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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Experimental Investigation of Rotorcraft Outwash in Ground Effect

The wake characteristics of a rotorcraft are affected by the proximity of a rotor to the ground surface, especially during hover. Ground effect is encountered when the rotor disk is within a distance of a few rotor radii above the ground surface and results in an increase in thrust for a given power relative to that same power condition with the rotor out of ground effect. Although this phenomenon has been highly documented and observed since the beginning of the helicopter age, there is still a relatively little amount of flow-field data existing to help understand its features. Joint Army and NASA testing was conducted at NASA Langley Research Center using a powered rotorcraft model in hover at various rotor heights and thrust conditions in order to contribute to the complete outwash data set. The measured data included outwash velocities and directions, rotor loads, fuselage loads, and ground pressures. The researchers observed a linear relationship between rotor height and percent download on the fuselage, peak mean outwash velocities occurring at radial stations between 1.7 and 1.8 r/R regardless of rotor height, and the measurement azimuthal dependence of the outwash profile for a model incorporating a fuselage. Comparisons to phase-locked PIV data showed similar contours but a more contracted wake boundary for the PIV data. This paper describes the test setup and presents some of the averaged results.

Tanner, Philip E.↗
NASA NTRS2003

MOBY, A Radiometric Buoy for Performance Monitoring and Vicarious Calibration of Satellite Ocean Color Sensors: Measurement and Data Analysis Protocols

The Marine Optical Buoy (MOBY) is the centerpiece of the primary ocean measurement site for calibration of satellite ocean color sensors based on independent in situ measurements. Since late 1996, the time series of normalized water-leaving radiances L(sub WN)(lambda) determined from the array of radiometric sensors attached to MOBY are the primary basis for the on-orbit calibrations of the USA Sea-viewing Wide Field-of-view Sensor (SeaWiFS), the Japanese Ocean Color and Temperature Sensor (OCTS), the French Polarization Detection Environmental Radiometer (POLDER), the German Modular Optoelectronic Scanner on the Indian Research Satellite (IRS1-MOS), and the USA Moderate Resolution Imaging Spectrometer (MODIS). The MOBY vicarious calibration L(sub WN)(lambda) reference is an essential element in the international effort to develop a global, multi-year time series of consistently calibrated ocean color products using data from a wide variety of independent satellite sensors. A longstanding goal of the SeaWiFS and MODIS (Ocean) Science Teams is to determine satellite-derived L(sub WN)(labda) with a relative combined standard uncertainty of 5 %. Other satellite ocean color projects and the Sensor Intercomparison for Marine Biology and Interdisciplinary Oceanic Studies (SIMBIOS) project have also adopted this goal, at least implicitly. Because water-leaving radiance contributes at most 10 % of the total radiance measured by a satellite sensor above the atmosphere, a 5 % uncertainty in L(sub WN)(lambda) implies a 0.5 % uncertainty in the above-atmosphere radiance measurements. This level of uncertainty can only be approached using vicarious-calibration approaches as described below. In practice, this means that the satellite radiance responsivity is adjusted to achieve the best agreement, in a least-squares sense, for the L(sub WN)(lambda) results determined using the satellite and the independent optical sensors (e.g. MOBY). The end result of this approach is to implicitly absorb unquantified, but systematic, errors in the atmospheric correction, incident solar flux, and satellite sensor calibration into a single correction factor to produce consistency with the in situ data.

Clark, Dennis K.↗
DOE OSTI2023

Improving nuclear data evaluations with predictive reaction theory and indirect measurements

Nuclear reaction data required for astrophysics and applications is incomplete, as not all nuclear reactions can be measured or reliably predicted. Neutron-induced reactions involving unstable targets are particularly challenging, but often critical for simulations. In response to this need, indirect approaches, such as the surrogate reaction method, have been developed. Nuclear theory is key to extract reliable cross sections from such indirect measurements. We describe ongoing efforts to expand the theoretical capabilities that enable surrogate reaction measurements. We focus on microscopic predictions for charged-particle inelastic scattering, uncertainty-quantified optical nucleon-nucleus models, and neural-network enhanced parameter inference.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Arctic shrub and Eriophorum leaf and root decomposition, northern Alaska, 2017-2018

