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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Reducing Barriers in Space Weather Research and Operations with Next-Generation Simulation Services at the Community Coordinated Modeling Center (CCMC)

Space weather forecasting capabilities are becoming increasingly important to the health of advanced technological infrastructure. The Community Coordinated Modeling Center (CCMC, https://ccmc.gsfc.nasa.gov) serves as a key liaison in the US space weather program between the research and operations communities by providing a wide range of tools and capabilities that help to evaluate, compare, exercise, and archive the results of simulations of a growing list of space weather models. With its unique toolset, CCMC supports space weather research and model development that advances our understanding of space weather phenomena and improves forecasting skill, while also facilitating development of space weather applications and deployment of operational capabilities. Guided by experience from over 20 years of providing simulation services, feedback from its research, operational and educational users world-wide, recommendations from CCMC Advisory Group and Programmatic Review Panel, the CCMC has begun work on the next generation system for its simulation services and model output archives. The new system has been envisioned to employ state-of-the-art technologies and standards to provide a user-oriented experience while improving ease of access, transparency, interoperability with partner systems, and enhancing reliability by incorporating advanced automation for performance monitoring and intelligent failover. In the presentation, we will give an overview of the current CCMC ecosystem and discuss updates to some of the key services of the system, including Runs-on-Request, Instant Runs, and Continuous Runs. We will also describe how a planned expansion and standardization of data archival activities will enhance the role of CCMC as a world-class provider of heliophysics information for the research and analysis of space weather. It is our hope that this evolution of the services can further reduce the barriers and burdens on researchers, forecasters and decision makers who rely on CCMC for their daily research and operations.

Space Weather↗
NASA NTRSDate not supplied

An Oculometric Standard to Assess the Performance of the Ocular System for Long Duration Spaceflight

Changes in the human brain, due to spaceflight, have been a challenge to the space program since its inception; a challenge that is becoming more acute as extended International Space Station (ISS) missions become routine, a return to crewed lunar exploration is about to begin, and a multi-year crewed trip to Mars is being planned. Determining the extent to which neurophysiological adaptations may adversely impact performance in operational tasks, assessing the full-time course of these changes, and identifying/mitigating any potential long-term health consequences are crucial steps required to enable safe crew-autonomous, long-duration, deep-space missions. Vision is the predominant perceptual sense used to guide cognition and motor control in humans. Thus, it is critical for the success of any future, long-duration mission to understand how long-term exposure to microgravity and to other stressors of spaceflight and their interactions, impact visual function. This is an especially challenging task given the small and disparate samples from which we have been and will be able to collect human performance data. NASA has recently been carefully tracking spaceflight-induced ophthalmic changes, driven in large part by the fluid shifts related to microgravity. In particular, a recent study of Optical Coherence Tomography (OCT) postflight measures revealed that more than two-thirds of US crew members experience significant increases in retinal thickness after 6month or longer ISS missions. Space Associated Neuro-ocular Syndrome (SANS) also includes visual acuity decrements and other ocular structural changes that could adversely impact in-flight performance as missions become longer. However, the impact of spaceflight on the visual system is not limited to the retina. Recent comparisons of pre-and post-flight brain images have revealed structural changes throughout the brain that implicate visual, visuomotor, and visual-cognitive pathways, consistent with observed functional impacts (e.g., decreased speed/accuracy/timing of fine goal-oriented movements). Current limitations on inflight testing make it very difficult to determine when and under what conditions SANS and other disruptions of human neurological subsystems arise. Currently, one cannot anticipate the time course and extent of impairment and recovery. This study addresses this knowledge gap by creating an integrated framework of the relevant existing literature on visual and oculomotor function during spaceflight with an eye towards complementing current structural measures of visuomotor impairment with new oculometric standard measures. Using eye-movement based visual function assessment and diagnostics would provide a valuable enhancement of crew health and performance monitoring in support of deepspace exploration.

