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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Analyzing Potential Grid Impacts from Future In-Motion Roadway Wireless Power Transfer Scenarios

This work examines the grid impact of in-motion roadway wireless power transfer through the examination of the electrification of high-capacity roadways inside a metropolitan area. The work uses data from a regional travel study and the Federal Highway Administration's Highway Performance Monitoring System to estimate the electrified roadway's hourly power use throughout a week. The data are then combined with hourly grid load estimates for the same metropolitan area to determine the overlay of traditional grid load with additional load from a future electrified roadway.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2017

Development of a Dynamic Lidar Uncertainty Framework

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote-sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote-sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote-sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards for quantifying remote sensing device uncertainty for power performance testing consider uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device and are generally fixed, leading to climatic uncertainty values that apply to the entire measurement campaign. However, real-world experience and a consideration of the fundamentals of the measurement process have shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we describe the development of a new dynamic lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from a field measurement site to assess the ability of the framework to predict errors in lidar-measured wind speed. The results show how uncertainty varies over time and can be used to help select data with different levels of uncertainty for different applications, for example, low uncertainty data for power performance testing versus all data for plant performance monitoring.

17 WIND ENERGY↗
DOE OSTI2018

Estimating Highway Volumes Using Vehicle Probe Data - Proof of Concept: Preprint

This paper examines the feasibility of using sampled commercial probe data in combination with validated continuous counter data to accurately estimate vehicle volume across the entire roadway network, for any hour during the year. Currently either real time or archived volume data for roadways at specific times are extremely sparse. 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 explores the use of commercial probe data to generate accurate volume measures that span the highway network providing ubiquitous coverage in space, and specific point-in-time measures for a specific date and time. The paper examines the need for the data, fundamental accuracy limitations based on a basic statistical model that take into account the sampling nature of probe data, and early results from a proof of concept exercise revealing the potential of probe type data calibrated with public continuous count data to meet end user expectations in terms of accuracy of volume estimates.

25 ENERGY STORAGE↗
DOE OSTI2018

CWA 15793 2011 Planning and Implementation Tool

This software, built on an open source platform called Electron (runs on Chromium and Node.js), is designed to assist organizations in the implementation of a biorisk management system consistent with the requirements of the international, publicly available guidance document CEN Workshop Agreement 15793:2011 (CWA 15793). The software includes tools for conducting organizational gap analysis against CWA 15793 requirements, planning tools to support the implementation of CWA 15793 requirements, and performance monitoring support. The gap analysis questions are based on the text of CWA 15793, and its associated guidance document, CEN Workshop Agreement 16393:2012. The authors have secured permission from the publisher of CWA 15793, the European Committee for Standardization (CEN), to use language from the document in the software, with the understanding that the software will be made available freely, without charge.

Gross, Alan↗
DOE OSTI2018

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

Currently real-time, 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 explores the use of state-of-art machine learning techniques to estimate accurate real-time 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

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 OSTI2018

Remote Robotic Equipment for Retrieval of High Level Waste to Support Environmental Closure - 18162

The Idaho Cleanup Project contractor, Fluor Idaho, LLC, is responsible for managing HLW calcine stored in the Calcined Solids Storage Facility at the Idaho Nuclear Technology and Engineering Center. The Idaho Nuclear Technology and Engineering Center is a DOE managed facility located at the INL Site in the remote portions of southeast Idaho. In 2016, DOE directed Fluor Idaho to develop and test a full-scale retrieval system to demonstrate the ability to safely retrieve HLW calcine from the Calcined Solids Storage Facility for future retrieval and processing. Removing HLW calcine from the State of Idaho is required by the 1995 Settlement Agreement, which mandates that DOE have HLW calcine ready to be moved out of Idaho by December 2035. Fluor Idaho is currently testing retrieval and transfer concepts to ensure that the Calcined Solids Storage Facility is placed in a final configuration that is protective of human health and the environment after HLW calcine has been removed to the maximum extent practical. This paper describes the full-scale prototype that will be used to prove retrieval and transfer system design capabilities, verify operating parameters, and establish a basis for calcine retrieval efficacy. Additionally, it describes the retrieval (bulk and residual) and visual monitoring systems that were designed and tested in 2017 and will continue to be tested with the full-scale prototype. Testing (i.e., full-scale mockup, bulk retrieval, residual clean-out, and visualization monitoring) performed by Fluor Idaho will demonstrate the maximum extent practical of HLW calcine removal that is achievable. This is critical to (1) establish criteria for ending calcine removal activities, (2) evaluate cost and benefits associated with performing additional removal, and (3) ensure clean up and closure standards under applicable requirements (e.g., statutes, orders, and agreements) are met. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Successful In Situ Remediation of Cr(VI)-Impacted Deeper Vadose Zone Soils for Groundwater Protection - 18603

Shallow soil at a former industrial wood treating facility was impacted with chromium, arsenic, and copper above cleanup goals and shallow soil has been excavated for protection of direct contact exposure pathways. Deeper vadose zone soil was predominantly impacted with chromium and was undergoing remediation for long-term groundwater protection. The 920-meter (m) long Cr(VI) groundwater plume has undergone full-scale remediation with groundwater recirculation and in situ bioremediation. Following approximately 4 years of operation, the Cr(VI) groundwater plume, with former source area groundwater concentrations over 10,000 micrograms per liter (μg/L), has been reduced to below the laboratory method reporting limit (0.50 μg/L). Approximately 23,000 cubic meters (m{sup 3}) of deeper (4.5 m - 11 m below ground surface, bgs) vadose zone soil required Cr(VI) remediation. The remediation strategy relied on reduction of Cr(VI) and geochemical fixation as trivalent chromium (Cr(III)) and iron hydroxide precipitates. Chromium reduction was facilitated by a carbon source (corn syrup and methanol) to establish and maintain biogeochemical reducing conditions and ferrous iron to support direct Cr(VI) chemical reduction. Combined, the injected soil moisture, carbon source and ferrous iron supported fast and sustained ferrous/ferric iron cycling and Cr(VI) reduction. Remedy performance monitoring included time-series water quality data from a network of vacuum lysimeters and confirmation soil borings. Reagent was applied either through liquid injection wells and direct-push injection points, or by deep soil mixing methodology in areas with difficult geology (i.e., fine-grained soils and discontinuous iron-silica hardpan layer). Following both phases of deep soil remediation, soils met long-term protection of groundwater remediation action objectives. In situ treatment of soils provides a cost-effective remedial solution compared to excavation, specifically when impacts are deep and long-term protection of groundwater is a primary remediation goal. The delivery methods employed in this case study can be applied to sites impacted with a broad range of contaminants amendable to geochemical fixation (e.g., chromium, uranium, strontium, nitrate, etc.). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
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.↗