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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 109 records · Page 6

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Load Profiles Data for the EVI-RoadTrip Web Tool

The dataset contains EVI-RoadTrip outputs, minute-by-minute load profiles in kW for each station in the simulation based on assumed utilization and network density. The load profiles are aggregated to lower spatial resolution (e.g., state-level, corridor-level) by summation of all station loads associated with the respective geography. This results in a load profile for each scenario that summarizes the corridor's, state's, or county's load profile in minute-level resolution.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Artificial intelligence based analysis of nanoindentation load–displacement data using a genetic algorithm

In this work, we developed an automated tool, Nanoindentation Neo package for the analysis of nanoindentation load–displacement curves using a Genetic Algorithm (GA) applied to the Oliver-Pharr method (Oliver et al.,1992). For some materials, such as polycrystalline isotropic graphites, Least Squares Fitting (LSF) of the unload curve can produce unrealistic fit parameters. These graphites exhibit sharply peaked unloading curves not easily fit using the LSF, which tends to overestimate the indenter tip geometry parameter. To tackle this problem, we extended our general materials characterization tool Neo for EXAFS analysis (Terry et al., 2021) to fit nanoindentation data. Nanoindentation Neo automatically processes and analyzes nanoindentation data with minimal user input while producing meaningful fit parameters. GA, a robust metaheuristic method, begins with a population of temporary solutions using model parameters called chromosomes; from these we evaluate a fitness value for each solution, and select the best solutions to mix with random solutions producing the next generation. A mutation operator then modifies existing solutions by random perturbations, and the optimal solution is selected. We tested the GA method using Silica and Al reference standards. We fit samples of graphite and a high entropy alloy (HEA) consisting of BCC and FCC phases.

42 ENGINEERING↗

NASA NTRS2011

Load Asymmetry Observed During Orion Main Parachute Inflation

The Crew Exploration Vehicle Parachute Assembly System (CPAS) has flight tested the first two generations of the Orion parachute program. Three of the second generation tests instrumented the dispersion bridles of the Main parachute with a Tension Measuring System. The goal of this load measurement was to better understand load asymmetry during the inflation process of a cluster of Main parachutes. The CPAS Main parachutes exhibit inflations that are much less symmetric than current parachute literature and design guides would indicate. This paper will examine loads data gathered on three cluster tests, quantify the degree of asymmetry observed, and contrast the results with published design guides. Additionally, the measured loads data will be correlated with videos of the parachute inflation to make inferences about the shape of the parachute and the relative load asymmetry. The goal of this inquiry and test program is to open a dialogue regarding asymmetrical parachute inflation load factors.

Morris, Aaron L.↗

DOE OSTI2020

Kosh

Kosh allows codes to store, query, share data via an easy-to-use Python API. Kosh lies on top of Sina and as a result can use any database backend supported by Sina. In adition Kosh aims to make data access and sharing as simple as possible. Via "loaders" kosh can open files associated with datasets in a seemless fashion independently of the actual file format. Kosh's loader can also load data in different format, although numpy is the most usual output type. Once loaded data from sources, data can be further processed via "transformers"

Doutriaux, Charles↗

NASA NTRS1994

Computation of Loads on the McDonnell Douglas Advanced Bearingless Rotor

Computed results from UMARC and DART analyses are compared with the blade bending moments and vibratory hub loads data obtained from a full-scale wind tunnel test of the McDonnell Douglas five-bladed advanced bearingless rotor. The 5 per-rev vibratory hub loads data are corrected using results from a dynamic calibration of the rotor balance. The comparison between UMARC computed blade bending moments at different flight conditions are poor to fair, while DART results are fair to good. Using the free wake module, UMARC adequately computes the 5P vibratory hub loads for this rotor, capturing both magnitude and variations with forward speed. DART employs a uniform inflow wake model and does not adequately compute the 5P vibratory hub loads for this rotor.

Nguyen, Khanh↗

NASA NTRS1993

Reduction of structural loads using maneuver load control on the Advanced Fighter Technology Integration (AFTI)/F-111 mission adaptive wing

A transonic fighter-bomber aircraft, having a swept supercritical wing with smooth variable-camber flaps was fitted with a maneuver load control (MLC) system that implements a technique to reduce the inboard bending moments in the wing by shifting the spanwise load distribution inboard as load factor increases. The technique modifies the spanwise camber distribution by automatically commanding flap position as a function of flap position, true airspeed, Mach number, dynamic pressure, normal acceleration, and wing sweep position. Flight test structural loads data were obtained for loads in both the wing box and the wing root. Data from uniformly deflected flaps were compared with data from flaps in the MLC configuration where the outboard segment of three flap segments was deflected downward less than the two inboard segments. The changes in the shear loads in the forward wing spar and at the roots of the stabilators also are presented. The camber control system automatically reconfigures the flaps through varied flight conditions. Configurations having both moderate and full trailing-edge flap deflection were tested. Flight test data were collected at Mach numbers of 0.6, 0.7, 0.8, and 0.9 and dynamic pressures of 300, 450, 600, and 800 lb/sq ft. The Reynolds numbers for these flight conditions ranged from 26 x 10(exp 6) to 54 x 10(exp 6) at the mean aerodynamic chord. Load factor increases of up to 1.0 g achieved with no increase in wing root bending moment with the MLC flap configuration.

