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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Cloud Algorithm Design and Performance for the 2002 Geoscience Laser Altimeter System Mission

A satellite borne lidar instrument, the Geoscience Laser Altimeter System (GLAS), is to be launched in late 2002 and will provide continuous profiling of atmospheric clouds and aerosol on a global basis. Data processing algorithms have been developed to provide operational data products in near real time. Basic data products for cloud observations are the height of the top and bottom of single to multiple cloud layers and the lidar calibrated observed backscatter cross section up to the level of signal attenuation. In addition the optical depth and vertical profile of visible extinction cross section of many transmissive cloud layers and most haze layers are to be derived. The optical thickness is derivable in some cases from the attenuation of the molecular scattering below cloud base. In other cases an assumption of the scattering phase function is required. In both cases a estimated correction for multiple scattering is required. The data processing algorithms have been tested in part from aircraft measurements used to simulated satellite data. The GLAS lidar observations will be made from an orbit that will allow inter comparison with all other existing satellite cloud measurements.

Spinhirne, J. D.↗
NASA NTRS2008

Effect of Surface Traffic Count on Taxi Time at Dallas-Fort Worth (DFW) International Airport

As the amount of air traffic increases over the years, most airports simply do not have the means of expanding to handle the intensified traffic on the surface that will ensue. Precise surveillance equipment and automation concepts, as well as advanced surface traffic algorithms are being developed to improve airport efficiency. These surface algorithms require inputs unique to each airport to ensure maximum efficiency, and minimal taxi delay. This study analyzes surface traffic at Dallas-Fort Worth International Airport (DFW) to determine the effect of the number of aircraft on the surface and the amount of stop and go situations they experience to the amount of additional taxi time encountered. If the surface capacity of an airport is known, minimal delay can be accomplished by limiting the number of taxiing aircraft to that capacity. This concept is related to highways, where traffic flow drastically decreases as more cars occupy the road. An attempt to minimize this effect on highways is seen with the use of metering lights at freeway on-ramps. Since the surface traffic at airports is highly regulated, and aircraft are less mobile on the ground, limiting the surface count to a certain number can greatly reduce the amount of additional taxi time encountered, as well as reduce hazardous emissions. This study will also find the regions of an airport that encounter the most additional taxi time when the number of aircraft in that area is increased. This could help surface traffic algorithms avoid congesting that area, or re-route aircraft to different runways when that area reaches its capacity. The relationship between the amount of stop and go situations an aircraft encounters and their effect on the taxi time of that aircraft will also be investigated. This will help to determine the effect of holding an aircraft on the taxiway as opposed to re-routing it. The lesser of the two should be used when developing surface traffic algorithms to further minimize the delay encountered. The fields investigated in this study include taxi time, the number of aircraft on the surface, the number of stop and go situations, and the time stopped for each aircraft. Taxi time is defined as spot to runway for departures, and runway to spot for arrivals. It does not include ramp area taxi time because the ramp area is controlled differently, and surface traffic schedulers do not currently incorporate them. Taxi time is found by finding the difference between take-off time (OFF) and spot crossing time for departures, and spot crossing time and landing time (ON) for arrivals. All surface data was either found directly using the Surface Operations Data Analysis and Adaptation (SODAA), a tool to analyze the Surface Management System (SMS) generated log files, or indirectly from SODAA using Matlab to derive values from SODAA data. The number of aircraft on the surface is found by looping through the ON times, OFF times, and spot times for each aircraft during a particular day. For each departure aircraft, surface counts are taken at its spot crossing and OFF time. The average of these two is used as the surface count for that aircraft. For arrivals, surface counts are taken at its ON time and its spot crossing time. The average of these two is used.

Kistler, Matthew Stephen↗
NASA NTRS2015

NASA Lessons Learned on Reusable and Expendable Launch Vehicle Operations and Their Application Towards DARPA's Experimental Spaceplane (XS-1) Program

Outline: Selected Historic Shuttle Operations Data; Shuttle Lessons Learned Recommendations for Lower Cost, Operationally Efficient Launch Vehicle Systems; Selected Expendable launch vehicle experiences; Past NASA Launch Vehicle Development Programs, Studies (1985 to present); Discussion: Suggested applications of NASA Lessons Learned to already-baselined contractor XS-1 Phase I concepts.

