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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Machine intelligence and autonomy for aerospace systems

The present volume discusses progress toward intelligent robot systems in aerospace applications, NASA Space Program automation and robotics efforts, the supervisory control of telerobotics in space, machine intelligence and crew/vehicle interfaces, expert-system terms and building tools, and knowledge-acquisition for autonomous systems. Also discussed are methods for validation of knowledge-based systems, a design methodology for knowledge-based management systems, knowledge-based simulation for aerospace systems, knowledge-based diagnosis, planning and scheduling methods in AI, the treatment of uncertainty in AI, vision-sensing techniques in aerospace applications, image-understanding techniques, tactile sensing for robots, distributed sensor integration, and the control of articulated and deformable space structures.

Heer, Ewald↗
NASA NTRS1999

STS-96 Crew Interview: Ellen Ochoa

Live footage of a preflight interview with Mission Specialist Ellen Ochoa is seen. The interview addresses many different questions including why Ochoa became an astronaut, the events that led to her interest, and her career path through research and engineering. Other interesting information that this one-on-one interview discusses is the logistics and supply mission, why it is important to send equipment to the International Space Station (ISS), and the Integrated Cargo Carrier (ICC). Ochoa mentions Discovery's anticipated docking with the ISS, her role during the scheduled space walk with Tamara E. Jernigan and Daniel T. Barry, and plans for the supply and equipment transfers. Ochoa also discusses her involvement in a Volatile Removal Assembly (VRA) experiment to remove contaminants from the water, the undocking of the spacecraft from the ISS, and a fly-around maneuver to take pictures of the ISS.

Source record↗
NASA NTRS2002

The Light Microscopy Module: An On-Orbit Multi-User Microscope Facility

The Light Microscopy Module (LMM) is planned as a remotely controllable on-orbit microscope subrack facility, allowing flexible scheduling and operation of fluids and biology experiments within the Fluids and Combustion Facility (FCF) Fluids Integrated Rack (FIR) on the International Space Station (ISS). The LMM will be the first integrated payload with the FIR to conduct four fluid physics experiments. A description of the LMM diagnostic capabilities, including video microscopy, interferometry, laser tweezers, confocal, and spectrophotometry, will be provided.

Motil, Susan M.↗
NASA NTRS2004

EUROPA2: Plan Database Services for Planning and Scheduling Applications

NASA missions require solving a wide variety of planning and scheduling problems with temporal constraints; simple resources such as robotic arms, communications antennae and cameras; complex replenishable resources such as memory, power and fuel; and complex constraints on geometry, heat and lighting angles. Planners and schedulers that solve these problems are used in ground tools as well as onboard systems. The diversity of planning problems and applications of planners and schedulers precludes a one-size fits all solution. However, many of the underlying technologies are common across planning domains and applications. We describe CAPR, a formalism for planning that is general enough to cover a wide variety of planning and scheduling domains of interest to NASA. We then describe EUROPA(sub 2), a software framework implementing CAPR. EUROPA(sub 2) provides efficient, customizable Plan Database Services that enable the integration of CAPR into a wide variety of applications. We describe the design of EUROPA(sub 2) from the perspective of both modeling, customization and application integration to different classes of NASA missions.

Bedrax-Weiss, Tania↗
NASA NTRS2015

A Vehicle Management End-to-End Testing and Analysis Platform for Validation of Mission and Fault Management Algorithms to Reduce Risk for NASA's Space Launch System