Vision↗
NASA NTRSDate not supplied

Limitations on the Use of Eye-Tracking Data to Understand Operator Awareness

Safety events in commercial aviation have implicated failures of attention and awareness, and the need for improved attention and awareness has pushed trainers toward eye-tracking systems as a primary tool for evaluating monitoring performance. Further, in the last 15 years, eye-tracking technology has become easier to acquire and use in operationally realistic settings. Although we believe that eye-tracking can be a useful evaluation tool, this paper describes basic limitations on relying on eye-tracking as the sole means for evaluating attention and awareness. A set of other measures are offered that can better identify where performance breakdowns occur.

aviation↗
NASA NTRSDate not supplied

Is "Sleepy Supervision" the New "Drowsy Driving"?

Human error has been implicated as a causal factor in a large proportion of road accidents. Automated driving systems purport to mitigate this risk, but self-driving systems that allow a driver to entirely disengage from the driving task also require the driver to monitor the environment and take control when necessary. Given that sleep loss impairs monitoring performance and there is a high prevalence of sleep deficiency in modern society, we hypothesized that supervising a self-driving vehicle would unmask latent sleepiness compared to manually controlled driving among individuals following their typical sleep schedules. We found that participants felt sleepier, had more involuntary transitions to sleep, had slower reaction times and more attentional failures, and showed substantial modifications in brain synchronization during and following an autonomous drive compared to a manually controlled drive. Our findings suggest that the introduction of partial self-driving capabilities in vehicles has the potential to paradoxically increase accident risk.

sleepiness↗
NASA NTRSDate not supplied

The Development and Application of Cognitive Performance Assessment for Exploration Class Mission EVAs

The objectives of our research are to: 1) Define cognitive performance requirements for future Lunar and Mars mission EVAs, 2) Characterize cognitive performance in subjects conducting simulated EVAs in analog studies, and 3) Develop and test unobtrusive EVA cognitive performance metrics. Our long-term goal is to develop exploration class cognitive performance monitoring capabilities for EVA and to provide data that can inform EVA planning for Lunar and Mars missions.

S R Anderson↗
NASA NTRS2024

Base Support Building Solid Waste Management Unit (SWMU) 014 PlumeStop Injection Pilot Study Work Plan

This document is a Pilot Study Work Plan (PSWP) for a PlumeStop® permeable reactive barrier (PRB) at the Base Support Building (BSB; the Site) located at the John F. Kennedy Space Center (KSC), Florida. The Site has been designated as Solid Waste Management Unit (SWMU) 014 under KSC’s Resource Conservation and Recovery Act (RCRA) Corrective Action program. This document was prepared by AECOM Technical Services, Inc. (AECOM) for the National Aeronautics and Space Administration (NASA) under Indefinite Delivery Indefinite Quantity Contract 80KSC019D0010, Task Order 80KSC021F0096. This PSWP details the permeable reactive barrier (PRB) being installed at the BSB to decrease concentrations of per- and polyfluoroalkyl substances (PFAS) in downgradient groundwater using PlumeStop®. PlumeStop® is a colloidal activated carbon that PFAS compounds readily adsorb to. This PSWP outlines the design criteria, injection plan, and performance monitoring schedule for the proposed PlumeStop® PRB study that will guide the possible selection for use and future design of future full-scale PlumeStop® PRBs at KSC.

PFAS↗
NASA NTRS2024

Ensuring Safe Decision-Making on the Moon and Mars: Cognitive Performance Assessment for Exploration Class Mission EVA

Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future partial gravity EVA on the Moon and Mars are expected to be higher compared to microgravity EVA currently conducted from the International Space Station (ISS). Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient data to characterize cognitive performance prior to, during, and following EVA. Furthermore, it is still unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis (CTA) of EVA to characterize the procedures, the cognitive demands required, and the critical safety decisions associated with decrements in cognitive performance. We used the applied cognitive task analysis method as outlined by Militello and Hutton (1998) and conducted interviews with subject matter experts (SMEs) in EVA at NASA Johnson Space Center in summer 2024. Interviews focused on exploration EVA and elicited feedback from SMEs on the cognitive skills required for specific EVA tasks including cognitive strategies, critical cues, and decision-making strategies. A cognitive demands table was assembled to consolidate and synthesize the information from all interviews. The information from this cognitive task analysis will inform ongoing work to identify the key cognitive domains for safe decision-making during exploration mission class EVA on the Moon and Mars. This work will importantly provide a link between cognitive domains identified in the cognitive task analysis and operationally-relevant performance metrics collected during spaceflight and simulation EVA that can be tracked and assessed by NASA mission operations. Ultimately, this work will aid in the development and application of exploration class cognitive performance monitoring capabilities to improve safe decision-making and inform EVA planning on future spaceflight missions to the Moon and Mars.

