Search NASA⌕ Search

SEARCH · Search NASA

Results for “Small Data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Updated neutrino mass constraints from galaxy clustering and CMB lensing-galaxy cross-correlation measurements

In this work, we revisit cosmological constraints on the sum of the neutrino masses Σm v from a combination of full-shape BOSS galaxy clustering [P(k)] data and measurements of the cross-correlation between Planck Cosmic Microwave Background (CMB) lensing convergence and BOSS galaxy overdensity maps [$C_{l}^{kg}$], using a simple but theoretically motivated model for the scale-dependent galaxy bias in auto- and cross-correlation measurements. We improve upon earlier related work in several respects, particularly through a more accurate treatment of the correlation and covariance between P(k) and $C_{l}^{kg}$ measurements. When combining these measurements with Planck CMB data, we find a 95% confidence level upper limit of Σm v sum of neutrino masses < 0.14 eV, while slightly weaker limits are obtained when including small-scale ACTPol CMB data, in agreement with our expectations. We confirm earlier findings that (once combined with CMB data) the full-shape information content is comparable to the geometrical information content in the reconstructed BAO peaks given the precision of current galaxy clustering data, discuss the physical significance of our inferred bias and shot noise parameters, and perform a number of robustness tests on our underlying model. While the inclusion of $C_{l}^{kg}$ measurements does not currently appear to lead to substantial improvements in the resulting Σm v of neutrino mass constraints, we expect the converse to be true for near-future galaxy clustering measurements, whose shape information content will eventually supersede the geometrical one.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗
NASA NTRS1983

The non-Z-squared response of the heavy nuclei cosmic ray detector on HEAO-3

A combination of ion chambers and Cerenkov radiators similar to the Heavy Nuclei Experiment flown on HEAO-3 was calibrated at the Bevalac heavy-ion accelerator using beams of Mn-25 nuclei at kinetic energies up to about 1700 MeV/nucleon and Au-79 nuclei up to about 1000 MeV/nucleon. The data show only a small deviation (about 2-3 charge units at Au) from the Z-squared scaling used previously (Binns et al., 1981, 1982, 1983) to analyze the HNE data. Although at lower energy, the calibration indicates that the published relative abundances of the Sn-50/Ba-56 group and the published upper-limit actinide abundances are not likely to be significantly affected by non-Z-squared effects.

Garrard, T. L.↗
NASA NTRS1994

Computer modeling the fatigue crack growth rate behavior of metals in corrosive environments

The objective of this task was to develop a method to digitize FCP (fatigue crack propagation) kinetics data, generally presented in terms of extensive da/dN-Delta K pairs, to produce a file for subsequent linear superposition or curve-fitting analysis. The method that was developed is specific to the Numonics 2400 Digitablet and is comparable to commercially available software products as Digimatic(sup TM 4). Experiments demonstrated that the errors introduced by the photocopying of literature data, and digitization, are small compared to those inherent in laboratory methods to characterize FCP in benign and aggressive environments. The digitizing procedure was employed to obtain fifteen crack growth rate data sets for several aerospace alloys in aggressive environments.

Richey, Edward, III↗
NASA NTRS1986

A study of large, medium and small scale structures in the topside ionosphere

Alouette and ISIS data were studied for large, medium, and small scale structures in the ionosphere. Correlation was also sought with measurements by other satellites, such as the Atmosphere Explorer C and E and the Dynamic Explorer 2 satellites, of both neutrals and ionization, and with measurements by ground facilities, such as the incoherent scatter radars. Large scale coherent wavelike structures were found from ISIS 2 electron density contours from above the F peak to nearly the satellite altitude. Such structures were also found to correlate with the observation by AE-C below the F peak during a conjunction of the two satellites. Vertical wavefronts found in the upper F region suggest the dominance of diffusion along field lines as well. Also discovered were multiple, evenly spaced field-aligned ducts in the F region that, at low latitudes, extended to the other hemisphere and were in the form of field-aligned sheets in the east-west direction. Low latitude heating events were discovered that could serve as sources for waves in the ionosphere.

Gross, Stanley H.↗
NASA NTRS1999

J6 Himalia: New Compositional Evidence and Interpretations for the Origin of Jupiter's Small Satellites

New narrowband spectrophotometric data of J6 Himalia, some of which are spatially resolved, support its C-type classification. The new spectra confirm the presence of a weak absorption feature centered near 0.7 micron attributed to oxidized iron in phyllosilicates, products of aqueous alteration, which varies in depth on opposite sides of the satellite. Evaluation of older UBV photometry of J6 and J7 Elara compared to UBV photometry of C-class (and subclass) asteroids showing spectral evidence of the 0.7-microns absorption feature suggests that J6 Himalia is an F-class asteroid. We propose that the parent body of the prograde Jovian satellites originated as part of the Nysa asteroid family. Evolutionary models of the Jovian system are used to address the capture and dispersal of the irregular satellites.