The development of the Space Launch System (SLS) launch vehicle requires cross discipline teams with extensive knowledge of launch vehicle subsystems, information theory, and autonomous algorithms dealing with all operations from pre-launch through on orbit operations. The characteristics of these systems must be matched with the autonomous algorithm monitoring and mitigation capabilities for accurate control and response to abnormal conditions throughout all vehicle mission flight phases, including precipitating safing actions and crew aborts. This presents a large complex systems engineering challenge being addressed in part by focusing on the specific subsystems handling of off-nominal mission and fault tolerance. Using traditional model based system and software engineering design principles from the Unified Modeling Language (UML), the Mission and Fault Management (M&FM) algorithms are crafted and vetted in specialized Integrated Development Teams composed of multiple development disciplines. NASA also has formed an M&FM team for addressing fault management early in the development lifecycle. This team has developed a dedicated Vehicle Management End-to-End Testbed (VMET) that integrates specific M&FM algorithms, specialized nominal and off-nominal test cases, and vendor-supplied physics-based launch vehicle subsystem models. The flexibility of VMET enables thorough testing of the M&FM algorithms by providing configurable suites of both nominal and off-nominal test cases to validate the algorithms utilizing actual subsystem models. The intent is to validate the algorithms and substantiate them with performance baselines for each of the vehicle subsystems in an independent platform exterior to flight software test processes. In any software development process there is inherent risk in the interpretation and implementation of concepts into software through requirements and test processes. Risk reduction is addressed by working with other organizations such as S&MA, Structures and Environments, GNC, Orion, the Crew Office, Flight Operations, and Ground Operations by assessing performance of the M&FM algorithms in terms of their ability to reduce Loss of Mission and Loss of Crew probabilities. In addition, through state machine and diagnostic modeling, analysis efforts investigate a broader suite of failure effects and detection and responses that can be tested in VMET and confirm that responses do not create additional risks or cause undesired states through interactive dynamic effects with other algorithms and systems. VMET further contributes to risk reduction by prototyping and exercising the M&FM algorithms early in their implementation and without any inherent hindrances such as meeting FSW processor scheduling constraints due to their target platform - ARINC 653 partitioned OS, resource limitations, and other factors related to integration with other subsystems not directly involved with M&FM. The plan for VMET encompasses testing the original M&FM algorithms coded in the same C++ language and state machine architectural concepts as that used by Flight Software. This enables the development of performance standards and test cases to characterize the M&FM algorithms and sets a benchmark from which to measure the effectiveness of M&FM algorithms performance in the FSW development and test processes. This paper is outlined in a systematic fashion analogous to a lifecycle process flow for engineering development of algorithms into software and testing. Section I describes the NASA SLS M&FM context, presenting the current infrastructure, leading principles, methods, and participants. Section II defines the testing philosophy of the M&FM algorithms as related to VMET followed by section III, which presents the modeling methods of the algorithms to be tested and validated in VMET. Its details are then further presented in section IV followed by Section V presenting integration, test status, and state analysis. Finally, section VI addresses the summary and forward directions followed by the appendices presenting relevant information on terminology and documentation.

Trevino, Luis↗
NASA NTRSDate not supplied

Early Assessments of Crew Timelines for the Lunar Surface Habitat

As NASA progresses towards sustained crewed space missions, crew timelines will become increasingly important to achieving mission goals. While it is desirable to spend as much time as possible during crewed space missions on science activities and experiments, there are a large number of activities that crew members must perform each day in order to maintain both crew and vehicle health and safety. The time available for science activities in space is highly dependent on mandatory tasks required for crew and vehicle health and safety. The different crewed activities need to be planned accordingly long before the start of a mission in order to optimize crew time for science. To begin assessing the potential crew time for available for science, an understanding of the requirements to maintain crew and vehicle health and safety is needed. These additional activities may include sleep, exercise, vehicle maintenance, logistics handling, crew personal time, as well as many other tasks. The remaining time outside of these required tasks, within a reasonable crew work schedule, can be dedicated to science operations. This paper will detail a collaborative effort to analyzing crew times for sustained spaceflight missions and how the results of that analysis are applied to the crew timeline for the proposed Lunar Surface Habitat (SH).To determine the crew time for all of these required activities, an analysis was conducted utilizing defined agency requirements and historical crewed mission data. Predicted crew activity times were integrated into a daily schedule in order to optimize the crew’s time during the mission. This methodology was utilized to produce expected crew timelines for NASA’s proposed Artemis Base Camp (ABC) missions. The current plans for the ABC contain two different sustained habitats, the Pressurized Rover (PR) and the Surface Habitat (SH). While the crew are separated between the two habitats, the timelines for each element are dependent on the other element’s operations, so the two element timelines are formed in conjunction with one another. The results described in this paper, however, will focus solely on the crew timeline in the SH. This paper will explain the methodology behind predicting the required crew time spent in the SH for each activity, and the process of incorporating these predicted crew times into a coherent schedule.

