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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 NTRS1999

Research Institute for Advanced Computer Science: Annual Report October 1998 through September 1999

The Research Institute for Advanced Computer Science (RIACS) carries out basic research and technology development in computer science, in support of the National Aeronautics and Space Administration's missions. RIACS is located at the NASA Ames Research Center (ARC). It currently operates under a multiple year grant/cooperative agreement that began on October 1, 1997 and is up for renewal in the year 2002. ARC has been designated NASA's Center of Excellence in Information Technology. In this capacity, ARC is charged with the responsibility to build an Information Technology Research Program that is preeminent within NASA. RIACS serves as a bridge between NASA ARC and the academic community, and RIACS scientists and visitors work in close collaboration with NASA scientists. RIACS has the additional goal of broadening the base of researchers in these areas of importance to the nation's space and aeronautics enterprises. RIACS research focuses on the three cornerstones of information technology research necessary to meet the future challenges of NASA missions: (1) Automated Reasoning for Autonomous Systems. Techniques are being developed enabling spacecraft that will be self-guiding and self-correcting to the extent that they will require little or no human intervention. Such craft will be equipped to independently solve problems as they arise, and fulfill their missions with minimum direction from Earth. (2) Human-Centered Computing. Many NASA missions require synergy between humans and computers, with sophisticated computational aids amplifying human cognitive and perceptual abilities; (3) High Performance Computing and Networking Advances in the performance of computing and networking continue to have major impact on a variety of NASA endeavors, ranging from modeling and simulation to data analysis of large datasets to collaborative engineering, planning and execution. In addition, RIACS collaborates with NASA scientists to apply information technology research to a variety of NASA application domains. RIACS also engages in other activities, such as workshops, seminars, and visiting scientist programs, designed to encourage and facilitate collaboration between the university and NASA information technology research communities.

Leiner, Barry M.↗

NASA NTRS2000

Research Institute for Advanced Computer Science

The Research Institute for Advanced Computer Science (RIACS) carries out basic research and technology development in computer science, in support of the National Aeronautics and Space Administration's missions. RIACS is located at the NASA Ames Research Center. It currently operates under a multiple year grant/cooperative agreement that began on October 1, 1997 and is up for renewal in the year 2002. Ames has been designated NASA's Center of Excellence in Information Technology. In this capacity, Ames is charged with the responsibility to build an Information Technology Research Program that is preeminent within NASA. RIACS serves as a bridge between NASA Ames and the academic community, and RIACS scientists and visitors work in close collaboration with NASA scientists. RIACS has the additional goal of broadening the base of researchers in these areas of importance to the nation's space and aeronautics enterprises. RIACS research focuses on the three cornerstones of information technology research necessary to meet the future challenges of NASA missions: (1) Automated Reasoning for Autonomous Systems. Techniques are being developed enabling spacecraft that will be self-guiding and self-correcting to the extent that they will require little or no human intervention. Such craft will be equipped to independently solve problems as they arise, and fulfill their missions with minimum direction from Earth; (2) Human-Centered Computing. Many NASA missions require synergy between humans and computers, with sophisticated computational aids amplifying human cognitive and perceptual abilities; (3) High Performance Computing and Networking. Advances in the performance of computing and networking continue to have major impact on a variety of NASA endeavors, ranging from modeling and simulation to data analysis of large datasets to collaborative engineering, planning and execution. In addition, RIACS collaborates with NASA scientists to apply information technology research to a variety of NASA application domains. RIACS also engages in other activities, such as workshops, seminars, and visiting scientist programs, designed to encourage and facilitate collaboration between the university and NASA information technology research communities.

Gross, Anthony R.↗

DOE OSTI2025

Recent Collaborations and Innovations to Demonstrate Next-Generation Techniques for Monitoring Subsurface Carbon Storage

World Carbon Capture, Utilization, and Storage (CCUS) Conference, Bergen, Norway, September 1–4, 2025. This talk provides a high-level overview of many novel and sustainable carbon storage-monitoring methods to accelerate the deployment of CCUS technologies at future CCUS sites across the United States. The Energy & Environmental Research Center’s work impacts the general CCUS industry by providing novel low-impact methods for tracking the injected plume’s migration and more autonomous data collection and processing techniques for performing assurance monitoring. Specifically, the results benefit 1) CCUS community members through knowledge sharing of lessons learned; 2) CCUS operators through commercialization of additional methods, including improvements to workflows and simplification of fieldwork; and 3) CCUS project stakeholders through implementation of low-impact and more autonomous monitoring solutions.