Vilas, Faith↗
NASA NTRS2010

Sensor Systems Collect Critical Aerodynamics Data

With the support of Small Business Innovation Research (SBIR) contracts with Dryden Flight Research Center, Tao of Systems Integration Inc. developed sensors and other components that will ultimately form a first-of-its-kind, closed-loop system for detecting, measuring, and controlling aerodynamic forces and moments in flight. The Hampton, Virginia-based company commercialized three of the four planned components, which provide sensing solutions for customers such as Boeing, General Electric, and BMW and are used for applications such as improving wind turbine operation and optimizing air flow from air conditioning systems. The completed system may one day enable flexible-wing aircraft with flight capabilities like those of birds.

Source record↗
DOE OSTI2022

Machine Learning-based Prediction of Departure from Nucleate Boiling Power for the PSBT Benchmark

Machine Learning (ML) has seen an exponential growth in its applications due to its advanced data driven prediction capabilities. The study presents a data-driven approach as a preliminary attempt to predict the power at which departure from nucleate boiling (DNB) occurs in pressurized water reactors (PWRs) by constructing an advanced ML algorithm that takes outlet pressure, inlet temperature and inlet mass flux as the input features. DNB is a critical heat flux (CHF) phenomenon seen in PWRs. The experimental data from the PWR subchannel and bundle tests (PSBT) benchmark is first used to train an artificial neural network (ANN) to predict the DNB power, which produces a root mean square error (RMSE) of 6.89 kW/m when tested on a blind subset of the PSBT data. Since the PSBT dataset is relatively small to train an accurate ANN, a data augmentation methodology based on generative adversarial networks (GANs) is used to expand the training dataset. By assuming that the real data follows a certain distribution, GANs try to learn that underlying distribution to generate similar synthetic data to augment the database and to improve the predictive capabilities of the ANN. The data generated from GANs are validated using 1-nearest neighbor and kernel maximum mean discrepancy. To further ensure data from GAN is similar to PSBT, the data is tested and filtered out using the sub-channel thermal-hydraulic code CTF. The results indicate that with the addition of 120 data points from GAN the RMSE reduces to 4.84 kW/m showing promising results for future developments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2004

Benchmarking Memory Performance with the Data Cube Operator

Data movement across a computer memory hierarchy and across computational grids is known to be a limiting factor for applications processing large data sets. We use the Data Cube Operator on an Arithmetic Data Set, called ADC, to benchmark capabilities of computers and of computational grids to handle large distributed data sets. We present a prototype implementation of a parallel algorithm for computation of the operatol: The algorithm follows a known approach for computing views from the smallest parent. The ADC stresses all levels of grid memory and storage by producing some of 2d views of an Arithmetic Data Set of d-tuples described by a small number of integers. We control data intensity of the ADC by selecting the tuple parameters, the sizes of the views, and the number of realized views. Benchmarking results of memory performance of a number of computer architectures and of a small computational grid are presented.

Frumkin, Michael A.↗
NASA NTRS1985

Hover and forward flight acoustics and performance of a small-scale helicopter rotor system

A 2.1-m diam., 1/6-scale model helicopter main rotor was tested in hover in the test section of the NASA Ames 40- by 80- Foot Wind Tunnel. Subsequently, it was tested in forward flight in the Ames 7- by 10-Foot Wind Tunnel. The primary objective of the tests was to obtain performance and noise data on a small-scale rotor at various thrust coefficients, tip Mach numbers, and, in the later case, various advance ratios, for comparisons with similar existing data on full-scale helicopter rotors. This comparison yielded a preliminary evaluation of the scaling of helicopter rotor performance and acoustic radiation in hover and in forward flight. Correlation between model-scale and full-scale performance and acoustics was quite good in hover. In forward flight, however, there were significant differences in both performance and acoustic characteristics. A secondary objective was to contribute to a data base that will permit the estimation of facility effects on acoustic testing.