This data package contains litter decomposition data collected from 170 plots of rapidly expanding shrub genera (Alnus, Betula, and Salix) and a widespread sedge (Eriophorum vaginatum) along a latitudinal and temperature gradient in northern Alaska. These data were produced from a litter bag experiment that took place from July 2017 to July 2018 and include mass loss and nitrogen loss decomposition metrics for both leaf and root litters. These raw data support a submitted manuscript that examines the variability in decomposition between shrub and graminoid leaf and root litters across a 1-year experiment across the graminoid-dominated Arctic tundra and reveals how deciduous shrub expansion affects litter decomposition in tundra ecosystems. Data are presented by site (n=5) and patch (shrub or sedge plot) in csv files. The site and plot location data and environmental measurement data are provided in Fraterrigo and Chen (2020). Additional methods regarding plot distribution and environmental measurements are in Chen et al. (2020) and Fraterrigo et al. (2024).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

RELAP5-3D Modeling of High Temperature Test Facility (HTTF) Test PG-26

The High Temperature Test Facility (HTTF) at Oregon State University (OSU) is a scaled integral effects experiment designed to investigate transient behavior in high-temperature gas-cooled nuclear reactors with prismatic fuel and reflector blocks. Several tests have been completed, and more are still planned to at the HTTF, including depressurized conduction cooldown (DCC) and pressurized conduction cooldown (PCC) transients. This report analyses test PG-26, a progression of the Double Ended Inlet-Outlet Crossover Duct Break transient that is referred to as a DCC. PG-26 has been performed at the HTTF between May 30 and June 30, 2019. Core initial conditions (i.e., before the DCC started) have been met using low power (<100 kW) and two of ten available electric heaters. The DCC transient was initiated during the 50 th hour of the test. The break valves were opened, and hot helium from the core and cold helium from the reactor cavity simulation tank (RCST) started mixing. The gases flowed in a countercurrent fashion, where the top half of the hot duct contained hot helium that flowed in one direction and cold helium that flowed in the other direction in the bottom half of the duct. After the pressure and density reached equilibrium, the event entered a diffusion mode. The onset of a reverse natural circulation was not observed during the DCC period of the test. Version 4.4.2ie of the RELAP5-3D computer code has been used to model the HTTF PG-26 test, and results have been compared to available high-quality measured data. The model used in this study is the quality-controlled HTTF RELAP5-3D model (HTTF base 2018-04-19 QA), originally developed by P. Bayless. The report includes RELAP5-3D results of the “base calculations” as well as some sensitivities to important uncertain model inputs, such as primary helium mass flow rate, core ceramic thermal properties, as well as heat evacuation and loop friction models. Using the base RELAP5-3D model predicts a countercurrent helium flow in the hot duct observed at the beginning of the DCC, but instead of going into a molecular diffusion mode, the model predicts the onset of natural convection. Increasing friction in the core and hot duct prevents the natural convection from happening in some of the simulations. Although some temperatures are well predicted (and even overpredicted), the general tendency is to underpredict the ceramic and helium temperatures and heat removal rates during the DCC, resulting in many of the assessment findings being in minimal or insufficient agreement with the data. It is worth noting that the described discrepancies between measured data and RELAP5-3D predictions are not RELAP5-3D code limitations. More so, they reflect limitations in boundary condition and thermal property knowledge. While the RELAP5-3D calculations of the test provide some insights into what happens during the transient, and point to missing or potentially uncertain data to which the experimenters can direct their attention, the principal conclusion is that the PG-26 test data are insufficient for a system code assessment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Al 0.68 Sc 0.32 N Lamb wave resonators with electromechanical coupling coefficients near 10.28%

Aluminum scandium nitride (Al 1-x Sc x N/AlScN) (x = 0.32) Lamb wave resonators (LWR) have been fabricated and tested to demonstrate electromechanical coupling coefficients (k t 2 ) in excess of 10%. The resonators exhibited an average k t 2 and unloaded quality factor (Q u ) of 10.28% and 711, respectively, when calculated from the measured data. Applying the Butterworth Van-Dyke (BVD) model to the measured data enabled the extraction of the resonator's lumped element parameters to calculate the motional quality factor (Q m ), which neglects the contributions of the electrical traces. For the best measured resonator response, results from the BVD model showed a Q m of 1184 and a resulting figure-of-merit (FOM = K 2 ·Q m ) of 100. Comparing the response of similar AlScN and AlN resonators shows that the AlScN LWR has a significantly lower motional resistance (R m ), suggesting that AlScN has a strong potential for use in piezoelectric microelectromechanical oscillators.