Crew Time↗
DOE OSTI2022

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Abstract In a manufacturing system, production control‐related decision‐making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation‐based virtual testbed that implements dynamic optimization, automatic information exchange, and decision‐making from the process‐level, system‐level, and factory‐level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python‐based numerical computation program, discrete‐event simulation software (Simul8), and an optimization solver (CPLEX) via a third‐party master program. The application of this simulation‐based virtual testbed is illustrated by a case study in a machining shop.

Sun, Yuting↗
DOE OSTI2025

Evaluating Economic Impact: An Investment Tool for Large Language Model Integration in Workweek Management

This paper explores the development and application of an investment tool designed to quantify the costs and potential savings associated with integrating large language models (LLMs) into work week management optimization (WMO) within the nuclear industry. LLMs, with their advanced natural language processing capabilities, can significantly enhance various aspects of work management, such as problem identification, prioritization, planning, scheduling, information retrieval, and information summary. Our investment tool focuses on evaluating the return on investment (ROI) for LLM applications in WMO by considering four pivotal decision factors: model selection, application, user training, and hosting options. This paper details the development and implementation of the ROI model and illustrates its application through multiple case studies, analyzing the impact of different variables, such as work time saved, number of requests, and model performance, on the computed ROI over two years. The computed ROI is also compared over different hosting solutions. Our findings indicate that ROI increases with enhanced work time savings and optimal request load but can decline with high request volumes or increased model costs. This model aids decision-makers in the nuclear industry by providing a structured approach to assessing the economic viability and potential savings from integrating LLMs into WMO processes.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Evaluating Economic Impact: An Investment Tool for Large Language Model Integration in Workweek Management

This paper explores the development and application of an investment tool designed to quantify the costs and potential savings associated with integrating large language models (LLMs) into work week management optimization (WMO) within the nuclear industry. LLMs, with their advanced natural language processing capabilities, can significantly enhance various aspects of work management, such as problem identification, prioritization, planning, scheduling, information retrieval, and information summary. Our investment tool focuses on evaluating the return on investment (ROI) for LLM applications in WMO by considering four pivotal decision factors: model selection, application, user training, and hosting options. This paper details the development and implementation of the ROI model and illustrates its application through multiple case studies, analyzing the impact of different variables, such as work time saved, number of requests, and model performance, on the computed ROI over two years. The computed ROI is also compared over different hosting solutions. Our findings indicate that ROI increases with enhanced work time savings and optimal request load but can decline with high request volumes or increased model costs. This model aids decision-makers in the nuclear industry by providing a structured approach to assessing the economic viability and potential savings from integrating LLMs into WMO processes.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Quantitative Metrics for Grid Resilience Evaluation and Optimization

Power system resilience has become a critical topic in recent years because of the increasing trend of extreme events and the growing integration of intermittent renewable energy sources. To enhance grid resilience against high-impact, low-frequency events, two questions should be answered: how to quantify the resilience of a given grid and how to incorporate the quantification into power system planning, operation, and restoration. Here this paper develops a new set of quantitative metrics with clear physical interpretation to comprehensively evaluate power system resilience. Using microgrids as an example, an event-based corrective scheduling (ECS) model and an online model predictive control (OMPC) model are developed to integrate the proposed quantitative resilience metrics into power system optimization models for resilience enhancement. The ECS model employs extreme event data to investigate the optimal restoration solution and to help microgrid operators prepare to respond to similar events. The OMPC model provides online decision-making support for operators to handle ongoing outages in the most resilient fashion. The effectiveness and superiority of the proposed quantitative resilience metrics and the resilience enhancement models are demonstrated through simulations and comparative studies on an IEEE test feeder and a real distribution feeder in Southern California.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