Thornton, Stephen V.↗

NASA NTRS2015

Complementary Aerodynamic Performance Datasets for Variable Speed Power Turbine Blade Section from Two Independent Transonic Turbine Cascades

Two independent experimental studies were conducted in linear cascades on a scaled, two-dimensional mid-span section of a representative Variable Speed Power Turbine (VSPT) blade. The purpose of these studies was to assess the aerodynamic performance of the VSPT blade over large Reynolds number and incidence angle ranges. The influence of inlet turbulence intensity was also investigated. The tests were carried out in the NASA Glenn Research Center Transonic Turbine Blade Cascade Facility and at the University of North Dakota (UND) High Speed Compressible Flow Wind Tunnel Facility. A large database was developed by acquiring total pressure and exit angle surveys and blade loading data for ten incidence angles ranging from +15.8deg to −51.0deg. Data were acquired over six flow conditions with exit isentropic Reynolds number ranging from 0.05×106 to 2.12×106 and at exit Mach numbers of 0.72 (design) and 0.35. Flow conditions were examined within the respective facility constraints. The survey data were integrated to determine average exit total-pressure and flow angle. UND also acquired blade surface heat transfer data at two flow conditions across the entire incidence angle range aimed at quantifying transitional flow behavior on the blade. Comparisons of the aerodynamic datasets were made for three "match point" conditions. The blade loading data at the match point conditions show good agreement between the facilities. This report shows comparisons of other data and highlights the unique contributions of the two facilities. The datasets are being used to advance understanding of the aerodynamic challenges associated with maintaining efficient power turbine operation over a wide shaft-speed range.

Cascade↗

NASA NTRS2015

Complementary Aerodynamic Performance Datasets for Variable Speed Power Turbine Blade Section from Two Independent Transonic Turbine Cascades

Turbine↗

DOE OSTI2021

Short-term nodal load forecasting based on machine learning techniques

This paper introduces an advanced Short-term Nodal Load Forecasting (STNLF) method that forecasts nodal load profiles for the next day in power systems, based on the combined use of three machine learning techniques. Least Absolute Shrinkage and Selection Operator (LASSO) is employed to reduce the number of features for a single nodal load forecasting. Principal Component Analysis (PCA) is used to capture the features of historical loads in low-dimensional space compared to the original high-dimensional load space where features are barely possible to depict. Additionally, Bayesian Ridge Regression (BRR) is utilized to decide the parameters of the prediction model from a statistics perspective. Tests based on modified PJM load data demonstrate the effectiveness of the proposed STNLF method compared to the state-of-the-art General Regression Neural Network (GRNN) method. Moreover, the reliability of the day-ahead Unit Commitment (UC) solution is shown to have been improved, based on the forecasted load data using the proposed STNLF method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Time and Frequency Analysis of Load Profile Data

Technology advancements and integration of modern advanced metering systems can monitor, forecast, inform, control, and operate the building's mechanical, electrical, and plumbing (MEP) systems. They offer a higher level of information, which can contribute to making smart buildings more energy efficient and to making them closer to becoming grid-interactive energy efficient buildings (GEB). This paper builds on the ongoing research on variability analysis of a case study building with a 1-minute load profile and examines the Discrete Wavelet Transform (DWT) process in the frequency domain to quantify the signal's energy in each bandwidth, with respect to each end-use category. Moreover, the amount of variability in the total variability is not similar among the end-use categories. This information is needed to understand the behavior of the variability in the frequency domain for future applications, such as generating synthetic load profiles with a similar frequency spectrum as the measured signal.

decomposition↗

DOE OSTI2022

Industrial battery operation and utilization in the presence of electrical load uncertainty using Bayesian decision theory

Behind the meter battery storage is becoming increasing popular in all sectors, though enthusiasm has recently lagged in the industrial sector. Even though there may be many factors contributing to this including lack of innovation, prohibitive costs, and undesirable rate structures, a difficulty arises in accounting for uncertainty of electrical load in industrial facilities while still attempting to utilize battery storage as much as possible all while trying to achieve fiscal profitability. Here this study utilizes Gaussian process regression and Bayesian decision theory to organize load data and quantify electrical load uncertainty to properly and effectively discharge industrial battery storage. The study employs a simulation model to set battery load setpoints for the span of the utility billing period according to the degree of risk aversion. This combination of economic analysis according to utility billing period and utilization of degree of risk aversion to make decisions on the uncertainty of the data has not before been applied to battery storage. The method resulted in an annual average reduction of peak demand by 3.8 % at the lowest amount of savings and lowest risk aversion. The highest risk aversion resulted in an annual average reduction of peak demand of 7.5 %. The maximum reduction of peak load in any month was 13.8 % in the month of December with a relatively high risk aversion. With a the highest amount risk aversion tested, the model reduced demand ten of the twelve months of the year.

