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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 NTRS2020

Terra MODIS: 20 years of on-orbit calibration and performance

Since its launch in December 1999, Terra Moderate Resolution Imaging Spectroradiometer (MODIS) has successfully operated for more than 20 years, with its observations generating a broad range of science data products that have greatly enabled the remote sensing community and users worldwide in their studies of many key geophysical parameters of the Earth’s systems. MODIS collects data in 36 spectral bands, covering wavelengths from 0.41 to 14.4 μm, which are calibrated by a set of onboard calibrators (OBCs). Also contributing to the sensor’s mission-long on-orbit calibration and characterization are near-monthly scheduled lunar observations and multiple time series of the sensor’s responses over select ground targets at a variety of scan angles. To a large extent, the quality of MODIS data products relies strongly on the dedicated efforts to operate and calibrate the instrument, to derive and update calibration parameters, and to develop and implement new calibration strategies and algorithms in response to on-orbit changes of the sensor’s characteristics and its OBC functions. We provide an overview of the Terra MODIS on-orbit operation and calibration activities over the last 20 years, including changes made to extend and preserve the instrument and OBC functions and their operation strategies. It also illustrates the sensor’s on-orbit performance with results derived from its OBC, lunar observations, and select ground targets and discusses major changes in sensor characteristics and corrections applied to the L1B algorithms as well as calibration lookup table updates. To date, the Terra MODIS instrument and its OBCs continue to operate and function normally. Except for those identified prelaunch, most spectral bands and detectors continue to meet their specified calibration requirements. Also discussed in our paper are lessons learned from Terra MODIS operation and calibration, as well as future efforts to further extend and maintain the quality of its long-term data records.

Terra↗
NASA NTRS1996

Surface and Atmospheric Parameter Retrieval From AVIRIS Data: The Importance of Non-Linear Effects

AVIRIS data represent a new and important approach for the retrieval of atmospheric and surface parameters from optical remote sensing data. Not only as a test for future space systems, but also as an operational airborne remote sensing system, the development of algorithms to retrieve information from AVIRIS data is an important step to these new approaches and capabilities. Many things have been learned since AVIRIS became operational, and the successive technical improvements in the hardware and the more sophisticated calibration techniques employed have increased the quality of the data to the point of almost meeting optimum user requirements. However, the potential capabilities of imaging spectrometry over the standard multispectral techniques have still not been fully demonstrated. Reasons for this are the technical difficulties in handling the data, the critical aspect of calibration for advanced retrieval methods, and the lack of proper models with which to invert the measured AVIRIS radiances in all the spectral channels. To achieve the potential of imaging spectrometry, these issues must be addressed. In this paper, an algorithm to retrieve information about both atmospheric and surface parameters from AVIRIS data, by using model inversion techniques, is described. Emphasis is put on the derivation of the model itself as well as proper inversion techniques, robust to noise in the data and an inadequate ability of the model to describe natural variability in the data. The problem of non-linear effects is addressed, as it has been demonstrated to be a major source of error in the numerical values retrieved by more simple, linear-based approaches. Non-linear effects are especially critical for the retrieval of surface parameters where both scattering and absorption effects are coupled, as well as in the cases of significant multiple-scattering contributions. However, sophisticated modeling approaches can handle such non-linear effects, which are especially important over vegetated surfaces. All the data used in this study were acquired during the 1991 Multisensor Airborne Campaign (MAC-Europe), as part of the European Field Experiment on a Desertification-threatened Area (EFEDA), carried out in Spain in June-July 1991.

Green Robert O.↗
NASA NTRS2014

Behavioral Health and Performance at NASA JSC: Recent Successes and Future Plan for BHP Research and Operations

The Behavioral Health and Performance discipline at NASA Johnson Space Center is organized into two distinct Divisions (Biomedical Research and Environmental Science Division and Space and Clinical Operations Division) but is integrated and interrelated in its day‐to‐day work. Ongoing operations supporting NASA's spaceflight goals benefit from the research portfolios that address risks to mission success. Similarly, these research portfolios are informed by operations to ensure investigations stay relevant given the dynamic environment of spaceflight. There are many success stories that can be presented where initial work begun as a BHP Research project, and funded through the Human Research Program, was fully implemented in operations or addressed an operational need. Examples include improving effectiveness of the debriefings used within Mission Control by the Mission Operations Directorate and countermeasures for fatigue management. There is also ongoing collaboration with research and operations for developing selection methods for future generation astronauts, and to enhance and inform the current family support function. The objective of this panel is to provide examples of recent success stories, describe areas where close collaboration is benefitting ongoing research and operations, and summarize how this will come together as NASA plans for the one year ISS mission - a unique opportunity for both BHP operations and research to learn more about preparing and supporting crewmembers for extended missions in space. The proposed panel will be comprised of six presentations, each describing a unique aspect of research or operations and the benefits to current and future spaceflight.

