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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

The effect of processing code, response modality and task difficulty on dual task performance and subjective workload in a manual system

This paper reports on the first experiment of a series studying the effect of task structure and difficulty demand on time-sharing performance and workload in both automated and corresponding manual systems. The experimental task involves manual control time-shared with spatial and verbal decisions tasks of two levels of difficulty and two modes of response (voice or manual). The results provide strong evidence that tasks and processes competing for common processing resources are time shared less effecively and have higher workload than tasks competing for separate resources. Subjective measures and the structure of multiple resources are used in conjunction to predict dual task performance. The evidence comes from both single-task and from dual-task performance.

Liu, Yili↗
NASA NTRS1990

NASA Information Sciences and Human Factors Program

Fiscal year 1989 descriptions of technical accomplishments in seven sections are presented: automation and robotics; communications; computer sciences; controls and guidance; data systems; human factors; and sensor technology.

Holcomb, Lee↗
NASA NTRS1991

Simulation Test Of Descent Advisor

Report describes piloted-simulation test of Descent Advisor (DA), subsystem of larger automation system being developed to assist human air-traffic controllers and pilots. Focuses on results of piloted simulation, in which airline crews executed controller-issued descent advisories along standard curved-path arrival routes. Crews able to achieve arrival-time precision of plus or minus 20 seconds at metering fix. Analysis of errors generated in turns resulted in further enhancements of algorithm to increase accuracies of its predicted trajectories. Evaluations by pilots indicate general support for DA concept and provide specific recommendations for improvement.

Davis, Thomas J.↗
NASA NTRS1991

NASA information sciences and human factors program

The FY-90 descriptions of technical accomplishments are contained in seven sections: Automation and Robotics, Communications, Computer Sciences, Controls and Guidance, Data Systems, Human Factors, and Sensor Technology.

Lee Holcomb↗
NASA NTRS1992

Cognitive engineering models in space systems

NASA space systems, including mission operations on the ground and in space, are complex, dynamic, predominantly automated systems in which the human operator is a supervisory controller. The human operator monitors and fine-tunes computer-based control systems and is responsible for ensuring safe and efficient system operation. In such systems, the potential consequences of human mistakes and errors may be very large, and low probability of such events is likely. Thus, models of cognitive functions in complex systems are needed to describe human performance and form the theoretical basis of operator workstation design, including displays, controls, and decision support aids. The operator function model represents normative operator behavior-expected operator activities given current system state. The extension of the theoretical structure of the operator function model and its application to NASA Johnson mission operations and space station applications is discussed.

Mitchell, Christine M.↗
NASA NTRS1988

Design of components for growing higher plants in space

The overall goal of this project is to design unique systems and components for growing higher plants in microgravity during long-term space missions (Mars and beyond). Specific design tasks were chosen to contribute to and supplement NASA's Controlled Ecological Life Support System (CELSS) project. Selected tasks were automated seeding of plants, plant health sensing, and food processing. Prototype systems for planting both germinated and nongerminated seeds were fabricated and tested. Water and air pressure differences and electrostatic fields were used to trap seeds for separation and transport for planting. An absorption spectrometer was developed to measure chlorophyll levels in plants as an early warning of plant health problems. In the area of food processing, a milling system was created using high-speed rotating blades which were aerodynamically configured to produce circulation and retractable to prevent leakage. The project produced significant results having substantial benefit to NASA. It also provided an outstanding learning experience for the students involved.

Source record↗
NASA NTRS1993

Cognitive engineering models in space systems

NASA space systems, including mission operations on the ground and in space, are complex, dynamic, predominantly automated systems in which the human operator is a supervisory controller. Models of cognitive functions in complex systems are needed to describe human performance and form the theoretical basis of operator workstation design, including displays, controls, and decision aids. Currently, there several candidate modeling methodologies. They include the Rasmussen abstraction/aggregation hierarchy and decision ladder, the goal-means network, the problem behavior graph, and the operator function model. The research conducted under the sponsorship of this grant focuses on the extension of the theoretical structure of the operator function model and its application to NASA Johnson mission operations and space station applications. The initial portion of this research consists of two parts. The first is a series of technical exchanges between NASA Johnson and Georgia Tech researchers. The purpose is to identify candidate applications for the current operator function model; prospects include mission operations and the Data Management System Testbed. The second portion will address extensions of the operator function model to tailor it to the specific needs of Johnson applications. At this point, we have accomplished two things. During a series of conversations with JSC researchers, we have defined the technical goal of the research supported by this grant to be the structural definition of the operator function model and its computer implementation, OFMspert. Both the OFM and OFMspert have matured to the point that they require infrastructure to facilitate use by researchers not involved in the evolution of the tools. The second accomplishment this year was the identification of the Payload Deployment and Retrieval System (PDRS) as a candidate system for the case study. In conjunction with government and contractor personnel in the Human-Computer Interaction Lab, the PDRS was identified as the most accessible system for the demonstration. Pursuant to this a PDRS simulation was obtained from the HCIL and an initial knowledge engineering effort was conducted to understand the operator's tasks in the PDRS application. The preliminary results of the knowledge engineering effort and an initial formulation of an operator function model (OFM) are contained in the appendices.