Launch site integration for mixed fleet operations

Launch site impacts and integration planning issues are presented to support launch operations for a mixed vehicle fleet (manned and cargo). Proposed ground systems and launch site configurations are described. Prelaunch processing scenarios and schedules are developed for candidate launch vehicles. Earth-to-orbit (ETO) vehicle architectures are presented to meet future launch requirements, including the Space Exploration Initiative (SEI). Flight vehicle design recommendations to enhance launch processing are discussed. The significance of operational designs for future launch vehicles is shown to be a critical factor in planning for mixed fleet launch site operations.

Scott, L. P.↗
NASA NTRS2009

Massively Parallel Dantzig-Wolfe Decomposition Applied to Traffic Flow Scheduling

Optimal scheduling of air traffic over the entire National Airspace System is a computationally difficult task. To speed computation, Dantzig-Wolfe decomposition is applied to a known linear integer programming approach for assigning delays to flights. The optimization model is proven to have the block-angular structure necessary for Dantzig-Wolfe decomposition. The subproblems for this decomposition are solved in parallel via independent computation threads. Experimental evidence suggests that as the number of subproblems/threads increases (and their respective sizes decrease), the solution quality, convergence, and runtime improve. A demonstration of this is provided by using one flight per subproblem, which is the finest possible decomposition. This results in thousands of subproblems and associated computation threads. This massively parallel approach is compared to one with few threads and to standard (non-decomposed) approaches in terms of solution quality and runtime. Since this method generally provides a non-integral (relaxed) solution to the original optimization problem, two heuristics are developed to generate an integral solution. Dantzig-Wolfe followed by these heuristics can provide a near-optimal (sometimes optimal) solution to the original problem hundreds of times faster than standard (non-decomposed) approaches. In addition, when massive decomposition is employed, the solution is shown to be more likely integral, which obviates the need for an integerization step. These results indicate that nationwide, real-time, high fidelity, optimal traffic flow scheduling is achievable for (at least) 3 hour planning horizons.

Rios, Joseph Lucio↗
NASA NTRS2012

Integrated Campaign Probabilistic Cost, Schedule, Performance, and Value for Program Office Support

This paper describes an integrated assessment tool developed at NASA Langley Research Center that incorporates probabilistic analysis of life cycle cost, schedule, launch performance, on-orbit performance, and value across a series of planned space-based missions, or campaign. Originally designed as an aid in planning the execution of missions to accomplish the National Research Council 2007 Earth Science Decadal Survey, it utilizes Monte Carlo simulation of a series of space missions for assessment of resource requirements and expected return on investment. Interactions between simulated missions are incorporated, such as competition for launch site manifest, to capture unexpected and non-linear system behaviors. A novel value model is utilized to provide an assessment of the probabilistic return on investment. A demonstration case is discussed to illustrate the tool utility.

Cornelius, David↗
NASA NTRS2019

LANDSAT 9: Mission Status and Prelaunch Instrument Performance Characterization and Calibration

Landsat 9 is currently under development as a joint effort between NASA and the United States Geological Survey (USGS). Landsat 9 is largely a rebuild of Landsat 8 and has the same two sensors, an Operational Land Imager (OLI) and a Thermal InfraRed Sensor(TIRS). The OLI-2 on Landsat 9, being built by Ball Aerospace, has completed its pre-launch characterization and calibration and is scheduled to be delivered in the summer of 2019. The TIRS-2, with improved stray light performance and increased redundancy for improved reliability, is being built by Goddard Space Flight Center. TIRS-2is undergoing testing through Spring 2019 and is also scheduled for summer2019 delivery. Several improvements to the characterization of both instruments have been incorporated into the testing plan, including improved spectral and radiometric characterization. The instruments will then be integrated onto the spacecraft being built by Northrop Grumman Innovation Systems (NGIS). The mission is targeted to launch as early as December 2020 on an Atlas V.