Steven R Anderson↗
DOE OSTI2020

Network-Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods

Currently ubiquitous volume data for roadway networks remains the key missing dimension in traffic operations. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper/poster explores the use of state-of-art machine learning techniques to estimate accurate volume measures that span the highway network providing ubiquitous coverage in space, and point-in-time measures for a specific date and time. Three tree-based ensemble learning models, random forest (RF), gradient boost machine (GBM), and extreme gradient boost (XGBoost), were tested for volume estimation by learning from combined dataset of commercial probe data provided by TomTom, the FHWA's Travel Monitoring Analysis System (TMAS) data, and other infrastructure attributes such as number of lanes, speed limit, and weather. The methods were tested on major corridors and freeways in the metropolitan area of Denver. All three machine learning methods were able to provide hourly volume estimates 24 hours a day, 7 days a week, and 365 days a year with around 18% mean absolute error to true volume and about 5% of error with respect to roadway capacity. The low error measures allow the potential application by transportation agencies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Hybrid Analytics Solution for Power Plant Operations

Expert Microsystems, Inc., MapEx Software Inc. and XMPLR Energy LLC have teamed to submit a proposal to the US Department of Energy’s Office of Fossil Energy to improve the efficiency, reliability and flexibility of existing coal-based power plants. The project, titled Hybrid Analytics Solution to Improve Coal Power Plant Operations (the “Hybrid Analytics” project), will develop, demonstrate and commercialize a novel approach that integrates two proven real-time monitoring techniques. The Hybrid Analytics approach integrates into a single, integrated solution: a) an established, advanced data-driven analytics solution that includes artificial intelligence, advanced pattern recognition and machine-learning techniques and b) a well-proven, first principle thermal heat balance model solution. The future role of coal plants will depend on the ability to cycle and follow loads to meet marginal power demands, accommodate renewables and support the grid. Essential to success in this new role is a plant’s ability to maintain high reliability, efficiency and flexibility. However, these changing operational demands put significant stresses on power plant components that directly impact the ability to deliver these requirements. In the past, plants have independently monitored performance using physics-based solutions (heat rate, boiler losses, etc.) and reliability using data-driven analytics (advanced pattern recognition). The Hybrid Analytics solution integrates both approaches to enhance fault detection, provide automated diagnostics and estimates of remaining time to act, and provide critical, enhanced and accurate real-time information to coal plant operators. As a result, coal plants can improve efficiency, reliability, and flexibility and can cost-effectively maintain their key role in the power delivery system.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Controller area network decoder (CAN-D)

A system and method for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This technology can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system and method can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.

Verma, Kiren E.↗
DOE OSTI2024

System for controller area network payload decoding

A system for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This system can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.

Verma, Kiren E.↗
NASA NTRS1991

Receiver autonomous integrity monitoring (RAIM) - Techniques, performance and potential

The goal of receiver autonomous integrity monitoring (RAIM) for the Global Positioning System (GPS) is to provide a sufficient level of navigation integrity for all phases of flight based on the signals transmitted by the GPS satellites only. Integrity requirements for airborne use of GPS are reviewed. This is followed by the description of a baseline fault detection algorithm which is shown to be capable of satisfying tentative integrity requirements. The related issue of testing the fault detection algorithm is also briefly addressed. Preliminary performance results for the baseline fault detection algorithm are presented, along with the potential of RAIM techniques for achieving GPS integrity.

