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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 NTRS2006

Spitzer Space Telescope Sequencing Operations Software, Strategies, and Lessons Learned

The Space Infrared Telescope Facility (SIRTF) was launched in August, 2003, and renamed to the Spitzer Space Telescope in 2004. Two years of observing the universe in the wavelength range from 3 to 180 microns has yielded enormous scientific discoveries. Since this magnificent observatory has a limited lifetime, maximizing science viewing efficiency (ie, maximizing time spent executing activities directly related to science observations) was the key operational objective. The strategy employed for maximizing science viewing efficiency was to optimize spacecraft flexibility, adaptability, and use of observation time. The selected approach involved implementation of a multi-engine sequencing architecture coupled with nondeterministic spacecraft and science execution times. This approach, though effective, added much complexity to uplink operations and sequence development. The Jet Propulsion Laboratory (JPL) manages Spitzer s operations. As part of the uplink process, Spitzer s Mission Sequence Team (MST) was tasked with processing observatory inputs from the Spitzer Science Center (SSC) into efficiently integrated, constraint-checked, and modeled review and command products which accommodated the complexity of non-deterministic spacecraft and science event executions without increasing operations costs. The MST developed processes, scripts, and participated in the adaptation of multi-mission core software to enable rapid processing of complex sequences. The MST was also tasked with developing a Downlink Keyword File (DKF) which could instruct Deep Space Network (DSN) stations on how and when to configure themselves to receive Spitzer science data. As MST and uplink operations developed, important lessons were learned that should be applied to future missions, especially those missions which employ command-intensive operations via a multi-engine sequence architecture.

missions operations↗
NASA NTRS2005

An Overview and Status of NASA's Radioisotope Power Conversion Technology NRA

NASA's Advanced Radioisotope Power Systems (RPS) development program is developing next generation radioisotope power conversion technologies that will enable future missions that have requirements that can not be met by either photovoltaic systems or by current Radioisotope Power System (RPS) technology. The Advanced Power Conversion Research and Technology project of the Advanced RPS development program is funding research and technology activities through the NASA Research Announcement (NRA) 02- OSS-01, "Research Opportunities in Space Science 2002" entitled "Radioisotope Power Conversion Technology" (RPCT), 13 August 2002. The objective of the RPCT NRA is to advance the development of radioisotope power conversion technologies to provide significant improvements over the state-of-practice General Purpose Heat Source/Radioisotope Thermoelectric Generator by providing significantly higher efficiency to reduce the number of radioisotope fuel modules, and increase specific power (watts/kilogram). Other Advanced RPS goals include safety, long-life, reliability, scalability, multi-mission capability, resistance to radiation, and minimal interference with the scientific payload. These advances would enable a factor of 2 to 4 decrease in the amount of fuel required to generate electrical power. The RPCT NRA selected advanced RPS power conversion technology research and development proposals in the following three areas: innovative RPS power conversion research, RPS power conversion technology development in a nominal 100We scale; and, milliwatt/multi-watt RPS (mWRPS) power conversion research. Ten RPCT NRA contracts were awarded in 2003 in the areas of Brayton, Stirling, thermoelectric (TE), and thermophotovoltaic (TPV) power conversion technologies. This paper will provide an overview of the RPCT NRA, and a brief summary of accomplishments over the first 18 months but focusing on advancements made over the last 6 months.

Anderson, David J.↗
NASA NTRS2004

Observation Planning Made Simple with Science Opportunity Analyzer (SOA)

As NASA undertakes the exploration of the Moon and Mars as well as the rest of the Solar System while continuing to investigate Earth's oceans, winds, atmosphere, weather, etc., the ever-existing need to allow operations users to easily define their observations increases. Operation teams need to be able to determine the best time to perform an observation, as well as its duration and other parameters such as the observation target. In addition, operations teams need to be able to check the observation for validity against objectives and intent as well as spacecraft constraints such as turn rates and acceleration or pointing exclusion zones. Science Opportunity Analyzer (SOA), in development for the last six years, is a multi-mission toolset that has been built to meet those needs. The operations team can follow six simple steps and define his/her observation without having to know the complexities of orbital mechanics, coordinate transformations, or the spacecraft itself.

