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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 NTRS2013

Sea Ice Thickness, Freeboard, and Snow Depth products from Operation IceBridge Airborne Data

The study of sea ice using airborne remote sensing platforms provides unique capabilities to measure a wide variety of sea ice properties. These measurements are useful for a variety of topics including model evaluation and improvement, assessment of satellite retrievals, and incorporation into climate data records for analysis of interannual variability and long-term trends in sea ice properties. In this paper we describe methods for the retrieval of sea ice thickness, freeboard, and snow depth using data from a multisensor suite of instruments on NASA's Operation IceBridge airborne campaign. We assess the consistency of the results through comparison with independent data sets that demonstrate that the IceBridge products are capable of providing a reliable record of snow depth and sea ice thickness. We explore the impact of inter-campaign instrument changes and associated algorithm adaptations as well as the applicability of the adapted algorithms to the ongoing IceBridge mission. The uncertainties associated with the retrieval methods are determined and placed in the context of their impact on the retrieved sea ice thickness. Lastly, we present results for the 2009 and 2010 IceBridge campaigns, which are currently available in product form via the National Snow and Ice Data Center

sea ice thickness↗
NASA NTRS2010

A Data Filter for Identifying Steady-State Operating Points in Engine Flight Data for Condition Monitoring Applications

This paper presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. If the standard deviation of the data falls below defined constraints, the engine is assumed to be at a steady-state operating point, and the mean measurement data at that point are archived for subsequent condition monitoring purposes. The fundamental design of the steady-state data filter is completely generic and applicable for any dynamic system. Additional domain-specific logic constraints are applied to reduce data outliers and variance within the collected steady-state data. The filter is designed for on-line real-time processing of streaming data as opposed to post-processing of the data in batch mode. Results of applying the steady-state data filter to recorded helicopter engine flight data are shown, demonstrating its utility for engine condition monitoring applications.

Simon, Donald L.↗
NASA NTRS1990

Operationally Efficient Propulsion System Study (OEPSS) data book. Volume 2: Ground operations problems

Operations problems and cost drivers were identified for current propulsion systems and design and technology approaches were identified to increase the operational efficiency and to reduce operations costs for future propulsion systems. To provide readily usable data for the ALS program, the results of the OEPSS study were organized into a series of OEPSS Data Books. This volume presents a detailed description of 25 major problems encountered during launch processing of current expendable and reusable launch vehicles. A concise description of each problem and its operational impact on launch processing is presented, along with potential solutions and technology recommendation.

Waldrop, Glen S.↗
DOE OSTI2021

Forecasting Solar-Thermal Systems Performance under Transient Operation Using a Data-Driven Machine Learning Approach Based on the Deep Operator Network Architecture

Modeling and prediction of the dynamic behavior of thermal systems operating under intermittent energy input and variable load requirements represent one of the greatest challenges in the development of efficient and reliable renewable-based power generation technologies. In this work, a data-driven machine learning modeling framework was developed based on a modified version of the Deep Operator Network architecture where the time coordinate in the trunk net is replaced with historical data of the predicting quantity. The modeling framework can be used to accurately predict the performance of renewable-based energy conversion technologies including wind- and solar-based power plants. This novel framework was applied on a solar-thermal system that consists of a solar collection loop using a flat plate collector, a power generation loop comprising an Organic Rankine Cycle, and a thermal energy storage tank connecting both loops. Variable solar irradiance, air temperature, and power load profiles were used by the Deep Operator Network to predict the State-of-Charge and the efficiency of the thermal system for several days. The results were compared with the State-of-Charge and efficiency functions calculated using a physics-based model. For a simple operation scenario, characterized by a clear sky solar irradiance profile and constant load, the standard deviation in the State-of-Charge prediction by Deep Operator Network is below 0.9% during a seven-day prediction time horizon. For the most realistic operation scenario that considers real solar irradiance and a rough load profile, the maximum standard deviation in the predictions for the State-of-Charge and efficiency are below 6.8% and 2.5%, respectively. A comparison between Deep Operator Network and Long Short Term Memory network was also performed. In general, both networks predict very well the State-of-Charge for different data density conditions; however, a higher accuracy, with a standard deviation below 2.0%, is obtained by the Deep Operator Network during three and half days using sparser training data of 20-minute points. The same accuracy for the State-of-Charge prediction with the Long Short Term Memory network is achieved only for 14 h. Average standard deviations for the State-of-Charge prediction of 1.1% with the Deep Operator Network and 1.5% with the Long Short Term Memory network are obtained for a four-day prediction time using a denser training data of 5-minute points.