02 PETROLEUM↗

NASA NTRS1992

Intelligent Processing Equipment Research Supported by the National Science Foundation

The research in progress on processes, workstations, and systems has the goal of developing a high level of understanding of the issues involved. This will enable the incorporation of a level of intelligence that will allow the creation of autonomous manufacturing systems that operate in an optimum manner, under a wide range of conditions. The emphasis of the research has been on the development of highly productive and flexible techniques to address current and future problems in manufacturing and processing. Several of these projects have resulted in well-defined and established models that can now be implemented in the application arena in the next few years.

Rao, Suren B.↗

DOE OSTI2024

Low-Flow Marine Hydrokinetic Turbine for Small Autonomous Unmanned Mobile Recharge Stations

A prototype low-flow marine current turbine for deployment from a small unmanned mobile floating platform has been developed for autonomously seeking and harnessing tidal/coastal currents. The support platform is an unmanned surface vehicle (USV), in the form of a catamaran with two electric outboard motors and with capabilities for autonomous navigation. The USV utilized is a WAM-V 16 vehicle that has been developed separately with support from the Office of Naval Research (ONR) [1]. The marine current turbine is based on a freestream waterwheel (FSWW), also known as an undershot waterwheel (FSWW), mounted on the stern of the USV. The concept of operation involves the USV autonomously navigating to a designated marine current resource. Upon arrival, the USV anchors itself, aligns with the current, and deploys the FSWW turbine using a custom cable-lift mechanism. The turbine harnesses the local current, and an onboard power-take-off (PTO) device converts the mechanical energy into electricity, which is stored in an onboard battery bank. When energy harvesting is completed, the turbine and the anchor are retrieved and the USV navigates to a selected location. These unmanned at-sea platforms can provide power to other unmanned maritime systems. Specifically, in this project, the power generated onboard can be used to charge aerial drones via a custom flight deck that has been developed for the USV. The recharging capabilities offered by a fleet of such strategically placed recharging stations can significantly benefit aerial drones operating in the maritime domain by eliminating the need to travel back and forth to land or ship based charging stations. The project has resulted in the development of subcomponents, including the FSWW turbine, a novel PTO, an automated anchoring system for the USV, an automated turbine deployment system, and a flight deck with capabilities onboard the USV for landing, direct-contact charging and takeoff of aerial drones. The design and development of these subsystems have culminated in the overall prototype marine hydrokinetic platform (MHK Platform, Fig. 1). Comprehensive lab and field testing have been conducted to validate the functionality and performance of the platform and its components. The project demonstrates the potential for autonomous, unmanned systems to harness renewable energy from marine currents, and provide sustainable power solutions for maritime applications such as coastal surveillance and environmental monitoring; shoreline mapping; search and rescue; oceanographic research; inspection and maintenance of offshore energy installations like wind turbines and oil rigs; oil spill response; maritime disaster response; and aerial surveys, as well as facilitation of data transfer drones and shore stations.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

ToPolyAgent: AI agents for coarse-grained bead-spring topological polymer simulations

We introduce ToPolyAgent, a multi-agent AI framework for performing coarse-grained molecular dynamics (MD) simulations of topological polymers through natural language instructions. By integrating large language models (LLMs) with domain-specific computational tools, ToPolyAgent supports both interactive and autonomous simulation workflows across diverse polymer architectures, including linear, ring, brush, and star polymers, as well as dendrimers. The system consists of four LLM-powered agents: a Config Agent for generating initial polymer–solvent configurations, a Simulation Agent for executing LAMMPS-based MD simulations and conformational analyses, a Report Agent for compiling markdown reports, and a Workflow Agent for streamlined autonomous operations. Interactive mode incorporates user feedback loops for iterative refinements, while autonomous mode enables end-to-end task execution from detailed prompts. We demonstrate ToPolyAgent's versatility through case studies involving diverse polymer architectures under varying solvent conditions, thermostats, and simulation lengths. Furthermore, we highlight its potential as a research assistant by directing it to investigate the effect of interaction parameters on the linear polymer conformation, and the influence of grafting density on the persistence length of the brush polymer. By coupling natural language interfaces with rigorous simulation tools, ToPolyAgent lowers barriers to complex computational workflows and advances AI-driven materials discovery in polymer science. It lays the foundation for autonomous and extensible multi-agent scientific research ecosystems.