observation planning operations↗
NASA NTRS2005

Results of the JIMO Follow-on Destinations Parametric Studies

NASA's proposed Jupiter Icy Moon Orbiter (JIMO) mission currently in conceptual development is to be the first one of a series of highly capable Nuclear Electric Propulsion (NEP) science driven missions. To understand the implications of a multi-mission capability requirement on the JIMO vehicle and mission, the NASA Prometheus Program initiated a set of parametric high-level studies to be followed by a series of more in-depth studies. The JIMO potential follow-on destinations identified include a Saturn system tour, a Neptune system tour, a Kuiper Belt Objects rendezvous, an Interstellar Precursor mission, a Multiple Asteroid Sample Return and a Comet Sample Return. This paper shows that the baseline JIMO reactor and design envelop can satisfy five out of six of the follow-on destinations. Flight time to these destinations can significantly be reduced by increasing the launch energy or/and by inserting gravity assists to the heliocentric phase.

nuclear electric propulsion↗
NASA NTRS2006

Sequencing System Building Blocks: Using a Component Architecture for Sequencing Software

Over the last few years software engineering has made significant strides in making more flexible architectures and designs possible. However, at the same time, spacecraft have become more complex and flight software has become more sophisticated. Typically spacecraft are often one-of-a-kind entities that have different hardware designs, different capabilities, different instruments, etc. Ground software has become more complex and operations teams have had to learn a myriad of tools that all have different user interfaces and represent data in different ways. At Jet Propulsion Laboratory (JPL) these themes have collided to require a new approach to producing ground system software. Two different groups have been looking at tackling this particular problem. One group is working for the JPL Mars Technology Program in the Mars Science Laboratory (MSL) Focused Technology area. The other group is the JPL Multi-Mission Planning and Sequencing Group. The major concept driving these two approaches on a similar path is to provide software that can be a more cohesive flexible system that provides a set of planning and sequencing system of services. This paper describes the efforts that have been made to date to create a unified approach from these disparate groups.

multi mission planning↗
NASA NTRS2004

Science Opportunity Analyzer (SOA): Science Planning Made Simple

.For the first time at JPL, the Cassini mission to Saturn is using distributed science operations for developing their experiments. Remote scientists needed the ability to: a) Identify observation opportunities; b) Create accurate, detailed designs for their observations; c) Verify that their designs meet their objectives; d) Check their observations against project flight rules and constraints; e) Communicate their observations to other scientists. Many existing tools provide one or more of these functions, but Science Opportunity Analyzer (SOA) has been built to unify these tasks into a single application. Accurate: Utilizes JPL Navigation and Ancillary Information Facility (NAIF) SPICE* software tool kit - Provides high fidelity modeling. - Facilitates rapid adaptation to other flight projects. Portable: Available in Unix, Windows and Linux. Adaptable: Designed to be a multi-mission tool so it can be readily adapted to other flight projects. Implemented in Java, Java 3D and other innovative technologies. Conclusion: SOA is easy to use. It only requires 6 simple steps. SOA's ability to show the same accurate information in multiple ways (multiple visualization formats, data plots, listings and file output) is essential to meet the needs of a diverse, distributed science operations environment.

observation planning operations↗
NASA NTRS2006

Technology Developments Integrating a Space Network Communications Testbed

As future manned and robotic space explorations missions involve more complex systems, it is essential to verify, validate, and optimize such systems through simulation and emulation in a low cost testbed environment. The goal of such a testbed is to perform detailed testing of advanced space and ground communications networks, technologies, and client applications that are essential for future space exploration missions. We describe the development of new technologies enhancing our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) that enable its integration in a distributed space communications testbed. MACHETE combines orbital modeling, link analysis, and protocol and service modeling to quantify system performance based on comprehensive considerations of different aspects of space missions. It can simulate entire networks and can interface with external (testbed) systems. The key technology developments enabling the integration of MACHETE into a distributed testbed are the Monitor and Control module and the QualNet IP Network Emulator module. Specifically, the Monitor and Control module establishes a standard interface mechanism to centralize the management of each testbed component. The QualNet IP Network Emulator module allows externally generated network traffic to be passed through MACHETE to experience simulated network behaviors such as propagation delay, data loss, orbital effects and other communications characteristics, including entire network behaviors. We report a successful integration of MACHETE with a space communication testbed modeling a lunar exploration scenario. This document is the viewgraph slides of the presentation.

hybrid simulation↗
NASA NTRS2005

Sequence System Building Blocks: Using a Component Architecture for Sequencing Software