Kitaplioglu, C.↗
NASA NTRS2015

Effect of MODIS Terra Radiometric Calibration Improvements on Collection 6 Deep Blue Aerosol Products: Validation and Terra/Aqua Consistency

The Deep Blue (DB) algorithm's primary data product is midvisible aerosol optical depth (AOD). DB applied to Moderate Resolution Imaging Spectroradiometer (MODIS) measurements provides a data record since early 2000 for MODIS Terra and mid-2002 for MODIS Aqua. In the previous data version (Collection 5, C5), DB production from Terra was halted in 2007 due to sensor degradation; the new Collection 6 (C6) has both improved science algorithms and sensor radiometric calibration. This includes additional calibration corrections developed by the Ocean Biology Processing Group to address MODIS Terra's gain, polarization sensitivity, and detector response versus scan angle, meaning DB can now be applied to the whole Terra record. Through validation with Aerosol Robotic Network (AERONET) data, it is shown that the C6 DB Terra AOD quality is stable throughout the mission to date. Compared to the C5 calibration, in recent years the RMS error compared to AERONET is smaller by approximately 0.04 over bright (e.g., desert) and approximately 0.01-0.02 over darker (e.g., vegetated) land surfaces, and the fraction of points in agreement with AERONET within expected retrieval uncertainty higher by approximately 10% and approximately 5%, respectively. Comparisons to the Aqua C6 time series reveal a high level of correspondence between the two MODIS DB data records, with a small positive (Terra-Aqua) average AOD offset <0.01. The analysis demonstrates both the efficacy of the new radiometric calibration efforts and that the C6 MODIS Terra DB AOD data remain stable (to better than 0.01 AOD) throughout the mission to date, suitable for quantitative scientific analyses.

Collection 6 Deep Blue↗
NASA NTRS1980

Validity of small scale tests for turret/fairing loads and cavity effects

Data from several wind tunnel experiments along with available flight test data are used to discuss the validity of small scale tests and their inherent limitations. Tests were performed at transonic speeds to measure the turbulence levels in a cavity with and without a forward porous fence, turret drag with and without an aerodynamic fairing, and turret/fairing unsteady pressures.

Mcdermott, D. J.↗
NASA NTRS2017

Jet Noise Prediction Using Hybrid RANS/LES with Structured Overset Grids

Hybrid RANS/LES simulations using the structured overset grid approach and a low dissipation finite-difference method within the Launch Ascent and Vehicle Aerodynamics (LAVA) solver framework are presented for jet noise prediction. The simulations are part of a validation effort to demonstrate jet noise prediction capability, to assess noise characteristics of next generation quiet supersonic commercial jets. Results are compared with experimental data acquired in the Small Hot Jet Acoustic Rig in the Aeroacoustic Propulsion Laboratory at NASA Glenn Research Center. Details of the structured overset grids, numerical discretization, and turbulence model are provided. Near-field comparisons to PIV data and far-field comparisons to microphone data are discussed. Excellent agreement of time-averaged mean quantities along the jet center-line and lip-line are obtained. Good agreement with root-mean-squared (RMS) quantities along the jet center-line are also obtained. An over prediction of lip-line RMS at the nozzle exit is observed leading to a small over-prediction of side-line far-field noise (less than 2 dB). Good agreement with far-field noise in the Mach wave radiation directions is achieved.

Housman, Jeffrey A.↗
NASA NTRS1976

A study and evaluation of image analysis techniques applied to remotely sensed data

An analysis of phenomena causing nonlinearities in the transformation from Landsat multispectral scanner coordinates to ground coordinates is presented. Experimental results comparing rms errors at ground control points indicated a slight improvement when a nonlinear (8-parameter) transformation was used instead of an affine (6-parameter) transformation. Using a preliminary ground truth map of a test site in Alabama covering the Mobile Bay area and six Landsat images of the same scene, several classification methods were assessed. A methodology was developed for automatic change detection using classification/cluster maps. A coding scheme was employed for generation of change depiction maps indicating specific types of changes. Inter- and intraseasonal data of the Mobile Bay test area were compared to illustrate the method. A beginning was made in the study of data compression by applying a Karhunen-Loeve transform technique to a small section of the test data set. The second part of the report provides a formal documentation of the several programs developed for the analysis and assessments presented.

