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Results for “Distributed data generation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Theoretical and experimental study of flow-control devices for inlets of indraft wind tunnels

The design of closed circuit wind tunnels has historically been performed using rule of thumb which have evolved over the years into a body of useful guidelines. The development of indraft wind tunnels, however, has not been as well documented. The design of indraft wind tunnels is therefore generally performed using a more intuitive approach, often resulting in a facility with disappointing flow quality. The primary problem is a lack of understanding of the flow in the inlet as it passes through the required antiturbulence treatment. For wind tunnels which employ large contraction ratio inlets, this lack of understanding is not serious since the relatively low velocity of the flow through the inlet treatment reduces the sensitivity to improper inlet design. When designing a small contraction ratio inlet, much more careful design is needed in order to reduce the flow distortions generated by the inlet treatment. As part of the National Full Scale Aerodynamics Complex Modification Project, 2-D computational methods were developed which account for the effect of both inlet screens and guide vanes on the test section velocity distribution. Comparisons with experimental data are presented which indicate that the methods accurately compute the flow distortions generated by a screen in a nonuniform velocity field. The use of inlet guide vanes to eliminate the screen induced distortion is also demonstrated both computationally and experimentally. Extensions of the results to 3-D is demonstrated and a successful wind tunnel design is presented.

Ross, James C.↗
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, Volt/VAr optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure (AMI)↗
DOE OSTI2020

Resilient Cooperative Voltage Control for Distribution Network with High Penetration Distributed Energy Resources

Here, this paper considers the problem of designing a resilient distributed voltage control algorithm for distribution systems with high penetration of distributed energy resources in the presence of an unknown cyber-attack. The purpose of the attack is to force the system to violate the operating voltage limit by intercepting its communication channels and inserting exogenous signals to perturb and/or modify the information being exchanged. We first review the cooperative voltage control proposed in our previous work and provide a new stability analysis for it. Next, we present a resilient cooperative voltage control algorithm by introducing a virtual system interconnected with the original system such that the voltage can be maintained within the operational limit under unknown attacks. The resiliency of the proposed algorithm is demonstrated via simulations on the IEEE 8500-node system when subjected to an attack which consists of corrupting the data being exchanged in the communication network between two generation units.

cooperative control↗
DOE OSTI2025

Deconvoluting thermomechanical effects in X-ray diffraction data using machine learning

X-ray diffraction is ideal for probing the sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and because of the inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach that uses combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis. The method builds on a previous effort to extract thermal strain distribution information from diffraction data. The new approach is applied to extract the evolution of the thermomechanical state during laser melting of an Inconel 625 wall specimen which produces significant residual stress upon cooling. A combination of heat transfer and fluid flow, elasto-plasticity and X-ray diffraction simulations is used to generate training data for machine-learning (Gaussian process regression, GPR) models that map diffracted intensity distributions to underlying thermomechanical strain fields. First-principles density functional theory is used to determine accurate temperature-dependent thermal expansion and elastic stiffness used for elasto-plasticity modeling. The trained GPR models are found to be capable of deconvoluting the effects of thermal and mechanical strains, in addition to providing information about underlying strain distributions, even from complex diffraction patterns with irregularly shaped peaks.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Sampling Functions from Gaussian Processes and Structured Covariance Gaussian Networks

When learning aerodynamic models from data, it is critical to incorporate estimates of model uncertainty. This motivates the design of probabilistic aerodynamic databases which can be sampled to generate physically and statistically plausible aerodynamic models. In this talk we discuss how to sample deterministic functions from two different kinds of probabilistic models and demonstrate their use. First, Gaussian Process Regressors (GPRs) are a widely used probabilistic kernel-based model which can be thought of as Gaussian distributions over functions. GPRs are generally trained by maximizing the marginal likelihood of seeing the training data over the kernel parameter space. Sample functions are easily generated by drawing points from the Gaussian distribution at desired input points. However, when the points are not known ahead of time, the classical sampling approach is not possible since successive function samples will generate different function realizations. We present an approach for sampling consistent function evaluations from a GPR over multiple samples. Second, we describe a neural network architecture which learns a conditional Gaussian distribution by maximizing the marginal likelihood at each point in the input space. We then discuss and compare several options for generating sample functions which match this distribution. Finally, we demonstrate the use of these probabilistic aerodynamic models in an atmospheric reentry simulation.

Gaussian process regression↗
NASA NTRS1994

2-D and 3-D oscillating wing aerodynamics for a range of angles of attack including stall

A comprehensive experimental investigation of the pressure distribution over a semispan wing undergoing pitching motions representative of a helicopter rotor blade was conducted. Testing the wing in the nonrotating condition isolates the three-dimensional (3-D) blade aerodynamic and dynamic stall characteristics from the complications of the rotor blade environment. The test has generated a very complete, detailed, and accurate body of data. These data include static and dynamic pressure distributions, surface flow visualizations, two-dimensional (2-D) airfoil data from the same model and installation, and important supporting blockage and wall pressure distributions. This body of data is sufficiently comprehensive and accurate that it can be used for the validation of rotor blade aerodynamic models over a broad range of the important parameters including 3-D dynamic stall. This data report presents all the cycle-averaged lift, drag, and pitching moment coefficient data versus angle of attack obtained from the instantaneous pressure data for the 3-D wing and the 2-D airfoil. Also presented are examples of the following: cycle-to-cycle variations occurring for incipient or lightly stalled conditions; 3-D surface flow visualizations; supporting blockage and wall pressure distributions; and underlying detailed pressure results.