42 ENGINEERING↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Inverter Model Validation and Calibration Using Phasor Measurement Unit Data

As the penetration of inverter-based renewable energy resources increases in the power grid, especially at the distribution and microgrid levels, the need to accurately represent them in planning studies increases as well. However, due to the lack of well-established standard procedures, and vendor reluctance towards the detailed sharing of proprietary models, automated dynamic model validation and parameter calibration tools for inverter based resources (IBRs) remain scarce. This work presents a model validation and parameter calibration platform for representing IBRs with generic phasor-domain models. Phasor measurements of power system events are used for continuous validation using the data playback method, and model parameters are re-calibrated if a significant mismatch between measurements and model response is observed. Unique features of the proposed platform include- (a) an iterative Bayesian optimization approach towards parameter calibration to address a possible mismatch between the structures of generic models implemented in simulation softwares and actual commercial inverters, (b) error metrics designed to account for a possible mismatch between the time resolution of simulation and measurements, and (c) analysis of the measurement-simulation mismatch to provide guidance to engineering personnel regarding model shortcomings. The performance of the platform has been illustrated using both simulated data and field measurements to validate/calibrate inverter models in GridLAB-D.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Gas-mapping 3D imager measurement techniques and method of data processing

Measurement approaches and data analysis methods are disclosed for combining 3D topographic data with spatially-registered gas concentration data to increase the efficiency of gas monitoring and leak detection tasks. Here, the metric for efficiency is defined as reducing the measurement time required to achieve the detection, or non-detection, of a gas leak with a desired confidence level. Methods are presented for localizing and quantifying detected gas leaks. Particular attention is paid to the combination of 3D spatial data with path-integrated gas concentration measurements acquired using remote gas sensing technologies, as this data can be used to determine the path-averaged gas concentration between the sensor and points in the measurement scene. Path-averaged gas concentration data is useful for finding and quantifying localized regions of elevated (or anomalous) gas concentration making it ideal for a variety of applications including: oil and gas pipeline monitoring, facility leak and emissions monitoring, and environmental monitoring.

Thorpe, Michael↗
NASA NTRS1992

Portable microwave test packages for beam-waveguide antenna performance evaluations

Portable microwave test packages used to evaluate a new 34-m-diameter beam-waveguide (BWG) antenna are described. The experimental methodology involved transporting test packages to different focal points of the BWG system and making noise temperature, antenna efficiency, and holography measurements. Comparisons of data measured at the different focal points enabled determinations of performance degradations caused by various mirrors in the BWG system. It is shown that, due to remarkable stabilities and accuracies of radiometric data obtained through the use of the microwave test packages, degradations caused by the BWG system were successfully determined.

Otoshi, Tom Y.↗
NASA NTRS1977

The epoch state navigation filter

The formulation of a recursive maximum likelihood navigation system employing reference position and velocity vectors as state variables is presented. Convenient forms of the required variational equations of motion are developed together with an explicit form of the associated state transition matrix needed to refer measurement data from the measurement time to the epoch time. Computational advantages accrue from this design in that the usual forward extrapolation of the covariance matrix of estimation errors can be avoided without incurring unacceptable system errors. Simulation data for earth orbiting satellites are provided to substantiate this assertion.

Battin, R. H.↗
DOE OSTI2020

Mid-Atlantic Bight Wave Hindcast To Support DOE Lidar Buoy Deployments: Model Validation

This study presents a shelf scale wave hindcast for the Mid-Atlantic Bight to provide accurate wave data to complement the measured data gathered by two DOE lidar buoy deployments off the coasts of Virginia and New Jersey. PNNL developed a ~2 km resolution model based on Wavewatch III and forced with analyzed winds and ocean currents; and executed a four-year hindcast for the period from January 2014 to December 2017. The model results are compared well against 16 wave-measuring buoys and data derived from six satellite-borne radar altimeters. In addition, the model results for two storm events that generated large waves, Hurricane Hermine and the January 2016 Blizzard, are discussed in detail.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Replication Data for: Measurement of the mean number of muons with energies above 500 GeV in air showers detected with the IceCube Neutrino Observatory

<b>Measurement of the mean number of muons with energies above 500 GeV in air showers detected with the IceCube Neutrino Observatory</b> <br><br> This data release accompanies results submitted to Physical Review D describing the measurement of the average multiplicity of TeV muons with IceCube. It contains the data necessary to reproduce the main plots from the paper (Figs. 7 and 9), i.e. the numerical results for the average number of muons with energies above 500 GeV as a function of primary cosmic ray energy. <br><br> For any questions about this data release, please write to analysis@icecube.wisc.edu. <br><br> Files included in this release: <ul> <li>A README file <li>Files including data to reproduce the results plots from the paper (see below for details) <li>An example python script showing how to read and plot the data </ul> <br> <u>What is in the files icecube_Nmu500_X_Y.txt:</u> <br> Y indicates wether the file contains values obtained from experimental data (Y="data") or air-shower simulations (Y="MC"). <br> X indicates the hadronic interaction model for which the plot is made. If Y="data", this means that the experimental data was interpreted using this model. If Y="MC", it means that the simulations were performed with this model. The three models included are Sibyll 2.1, QGSJet-II.04, and EPOS-LHC (see paper for references). The file with X="modelaverage" gives the average over the three individual results with the deviations from the average included in the systematic uncertainties. <br><br> Please see the README file for details on how the data is structured in the files.