Reusable launch vehicle↗
NASA NTRS2022

Working Toward A National Coordinated Soil Moisture Monitoring Network: Vision, Progress, and Future Directions

Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.

C. Bruce Baker↗
NASA NTRS1989

Surface energy and radiation balance systems - General description and improvements

Surface evaluation of sensible and latent heat flux densities and the components of the radiation balance were desired for various vegetative surfaces during the ASCOT84 experiment to compare with modeled results and to relate these values to drainage winds. Five battery operated data systems equipped with sensors to determine the above values were operated for 105 station days during the ASCOT84 experiment. The Bowen ratio energy balance technique was used to partition the available energy into the sensible and latent heat flux densities. A description of the sensors and battery operated equipment used to collect and process the data is presented. In addition, improvements and modifications made since the 1984 experiment are given. Details of calculations of soil heat flow at the surface and an alternate method to calculate sensible and latent heat flux densities are provided.

Fritschen, Leo J.↗
NASA NTRS1992

The National Space Science Data Center: An operational perspective

The National Space Science Data Center (NSSDC) manages over 110,000 data tapes with over 4,000 data sets. The size of the digital archive is approximately 6,000 GBytes and is expected to grow to more than 28,000 GBytes by 1995. The NSSDC is involved in several initiatives to better serve the scientific community and improve the management of current and future data holdings. These initiatives address the need to manage data to ensure ready access by the user and manage the media to ensure continuing accessibility and integrity of the data. This paper will present an operational view of the NSSDC, outlining current policies and procedures that were implemented to ensure the effective use of available resources to support service and mission goals, and maintain compliance with prescribed data management directives.

Blitstein, Ronald↗
DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1952

Preliminary Survey of Icing Conditions Measured During Routine Transcontinental Airline Operation

Icing data collected on routine operations by four DC-4-type aircraft equipped with NACA pressure-type icing-rate meters are presented as preliminary information obtained from a statistical icing data program sponsored by the NACA with the cooperation of many airline companies and the United States Air Force. The program is continuing on a much greater scale to provide large quantities of data from many air routes in the United States and overseas. Areas not covered by established air routes are also being included in the survey. The four aircraft which collected the data presented in this report were operated by United Air Lines over a transcontinental route from January through May, 1951. An analysis of the pressure-type icing-rate meter was satisfactory for collecting statistical data during routine operations. Data obtained on routine flight icing encounters from.these four instrumented aircraft, although insufficient for a conclusive statistical analysis, provide a greater quantity and considerably more realistic information than that obtained from random research flights. A summary of statistical data will be published when the information obtained daring the 1951-52 icing season and that to be obtained during the 1952-53 season can be analyzed and assembled. The 1951-52 data already analyzed indicate that the quantity, quality, and range of icing information being provided by this expanded program should afford a sound basis for ice-protection-system design by defining the important meteorological parameters of the icing cloud.

Perkins, Porter J.↗
NASA NTRS2022

Information Fusion and Data Analytics for Human Lunar Exploration (CIF REPORT: Detailed PI Write-up)

The Information Fusion & Data Analytics (IFDA) project commenced in FY20, continued through FY21, and its final platform development phase continues in FY22. The objective remains the fusion and rapid accessibility of large quantities of disparate sourced human spaceflight data. IFDA is a platform tailored for NA (S&MA) to develop highly advanced operational data integration and analysis techniques. IFDA leverages the JSC ER7 modeling, simulation,and data fusion capabilities to collect, warehouse, and augment data human exploration data integration and analysis techniques. The IFDA project’s integrated data visualizations have been demonstrated in two validation scenarios in FY21, and provided the architecture and platform basis for development of a full-scale data analysis suite and storage solution useful to all JSC organizations engaged in real time operations and safety tasks. Scenarioand prototypical development including the construction of a full scale data analysis suite and storage solution, useful to all JSC organizations engaged in real time operations and safety tasks, is central to IFDA Phase 3 and provides a demonstrable pathway for the Digital Transformation Program. IFDA Phase 3 is focused on data provider, data utilizer, and SME hands-on workshops that will conclude the Dem / Valphase and deliver a program-ready data integration tool as a product.