25 ENERGY STORAGE↗

DOE OSTI2024

How robust are estimates of key parameters in standard viral dynamic models?

Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1975

Application of a dedicated micro-processor for automatic data acquisition of load strain and acoustic-emission data

This paper describes an automatic system for acquisition, logging and analysis of stress, strain and acoustic emission data. The complete system is a combination of several standard items of equipment and a newly developed intelligent data logger which has the capability to send data directly to a large scale computer for reduction and plotting. The data logging and all interfacing with peripheral equipment are accomplished using digital electronics. The heart of the system is a four-bit microprocessor and digital cassette tape unit. The system, which is controlled by instructions from a cathode ray tube terminal, has the capability to transmit data from the cassette unit to a large computer via a time sharing option. Typical results are presented for acoustic emission count rate and total count curves superimposed on stress-strain diagrams for boron/epoxy advanced composites.

Wilson, G. H.↗

NASA NTRS2001

Corner Wrinkling at a Square Membrane Due to Symmetric Mechanical Loads

Thin-film membrane structures are under consideration for use in many future gossamer spacecraft systems. Examples include sunshields for large aperture telescopes, solar sails, and membrane optics. The development of capabilities for testing and analyzing pre-tensioned, thin film membrane structures is an important and challenging aspect of gossamer spacecraft technology development. This paper presents results from experimental and computational studies performed to characterize the wrinkling behavior of thin-fi[m membranes under mechanical loading. The test article is a 500 mm square membrane subjected to symmetric comer loads. Data is presented for loads ranging from 0.49 N to 4.91 N. The experimental results show that as the load increases the number of wrinkles increases, while the wrinkle amplitude decreases. The computational model uses a finite element implementation of Stein-Hedgepeth membrane wrinkling theory to predict the behavior of the membrane. Comparisons were made with experimental results for the wrinkle angle and wrinkled region. There was reasonably good agreement between the measured wrinkle angle and the predicted directions of the major principle stresses. The shape of the wrinkle region predicted by the finite element model matches that observed in the experiments; however, the size of the predicted region is smaller than that determined in the experiments.

Blandino, Joseph R.↗

NASA NTRS2003

Experimental Performance Evaluation of a Supersonic Turbine for Rocket Engine Applications

In order to mitigate the risk of rocket propulsion development, efficient, accurate, detailed fluid dynamics analysis and testing of the turbomachinery is necessary. To support this requirement, a task was developed at NASA Marshall Space Flight Center (MSFC) to improve turbine aerodynamic performance through the application of advanced design and analysis tools. These tools were applied to optimize a supersonic turbine design suitable for a reusable launch vehicle (RLV). The hot gas path and blading were redesigned-to obtain an increased efficiency. The goal of the demonstration was to increase the total-to- static efficiency of the turbine by eight points over the baseline design. A sub-scale, cold flow test article modeling the final optimized turbine was designed, manufactured, and tested in air at MSFC s Turbine Airflow Facility. Extensive on- and off- design point performance data, steady-state data, and unsteady blade loading data were collected during testing.

Snellgrove, Lauren M.↗

NASA NTRS1975

Impact of remote sensing upon the planning, management, and development of water resources

Principal water resources users were surveyed to determine the impact of remote data streams on hydrologic computer models. Analysis of responses demonstrated that: most water resources effort suitable to remote sensing inputs is conducted through federal agencies or through federally stimulated research; and, most hydrologic models suitable to remote sensing data are federally developed. Computer usage by major water resources users was analyzed to determine the trends of usage and costs for the principal hydrologic users/models. The laws and empirical relationships governing the growth of the data processing loads were described and applied to project the future data loads. Data loads for ERTS CCT image processing were computed and projected through the 1985 era.

Castruccio, P. A.↗

NASA NTRSDate not supplied

Cross-Validation of Computational and Experimental Distributed Surface Pressures on the Space Launch System

This paper presents a new workflow for comparing experimental pressure-sensitive paint (PSP) data to computational fluid dynamic (CFD) simulations by way of mapping data from corresponding grids utilizing interpolation methods. In addition to generating quantitative and qualitative point-to-point comparisons between PSP and CFD data, this workflow extracts sectional loading data from both grids and generates lineload comparison charts for corresponding PSP and CFD runs. Experimental PSP data presented in this paper were taken from a 2016 NASA Ames Research Center Unitary Plan Wind Tunnel 11- by 11-Foot Transonic WindTunnel Facility test of the NASA Space Launch System. CFD simulation data for comparison purposes were generated using the FUN3D code. Overall, interpolation onto PSP grids versus CFD grids yields comparable surface pressure fields. However, lineload comparisons are easier to make on the CFD grid-mapped data due to the grid topology and the current capabilities of the lineload analysis tools at NASA Langley Research Center. This workflow is written using contemporary software (Python, Tecplot, PyTecplot), is compatible with existing tools at NASA Langley, and is developed to be adaptable depending on the situation.

SLS↗

DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