Astroparticle Physics↗
DOE OSTI2020

Cyber physical attack detection

A cyber-security threat detection system and method stores physical data measurements from a cyber-physical system and extracts synchronized measurement vectors synchronized to one or more timing pulses. The system and method synthesizes data integrity attacks in response to the physical data measurements and applies alternating parametrized linear and non-linear operations in response to the synthesized data integrity attacks. The synthesis renders optimized model parameters used to detect multiple cyber-attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Cyber physical attack detection

A cyber-security threat detection system and method stores physical data measurements from a cyber-physical system and extracts synchronized measurement vectors synchronized to one or more timing pulses. The system and method synthesize data integrity attacks in response to the physical data measurements and applies alternating parameterized linear and non-linear operations in response to the synthesized data integrity attacks. The synthesis renders optimized model parameters used to detect multiple cyber-attacks.

Ferragut, Erik M.↗
DOE OSTI2023

Bayesian GAN-Based False Data Injection Attack Detection in Active Distribution Grids With DERs

Advancements in information and communication technologies have revolutionized monitoring and control capabilities within smart grids. However, it also brings new vulnerabilities to data acquisition systems and state estimation functions, which attackers can subtly tamper with the measurement data through compromising the communication network. Moreover, the high penetration of renewable energy sources with the inherited characteristics of uncertainty and variability further complicates the design of effective intrusion detection systems. In this paper, a Bayesian deep learning-based approach is developed to detect cyber attacks and maintain the security of smart grids. Our method specifically addresses the prevalent issue of imbalanced data in real power systems, which arises from the predominance of normal system operations over compromised or attacked states. Employing a novel Bayesian GAN-based technique, our approach successfully discriminates between secure and compromised measurement data, even in scenarios with significant data imbalance. Furthermore, the proposed method accommodates various practical application factors, ensuring accurate intrusion detection despite the presence of measurement noise. The feasibility and effectiveness of the proposed detection mechanism are validated by testing on IEEE 13-node and 123-node test systems. Simulation results and comparisons with literature methods demonstrate the superiority of proposed cybersecurity solutions.

Bayesian GAN↗
DOE OSTI2026

Corn and Switchgrass Derived Sustainable Aviation Fuel Carbon Intensity

The production and use of sustainable aviation fuel (SAF) could lower aviation's carbon footprint. Feedstock agricultural management can optimize—or potentially determine—SAF sustainability and profitability, but long-term SAF feedstock comparisons are limited. Life cycle assessments (LCA) are commonly used to estimate the carbon intensity of producing SAF from emerging feedstocks due to the limited availability of long-term, primary field data. We quantified the agricultural phase and well-to-wake (WTWa) carbon intensities of producing SAF from no-till, continuous corn (Zea mays L.) under 120 kg N ha−1 year−1 (120 N corn) and switchgrass (Panicum virgatum L.) under 60 or 120 kg N ha−1 year−1 (60 N and 120 N SWG, respectively) via LCAs based on measured data from a long-term (1998-present) field experiment on marginally productive cropland located in Eastern NE, USA. Measured data included crop productivity, long-term changes in soil organic carbon (SOC) stocks (0–150 cm), annual soil N2O emissions, and field management practices. Both 120 N corn (2078 L ha−1) and 120 N SWG (1752 L ha−1) produced more SAF than 60 N SWG (1229 L ha−1) (p < 0.0001). However, making SAF from corn had greater life cycle GHG emissions (i.e., WTWa carbon intensity) than making SAF from 120 N and 60 N SWG (61.5 > −14.5 and −27.5 g CO2e MJ−1 SAF, respectively; p = 0.0007), primarily due to SOC accrual during crop production. Here, we demonstrate that agricultural management (e.g., crop type, fertilizer use) can determine the sustainably and productivity of producing SAF by using long-term, measured field data in LCAs.

carbon intensity↗