DeepONet↗
NASA NTRS2003

Hall Effect Thruster Interactions Data from the Russian Express-A2 and Express-A3 Satellites: Acquire Express-A3 SPT?100 Based Propulsion Subsystem and Other Subsystem Flight Operation TM-Data, Task 31 - Part 5

This 12-part report documents the data obtained from various sensor measurements taken aboard the Russian Express-A2 and Express-A3 spacecraft in Geosynchronous Earth Orbit (GEO). These GEO communications satellites, which were designed and built by NPO Prikladnoy Mekhaniki (NPO PM) of Zheleznogorsk, Russia, utilize Hall thruster propulsion systems for north-south and east-west stationkeeping and as of June 2002, were still operating at 80deg E. and 11deg W., respectively. Express-A2 was launched on March 12, 2000, while Express-A3 was launched on June 24, 2000. The diagnostic equipment from which these data were taken includes electric field strength sensors, ion current and energy sensors, and pressure sensors. The diagnostics and the Hall thruster propulsion systems are described in detail along with lists of tabular data from those diagnostics and propulsion system and other satellite systems. Space Power, Inc., now part of Pratt & Whitney's Chemical Systems Division, under contract NAS3-99151 to the NASA Glenn Research Center, obtained these data over several periods from March 12, 2000, through September 30, 2001. Each of the 12 individual reports describe, in detail, the propulsion systems as well as the diagnostic sensors utilized. Finally, parts 11 and 12 include the requirements to which NPO PM prepared and delivered these data.

Sitnikova, N.↗
NASA NTRS2003

Hall Effect Thruster Interactions Data From the Russian Express-A2 and Express-A3 Satellites: Acquire Express-A3 SPT-100 Based Propulsion Subsystem and Other Subsystem Flight Operation TM-Data, Task 32 - Part 7

This 12-part report documents the data obtained from various sensor measurements taken aboard the Russian Express-A2 and Express-A3 spacecraft in Geosynchronous Earth Orbit (GEO). These GEO communications satellites, which were designed and built by NPO Prikladnoy Mekhaniki (NPO PM) of Zheleznogorsk, Russia, utilize Hall thruster propulsion systems for north-south and east-west stationkeeping and as of June 2002, were still operating at 80 E. and 11 W., respectively. Express-A2 was launched on March 12, 2000, while Express-A3 was launched on June 24, 2000. The diagnostic equipment from which these data were taken includes electric field strength sensors, ion current and energy sensors, and pressure sensors. The diagnostics and the Hall thruster propulsion systems are described in detail along with lists of tabular data from those diagnostics and propulsion system and other satellite systems. Space Power, Inc., now part of Pratt & Whitney's Chemical Systems Division, to the NASA Glenn Research Center, obtained these data over several periods from March 12, 2000, through September 30, 2001. Each of the 12 individual reports describe, in detail, the propulsion systems as well as the diagnostic sensors utilized. Finally, parts 11 and 12 include the requirements to which NPO PM prepared and delivered these data.

Sitnikova, N.↗
NASA NTRS2003

Hall Effect Thruster Interactions Data From the Russian Express-A2 and Express-A3 Satellites: Acquire Express-A3 SPT-100 Based Propulsion Subsystem and Other Subsystem Flight Operation TM-Data for the Period of June 24, 2000 to and Including September 30, 2000, Task 30 - Part 3

This 12-part report documents the data obtained from various sensor measurements taken aboard the Russian Express-A2 and Express-A3 spacecraft in Geosynchronous Earth Orbit (GEO). These GEO communications satellites, which were designed and built by NPO Prikladnoy Mekhaniki (NPO PM) of Zheleznogorsk, Russia, utilize Hall thruster propulsion systems for north-south and east-west stationkeeping and as of June 2002, were still operating at 80 E. and 11 W., respectively. Express-A2 was launched on March 12, 2000, while Express-A3 was launched on June 24, 2000. The diagnostic equipment from which these data were taken includes electric field strength sensors, ion current and energy sensors, and pressure sensors. The diagnostics and the Hall thruster propulsion systems are described in detail along with lists of tabular data from those diagnostics and propulsion system and other satellite systems. Space Power, Inc., now part of Pratt & Whitney's Chemical Systems Division, under contract NAS3 99151 to the NASA Glenn Research Center, obtained these data over several periods from March 12, 2000, through September 30, 2001. Each of the 12 individual reports describe, in detail, the propulsion systems as well as the diagnostic sensors utilized. Finally, parts 11 and 12 include the requirements to which NPO PM prepared and delivered these data.

