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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation

This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). Here, we propose a Generator structure consisting of a coarse network and a fine-tuning network. The coarse network provides an initial estimation of the data segment in the inpainting period. The fine-tuning network consists of self-attention blocks and gated convolution layers for adjusting the initial estimations. Loss functions are specially designed for the fine-tuning and the discriminator networks to enhance both the point-to-point accuracy and realisticness of the results. We test the Load-PIN on three real-world data sets for two applications: patching missing data and deriving baselines of conservation voltage reduction (CVR) events. We benchmark the performance of Load-PIN with five existing deep-learning methods. Our simulation results show that, compared with the state-of-the-art methods, Load-PIN can handle varying-length missing data events and achieve 15-30% accuracy improvement.

14 SOLAR ENERGY↗
DOE OSTI2012

Using Utility Load Data to Estimate Demand for Space Cooling and Potential for Shiftable Loads

This paper describes a simple method to estimate hourly cooling demand from historical utility load data. It compares total hourly demand to demand on cool days and compares these estimates of total cooling demand to previous regional and national estimates. Load profiles generated from this method may be used to estimate the potential for aggregated demand response or load shifting via coldstorage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1978

Load and dynamic assessment of B-52B-008 carrier aircraft for finned configuration 1 space shuttle solid rocket booster deceleration subsystem drop test vehicle. Volume 4: Pylon load data

The pylon loading at the drop test vehicle and wing interface attack points is presented. The loads shown are determined using a stiffness method, which assumes the side stiffness of the forward hook guide and the fore and aft stiffness of each drag pin to be equal. The net effect of this assumption is that the forward hook guide reacts approximately 96% of the drop test vehicle yawing moment. For a comparison of these loads to previous X-15 analysis design loadings, see Volume 1 of this document.

Quade, D. A.↗
DOE OSTI2014

Trend of tunnel magnetoresistance and variation in threshold voltage for keeping data load robustness of metal–oxide–semiconductor/magnetic tunnel junction hybrid latches

The robustness of data load of metal–oxide–semiconductor/magnetic tunnel junction (MOS/MTJ) hybrid latches at power-on is examined by using Monte Carlo simulation with the variations in magnetoresistances for MTJs and in threshold voltages for MOSFETs involved in 90 nm technology node. Three differential pair type spin-transfer-torque-magnetic random access memory cells (4T2MTJ, 6T2MTJ, and 8T2MTJ) are compared for their successful data load at power-on. It is found that the 4T2MTJ cell has the largest pass area in the shmoo plot in TMR ratio (tunnel magnetoresistance ratio) and V{sub dd} in which a whole 256 kb cell array can be powered-on successfully. The minimum TMR ratio for the 4T2MTJ in 0.9 V < V{sub dd} < 1.9 V is 140%, while the 6T2MTJ and the 8T2MTJ cells require TMR ratio larger than 170%.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1993

Federal Communications Commission (FCC) Transponder Loading Data Conversion Software. User's guide and software maintenance manual, version 1.2

This volume contains the complete software system documentation for the Federal Communications Commission (FCC) Transponder Loading Data Conversion Software (FIX-FCC). This software was written to facilitate the formatting and conversion of FCC Transponder Occupancy (Loading) Data before it is loaded into the NASA Geosynchronous Satellite Orbital Statistics Database System (GSOSTATS). The information that FCC supplies NASA is in report form and must be converted into a form readable by the database management software used in the GSOSTATS application. Both the User's Guide and Software Maintenance Manual are contained in this document. This volume of documentation passed an independent quality assurance review and certification by the Product Assurance and Security Office of the Planning Research Corporation (PRC). The manuals were reviewed for format, content, and readability. The Software Management and Assurance Program (SMAP) life cycle and documentation standards were used in the development of this document. Accordingly, these standards were used in the review. Refer to the System/Software Test/Product Assurance Report for the Geosynchronous Satellite Orbital Statistics Database System (GSOSTATS) for additional information.

