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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

NASA Lessons Learned on Reusable and Expendable Launch Vehicle Operations and Their Application Towards DARPA's Experimental Spaceplane (XS-1) Program

Outline: Selected Historic Shuttle Operations Data; Shuttle Lessons Learned Recommendations for Lower Cost, Operationally Efficient Launch Vehicle Systems; Selected Expendable launch vehicle experiences; Past NASA Launch Vehicle Development Programs, Studies (1985 to present); Discussion: Suggested applications of NASA Lessons Learned to already-baselined contractor XS-1 Phase I concepts.

Reusable launch vehicle↗
NASA NTRS1994

The Network Operations Control Center upgrade task: Lessons learned

This article synthesizes and describes the lessons learned from the Network Operations Control Center (NOCC) upgrade project, from the requirements phase through development and test and transfer. At the outset, the NOCC upgrade was being performed simultaneously with two other interfacing and dependent upgrades at the Signal Processing Center (SPC) and Ground Communications Facility (GCF), thereby adding a significant measure of complexity to the management and overall coordination of the development and transfer-to-operations (DTO) effort. Like other success stories, this project carried with it the traditional elements of top management support and exceptional dedication of cognizant personnel. Additionally, there were several NOCC-specific reasons for success, such as end-to-end system engineering, adoption of open-system architecture, thorough requirements management, and use of appropriate off-the-shelf technologies. On the other hand, there were several difficulties, such as ill-defined external interfaces, transition issues caused by new communications protocols, ambivalent use of two sets of policies and standards, and mistailoring of the new JPL management standard (due to the lack of practical guidelines). This article highlights the key lessons learned, as a means of constructive suggestions for the benefit of future projects.

Sherif, J. S.↗
NASA NTRS2014

Fifteen Years of Chandra Operation: Scientific Highlights and Lessons Learned

NASA's Chandra X-Ray Observatory, designed for three years of operation with a goal of five years is now entering its 15-th year of operation. Thanks to its superb angular resolution, the Observatory continues to yield new and exciting results, many of which were totally unanticipated prior to launch. We will review some scientific highlights and present "lessons learned" from the experience of operating this great observatory.

Weisskopf, Martin C.↗
NASA NTRS2022

Operational maize yield forecasts for Sub-Saharan Africa using Earth observation data and machine learning

Food insecurity continues to grow in Sub-Saharan Africa (SSA). In 2019, chronically malnourished people numbered nearly 240 million, or 20% of the population in SSA. Globally, numerous efforts have been made to anticipate potential droughts, crop conditions, and food shortages to foster improved food insecurity early warning and risk management. To support this goal, we develop an Earth Observation (EO) and machine-learning-based operational, subnational maize yield forecast system and evaluate its out-of-sample forecast skills during the growing seasons for Kenya, Somalia, Malawi, and Burkina Faso. In general, forecast skills improve substantially during the vegetative growth period (VP) and gradually during the reproductive development period (RP). Thus, mid-season assessment can provide effective early warning months before harvest. Skillful forecasts (Nash Sutcliffe Efficiency (NSE) > 0.6 and Mean Absolute Percentage Error (MAPE) < 20%) appear approximately two dekads after the VP; for example, they appear in May in Kenya and Somalia, January in Malawi, and July in Burkina Faso.

Donghoon Lee↗
DOE OSTI2026

Explainable Graph Learning for Particle Accelerator Operations

Particle accelerators are vital tools in physics, medicine, and industry, requiring precise tuning to ensure optimal beam performance. However, real-world deviations from idealized simulations make beam tuning a time-consuming and error-prone process. In this work, we propose an explanation-driven framework for providing actionable insight into beamline operations, with a focus on the injector beamline at the Continuous Electron Beam Accelerator Facility (CEBAF). We represent beamline configurations as heterogeneous graphs, where setting nodes represent elements that human operators can actively adjust during beam tuning, and reading nodes passively provide diagnostic feedback. To identify the most influential setting nodes responsible for differences between any two beamline configurations, our approach first predicts the resulting changes in reading nodes caused by variations in settings, and then learns importance scores that capture the joint influence of multiple setting nodes. Experimental results on real-world CEBAF injector data demonstrate the framework’s ability to generate interpretable insights that can assist human operators in beamline tuning and reduce operational overhead.

Wang, Song [Univ. of Virginia, Charlottesville, VA↗
NASA NTRS1993

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Special approach/docking testcase results

As part of the RICIS project, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use these two terms interchangeably to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS) and programming/testing support from other contractor personnel. This report is the final deliverable D4 in our milestones and project activity. It provides the test results for the special testcase of approach/docking scenario for the shuttle and SMM satellite. Based on our experience and analysis with the attitude and translational controllers, we have modified the basic configuration of the reinforcement learning algorithm in ARIC. The shuttle translational controller and its implementation in ARIC is described in our deliverable D3. In order to simulate the final approach and docking operations, we have set-up this special testcase as described in section 2. The ARIC performance results for these operations are discussed in section 3 and conclusions are provided in section 4 along with the summary for the project.