Ding, Lijie [Oak Ridge National Laboratory (ORNL),↗

NASA NTRS2016

The NASA/AFRL Meter Class Autonomous Telescope

For the past decade, the NASA Orbital Debris Program Office (ODPO) has relied on using various ground-based telescopes in Chile to acquire statistical survey data as well as photometric and spectroscopic data of orbital debris in geosynchronous Earth orbit (GEO). The statistical survey data have been used to supply the Orbital Debris Engineering Model (ORDEM) v.3.0 with debris detections in GEO to better model the environment at altitudes where radar detections are limited. The data produced for the statistical survey ranged from 30 to 40 nights per year, which only accounted for ~10% of the possible observing time. Data collection was restricted by ODPO resources and weather conditions. In order to improve the statistical sampling in GEO, as well as observe and sample other orbits, NASA's ODPO with support from the Air Force Research Laboratory (AFRL), has constructed a new observatory dedicated to orbital debris - the Meter Class Autonomous Telescope (MCAT) on Ascension Island. This location provides MCAT with the unique ability to access targets orbiting at an altitude of less than 1,000 km and low inclinations (< 20 deg). This orbital regime currently has little to no coverage by the U.S. Space Surveillance Network. Unlike previous ODPO optical assets, the ability to operate autonomously will allow rapid response observations of break-up events, an observing mode that was only available via radar tasking prior to MCAT's deployment. The primary goal of MCAT is to statistically characterize GEO via daily tasking files uploaded from ODPO. These tasking files define which operating mode to follow, providing the field center, rates, and/or targets to observe over the entire observing period. The system is also capable of tracking fast-moving targets in low Earth orbit (LEO), middle Earth orbit (MEO), as well as highly eccentric orbits like geostationary transfer orbits. On 25 August 2015, MCAT successfully acquired scientific first light, imaging the Bug Nebula and tracked objects in LEO, MEO, and GEO. NASA is working towards characterizing the system and thoroughly testing the integrated hardware and software control to achieve fully autonomous operations by late 2016. This paper will review the history and current status of the MCAT project, the details of the telescope system, and its five currently manifested operating modes.

Cowardin, H.↗

NASA NTRS2004

Advanced Life Support Project: Crop Experiments at Kennedy Space Center

Crop production systems provide bioregenerative technologies to complement human crew life support requirements on long duration space missions. Kennedy Space Center has lead NASA's research on crop production systems that produce high value fresh foods, provide atmospheric regeneration, and perform water processing. As the emphasis on early missions to Mars has developed, our research focused on modular, scalable systems for transit missions, which can be developed into larger autonomous, bioregenerative systems for subsequent surface missions. Components of these scalable systems will include development of efficient light generating or collecting technologies, low mass plant growth chambers, and capability to operate in the high energy background radiation and reduced atmospheric pressures of space. These systems will be integrated with air, water, and thermal subsystems in an operational system. Extensive crop testing has been done for both staple and salad crops, but limited data is available on specific cultivar selection and breadboard testing to meet nominal Mars mission profiles of a 500-600 day surface mission. The recent research emphasis at Kennedy Space Center has shifted from staple crops, such as wheat, soybean and rice, toward short cycle salad crops such as lettuce, onion, radish, tomato, pepper, and strawberry. This paper will review the results of crop experiments to support the Exploration Initiative and the ongoing development of supporting technologies, and give an overview of capabilities of the newly opened Space Life Science (SLS) Lab at Kennedy Space Center. The 9662 square m (104,000 square ft) SLS Lab was built by the State of Florida and supports all NASA research that had been performed in Hanger-L. In addition to NASA research, the SLS Lab houses the Florida Space Research Institute (FSRI), responsible for co-managing the facility, and the University of Florida (UF) has established the Space Agriculture and Biotechnology Research and Education (SABRE) Center with several faculty.