Landsat↗
NASA NTRSDate not supplied

Simulations of Urban Air Mobility Operations

Urban Air Mobility (UAM) aims to offer air taxi service as an alternative to driving on the congested roads. Integration of UAM operations into the National Airspace System (NAS) has been the focus of the research conducted at NASA Ames Research Center. In this talk, I present results from simulations performed during FY2019 to investigate if NASA’s UAS Traffic Management (UTM) architecture and its implementation are extensible for UAM operations. These simulations also tested a set of core airspace management services tailored to controlled airspace access. In the latter half of this talk, I present the efforts made towards the integration of two such services – a strategic scheduling and a tactical separation service – in a simulation environment under ideal conditions. I will conclude this talk by presenting the future work planned towards enabling UAM operations

Urban Air Mobility↗
NASA NTRS2021

Simulations of Urban Air Mobility Operations

Urban Air Mobility (UAM) aims to offer air taxi service as an alternative to driving on the congested roads. Integration of UAM operations into the National Airspace System (NAS) has been the focus of the research conducted at NASA Ames Research Center. In this talk, I present results from simulations performed during FY2019 to investigate if NASA’s UAS Traffic Management (UTM) architecture and its implementation are extensible for UAM operations. These simulations also tested a set of core airspace management services tailored to controlled airspace access. In the latter half of this talk, I present the efforts made towards the integration of two such services – a strategic scheduling and a tactical separation service – in a simulation environment under ideal conditions. I will conclude this talk by presenting the future work planned towards enabling UAM operations

Urban Air Mobility↗
NASA NTRS2007

DSN Resource Scheduling

TIGRAS is client-side software, which provides tracking-station equipment planning, allocation, and scheduling services to the DSMS (Deep Space Mission System). TIGRAS provides functions for schedulers to coordinate the DSN (Deep Space Network) antenna usage time and to resolve the resource usage conflicts among tracking passes, antenna calibrations, maintenance, and system testing activities. TIGRAS provides a fully integrated multi-pane graphical user interface for all scheduling operations. This is a great improvement over the legacy VAX VMS command line user interface. TIGRAS has the capability to handle all DSN resource scheduling aspects from long-range to real time. TIGRAS assists NASA mission operations for DSN tracking of station equipment resource request processes from long-range load forecasts (ten years or longer), to midrange, short-range, and real-time (less than one week) emergency tracking plan changes. TIGRAS can be operated by NASA mission operations worldwide to make schedule requests for the DSN station equipment.

Wang, Yeou-Fang↗
NASA NTRS1987

Spacelab Life Sciences 1 and 2 scientific research objectives

The pressurized Spacelab module was designed and built to allow investigators to conduct research in space in an environment approximating that of a ground-based laboratory. It is configured to allow multiple investigations employing both human and nonhuman subjects. This flexability is exemplified by the SLS-1, SLS-2, and SLS-3 experiment complement. A total of 21 experiments are scheduled for these missions; the areas to be investigated are renal/endocrine function, cardiovascular/cardiopulmonary function, hematology, immunology, metabolic activity of muscle, Ca metabolism, the vestibular system, and general biology. A plan for integration of measurements will allow each investigator to use data from other experiments. The experiments make up a scientifically balanced payload that addresses fundamental biomedical problems associated with space flight and provides the first opportunity to study the acute effects of weightlessness in a comprehensive, interrelated fashion.

Leach, Carolyn S.↗