Mitchell, Christine M.↗
NASA NTRS1993

The application of automated operations at the Institutional Processing Center

The JPL Institutional and Mission Computing Division, Communications, Computing and Network Services Section, with its mission contractor, OAO Corporation, have for some time been applying automation to the operation of JPL's Information Processing Center (IPC). Automation does not come in one easy to use package. Automation for a data processing center is made up of many different software and hardware products supported by trained personnel. The IPC automation effort formally began with console automation, and has since spiraled out to include production scheduling, data entry, report distribution, online reporting, failure reporting and resolution, documentation, library storage, and operator and user education, while requiring the interaction of multi-vendor and locally developed software. To begin the process, automation goals are determined. Then a team including operations personnel is formed to research and evaluate available options. By acquiring knowledge of current products and those in development, taking an active role in industry organizations, and learning of other data center's experiences, a forecast can be developed as to what direction technology is moving. With IPC management's approval, an implementation plan is developed and resources identified to test or implement new systems. As an example, IPC's new automated data entry system was researched by Data Entry, Production Control, and Advance Planning personnel. A proposal was then submitted to management for review. A determination to implement the new system was made and elements/personnel involved with the initial planning performed the implementation. The final steps of the implementation were educating data entry personnel in the areas effected and procedural changes necessary to the successful operation of the new system.

Barr, Thomas H.↗
NASA NTRS2000

A Comparison of Center/TRACON Automation System and Airline Time of Arrival Predictions

Benefits from information sharing between an air traffic service provider and a major air carrier are evaluated. Aircraft arrival time schedules generated by the NASA/FAA Center/TRACON Automation System (CTAS) were provided to the American Airlines System Operations Control Center in Fort Worth, Texas, during a field trial of a specialized CTAS display. A statistical analysis indicates that the CTAS schedules, based on aircraft trajectories predicted from real-time radar and weather data, are substantially more accurate than the traditional airline arrival time estimates, constructed from flight plans and en route crew updates. The improvement offered by CTAS is especially advantageous during periods of heavy traffic and substantial terminal area delay, allowing the airline to avoid large predictive errors with serious impact on the efficiency and profitability of flight operations.

Heere, Karen R.↗
NASA NTRS1998

Overview of NASA Advanced Transportation Technologies Program

A General Overview of NASA Advanced Transportation Technologies Program is presented. The contents include: 1) Center-TRACON Automation System (CTAS) which provides automation tools to assist air traffic controllers in planning and controlling air traffic arriving into major airports; 2) Surface Movement Advisor (SMA) for expediting and optimizing aircraft operations on the airport surface; and 3) Terminal Area Productivity Program (TAP), which is aimed at improving airport throughput in instrument meteorological conditions to match that attainable in clear weather.

Ashford, Rose↗
NASA NTRS1998

Automated System Checkout to Support Predictive Maintenance for the Reusable Launch Vehicle

The Propulsion Checkout and Control System (PCCS) is a predictive maintenance software system. The real-time checkout procedures and diagnostics are designed to detect components that need maintenance based on their condition, rather than using more conventional approaches such as scheduled or reliability centered maintenance. Predictive maintenance can reduce turn-around time and cost and increase safety as compared to conventional maintenance approaches. Real-time sensor validation, limit checking, statistical anomaly detection, and failure prediction based on simulation models are employed. Multi-signal models, useful for testability analysis during system design, are used during the operational phase to detect and isolate degraded or failed components. The TEAMS-RT real-time diagnostic engine was developed to utilize the multi-signal models by Qualtech Systems, Inc. Capability of predicting the maintenance condition was successfully demonstrated with a variety of data, from simulation to actual operation on the Integrated Propulsion Technology Demonstrator (IPTD) at Marshall Space Flight Center (MSFC). Playback of IPTD valve actuations for feature recognition updates identified an otherwise undetectable Main Propulsion System 12 inch prevalve degradation. The algorithms were loaded into the Propulsion Checkout and Control System for further development and are the first known application of predictive Integrated Vehicle Health Management to an operational cryogenic testbed. The software performed successfully in real-time, meeting the required performance goal of 1 second cycle time.