Mallasch, Paul G.↗
DOE OSTI2021

Improving the Accuracy of Clustering Electric Utility Net Load Data using Dynamic Time Warping

Identifying patterns in electric utility net load data in a time-series format is very useful in preparing the operation for next day. Machine learning algorithms have been used in other domains and those concepts are applied in this paper on real-world net load measurement data. Clustering is the practice of grouping data with similar characteristics as determined by the distance measure. The K-means clustering algorithm is utilized here with actual electric utility data. The paper uses the standard distance measure, Euclidean distance (ED), and compares its performance against the dynamic time warping (DTW) measure. An actual case study with real data is presented, and DTW distance measure-based method observed to result better accuracy compared to the ED based method for substation net load measurements predominantly with residential customers.

clustering↗
DOE OSTI2012

Extracting Operating Modes from Building Electrical Load Data: Preprint

Empirical techniques for characterizing electrical energy use now play a key role in reducing electricity consumption, particularly miscellaneous electrical loads, in buildings. Identifying device operating modes (mode extraction) creates a better understanding of both device and system behaviors. Using clustering to extract operating modes from electrical load data can provide valuable insightsinto device behavior and identify opportunities for energy savings. We present a fast and effective heuristic clustering method to identify and extract operating modes in electrical load data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Enriching Load Data Using Micro-PMUs and Smart Meters

In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record high-resolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption, which cannot sufficiently reflect the actual instantaneous load volatility within each sampling interval. In this paper, we have proposed a novel approach for enriching load data for service transformers that only have low-resolution smart meters. The key to our approach is to statistically recover the high-resolution load data, which is masked by the low-resolution data, using trained probabilistic models of service transformers that have both high- and low-resolution data sources, i.e., micro-PMUs and smart meters. The overall framework consists of two steps: first, for the transformers with micro-PMUs, a Gaussian Process is leveraged to capture the relationship between the maximum/minimum load and average load within each low-resolution sampling interval of smart meters; a Markov chain model is employed to characterize the transition probability of known high-resolution load. Next, the trained models are used as teachers for the transformers with only smart meters to decompose known low-resolution load data into targeted high-resolution load data. The enriched data can recover instantaneous load uncertainty and significantly enhance distribution system observability and situational awareness. Here, we have verified the proposed approach using real high- and low-resolution load data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1984

Strip and load data

The method of taking batch data files and loading these files into the ADABAS data base management system (DBMS) is examined. This strip and load process allows the user to quickly become productive. Techniques for data fields and files definition are also included.

Jones, R. H.↗
NASA NTRS1939

Resume of air-load data on slats and flaps

A resume of the generally available test data regarding air loads on slats and flaps is presented and data attained up to the fall of 1938 are included. The data are given in the form of N.A.C.A. standard coefficients of air forces and moments on the lift-increasing device and, when available, the aerodynamic characteristics of the combined wing and high-lift device are included. Slats of the Handley Page type, fixed auxiliary airfoils, and flaps of several different types are covered.

Wenzinger, Carl J↗
NASA NTRS1976

Vibratory hub load data reduction and analysis from the reverse velocity rotor wind tunnel test, phase 2B

The vibratory hub loads data analysis from the reverse velocity rotor wind tunnel test is reported. Vibratory loads were obtained from the rotating hub balance and also by synthesis of generalized coordinates from the blade flap bending moments. Load trends were defined as a function of speed, rotor thrust and 2 per rev cyclic from each of the data methods. These trends were compared to determine the degree of agreement between each method and provide substantiation for the generalized coordinate approach.