information fusion↗
NASA NTRS2022

Information Fusion & Analytics for Human Lunar Exploration

The Information Fusion & Data Analytics (IFDA) project commenced in FY20, continued through FY21, and its final platform development phase continues in FY22. The objective remains the fusion and rapid accessibility of large quantities of disparate sourced human spaceflight data. IFDA is a platform tailored for NA (S&MA) to develop highly advanced operational data integration and analysis techniques. IFDA leverages the JSC ER7 modeling, simulation,and data fusion capabilities to collect, warehouse, and augment data human exploration data integration and analysis techniques. The IFDA project’s integrated data visualizations have been demonstrated in two validation scenarios in FY21, and provided the architecture and platform basis for development of a full-scale data analysis suite and storage solution useful to all JSC organizations engaged in real time operations and safety tasks. Scenarioand prototypical development including the construction of a full scale data analysis suite and storage solution, useful to all JSC organizations engaged in real time operations and safety tasks, is central to IFDA Phase 3 and provides a demonstrable pathway for the Digital Transformation Program. IFDA Phase 3 is focused on data provider, data utilizer, and SME hands-on workshops that will conclude the Dem / Valphase and deliver a program-ready data integration tool as a product.

information fusion↗
NASA NTRS1990

Space Station Freedom Data Assessment Study

The SSF Data Assessment Study was initiated to identify payload and operations data requirements to be supported in the Space Station era. To initiate the study payload requirements from the projected SSF user community were obtained utilizing an electronic questionnaire. The results of the questionnaire were incorporated in a personal computer compatible database used for mission scheduling and end-to-end communications analyses. This paper discusses data flow paths and associated latencies, communications bottlenecks, resource needs versus availability, payload scheduling 'warning flags' and payload data loading requirements for each major milestone in the Space Station buildup sequence. This paper also presents the statistical and analytical assessments produced using the data base, an experiment scheduling program, and a Space Station unique end-to-end simulation model. The modeling concepts and simulation methodologies presented in this paper provide a foundation for forecasting communication requirements and identifying modeling tools to be used in the SSF Tactical Operations Planning (TOP) process.

Johnson, Anngienetta R.↗
NASA NTRS1990

Operationally Efficient Propulsion System Study (OEPSS) data book. Volume 3: Operations technology

The study was initiated to identify operational problems and cost drivers for current propulsion systems and to identify technology and design approaches to increase the operational efficiency and reduce operations costs for future propulsion systems. To provide readily usable data for the Advanced Launch System (ALS) program, the results of the OEPSS study were organized into a series of OEPSS Data Books. This volume describes operations technologies that will enhance operational efficiency of propulsion systems. A total of 15 operations technologies were identified that will eliminate or mitigate operations problems described in Volume 2. A recommended development plan is presented for eight promising technologies that will simplify the propulsion system and reduce operational requirements.

Vilja, John O.↗
NASA NTRS1987

The International Satellite Cloud Climatology Project - Preliminary results and its potential aspects

The ISCCP (International Satellite Cloud Climatology Project) began its operational data collection phase on July 1, 1983. Considerable research efforts ensured that the compression of basic satellite data did not reduce the information content on cloudiness. At present an operational algorithm has been developed and tested with data from several months, whereas a target for very intensive use by the research community the data from April 1985 are selected. These concur with a simultaneous data set of the ERBE. Special efforts are now underway to map also clouds over both polar regions and may lead to the development of new specific algorithms. Several preliminary results are presented in this paper.