Over the last few years software engineering has made significant strides in making more flexible architectures and designs possible. However, at the same time, spacecraft have become more complex and flight software has become more sophisticated. Typically spacecraft are often one-of-a-kind entities that have different hardware designs, different capabilities, different instruments, etc. Ground software has become more complex and operations teams have had to learn a myriad of tools that all have different user interfaces and represent data in different ways. At Jet Propulsion Laboratory (JPL) these themes have collided to require an new approach to producing ground system software. Two different groups have been looking at tackling this particular problem. One group is working for the JPL Mars Technology Program in the Mars Science Laboratory (MSL) Focused Technology area. The other group is the JPL Multi-Mission Planning and Sequencing Group . The major concept driving these two approaches on a similar path is to provide software that can be a more cohesive flexible system that provides a act of planning and sequencing system of services. This paper describes the efforts that have been made to date to create a unified approach from these disparate groups.

flexible architectures↗
NASA NTRS2006

Flight-Tested Prototype of BEAM Software

Researchers at JPL have completed a software prototype of BEAM (Beacon-based Exception Analysis for Multi-missions) and successfully tested its operation in flight onboard a NASA research aircraft. BEAM (see NASA Tech Briefs, Vol. 26, No. 9; and Vol. 27, No. 3) is an ISHM (Integrated Systems Health Management) technology that automatically analyzes sensor data and classifies system behavior as either nominal or anomalous, and further characterizes anomalies according to strength, duration, and affected signals. BEAM (see figure) can be used to monitor a wide variety of physical systems and sensor types in real time. In this series of tests, BEAM monitored the engines of a Dryden Flight Research Center F-18 aircraft, and performed onboard, unattended analysis of 26 engine sensors from engine startup to shutdown. The BEAM algorithm can detect anomalies based solely on the sensor data, which includes but is not limited to sensor failure, performance degradation, incorrect operation such as unplanned engine shutdown or flameout in this example, and major system faults. BEAM was tested on an F-18 simulator, static engine tests, and 25 individual flights totaling approximately 60 hours of flight time. During these tests, BEAM successfully identified planned anomalies (in-flight shutdowns of one engine) as well as minor unplanned anomalies (e.g., transient oil- and fuel-pressure drops), with no false alarms or suspected false-negative results for the period tested. BEAM also detected previously unknown behavior in the F- 18 compressor section during several flights. This result, confirmed by direct analysis of the raw data, serves as a significant test of BEAM's capability.

Mackey, Ryan↗
NASA NTRS2004

Analyzing Spacecraft Telecommunication Systems

Multi-Mission Telecom Analysis Tool (MMTAT) is a C-language computer program for analyzing proposed spacecraft telecommunication systems. MMTAT utilizes parameterized input and computational models that can be run on standard desktop computers to perform fast and accurate analyses of telecommunication links. MMTAT is easy to use and can easily be integrated with other software applications and run as part of almost any computational simulation. It is distributed as either a stand-alone application program with a graphical user interface or a linkable library with a well-defined set of application programming interface (API) calls. As a stand-alone program, MMTAT provides both textual and graphical output. The graphs make it possible to understand, quickly and easily, how telecommunication performance varies with variations in input parameters. A delimited text file that can be read by any spreadsheet program is generated at the end of each run. The API in the linkable-library form of MMTAT enables the user to control simulation software and to change parameters during a simulation run. Results can be retrieved either at the end of a run or by use of a function call at any time step.

Kordon, Mark↗
NASA NTRS2003

Software Supports Distributed Operations via the Internet

Multi-mission Encrypted Communication System (MECS) is a computer program that enables authorized, geographically dispersed users to gain secure access to a common set of data files via the Internet. MECS is compatible with legacy application programs and a variety of operating systems. The MECS architecture is centered around maintaining consistent replicas of data files cached on remote computers. MECS monitors these files and, whenever one is changed, the changed file is committed to a master database as soon as network connectivity makes it possible to do so. MECS provides subscriptions for remote users to automatically receive new data as they are generated. Remote users can be producers as well as consumers of data. Whereas a prior program that provides some of the same services treats disconnection of a user from the network of users as an error from which recovery must be effected, MECS treats disconnection as a nominal state of the network: This leads to a different design that is more efficient for serving many users, each of whom typically connects and disconnects frequently and wants only a small fraction of the data at any given time.

Norris, Jeffrey↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