Piziali, R. A.↗
NASA NTRS1992

Physical retrieval of precipitation water contents using multisensor microwave data and model constraints

A physical retrieval method is proposed for estimating precipitation water distributions and other geophysical parameters from the Special Sensor Microwave/Imager (SSM/I) data. In the proposed method, sensor antenna functions are explicitly included to accommodate varying channel resolution. An embedded one-dimensional cloud model is utilized to generate vertical distributions of precipitating and nonprecipitating water, and spectral solutions are sought for certain background parameters, such as humidity, which vary more slowly in the horizontal than the cloud and precipitation water contents. The method allows the incorporation of measurements from SSM/T and geostationary infrared measurements in addition to SSM/I data, as well as information from conventional sources (e.g., radiosondes) or numerical forecast model fields.

Olson, William S.↗
NASA NTRS1999

Experimental Studies of the Distribution of Minor Constituents and Dynamic Processes in the Atmosphere

Minor constituents play an important role in atmospheric chemistry and serve as tracers in transport and mixing studies in tropospheric-stratospheric exchange processes. Measurements of trace gases are essential to an understanding of the mechanisms by which minor constituents originating in the troposphere, both naturally occurring and anthropogenic, reach the stratosphere. Many of these gases are sources of species directly involved in the chemistry of ozone depletion in the stratosphere and ozone generation in the troposphere. Some contribute to the warming of the atmosphere. Data on tracer distributions are important in the development of models for predicting photochemical effects in the stratosphere and troposphere. Data on atmospheric dynamics are important for the development of models for predicting the transport of photochemical species and for characterizing the nature of stratospheric motions.

Goodman, Jindra↗
NASA NTRS1984

Numerical simulations of loops heated to solar flare temperatures. III - Asymmetrical heating

A numerical model is defined for asymmetric full solar flare loop heating and comparisons are made with observational data. The Dynamic Flux Tube Model is used to describe the heating process in terms of one-dimensional, two fluid conservation equations of mass, energy and momentum. An adaptive grid allows for the downward movement of the transition region caused by an advancing conduction front. A loop 20,000 km long is considered, along with a flare heating system and the hydrodynamic evolution of the loop. The model was applied to generating line profiles and spatial X-ray and UV line distributions, which were compared with SMM, P78-1 and Hintori data for Fe, Ca and Mg spectra. Little agreement was obtained, and it is suggested that flares be treated as multi-loop phenomena. Finally, it is concluded that chromospheric evaporation is not an effective mechanism for generating the soft X-ray bursts associated with flares.

Cheng, C.-C.↗
NASA NTRS2009

The Cranked Arrow Wing Aerodynamics Project (CAWAP) and its Extension to the International Community as CAWAPI: Objectives and Overview

This paper provides a brief history of the F-16XL-1 aircraft, its role in the High Speed Research (HSR) program and how it was morphed into the Cranked Arrow Wing Aerodynamics Project (CAWAP). Various flight, wind-tunnel and Computational Fluid Dynamics (CFD) data sets were generated during the CAWAP. These unique and open flight datasets for surface pressures, boundary-layer profiles and skin-friction distributions, along with surface flow data, are described and sample data comparisons given. This is followed by a description of how the project became internationalized to be known as Cranked Arrow Wing Aerodynamics Project International (CAWAPI) and is concluded by an introduction to the results of a 5-year CFD predictive study of data.

Lamar, John E.↗
DOE OSTI2021

A deep generative model enables automated structure elucidation of novel psychoactive substances

Over the past decade, the illicit drug market has been reshaped by the proliferation of clandestinely produced designer drugs. These agents, referred to as new psychoactive substances (NPSs), are designed to mimic the physiological actions of better-known drugs of abuse while skirting drug control laws. The public health burden of NPS abuse obliges toxicological, police and customs laboratories to screen for them in law enforcement seizures and biological samples. However, the identification of emerging NPSs is challenging due to the chemical diversity of these substances and the fleeting nature of their appearance on the illicit market. Here, in this study, we present DarkNPS, a deep learning-enabled approach to automatically elucidate the structures of unidentified designer drugs using only mass spectrometric data. Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the structural prior. We show that the structural prior allows DarkNPS to elucidate the exact chemical structure of an unidentified NPS with an accuracy of 51% and a top-10 accuracy of 86%. Our generative approach has the potential to enable de novo structure elucidation for other types of small molecules that are routinely analysed by mass spectrometry.