Sitnikova, N.↗
NASA NTRS2003

Hall Effect Thruster Interactions Data From the Russian Express-A2 and Express-A3 Satellites. Acquire Express-A2 SPT-100 Based Propulsion Subsystem and Other Subsystem Flight Operation TM-Data for the Period of March 12, 2000 to and Including June 15, 2000, Task 29

This 12-part report documents the data obtained from various sensor measurements taken aboard the Russian Express-A2 and Express-A3 spacecraft in Geosynchronous Earth Orbit (GEO). These GEO communications satellites, which were designed and built by NPO Prikladnoy Mekhaniki (NPO PM) of Zheleznogorsk, Russia, utilize Hall thruster propulsion systems for north-south and east-west stationkeeping and as of June 2002, were still operating at 80 E. and 11 W., respectively. Express-A2 was launched on March 12, 2000, while Express-A3 was launched on June 24, 2000. The diagnostic equipment from which these data were taken includes electric field strength sensors, ion current and energy sensors, and pressure sensors. The diagnostics and the Hall thruster propulsion systems are described in detail along with lists of tabular data from those diagnostics and propulsion system and other satellite systems. Space Power, Inc., now part of Pratt & Whitney s Chemical Systems Division, under contract NAS3 99151 to the NASA Glenn Research Center, obtained these data over several periods from March 12, 2000, through September 30, 2001. Each of the 12 individual reports describe, in detail, the propulsion systems as well as the diagnostic sensors utilized. Finally, parts 11 and 12 include the requirements to which NPO PM prepared and delivered these data.

Sitnikova, N.↗
NASA NTRSDate not supplied

Upping the TEMPO on Air Pollution Observations from Space for Enhanced Science Applications

The Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission was launched into Geostationary Earth Orbit (GEO) to 91°W longitude on April 7, 2023, and had a successful First Light period from July 31 – August 2 where the first hourly daytime scans across the TEMPO Field of Regard (FoR) covering greater North America were performed. First light images of nitrogen dioxide (NO2) were released to the public on August 24. In addition to NO2, TEMPO will provide hourly daytime observations of formaldehyde (HCHO), sulfur dioxide (SO2), ozone (O3), bromine (BrO), water vapor (H2O), and aerosols, at high spatial resolution (~2.0 x 4.75 km2) across the FoR. The hyperspectral ultraviolet-to-visible measurements from the TEMPO imaging grating spectrometer will permit an ozone profile retrieval capable of monitoring the diurnal evolution of ozone in the planetary boundary layer. The non-standard or special scan operations of TEMPO at sub-hourly frequency (e.g., 2-10 minutes) over selected slices of the FoR will further enhance monitoring capabilities during air quality disasters (e.g., wildfires, volcanic eruptions, dust storms, industrial accidents), research studies, and field campaign operations. A large diversity of stakeholders and end-users have been engaged in the TEMPO Early Adopters Program, supported by the NASA Applied Sciences Program, which aims to enhance health and air quality applications and maximize the societal benefit of TEMPO data when operational data becomes publicly available in the spring of 2024. Key outcomes from the Early Adopters Program include the pre-launch use of TEMPO proxy data to prepare the community for operational data, tailoring the data dissemination and visualization tools for the mission based on user needs, and enhancing the TEMPO special operations component of the mission through early adopter studies. This presentation will provide the latest TEMPO mission and Early Adopters Program updates, initiatives from the TEMPO special operations, and early demonstrations of science applications enabled through TEMPO data, while also showcasing the First Light imagery.

Air Quality↗
NASA NTRS1992

Operationally efficient propulsion system study (OEPSS) data book: Space Transfer Propulsion Operational Efficiency Study Task of OEPSS - Volume 6

This document is the final report for the Space Transfer Propulsion Operational Efficiency Study Task of the Operationally Efficient Propulsion System Study (OEPSS) conducted by the Rocketdyne Division of Rockwell International. This Study task studied, evaluated and identified design concepts and technologies which minimized launch and in-space operations and optimized in-space vehicle propulsion system operability.

Harmon, Timothy J.↗
NASA NTRS1993

SpaceOps 1992: Proceedings of the Second International Symposium on Ground Data Systems for Space Mission Operations

The Second International Symposium featured 135 oral presentations in these 12 categories: Future Missions and Operations; System-Level Architectures; Mission-Specific Systems; Mission and Science Planning and Sequencing; Mission Control; Operations Automation and Emerging Technologies; Data Acquisition; Navigation; Operations Support Services; Engineering Data Analysis of Space Vehicle and Ground Systems; Telemetry Processing, Mission Data Management, and Data Archiving; and Operations Management. Topics focused on improvements in the productivity, effectiveness, efficiency, and quality of mission operations, ground systems, and data acquisition. Also emphasized were accomplishments in management of human factors; use of information systems to improve data retrieval, reporting, and archiving; design and implementation of logistics support for mission operations; and the use of telescience and teleoperations.