Raschke, E.↗
DOE OSTI2025

LCLS Big Data Handling – How I Learned to Stop Worrying and Love the Data Deluge

Advanced data and computing systems are vital to Linac Coherent Light Source (LCLS) operations, data interpretation and overall scientific productivity. The transition to MHz-era operation marks a fundamental change in scale that requires new infrastructure and architectures to link LCLS to the required scale of computing needed for scientific interpretation. The LCLS-II Data System meets big data challenges by implementing configurable data reduction that can adapt to multiple science areas, real-time analysis frameworks to provide visualization and fast feedback, and the ability to transfer data to local and remote computational facilities for near real time analysis at the appropriate scale. Feature extracted information generated in the data analysis pipeline - at the edge, local compute, or remote High-Performance Computing (HPC) resources - can be used to steer experiments and inform user decisions during beam time. Artificial Intelligence and Machine Learning (AI/ML) techniques present new opportunities to rapidly analyse large datasets and direct experiments, but create new challenges in scaling, adaptability, complexity, and trustworthiness. We describe how the LCLS-II Data System architecture addresses its data-driven challenges in the areas of data acquisition, data processing, data management, and workflow orchestration to decrease the overall time-to-science and provide a vision for future developments.

artificial intelligence↗
DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

EGS-Collab Experiment 1: Time-lapse ERT Data and E4D Inversion Files for 10-24-2018 through 11-07-2018 Flow Test

This data submission includes the raw time-lapse ERT (electrical resistivity tomography) monitoring data, flow system data, operator logs, E4D (https://e4d.pnnl.gov) inversion files, and meta-data necessary to reproduce the 4D ERT inversion for the Oct. 24 through Nov. 7 2018 post-stimulation flow test in test bed 1. The tests were done at the Sanford Underground Research Facility at Homestake Mine in South Dakota.

15 GEOTHERMAL ENERGY↗
NASA NTRS2003

Brayton Power Conversion Unit Tested: Provides a Path to Future High-Power Electric Propulsion Missions

Closed-Brayton-cycle conversion technology has been identified as an excellent candidate for nuclear electric propulsion (NEP) power conversion systems. Advantages include high efficiency, long life, and high power density for power levels from about 10 kWe to 1 MWe, and beyond. An additional benefit for Brayton is the potential for the alternator to deliver very high voltage as required by the electric thrusters, minimizing the mass and power losses associated with the power management and distribution (PMAD). To accelerate Brayton technology development for NEP, the NASA Glenn Research Center is developing a low-power NEP power systems testbed that utilizes an existing 2- kWe Brayton power conversion unit (PCU) from previous solar dynamic technology efforts. The PCU includes a turboalternator, a recuperator, and a gas cooler connected by gas ducts. The rotating assembly is supported by gas foil bearings and consists of a turbine, a compressor, a thrust rotor, and an alternator on a single shaft. The alternator produces alternating-current power that is rectified to 120-V direct-current power by the PMAD unit. The NEP power systems testbed will be utilized to conduct future investigations of operational control methods, high-voltage PMAD, electric thruster interactions, and advanced heat rejection techniques. The PCU was tested in Glenn s Vacuum Facility 6. The Brayton PCU was modified from its original solar dynamic configuration by the removal of the heat receiver and retrofitting of the electrical resistance gas heater to simulate the thermal input of a steady-state nuclear source. Then, the Brayton PCU was installed in the 3-m test port of Vacuum Facility 6, as shown. A series of tests were performed between June and August of 2002 that resulted in a total PCU operational time of about 24 hr. An initial test sequence on June 17 determined that the reconfigured unit was fully operational. Ensuing tests provided the operational data needed to characterize PCU performance over its full operating range. The primary test variables used in operating the Brayton PCU were heater input power and rotor speed. Testing demonstrated a maximum steady-state alternating-current power output of 1835 W at a gas heater power of 9000 W and a rotor speed of 52000 rpm. The corresponding measured turbine inlet gas temperature was 1076 K, and the compressor inlet gas temperature was 282 K. When insulation losses from the gas heater were neglected, the Brayton cycle efficiency for the maximum power point was calculated to be 24 percent. The net direct-current power output was 1750 W, indicating a PMAD efficiency of about 95 percent.

Mason, Lee S.↗