Taylor, R. B.↗
NASA NTRS2022

Apollo Crew Loads Data Mining

INTRODUCTION Human exposure data to dynamic loads in spaceflight are sparse not only because very few crewmembers have flown in space, but also because much of the historic data is no longer readily available. Earth and Moon landings of the Apollo missions are unique and valuable for understanding human tolerance in water landing conditions for both short-duration and long-duration crewmembers as well as informing crew tolerance in standing postures during planetary landings. The goal of this task was to find relevant Apollo data to better understand human tolerance in these scenarios, in order to apply this knowledge to future missions. METHODS The goal of this task was to recover time history data from dynamic phases of flight from crewed Apollo missions. The first phase of the task was to search available records at JSC and other centers to locate the appropriate records at the National Archives in Fort Worth, TX. Once the possible data locations were located, it was planned that one researcher would travel to the National Archives in Fort Worth, TX to collect the relevant Apollo data. Once the data was retrieved, they would be digitized for future use and evaluation, and archived within the Human Physiology, Performance, Protection, and Operations (H-3PO) laboratory within the Human Health and Performance Directorate. These data would be used to assess the feasibility and acceptability of current standards and vehicle design requirements for lunar and Mars missions. Additionally, any medical monitoring required, or physiological changes noted in crewmembers based on dynamic loads would be documented for future use. RESULTS Relevant records were found at Ft. Worth, TX and College Park, MD using the National Archives Online Catalog. In addition to the online catalog, an Archives staff member was able to share an additional inventory list of NASA JSC records held in Ft. Worth, TX. Due to COVID-19, NASA has only recently allowed non-mission essential travel. In addition, the National Archives has also been closed to the public since March 2020. Because of this, travel to the National Archives to search records there has not been possible. CONCLUSION The findings include collections at the National Archives at both Ft. Worth, TX and College Park, MD that could possibly hold the data we are looking for. Unfortunately, the descriptions of each collection are very broad. Though we are hopeful that relevant data is stored in one of these collections, we cannot be sure that we will find what we are looking for. At this time, it is not clear when the Archives will reopen to the public or when NASA will allow travel. Travel to the Archives has been postponed, and will be reassessed at a future date.

T Reiber↗
NASA NTRS1977

A compilation of spacecraft loads data from four Titan Centaur Launch Vehicle flights. Volume 1: Acoustics data

The payloads carried by the first four Titan Centaur launch vehicle flights were, a Viking spacecraft dynamic simulator, the Helios A spacecraft, and the two Viking spacecraft. Dynamic loads data were accumulated from these four flights for application to the Viking spacecraft. The accoustics data include time histories and spectral analysis plots of accoustic pressure measurements made in the Centaur and payload compartment during launch flight. Summary plots are presented to show composite spectra for liftoff and Mach 1/Max Q periods of flight.

Kachadourian, G.↗
NASA NTRS1978

Load and dynamic assessment of B-52B-008 carrier aircraft for finned configuration 1 space shuttle solid rocket booster decelerator subsystem drop test vehicle. Volume 3: Pylon load data method 1

The pylon loading at the drop test vehicle and wing interface attach points is presented. The loads shown are determined using a stiffness method, which assumes the side stiffness of the foreward hook guide to be one-fourth of the fore and aft stiffness of each drag pin. The net effect of this assumption is that the forward hook guide reacts approximately 85% of the drop test vehicle yawing moment. For a comparison of these loads to previous X-15 analysis design loadings, see Volume 1 of this document.

Quade, D. A.↗
NASA NTRS1997

Performance and Vibratory Loads Data From a Wind-Tunnel Test of a Model Helicopter Main-Rotor Blade With a Paddle-Type Tip

An investigation was conducted in the Langley Transonic Dynamics Tunnel to obtain data to permit evaluation of paddle-type tip technology for possible use in future U.S. advanced rotor designs. Data was obtained for both a baseline main-rotor blade and a main-rotor blade with a paddle-type tip. The baseline and paddle-type tip blades were compared with regard to rotor performance, oscillatory pitch-link loads, and 4-per-rev vertical fixed-system loads. Data was obtained in hover and forward flight over a nominal range of advance ratios from 0.15 to 0.425. Results indicate that the paddle-type tip offers no performance improvements in either hover or forward flight. Pitch-link oscillatory loads for the paddle-type tip are higher than for the baseline blade, whereas 4-per-rev vertical fixed-system loads are generally lower.

Yeager, William T., Jr.↗
NASA NTRS1971

Deployment loads data from a free-flight investigation of all flexible parawings having 371.612 sq meters (4000 sq feet) of wing area

A free-flight test program to determine the deployment characteristics of all-flexible parawings was conducted. Both single-keel and twin-keel parawings having a wing area of 4000 square feet with a five-stage reefing system were tested by use of a bomb-type instrumented test vehicle. Several twin-keel-parawing tests were also made by using an instrumented controllable sled-type test vehicle. The systems were launched from either a C-130 or a C-119 carrier airplane, and a programer parachute was used to bring the test vehicle to a proper dynamic pressure and near-vertical flight path prior to deployment of the parawing system. The free-flight deployment loads data are presented in the form of time histories of individual suspension-line loads and total loads.

Croom, D. R.↗