Leveton, L. B.↗
NASA NTRS1995

Advanced Transportation System Studies Technical Area 2 (TA-2) Heavy Lift Launch Vehicle Development Contract: Technical Results - Volume 2

The sections in this report include: Single Stage to Orbit (SSTO) Design Ground-rules; Operations Issues and Lessons Learned; Vertical-Takeoff/Landing Versus Vertical-Takeoff/Horizontal-Landing; SSTO Design Results; SSTO Simulation Results; SSTO Assessment Results; SSTO Sizing Tool User's Guide; SSto Turnaround Assessment Report; Ground Operations Assessment First Year Executive Summary; Health Management System Definition Study; Major TA-2 Presentations; First Lunar Outpost Heavy Lift Launch Vehicle Design and Assessment; and the section, Russian Propulsion Technology Assessment Reports.

Source record↗
NASA NTRS1995

Space Mechanisms Lessons Learned Study. Volume 1: Summary

Hundreds of satellites have been launched to date. Some have operated extremely well and others have not. In order to learn from past operating experiences, a study was conducted to determine the conditions under which space mechanisms (mechanically moving components) have previously worked or failed. The study consisted of: (1) an extensive literature review that included both government contractor reports and technical journals; (2) communication and visits (when necessary) to the various NASA and DOD centers and their designated contractors (this included contact with project managers of current and prior NASA satellite programs as well as their industry counterparts); (3) requests for unpublished information to NASA and industry; and (4) a mail survey designed to acquire specific mechanism experience. The information obtained has been organized into two volumes. Volume 1 provides a summary of the lessons learned, the results of a needs analysis, responses to the mail survey, a listing of experts, a description of some available facilities and a compilation of references. Volume 2 contains a compilation of the literature review synopsis.

Shapiro, Wilbur↗
NASA NTRS2006

Human Factors Lessons Learned on the International Space Station

Experience on International Space Station (ISS) provides many important lessons for future space flight. NASA human factors engineers have been systematically collecting lessons learned from crew debriefs, as well as working with ground support teams to continuously improve crew operations. This paper describes the methods for collecting data from debriefs, lessons learned through that process, and an example of a technology development task funded through the Space Human Factors Engineering (SHFE) program element in response to an identified operational need. Each ISS increment crew spends many hours after the flight answering questions from the various subsystem leads. The Flight Crew Integration subsystem lead asks questions specific to human factors and habitability issues. In addition, crew comments on many other subsystems provide insight into interface designs, operability and maintainability. The debrief comments are unique to each crew, and must be categorized to provide operational lessons learned. Personal identifiers are removed and comments aggregated to separate consistent issues from personal preferences. Examples will be given, and the procedure for incorporating the lessons into requirements and guidelines for the next human space vehicle will be described. In flight, very few astronauts are medical doctors. Written medical procedures during flight need to be easy to follow and quick to understand. The problem was analyzed as part of a SHFE task. Organization was analyzed and reorganizations were created and tested. Results will be reported. The ISS is a very important analog for planning future long-term missions. Collection of data from debriefs, studying the lessons learned and focusing on requirements for future missions are examples of the accomplishments through the SHFE program.

Woolford, Barbara↗
NASA NTRS2011

Compliant Task Execution and Learning for Safe Mixed-Initiative Human-Robot Operations

We introduce a novel task execution capability that enhances the ability of in-situ crew members to function independently from Earth by enabling safe and efficient interaction with automated systems. This task execution capability provides the ability to (1) map goal-directed commands from humans into safe, compliant, automated actions, (2) quickly and safely respond to human commands and actions during task execution, and (3) specify complex motions through teaching by demonstration. Our results are applicable to future surface robotic systems, and we have demonstrated these capabilities on JPL's All-Terrain Hex-Limbed Extra-Terrestrial Explorer (ATHLETE) robot.

multi-agent collaboration↗
NASA NTRS2020

MSL Telecom Automated Anomaly Detection

The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.