Sager, John C.↗

DOE OSTI2023

Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells

Abstract Simultaneously optimizing the processing parameters of functional thin films remains a challenge. The design and utilization of a fully automated platform called SPINBOT is presented for the engineering of solution‐processed functional thin films. The SPINBOT is capable of performing experiments with high sampling variability through the unsupervised processing of hundreds of substrates with exceptional experimental control. Through the iterative optimization process enabled by the Bayesian optimization (BO) algorithm, the SPINBOT explores an intricate parameter space, continuously improving the quality and reproducibility of the produced thin films. This machine learning (ML)‐guided reliable SPINBOT platform enables the acceleration of the optimization process of perovskite solar cells via a simple photoluminescence characterization of films. As a result, this study arrives at an optimal film that, when processed into a solar cell in an ambient atmosphere, immediately yields a champion power conversion efficiency (PCE) of 21.6% with satisfactory performance reproducibility. The unsealed devices retain 90% of their initial efficiency after 1100 h of continuous operation at 60–65 °C under metal‐halide lamps. It is anticipated that the integration of robotic platforms with the intelligent algorithm will facilitate the widespread adoption of effective autonomous experimentation to address the evolving needs and constraints within the materials science research community.

14 SOLAR ENERGY↗

DOE OSTI2021

Smart Methane Emission Detection System Development Final Report

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators.

03 NATURAL GAS↗

NASA NTRS1974

On-orbit performance of the Skylab astronaut maneuvering research vehicle

The present work evaluates the results of flight tests of the Astronaut Maneuvering Research Vehicle (AMRV) conducted inside the Skylab Orbital Workshop. The vehicle is an autonomous mobility aid which allows the pilot to control his position and orientation in space without the necessity of reacting against the spacecraft. The purpose of the tests was to obtain operation experience and engineering/human performance data in a zero gravity environment, to correlate on-orbit data with simulation data, and to recommend a design configuration for future operational maneuvering units. Four different techniques for controlling AMRV dynamics were tested: hand-held maneuvering unit, direct, rate gyro, and control moment gyro operating modes.

Murtagh, T. B.↗

NASA NTRS1986

Chemical production on Mars using in situ propellant production technology

In situ propellant production (ISPP) was examined in terms of its applicability to a manned Mars mission. Production of oxygen from Martian atmosphere was used as the baseline system for ISPP technology assessment. It was concluded that production of oxygen was an important element in a manned Mars mission which could be developed in terrestrial laboratories. Expert system methodology will be required to enable reliable, autonomous production of oxygen. Furthermore, while no major technical breakthroughs are required, this research requires a long lead time to permit its systematic evolution.

Robert L Ash↗

NASA NTRS1990

The real-time learning mechanism of the Scientific Research Associates Advanced Robotic System (SRAARS)

Scientific research associates advanced robotic system (SRAARS) is an intelligent robotic system which has autonomous learning capability in geometric reasoning. The system is equipped with one global intelligence center (GIC) and eight local intelligence centers (LICs). It controls mainly sixteen links with fourteen active joints, which constitute two articulated arms, an extensible lower body, a vision system with two CCD cameras and a mobile base. The on-board knowledge-based system supports the learning controller with model representations of both the robot and the working environment. By consecutive verifying and planning procedures, hypothesis-and-test routines and learning-by-analogy paradigm, the system would autonomously build up its own understanding of the relationship between itself (i.e., the robot) and the focused environment for the purposes of collision avoidance, motion analysis and object manipulation. The intelligence of SRAARS presents a valuable technical advantage to implement robotic systems for space exploration and space station operations.

Chen, Alexander Y.↗

NASA NTRS1989

Experiences with the JPL telerobot testbed: Issues and insights

The Jet Propulsion Laboratory's (JPL) Telerobot Testbed is an integrated robotic testbed used to develop, implement, and evaluate the performance of advanced concepts in autonomous, tele-autonomous, and tele-operated control of robotic manipulators. Using the Telerobot Testbed, researchers demonstrated several of the capabilities and technological advances in the control and integration of robotic systems which have been under development at JPL for several years. In particular, the Telerobot Testbed was recently employed to perform a near completely automated, end-to-end, satellite grapple and repair sequence. The task of integrating existing as well as new concepts in robot control into the Telerobot Testbed has been a very difficult and timely one. Now that researchers have completed the first major milestone (i.e., the end-to-end demonstration) it is important to reflect back upon experiences and to collect the knowledge that has been gained so that improvements can be made to the existing system. It is also believed that the experiences are of value to the others in the robotics community. Therefore, the primary objective here will be to use the Telerobot Testbed as a case study to identify real problems and technological gaps which exist in the areas of robotics and in particular systems integration. Such problems have surely hindered the development of what could be reasonably called an intelligent robot. In addition to identifying such problems, researchers briefly discuss what approaches have been taken to resolve them or, in several cases, to circumvent them until better approaches can be developed.