Atkinson, R. J.↗
NASA NTRS1988

Aeroacoustic research programs at the Army Aviation Research and Technology Activity

The Army rotorcraft aeroacoustic programs are reviewed, highlighting the theoretical and experimental progress made by Army researchers in the physical understanding of helicopter impulsive noise. The two impulsive noise sources addressed over this past decade are high-speed impulsive noise and blade-vortex interaction noise, both of which have had and will continue to have an increasing influence on Army rotorcraft design and operations. The advancements discussed are in the areas of in-flight data acquisition techniques, small-scale-model tests in wind tunnels, holographic interferometry/tomographic techniques, and the expanding capabilities of computational fluid dynamics in rotorcraft acoustic problems. Current theoretical prediction methods are compared with experimental data, and parameters that govern model scaling are established. The very successful cooperative efforts between the Army, NASA, and industry are also addressed

Yu, Yung H.↗
NASA NTRS2007

Testing of the Crew Exploration Vehicle in NASA Langley's Unitary Plan Wind Tunnel

As part of a strategic, multi-facility test program, subscale testing of NASA s Crew Exploration Vehicle was conducted in both legs of NASA Langley s Unitary Plan Wind Tunnel. The objectives of these tests were to generate aerodynamic and surface pressure data over a range of supersonic Mach numbers and reentry angles of attack for experimental and computational validation and aerodynamic database development. To provide initial information on boundary layer transition at supersonic test conditions, transition studies were conducted using temperature sensitive paint and infrared thermography optical techniques. To support implementation of these optical diagnostics in the Unitary Wind Tunnel, the experiment was first modeled using the Virtual Diagnostics Interface software. For reentry orientations of 140 to 170 degrees (heat shield forward), windward surface flow was entirely laminar for freestream unit Reynolds numbers equal to or less than 3 million per foot. Optical techniques showed qualitative evidence of forced transition on the windward heat shield with application of both distributed grit and discreet trip dots. Longitudinal static force and moment data showed the largest differences with Mach number and angle of attack variations. Differences associated with Reynolds number variation and/or laminar versus turbulent flow on the heat shield were very small. Static surface pressure data supported the aforementioned trends with Mach number, Reynolds number, and angle of attack.

Murphy, Kelly J.↗
NASA NTRSDate not supplied

Broadband Noise Prediction of Two Small Hovering Rotors using FUN3D-ANOPP2

A computational study was performed on two small hovering rotors, the ideally twisted rotor and the optimum hovering 2-bladed rotor, using FUN3D-ANOPP2. The effects of different inviscid upwinding techniques and temporal resolutions on aerodynamic performance and acoustic prediction were compared against experimental data obtained in the Small Hover Anechoic Chamber facility at the NASA Langley Research Center. Decreasing the numerical dissipation with 𝜅 tuning or by using a higher-order inviscid flux reconstruction scheme was shown to improve aerodynamic performance predictions when compared to measured data for most cases. The effect of dissipation on the vorticity field was also seen to influence the amount of turbulence surrounding the vortex core of the first blade-vortex interaction (BVI), the coherence of the second and higher BVI vortex cores, and the downstream breakdown of the rotor wake for both rotor geometries. Tonal and broadband noise predictions from simulation cases with lower dissipation were seen to agree better with the experiment for the ideally twisted rotor, whereas temporal resolution differences had negligible impact on the broadband noise results. Similar fundamental BPF directivity patterns were obtained by all simulation cases for the optimum hovering 2-bladed rotor, while the most accurate 2*BPF directivity trend, compared to the experiment, was predicted by the lower-dissipation case at the coarse temporal resolution, albeit with an amplitude underprediction. Broadband noise spectral roll-off behavior was seen to occur at a higher frequency when using a finer temporal resolution for the optimum hovering 2-bladed rotor, unlike for the ideally twisted rotor geometry. The broadband noise prediction for the optimum hovering 2-bladed rotor from the higher-dissipation case at the coarse temporal resolution was also seen to predict a tone centered around approximately 8.5 kHz, which was similar in amplitude to the measured tone around 16.5 kHz.

Christopher S. Thurman↗
DOE OSTI2021

Synchrophasor Data Compression Under Disturbance Conditions via Cross-Entropy-Based Singular Value Decomposition

The increasing deployment of phasor measurement units and the advances of their reporting rates are challenging the present data centers in terms of storing and analyzing large-volume data. Under power system disturbance conditions, it is difficult to retain critical information while compressing the synchrophasor data effectively. This article combines the cross entropy and the singular value decomposition, proposing a novel model to compress the synchrophasor data to an extremely small size yet keep superior accuracy. The proposed model is extensively tested and compared with the state-of-the-art algorithms using the simulated and the FNET/GridEye field-collected data. The result indicates that the proposed algorithm has superior performance in compressing the data while retaining critical information under disturbance conditions.

42 ENGINEERING↗