Patterson-Hine, Ann↗
NASA NTRSDate not supplied

Training the Powered-Lift Evaluation Pilot

This paper focuses on the preparation and training of evaluation pilots during the second of two research studies: Automation Enabled Pilot studies 1 and 2 (AEP-1 and AEP-2). These studies are intended to assess novel aircraft automation concepts for electric powered aircraft equipped with Indirect Flight Control Systems (IFCS) capable of Vertical Takeoff and Landing (eVTOL). The AEP-1 study importantly introduced the concept of future evaluation methods used in the AEP-1 study for eVTOL equipped with IFCS that are agnostic and cut cross both airworthiness and operational requirements. The AEP-2 study utilized an updated Lift Plus Cruise aircraft model and examined pilot interaction with novel procedures and interfaces. Specifically, it explored challenges related to transitioning from forward flight to landing with an industry representative Urban Air Mobility (UAM) approach procedure, establishing baselines for future automation studies.

AAM↗
NASA NTRSDate not supplied

Training the Powered-Lift Evaluation Pilot

This presentation focuses on the preparation and training of evaluation pilots during the second of two research studies: Automation Enabled Pilot studies 1 and 2 (AEP-1 and AEP-2). These studies are intended to assess novel aircraft automation concepts for electric powered aircraft equipped with Indirect Flight Control Systems (IFCS) capable of Vertical Takeoff and Landing (eVTOL). The AEP-1 study importantly introduced the concept of future evaluation methods used in the AEP-1 study for eVTOL equipped with IFCS that are agnostic and cut cross both airworthiness and operational requirements. The AEP-2 study utilized an updated Lift Plus Cruise aircraft model and examined pilot interaction with novel procedures and interfaces. Specifically, it explored challenges related to transitioning from forward flight to landing with an industry representative Urban Air Mobility (UAM) approach procedure, establishing baselines for future automation studies.

AAM↗
NASA NTRSDate not supplied

Building Automation System Replacement and Improvements during Concurrent Integration and Testing for the Spacecraft Systems Development and Integration Facility

The Spacecraft Systems Development and Integration Facility (SSDIF), the premier cleanroom for NASA Goddard Space Flight Center (GSFC), has gone through multiple iterations of changes to its building automation system (BAS). NASA flight projects require more stringent environmental controls for temperature, humidity, air exchange rates, and pressurization. This led to increasingly difficult circumstances with replacing and improving the BAS during Integration and Testing (I&T) operations. The SSDIF was partially transitioned to a new BAS during November and December of 2023. Transition occurred in a live environment, while regular I&T activities resumed for the Roman Space Telescope. This resulted in the development and execution of a comprehensive method of procedure. Strategies such as sequencing equipment transition, and using temporary controls for critical equipment, avoided detriments to cleanroom environment. While the new BAS was being built, data points on the previous system would become obsolete, leading to data gaps and uncertainties. Critical data points were distinguished and trended on the new system, providing key insights on equipment functionality, while the BAS was completed. Control hunting and environmental deviations were persistent cases with the SSDIF. In some instances, proportional-integral-derivative (PID) tuning was implemented on current logic to bring smooth PID loop hunting. In other situations, controls logic was completely altered by changing setpoints or sequences. This provided greater laminar flow, more stable pressurization, and improved temperature and humidity controls. This paper documents the transitionary procedure to a new BAS, the improvements made, and challenges faced when altering a BAS during I&T operations.