Mukai, Ryan↗
DOE OSTI2025

Deep Learning for Subsurface Flow: A Comparative Study of U‐Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage

Subsurface flow research is essential for the sustainable management of natural resources and the environment. Deep learning (DL) has significantly advanced this field by developing efficient and accurate surrogate models to replace computationally expensive physics‐based simulations. These surrogate models are commonly used to predict the spatiotemporal evolution of state variables, such as gas saturation and reservoir pressure, in heterogeneous geological formations. Despite the various DL models applied to this task, there is a lack of studies systematically comparing their performance. This absence of comparative analysis leads to somewhat arbitrary DL model selection in subsurface flow research, resulting in suboptimal performance and potentially inaccurate predictions. To bridge this gap, we conduct a systematic comparison study of three popular DL architectures—U‐Net, Fourier Neural Operators (FNO), and Segmentation Transformer (SETR)—in surrogate modeling of underground hydrogen storage (UHS). We focus on UHS due to its promise of enhancing clean energy resilience and its cyclic operational conditions that represent common scenarios in various subsurface applications. We evaluate the models based on accuracy, training cost, and inference speed. The comparison shows that U‐Net achieves the highest accuracy, followed by SETR and FNO. Despite its lower accuracy, FNO has the highest inference speed. SETR offers competitive accuracy with the least training memory usage, demonstrating the potential of transformers in learning subsurface flow. Our results provide guidance for selecting DL models for surrogate modeling in a wide range of subsurface flow problems.

42 ENGINEERING↗
NASA NTRS2011

Manned Mission Planning Considerations when Using a Non-Regenerable CO2 Removal System

As the commercial spacecraft industry increases in size, there will be a corresponding increase in the number of manned spacecraft built and operationally flown each year. Industry teams for these new spacecraft will have multiple design and operational choices to make for each of these spacecraft s subsystems. The carbon dioxide (CO2) removal subsystem of the environmental control and life support system is one that presents such challenges. This paper seeks to aid industry in making design and operations choices by providing a document containing lessons learned by the Space Shuttle Program s Operations team, with specific focus given to the non-regenerable CO2 removal system currently used by the Space Shuttle. Carbon dioxide, one of the key byproducts of respiration, can lead to injury and death if allowed to build up in a spacecraft s habitable environment. Therefore, any spacecraft s environmental control and life support system must contain a method for removing this hazard. These removal systems can either be non-regenerable or regenerable. While this paper defines the difference between these types and presents a generic comparison of their capabilities, the focus is specifically on the Space Shuttle s CO2 removal systems. This will include a short discussion of the Space Shuttle s regenerable amine solid absorption system that was part of the Extended Duration Orbiter (EDO) modification, however, emphasis is given to the non-regenerable Lithium Hydroxide canister system used as the prime removal method employed by the Space Shuttle, including a discussion on why this method was chosen over a regenerable system. A full exposition is given on all of the considerations required for mission planning when using a non-regenerable CO2 removal system. Key discussion items include: airflow lessons learned, recent physiological issues related to short term and long term exposure and how lower levels may be more harmful than previously thought, lithium hydroxide dust issues, mass vs. stowage comparisons, cost, complexity considerations, operational margin considerations, timing considerations with respect to the crew s schedule, and logistical management. The conclusion of the paper finalizes the discussion and contains a listing of considerations in regards to CO2 management using a non-regenerable removal system in the near future.

DeSimpelaere, Edward↗
NASA NTRS2005

Learning from the Mars Rover Mission: Scientific Discovery, Learning and Memory

Purpose: Knowledge management for space exploration is part of a multi-generational effort. Each mission builds on knowledge from prior missions, and learning is the first step in knowledge production. This paper uses the Mars Exploration Rover mission as a site to explore this process. Approach: Observational study and analysis of the work of the MER science and engineering team during rover operations, to investigate how learning occurs, how it is recorded, and how these representations might be made available for subsequent missions. Findings: Learning occurred in many areas: planning science strategy, using instrumen?s within the constraints of the martian environment, the Deep Space Network, and the mission requirements; using software tools effectively; and running two teams on Mars time for three months. This learning is preserved in many ways. Primarily it resides in individual s memories. It is also encoded in stories, procedures, programming sequences, published reports, and lessons learned databases. Research implications: Shows the earliest stages of knowledge creation in a scientific mission, and demonstrates that knowledge management must begin with an understanding of knowledge creation. Practical implications: Shows that studying learning and knowledge creation suggests proactive ways to capture and use knowledge across multiple missions and generations. Value: This paper provides a unique analysis of the learning process of a scientific space mission, relevant for knowledge management researchers and designers, as well as demonstrating in detail how new learning occurs in a learning organization.

Linde, Charlotte↗
NASA NTRS1995

Space Mechanisms Lessons Learned Study. Volume 2: Literature Review

Hundreds of satellites have been launched to date. Some have operated extremely well and others have not. In order to learn from past operating experiences, a study was conducted to determine the conditions under which space mechanisms (mechanically moving components) have previously worked or failed. The study consisted of an extensive literature review that included both government contractor reports and technical journals, communication and visits (when necessary) to the various NASA and DOD centers and their designated contractors (this included contact with project managers of current and prior NASA satellite programs as well as their industry counterparts), requests for unpublished information to NASA and industry, and a mail survey designed to acquire specific mechanism experience. The information obtained has been organized into two volumes. Volume 1 provides a summary of the lesson learned, the results of a needs analysis, responses to the mail survey, a listing of experts, a description of some available facilities, and a compilation of references. Volume 2 contains a compilation of the literature review synopsis.