Farrell, James L.↗
DOE OSTI2024

Monitoring of Photovoltaic (PV) Performance and Degradation: Integrated Renewable Energy Systems (IRES) - PV Monitoring Task

The Integrated Renewable Energy System (IRES) testbed demonstration at the Pacific Northwest National Laboratory (PNNL) Sequim campus includes development of a suite of capabilities for tracking the performance of photovoltaic (PV) renewable energy components located on a floating platform (floating PV) and on a shoreline building rooftop. The marine environment represents potentially harsh and corrosive conditions for PV modules. Compared to terrestrial PV, potential concerns for offshore PV arising from high humidity and occasional contact with saltwater, marine wildlife, and aquaculture. These environmental factors can reduce electricity generation efficiency and increase the risk of electrical faults, polymer insulation or jacketing degradation and hydrolysis of the PV cell encapsulant materials. Offshore PV modules may also be exposed to lower temperatures and buoyant and vibrating motions with a floating platform. It is not clear how the long-term performance of floating PV will be affected by these factors. To understand expected energy generation through solar cells on a floating platform, monitoring of the performance of PV modules in the marine environment is needed. This report outlines a plan for long-term testing of IRES PV components utilizing resources of the PNNL Material Aging and Detection (MAaD) Science team and the Marine and Coastal Research Laboratory (MCRL). Equipment applicable for onsite testing and real-time monitoring of the PV performance associated with environmental conditions including temperature, solar irradiance, shading and soiling is included. In case of performance loss, equipment for fault detection, failure analysis, material testing, and further troubleshooting are also available. Through establishment of this capabilities, the enabling IRES project sets the stage for future research to advance off-shore and near-shore energy options for businesses and communities.

14 SOLAR ENERGY↗
DOE OSTI2021

BETO 2021 Peer Review - Process Monitoring and Predictions of Biorefinery Performance

Online process monitoring coupled with rapid predictive tools will be essential to refineries of the future to provide real-time feedback and process control on new, renewable feeds and their accompanying processes and products. This project will provide refinery operators with tools to predict product component concentrations in minutes from online, slip stream mass spectra, allowing for rapid detection of off-specification product. We will arrive at a template for predictive tool generation through the development of a specific tool as a starting point -- co-processing of pyrolysis oils and vacuum gas oil over fluid catalytic cracking (FCC) catalysts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1994

A graphic system for telemetry monitoring and procedure performing at the Telecom SCC

The increasing amount of telemetry parameters and the increasing complexity of procedures used for the in-orbit satellite follow-up has led to the development of new tools for telemetry monitoring and procedures performing. The name of the system presented here is Graphic Server. It provides an advanced graphic representation of the satellite subsystems, including real-time telemetry and alarm displaying, and a powerful help for decision making with on line contingency procedures. Used for 2.5 years at the TELECOM S.C.C. for procedure performing, it has become an essential part of the S.C.C.

Loubeyre, Jean Philippe↗
NASA NTRS1991

Real-time in-flight engine performance and health monitoring techniques for flight research application

Procedures for real time evaluation of the inflight health and performance of gas turbine engines and related systems were developed to enhance flight test safety and productivity. These techniques include the monitoring of the engine, the engine control system, thrust vectoring control system health, and the detection of engine stalls. Real time performance techniques were developed for the determination and display of inflight thrust and for aeroperformance drag polars. These new methods were successfully shown on various research aircraft at NASA-Dryden. The capability of NASA's Western Aeronautical Test Range and the advanced data acquisition systems were key factors for implementation and real time display of these methods.

Ray, Ronald J.↗
NASA NTRS1978

Maintaining calibration of long term satellite monitoring devices as performed on the ozone sensing BUV instruments

Several potential calibration updating techniques for satellite-borne instruments for monitoring the stratosphere are reviewed. The Nimbus 4 BUV instrument was checked against an Atmosphere Explorer 5 BUV launched in November 1975. The problems encountered in this exercise are discussed and generalized to establish an initial checklist for future calibration experiments of satellite sensors using Shuttle-borne sensors.

Guenther, B.↗