Source record↗
NASA NTRSDate not supplied

Clifford Neural Operators on Atmospheric Data Influenced Partial Differential Equations

Mathematical representations of the atmosphere are key to forecasting and research tasks across Earth science. Numerically solving the underlying partial differential equations(PDEs) of the atmosphere, however, can be difficult and computationally expensive with numerous trade-offs between computing efficiency and accuracy. Utilizing neural net-works to learn approximations of the PDE solutions from the data can help us model complex phenomena more efficiently than traditional numerical schemes. Here, we have applied Clifford algebra-based neural operators for predicting atmospheric variables. Clifford Fourier neural operators are used with two different backbone architectures, ResNet and UNet, on custom data of U10, V10 and surface pressure as well as U500, V500 and Z500. Clifford Fourier neural operators, coupled with ResNet and UNet architectures, are applied to a key reanalysis dataset. Model performance is initially strong, but we observe increasing errors, resulting in the model becoming highly unstable.

Sujit Roy↗
NASA NTRS2009

General Purpose Data-Driven Monitoring for Space Operations

As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault and anomaly detection algorithms and diagnosis tools with executive and adaptive planning functions contained in the flight software on-board the Air Force Research Laboratory TacSat-3 satellite. The TVSM software package will be uploaded after launch to monitor spacecraft subsystems such as power and guidance, navigation, and control (GN&C). It will analyze data in real-time to demonstrate detection of faults and unusual conditions, diagnose problems, and react to threats to spacecraft health and mission goals. The experiment will demonstrate the feasibility and effectiveness of integrated system health management (ISHM) technologies with both ground and on-board experiments.

Iverson, David L.↗
NASA NTRS2002

Fuel Consumption Modeling of a Transport Category Aircraft Using Flight Operations Quality Assurance Data: A Literature Review

Fuel is a major cost expense for air carriers. A typical airline spends 10% of its operating budget on the purchase of jet fuel, which even exceeds its expenditures on aircraft acquisitions. Thus, it is imperative that fuel consumption be managed as wisely as possible. The implementation of Flight Operations Quality Assurance (FOQA) programs at airlines may be able to assist in this management effort. The purpose of the study is to examine the literature regarding fuel consumption by air carriers, the literature related to air carrier fuel conservation efforts, and the literature related to the appropriate statistical methodologies to analyze the FOQA-derived data.

Stolzer, Alan J.↗
NASA NTRS2011

The Landsat Data Continuity Mission Operational Land Imager: Radiometric Performance

The Operational Land Imager (OLI) is one of two instruments to fly on the Landsat Data Continuity Mission (LDCM), which is scheduled to launch in December 2012 to become the 8th in the series of Landsat satellites. The OLI images in the solar reflective part of the spectrum, with bands similar to bands 1-5, 7 and the panchromatic band on the Landsat-7 ETM+ instrument. In addition, it has a 20 nm bandpass spectral band at 443 nm for coastal and aerosol studies and a 30 nm band at 1375 nm to aid in cirrus cloud detection. Like ETM+, spatial resolution is 30 m in the all but the panchromatic band, which is 15 meters. OLI is a pushbroom radiometer with approximately 6000 detectors per 30 meter band as opposed to the 16 detectors per band on the whiskbroom ETM+. Data are quantized to 12 bits on OLI as opposed to 8 bits on ETM+ to take advantage of the improved signal to noise ratio provided by the pushbroom design. The saturation radiances are higher on OLI than ETM+ to effectively eliminate saturation issues over bright Earth targets. OLI includes dual solar diffusers for on-orbit absolute and relative (detector to detector) radiometric calibration. Additionally, OLI has 3 sets of on-board lamps that illuminate the OLI focal plane through the full optical system, providing additional checks on the OLI's response[l]. OLI has been designed and built by Ball Aerospace & Technology Corp. (BATC) and is currently undergoing testing and calibration in preparation for delivery in Spring 2011. Final pre-launch performance results should be available in time for presentation at the conference. Preliminary results will be presented below. These results are based on the performance of the Engineering Development Unit (EDU) that was radiometrically tested at the integrated instrument level in 2010 and assembly level measurements made on the flight unit. Signal-to-Noise (SNR) performance: One of the advantages of a pushbroom system is the increased dwell time of the detectors allowing for significantly higher SNR than equivalent aperture whiskbroom systems. OLI performance based on the EDU at the "typical" radiance level as specified in the OLI requirements document are about 10 times better than ETM+ performance and 2-3 times better than the requirements for OLI (Table 1).

Markham, Brian↗
NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