Shapiro, Wilbur↗
DOE OSTI2024

Implementation of Detailed Polyethylene Pyrolysis Kinetics into CFD Simulations using Machine Learning

Municipal solid waste (MSW) and waste plastics have received significant attention due to the issues of waste generation and storage, as well as their potential as an energy resource. High-density polyethylene (HDPE) makes up a large portion of plastic waste and has been the subject of several conversion studies. However, the mechanisms associated with converting HDPE through pyrolysis and gasification are extensive and complex making them difficult to implement into high-fidelity computational fluid dynamic (CFD) simulations. For this project, a primary pyrolysis mechanism containing 42 unique species and 737 heterogeneous reactions was used to generate kinetic data over a range of operating conditions. A machine learning (ML) model was developed to replicate the results of the detailed pyrolysis mechanism while significantly increasing the computational efficiency. A deep operator network (DeepONet) architecture was adopted to train the model using time steps relevant to CFD simulations. The ML used physics-based loss functions to ensure mass conservation. The ML model has been deployed in simple MFiX CFD simulations, single particle, and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗
NASA NTRSDate not supplied

Real-Time Science Decisioning During High Tempo-High Intensity Mission Operations and the Role of Analogs

Introduction: NASA’s VIPER mission presents a unique operational paradigm within the history of robotic spaceflight. The proximity of the Moon to the Earth and the terrain elements (surface characteristics, light/shadow dynamics, communication links) of the lunar South Polar landing site create unprecedented operational conditions between these two planetary bodies. Apollo era lunar science and exploration included humans in situ to operate instruments and assimilate observational inputs in real-time. Previous lunar orbital missions have worked to operational timescales, e.g., decisional timelines and communication exchanges, that were weeks in length. Mars rover missions have worked to operational timescales, e.g., decisional timelines and communication exchanges between Mars and Earth, that were hours, days, and weeks in length. In the case of the VIPER mission, our operational decisioning for rover driving and instrument commanding will be compressed to minute-scale timeframes. These operational conditions directly impact the manner and speed with which the VIPER Science Team (VST) is required to synthesize and analyze data and produce timely science-driven decisions throughout surface mission operations. The VST shall provide mission enhancing scientific input to guide rover traverse planning and drill site confirmation and selection throughout surface operations. Further, the VST input will be of vital importance to the mission’s ability to maximize science return and to meet broader NASA objectives for future lunar in-situ resource utilization (ISRU)and exploration activities. The VST co-located in the Mission Science Center (MSC) will be responsive to the tactical operational cadence of the Mission Operations Center (MOC) and will provide further strategic and Long-Term Planning (LTP) guidance to the mission. The VIPER Science Operations & Integration(SO&I)team has developed an architecture that is focused on the infusion of science-decisioning into the operational framework and execution cadence of VIPER. NASA analog research has played a significant role in the construction of the VIPER science operations systems. As an example, the SO&I team has led analog missions that have focused on bringing together expertise in the sciences (natural, applied and social) and in operations in service of learning how to build and hold together interdisciplinary work environments and what tools are needed to support high tempo, high intensity integrated decisioning. These experiences have provided an essential foundation of knowledge to the VIPER team. Those analogs that specifically influenced the VIPER science operations construct were identified through a process of comparative analysis to prioritize those that offered relevance in whole or in part, and those that did not. The analog research output that provided extensibility to the VIPER science operations architecture included remote teams of humans and robots in cooperation (synchronous and asynchronous) with simulated earthbound systems, engineering and science teams, and the integrated assembly of tools that supported scientific analysis and data synthesis and provided infrastructure for the remote testing framework. Analogs which included real-time data monitoring, synthesis, visualization and access in a democratized and operationalized manner were of particular interest to the development of the VIPER MSC toolset both in terms of the technology and the processes used to develop the supporting infrastructure. We anticipate that each subsequent mission to the lunar south pole, whether with robots or humans, will be able to optimize science and exploration return by evolving strategies to infuse real-time collaborative science-decisioning. Furthermore, these efforts will result in a foundation for science operations development in support of human-robotic exploration of deep space and Mars. NASA analogs can continue to provide the opportunity to prepare, test and iterate on the operational concepts and tools that will support these ever-expanding space exploration efforts. Our presentation will include an overview of the VIPER Science Operations & Integration development process and specifics on what aspects of analog research have had a significant impact on our work systems.

D S S Lim↗
DOE OSTI2024

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach

Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper’s advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The numerical experiments show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNO in ocean dynamics forecasting, offering a scalable solution with improved precision.

97 MATHEMATICS AND COMPUTING↗