Cheminformatics↗
DOE OSTI2026

Ensemble Federated Machine Learning‐Based Cybersecurity Situational Awareness in Microgrid Network

Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Measurement of charged-current muon neutrino-argon interactions without pions in the final state using the MicroBooNE detector

We report a new measurement of flux-integrated differential cross sections for charged-current (CC) muon neutrino interactions with argon nuclei that produce no final-state pions (𝜈 𝜇 ⁢CC⁢0⁢𝜋). These interactions are of particular importance as a topologically defined signal dominated by quasielasticlike interactions. This measurement was performed with the MicroBooNE liquid argon time projection chamber detector located at the Fermilab Booster Neutrino Beam and uses an exposure of 1.3 ×10 21 protons on target collected between 2015 and 2020. The results are presented in terms of single- and double-differential cross sections as a function of the final-state muon momentum and angle. The data are compared with widely used neutrino event generators. We find good agreement with the single-differential measurements, while only a subset of generators are also able to adequately describe the data in double-differential distributions. This work facilitates comparison with Cherenkov detector measurements, including those located at the Booster Neutrino Be

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Substitution or Shared Utilization? Intrahousehold Vehicle Use in Mixed-Powertrain Households

While previous research has focused heavily on understanding the factors deriving alternative fuel vehicle adoption rates, there remains a significant gap in understanding how households distribute mileage across different powertrains. This study utilizes data from the 2022 Next Generation National Household Travel Survey to investigate vehicle miles traveled within a sample of 150 plug-in electric vehicle (PEV)-owning households (in which at least one battery electric vehicle is present), characterizing how different powertrains are integrated into daily mobility. Leveraging a Seemingly Unrelated Regression (SUR) framework the study jointly models the utilization of PEVs, hybrid electric vehicles (HEV), and internal combustion engine vehicles (ICEVs) while accounting for household-level substitution effects. The results provide evidence of an asymmetric substitution effect. In households with mixed-powertrain configurations, the ICEV captures a substantially higher share of household miles (compared with the PEV), acting as a utility sponge. Conversely, the model identifies specific socioeconomic and geographic cohorts that prioritize PEV as the primary household workhorse, indicating a systematic sorting effect. Although the sample size limits broader generalizability, these findings suggest that PEVs are used for frequent, specific routine-intensive roles, whereas the ICEV remains a specialized utility vehicle. These insights highlight distinct intrahousehold vehicle use behaviors that are often obscured by aggregate fleetwide statistics.

25 ENERGY STORAGE↗
DOE OSTI2026

2000 Delaware Valley Regional Planning Commission Household Travel Survey

This survey was conducted from March through December 2000 under the auspices of the Delaware Valley Regional Planning Commission and the Southern Jersey Transportation Planning Organization and funded through the Pennsylvania and New Jersey departments of transportation. Cambridge Systematics provided quality assurance under a subcontract to NuStats. The purpose of the survey was to obtain information about work and non-work trip generation, trip distribution, modal choice, and traffic assignment, as well as to obtain data on average vehicle occupancy. The dataset contains demographic and travel data on 5,677 households in the 14-county study area. Of those households, 4,217 were from the 10-county Delaware Valley region of Pennsylvania and New Jersey. The 4,217 Delaware Valley households, when weighted, represent 9,358 members and 6,069 vehicles, and reported a total of 31,631 trips.

1Hz data↗
DOE OSTI2020

Adaptive Machine Learning for Bragg Coherent Diffraction Imaging (BCDI) of 3D Electron Density Maps with Application to La 2-x Ba x CuO 4 (LBCO) High Temperature Superconductor Studies [PowerPoint]

Understanding mesoscale heterogeneity is important for evaluating fatigue and failure of structural materials and for manufacturing processes. Data-driven tools can facilitate in guiding effort investment (measurements and computations) at various stages of the materials development. Our 3D reconstruction approach is to find a set of coefficients which define a bounding surface of the electron density. Data for training the 3D CNN was generated by sampling coefficients from uniform distributions. Test vs. prediction values are shown for the 28 even-valued coefficients for 1000 test structures, with the best and worst performers highlighted. The adaptive part of this work utilized a model independent extremum seeking (ES). The predictions of the CNN were used as the starting point for the ES algorithm. The convergence of the ES algorithm for 3 different structures is illustrated, and the robustness of the adaptive ML approach was demonstrated on experimentally measured 3D crystal from high energy diffraction microscopy. Reconstruction of a non-uniform density volume with different levels of Poisson noise is also shown. Preliminary attempts at reconstructing measured La 2-x Ba x CuO 4 diffraction patterns have so far failed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1979

CLASSY: An adaptive maximum likelihood clustering algorithm

The CLASSY clustering method alternates maximum likelihood iterative techniques for estimating the parameters of a mixture distribution with an adaptive procedure for splitting, combining, and eliminating the resultant components of the mixture. The adaptive procedure is based on maximizing the fit of a mixture of multivariate normal distributions to the observed data using its first through fourth central moments. It generates estimates of the number of multivariate normal components in the mixture as well as the proportion, mean vector, and covariance matrix for each component. The basic mathematical model for CLASSY and the actual operation of the algorithm as currently implemented are described. Results of applying CLASSY to real and simulated LANDSAT data are presented and compared with those generated by the iterative self-organizing clustering system algorithm on the same data sets.

Lennington, R. K.↗