on a curated dataset of high-resolution FF-TEM micrographs of Physcomitrium patens, expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50-95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high-throughput applications. Our results show that the pipeline achieves human-expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)-assisted workflows in biological imaging.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Impact of NASA’s Entry Systems Modeling Project on Planetary Mission Design

Planetary missions continue to grow larger and more complex. Furthermore, the current focus on human exploration of the Moon and Mars, as well as Mars Sample Return(MSR), place increas-ingly stringent requirements on the reliability of the entry, descent, and landing (EDL) system that ensures the safe delivery of payload or crew to their destination. Planetary EDL is an area in which mission designers are critically reliant on modeling and simulation to demonstrate the reliability of the system, as there are no ground facilities that are able to fully test these systems in a flight-relevant environment. NASA’s state-of-the-art modeling and simulation capability must continually evolve to meet the needs of the next generation of planetary EDL. To accomplish this aim, NASA’s Entry Systems Modeling (ESM) Project was formed in 2013and is funded bythe Space Technology Mission Directorate(STMD) and Science Mission Directorate (SMD). ESM is the Agency’s only cross-cutting effort for advancing entry systems modeling and simulation capabilities across a range of technical disciplines and Solar System destinations. ESM is a portfolio project covering a variety of mid-TRL research efforts within four core EDL-related areas of investment: (1) Thermal protection material modeling, (2) Shock layer kinetics and radiation, (3) Aerosciences, and (4) Guidance, navigation, and con-trol. The material modeling group creates detailed material response modelsof thermal protection systems (TPS)from the micro to macro scale, and at the fun-damental and engineering levels. Shock layer kinetics and radiation focuses on radiative heating of space-craft, quantum chemistry and benchmark experiments for validation.Aerosciences is a broad research area that impacts many aspects of entry systems, including parachutes, aerodynamics, and turbulent heating augmentation due to TPS roughness.The guidance, navigation,and control effort under ESM is expanding the capabilities of NASA’s main flight mechanics tool, POST2, for use on high-performance computing architectures and to generalize interoperability with external applications for more detailed end-to-end simula-tion.In addition, several focusedresearch topics have been approvedto augment ESM’s core portfolio. These include efforts for deep post-flight analysis of Mars 2020/MEDLI2 flight data; development ofTPS failure models; improvement of hypersonic wakeflow models; and a recently concluded effort to provide material response models for NuSil-coated PICA heat-shield material. This presentation will discuss each of these investment areas and demonstrate via real mission examples how advances to the state-of-the-art enabled by ESM are directly impacting the missions of today and tomorrow, including InSight, Mars 2020, Mars Sample Return, Orion, and Dragonfly.

Entry Systems Modeling↗
NASA NTRS2012

Telerobotics Workstation (TRWS) for Deep Space Habitats

On medium- to long-duration human spaceflight missions, latency in communications from Earth could reduce efficiency or hinder local operations, control, and monitoring of the various mission vehicles and other elements. Regardless of the degree of autonomy of any one particular element, a means of monitoring and controlling the elements in real time based on mission needs would increase efficiency and response times for their operation. Since human crews would be present locally, a local means for monitoring and controlling all the various mission elements is needed, particularly for robotic elements where response to interesting scientific features in the environment might need near- instantaneous manipulation and control. One of the elements proposed for medium- and long-duration human spaceflight missions, the Deep Space Habitat (DSH), is intended to be used as a remote residence and working volume for human crews. The proposed solution for local monitoring and control would be to provide a workstation within the DSH where local crews can operate local vehicles and robotic elements with little to no latency. The Telerobotics Workstation (TRWS) is a multi-display computer workstation mounted in a dedicated location within the DSH that can be adjusted for a variety of configurations as required. From an Intra-Vehicular Activity (IVA) location, the TRWS uses the Robot Application Programming Interface Delegate (RAPID) control environment through the local network to remotely monitor and control vehicles and robotic assets located outside the pressurized volume in the immediate vicinity or at low-latency distances from the habitat. The multiple display area of the TRWS allows the crew to have numerous windows open with live video feeds, control windows, and data browsers, as well as local monitoring and control of the DSH and associated systems.