Stone, Henry W.↗

NASA NTRS1989

First results in terrain mapping for a roving planetary explorer

To perform planetary exploration without human supervision, a complete autonomous rover must be able to model its environment while exploring its surroundings. Researchers present a new algorithm to construct a geometric terrain representation from a single range image. The form of the representation is an elevation map that includes uncertainty, unknown areas, and local features. By virtue of working in spherical-polar space, the algorithm is independent of the desired map resolution and the orientation of the sensor, unlike other algorithms that work in Cartesian space. They also describe new methods to evaluate regions of the constructed elevation maps to support legged locomotion over rough terrain.

Krotkov, E.↗

NASA NTRS1991

KC-135 materials handling robotics

Robot dynamics and control will become an important issue for implementing productive platforms in space. Robotic operations will become necessary for man-tended stations and for efficient performance of routine operations in a manned platform. The current constraints on the use of robotic devices in a microgravity environment appears to be due to an anticipated increase in acceleration levels due to manipulator motion and for safety concerns. The objective of this study will be to provide baseline data to meet that need. Most texts and papers dealing with the kinematics and dynamics of robots assume that the manipulator is composed of joints separated by rigid links. However, in recent years several groups have begun to study the dynamics of flexible manipulators, primarily for applying robots in space and for improving the efficiency and precision of robotic systems. Robotic systems which are being planned for implementation in space have a number of constraints to overcome. Additional concepts which have to be worked out in any robotic implementation for a space platform include teleoperation and degree of autonomous control. Some significant results in developing a robotic workcell for performing robotics research on the KC-135 aircraft in preperation for space-based robotics applications in the future were generated. In addition, it was shown that TREETOPS can be used to simulate the dynamics of robot manipulators for both space and ground-based applications.

Workman, Gary L.↗

NASA NTRS1994

KATE: From the lab to the firing room

For the past 9 years, the AI group at NASA's Kennedy Space Center (KSC) has been developing a real-time model-based reasoning (MBR) system that can diagnose faults and provide control advisories. The system resulting from this effort is called KATE (Knowledge-based Autonomous Test Engineer). This article describes the transition of KATE from the applied research lab to the firing room (the firing room is the main control and monitoring center for all Shuttle ground support and launch processing). Because the problems that were countered along the way are not unique, we decided to share some of our experiences. This article also includes a brief overview of the latest application of KATE.

Pepe, Charles↗

NASA NTRS2003

Pseudo Linear Gyro Calibration

Previous high fidelity onboard attitude algorithms estimated only the spacecraft attitude and gyro bias. The desire to promote spacecraft and ground autonomy and improvements in onboard computing power has spurred development of more sophisticated calibration algorithms. Namely, there is a desire to provide for sensor calibration through calibration parameter estimation onboard the spacecraft as well as autonomous estimation on the ground. Gyro calibration is a particularly challenging area of research. There are a variety of gyro devices available for any prospective mission ranging from inexpensive low fidelity gyros with potentially unstable scale factors to much more expensive extremely stable high fidelity units. Much research has been devoted to designing dedicated estimators such as particular Extended Kalman Filter (EKF) algorithms or Square Root Information Filters. This paper builds upon previous attitude, rate, and specialized gyro parameter estimation work performed with Pseudo Linear Kalman Filter (PSELIKA). The PSELIKA advantage is the use of the standard linear Kalman Filter algorithm. A PSELIKA algorithm for an orthogonal gyro set which includes estimates of attitude, rate, gyro misalignments, gyro scale factors, and gyro bias is developed and tested using simulated and flight data. The measurements PSELIKA uses include gyro and quaternion tracker data.

Harman, Richard↗