Cleanroom↗
DOE OSTI2024

SUBTASK 1.6 – BASIN ELECTRIC CARBON STORAGE RESEARCH PROJECT: NOVEL MONITORING TECHNIQUES

The Energy & Environmental Research Center (EERC) conducted baseline activities associated with an applied research project at Basin Electric Power Cooperative’s (Basin’s) carbon capture and storage (CCS) site in Beulah, North Dakota, to establish novel carbon storage-monitoring techniques as commercial methods under Cooperative Agreement No. DE-FE0024233, Subtask 1.6. The following report summarizes the baseline activities performed and briefly describes the subsequent (operational monitoring) activities that have been proposed to the U.S. Department of Energy (DOE) as part of the overall project to develop and demonstrate novel monitoring techniques at North America’s largest permitted CCS operation. Dakota Gasification Company (DGC), a wholly owned subsidiary of Basin, owns and operates the Great Plains Synfuels Plant (GPSP) approximately 5 miles northwest of the town of Beulah, North Dakota (Figure 1). In 2023, DGC received approval from the North Dakota Industrial Commission (NDIC) to develop a storage facility on-site for injecting a stream of carbon dioxide (CO2) captured from GPSP. DGC will transport the captured CO2 stream with approximately 6.8 miles of transmission lines that extend north of GPSP and inject >1 million tonnes (MMt) of CO2 annually (>1 MMt/yr) over a 12-year period with up to six underground injection control (UIC) Class VI-compliant injection wells completed in the Broom Creek Formation, a predominantly sandstone reservoir and saline aquifer underlying GPSP. The Broom Creek Formation lies approximately 5900 feet (ft) below ground surface (bgs) at GPSP. The commercial scale (i.e., >1 MMt/yr) of DGC’s permitted carbon storage project is ideal for developing and testing the novel monitoring techniques included within Subtask 1.6. The goals of this project are to demonstrate 1) the cost-effectiveness of novel monitoring technologies included as part of this research, 2) technology capability for tracking the CO2 plume and/or associated pressure response in the subsurface and monitoring out-of-zone migration, and 3) compliance with UIC Class VI program requirements. The research activities proposed for the overall project include 1) design of an automated, integrated, modular (AIM) monitoring station; 2) time-lapse electromagnetic (EM) field surveys; 3) drone-based surveillance studies; 4) time-lapse monitoring with seismic methods; 5) advanced wellbore-monitoring methods; 6) deployment of an AIM monitoring network; 7) EM monitoring of CO2 with real-time data processing; 8) continued seasonal drone-based surveillance studies; 9) seismic monitoring with passive and active surveys; and 10) wellbore monitoring with nuclear magnetic resonance (NMR) for near-surface characterization. Completion of Activities 1.0–5.0 (baseline activities) are described in this report. Upon authorization of funding by DOE, the EERC will initiate Activities 6.0– 10.0 (operational monitoring activities). Current state-of-the-art (SOA) carbon storage-monitoring techniques require countless labor hours dedicated to the acquisition of data. Once data are gathered, these SOA techniques often rely on commercial facilities to process raw data from the field. However, it is anticipated that next-generation monitoring techniques, such as those being demonstrated, will lower acquisition footprints, be less operationally intensive, and improve data acquisition efficiencies. These new techniques are more conducive to the application of machine learning, artificial intelligence, and automation, thus providing a pathway for integration into active control systems, informing site operability, and improving the integration of data for future CCS projects across the United States. Additionally, reclaimed and active mining lands are present within the project site, creating a unique opportunity to demonstrate the effectiveness of remote sensing and surface-based geophysics monitoring techniques at similar project sites that may include disturbed, unconsolidated, or actively excavated near-surface environments. The efforts included in the overall project will produce necessary designs, learnings, and data acquired during the baseline and operational monitoring periods that are necessary for time-lapse demonstration and validation of the described monitoring techniques. In addition, it is anticipated that the monitoring technologies included in this study will be compliant with UIC Class VI requirements to enable the potential for implementation at other CCS sites across the United States.

42 ENGINEERING↗
NASA NTRS1993

PILOT: An intelligent distributed operations support system

The Real-Time Data System (RTDS) project is exploring the application of advanced technologies to the real-time flight operations environment of the Mission Control Centers at NASA's Johnson Space Center. The system, based on a network of engineering workstations, provides services such as delivery of real time telemetry data to flight control applications. To automate the operation of this complex distributed environment, a facility called PILOT (Process Integrity Level and Operation Tracker) is being developed. PILOT comprises a set of distributed agents cooperating with a rule-based expert system; together they monitor process operation and data flows throughout the RTDS network. The goal of PILOT is to provide unattended management and automated operation under user control.

Rasmussen, Arthur N.↗
DOE OSTI2022

Feedback Control Approaches for Restoration of Power Grids from Blackouts

The automated restoration of power systems with variable energy resources is a timely problem to tackle. Automated restoration advice can support operators in deciding on strategic actions to restore power grids from a blackout with a mix of conventional and renewable generation resources. To this end, this paper frames the restoration process of power grids with solar resources as a nonlinear dynamic model with algebraic constraints in discrete time which is steered by feedback control loops. We discuss two feedback-control strategies based on greedy and reinforcement learning algorithms, and contrast their performance with restoration plans generated by a mixed-integer linear program. We found that the reinforcement learning algorithm infers restoration actions faster than the greedy one. However, the tuning process of the reinforcement learning parameters is slower than for the greedy one.

machine learning↗