Jani, Yashvant↗
NASA NTRS2014

Fifteen Years of Chandra Operation: Scientific Highlights and Lessons Learned

NASA's Chandra X-Ray Observatory, designed for three years of operation with a goal of five years is now entering its 15-th year of operation. Thanks to its superb angular resolution, the Observatory continues to yield new and exciting results, many of which were totally unanticipated prior to launch. We discuss the current technical status, review recent scientific highlights, indicate a few future directions, and present what we feel is the most important lesson learned from our experience of building and operating this great observatory.

Weisskopf, Martin C.↗
NASA NTRS2016

Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?

Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations.

Sridhar, Banavar↗
NASA NTRS2021

Small-Body Proximity Operations & TAG: Navigation Experiences & Lessons Learned from the OSIRIS-REx Mission

On October 20th, 2020, the nearly two-year proximity operations campaign for the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx)mission at the near-Earth asteroid (101955) Bennu culminated in a successful Touch-and-Go (TAG) sample collection event. Navigation performance was a significant driver for flight activities at Bennu, which consisted of multiple phases geared towards characterizing the asteroid, selecting a sample site, and safely guiding the spacecraft to and from the surface in order to collect at least 60 g of pristine regolith. The entire operations team gained a tremendous amount of experience operating in the challenging small body environment and overcame many challenges. In this paper, we summarize navigation-focused experiences and lessons learned from OSIRIS-REx proximity operations at Bennu that are applicable to future missions to small asteroids, comets, and planetary moons. Areas of focus include staffing and organization, ground system infrastructure, mission phase planning, navigation operations, and spacecraft and payload considerations.

Kenneth M. Getzandanner↗
NASA NTRS1992

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Attitude control results

As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.

Jani, Yashvant↗
NASA NTRS2013

International Space Station Common Cabin Air Assembly Condensing Heat Exchanger Hydrophilic Coating Operation, Recovery, and Lessons Learned

The ability to control the temperature and humidity of an environment or habitat is critical for human survival. These factors are important to maintaining human health and comfort, as well as maintaining mechanical and electrical equipment in good working order to support the human and to accomplish mission objectives. The temperature and humidity of the International Space Station (ISS) United States On-orbit Segment (USOS) cabin air is controlled by the Common Cabin Air Assembly (CCAA). The CCAA consists of a fan, a condensing heat exchanger (CHX), an air/water separator, temperature and liquid sensors, and electrical controlling hardware and software. The CHX is the primary component responsible for control of temperature and humidity. The CCAA CHX contains a chemical coating that was developed to be hydrophilic and thus attract water from the humid influent air. This attraction forms the basis for water removal and therefore cabin humidity control. However, there have been several instances of CHX coatings becoming hydrophobic and repelling water. When this behavior is observed in an operational CHX in the ISS segments, the unit s ability to remove moisture from the air is compromised and the result is liquid water carryover into downstream ducting and systems. This water carryover can have detrimental effects on the ISS cabin atmosphere quality and on the health of downstream hardware. If the water carryover is severe and widespread, this behavior can result in an inability to maintain humidity levels in the USOS. This paper will describe the operation of the five CCAAs within the USOS, the potential causes of the hydrophobic condition, and the impacts of the resulting water carryover to downstream systems. It will describe the history of this behavior and the actual observed impacts to the ISS USOS. Information on mitigation steps to protect the health of future CHX hydrophilic coatings as well as remediation and recovery of the full heat exchanger will be discussed.

Balistreri, Steven F.↗
NASA NTRS1991

The Space Flight Operations Center first phase - Lessons learned

This paper is a retrospective look at the Space Flight Operations Center Project; a large, complex, multimission ground data system which is currently under development at the Jet Propulsion Laboratory (JPL), Pasadena. The Project was established in February 1984 and will be completed in April 1992 when the Magellan, Voyager, Ulysses, Galileo and Mars Observer Projects will be fully supported. The first phase of this project covers the development of a subset of multimission Baseline subsystems, the development of the mission-specific adaptations to this Baseline set for the Magellan Project, the development of a Magellan-specific high rate processor together with the delivery of these subsystems to operations. This first phase became operational in January 1989 in readiness for the Magellan launch in May 1989.

Gainsborough, A. J.↗
NASA NTRS2011

The 2010 Desert Rats Science Operations Test: Outcomes and Lessons Learned

The Desert RATS 2010 Team tested a variety of science operations management techniques, applying experience gained during the manned Apollo missions and the robotic Mars missions. This test assessed integrated science operations management of human planetary exploration using real-time, tactical science operations to oversee daily crew science activities, and a night shift strategic science operations team to conduct strategic level assessment of science data and daily traverse results. In addition, an attempt was made to collect numerical metric data on the outcome of the science operations to assist test evaluation. The two most important outcomes were 1) the production of significant (almost overwhelming) volume of data produced during daily traverse operations with two rovers, advanced imaging systems and well trained, scientifically proficient crew-members, and 2) the degree to which the tactical team s interaction with the surface crew enhanced science return. This interaction depended on continuous real-time voice and data communications, and the quality of science return from any human planetary exploration mission will be based strongly on the aggregate interaction between a well trained surface crew and a dedicated science operations support team using voice and imaging data from a planet s surface. In addition, the scientific insight developed by both the science operations team and the crews could not be measurable by simple numerical quantities, and its value will be missed by a purely metric-based evaluation of test outcome. In particular, failure to recognize the critical importance of this qualitative type interaction may result in mission architecture choices that will reduce science return.

Eppler, D. B.↗