Mittman, David S.↗
NASA NTRSDate not supplied

Communicating Metrics of Land Surface Temperature Variability Using Multi-sensor Machine Learning

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Further, time series clustering approaches are examined with the objective of identifying key indicators of change in diurnal cycling and extremes on a continental scale. Communicating LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Duffy↗
NASA NTRSDate not supplied

LEO Sensor to GEO Sensor Algorithm Transfer Models for Land Surface Temperature

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Communicating diurnal LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Marie Duffy↗
NASA NTRS1989

Software package for performing experiments about the convolutionally encoded Voyager 1 link

A software package enabling engineers to conduct experiments to determine the actual performance of long constraint-length convolutional codes over the Voyager 1 communication link directly from the Jet Propulsion Laboratory (JPL) has been developed. Using this software, engineers are able to enter test data from the Laboratory in Pasadena, California. The software encodes the data and then sends the encoded data to a personal computer (PC) at the Goldstone Deep Space Complex (GDSC) over telephone lines. The encoded data are sent to the transmitter by the PC at GDSC. The received data, after being echoed back by Voyager 1, are first sent to the PC at GDSC, and then are sent back to the PC at the Laboratory over telephone lines for decoding and further analysis. All of these operations are fully integrated and are completely automatic. Engineers can control the entire software system from the Laboratory. The software encoder and the hardware decoder interface were developed for other applications, and have been modified appropriately for integration into the system so that their existence is transparent to the users. This software provides: (1) data entry facilities, (2) communication protocol for telephone links, (3) data displaying facilities, (4) integration with the software encoder and the hardware decoder, and (5) control functions.

Cheng, U.↗
NASA NTRS2004

Liquid Crystal-Based Beam Steering Device Development for NASA Applications

The NASA Computing, Information and Communications Technology (CICT) Program is supporting the development of liquid crystal-based beam steering devices. The device would use inexpensive, light-weight, optical components, and it would have the following capabilities: electronic beam scanning to angles above 1 milliradian, and submicroradian beam pointing accuracy. In order to correct for the imperfections resulting from the space-deployable optics, the technique of wave-front correction would be implemented. Hence, the output beam quality would be maintained. The potential applications could include satellite tracking, near-Earth inter-satellite communications, deep-space communications, and optical phased array systems. The status of the beam steering device development based on the liquid crystal technology and its relationship to prospective NASA mission scenarios will be described.

Pouch, John↗
NASA NTRS1977

An improved digital algorithm for fast amplitude approximations of quadrature pairs

A computationally fast algorithm for approximating the amplitude (A) of a quadrature pair (I,Q) has been discovered. The piecewise linear formula, the unbiased estimate of A=X+1/8y when X or = 3Y and 7/8X + 1/2Y when X or = 3Y applies where X is congruent to the maximum absolute value of (I,Q) and Y is congruent to the minimum absolute value of (I,Q). This algorithm has been proven to be far more accurate then currently used approximations. An immediate application is the wideband digital spectrum analyzer under development for monitoring radio frequency interference at Deep Space Network stations.

Levitt, B. K.↗
NASA NTRS1993

Radio Doppler navigation of interplanetary spacecraft using different data processing modes

Doppler shift measurements derived from closed-loop radio tracking of distant spacecraft by ground stations of the Deep Space Network (DSN) are one of the principal means used for interplanetary navigation. This paper describes an investigation of the navigation accuracy that can be achieved with two-way coherent X-band (7.2 to 8.4 GHz) Doppler phase and frequency data; both theoretical and practical aspects of the two Doppler formulations are addressed. A new filtering strategy is also proposed, which differs from current approaches in that most of the ground system calibration errors affecting the Doppler data are represented as filter parameters, in addition to the spacecraft trajectory parameters. Error covariance computations for application of the proposed filter to a navigation scenario derived from the Mars Observer mission are provided, in order to assess the performance that might be obtained in practice. The results indicate that with a phase formulation of the Doppler data and the new filter, navigation accuracies of 15 km at Mars (0.05 microrad in an angular sense) are possible with the DSN's present X-band Doppler tracking system.

Thurman, S. W.↗