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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Mid-Air Range-Visual-Inertial Estimator Initialization for Micro Air Vehicles

Monocular Visual-Inertial Odometry (VIO) has become ubiquitous for navigation of autonomous Micro Air Vehicles (MAVs). Yet, state-of-the-art VIO is still very failure- prone, which can have dramatic consequences. To prevent this, VIO must be able to re-initialize in mid-air, either during a free fall or on a constant horizontal velocity trajectory after attitude control has been re-established. However, for both of these trajectories, the visual scale cannot be observed with VIO batch initializers because of the absence of acceleration change. We propose to use a small and lightweight laser range finder and a scene facet model to initialize vision-based navigation at the right scale under any motion condition and over any scene structure. This new range constraint is integrated into a visual-inertial bundle adjustment initializer. We demonstrate in simulation and real data how this approach can address mid-air state estimation failure in real-time and analyze the sensitivity of estimators to initial state errors.

Brockers, Roland↗
DOE OSTI2015

ESTIMATING BLACK HOLE MASSES IN HUNDREDS OF QUASARS

We explore the practical feasibility of active galactic nucleus (AGN) broadband reverberation mapping and present first results. We lay out and apply a rigorous approach for the stochastic reverberation mapping of unevenly sampled multi-broadband flux measurements, assuming that the broad-line region (BLR) line flux is contributing up to 15% in some bands, and is directly constrained by one spectroscopical epoch. The approach describes variations of the observed flux as the continuum, modeled as a stochastic Gaussian process, and emission line contribution, modeled as a scaled, smoothed, and delayed version of the continuum. This approach can be used not only to interpolate in time between measurements, but also to determine confidence limits on continuum—line emission delays. This approach is applied to Sloan Digital Sky Survey observations in Stripe 82 (S82), providing flux measurements that are precise to 2% at ∼60 epochs over ∼10 yr. The strong annual variations in the epoch sampling prove a serious limitation in practice. In addition, suitable redshift ranges must be identified where strong, broad emission lines contribute to one filter, but not to another. By generating and evaluating problem-specific mock data, we verify that S82-like data can constrain τ{sub delay} for a simple transfer function model. In application to real data, we estimate τ{sub delay} for 323 AGNs with 0.225 < z < 0.846, combining information for different objects through the ensemble-scaling relationships for BLR size and black hole mass. Our analysis tentatively indicates a 1.7 times larger BLR size of Hα and Mg II compared to Kaspi et al. and Vestergaard, but the seasonal data sampling casts doubt on the robustness of the inference.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Extreme data compression for the CMB

We apply the Karhunen-Loéve methods to cosmic microwave background (CMB) data sets, and show that we can recover the input cosmology and obtain the marginalized likelihoods in Λ cold dark matter cosmologies in under a minute, much faster than Markov chain Monte Carlo methods. This is achieved by forming a linear combination of the power spectra at each multipole l, and solving a system of simultaneous equations such that the Fisher matrix is locally unchanged. Instead of carrying out a full likelihood evaluation over the whole parameter space, we need evaluate the likelihood only for the parameter of interest, with the data compression effectively marginalizing over all other parameters. The weighting vectors contain insight about the physical effects of the parameters on the CMB anisotropy power spectrum C l . The shape and amplitude of these vectors give an intuitive feel for the physics of the CMB, the sensitivity of the observed spectrum to cosmological parameters, and the relative sensitivity of different experiments to cosmological parameters. We test this method on exact theory C l as well as on a Wilkinson Microwave Anisotropy Probe (WMAP)-like CMB data set generated from a random realization of a fiducial cosmology, comparing the compression results to those from a full likelihood analysis using CosmoMC. Furthermore, after showing that the method works, we apply it to the temperature power spectrum from the WMAP seven-year data release, and discuss the successes and limitations of our method as applied to a real data set.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

CHiPBETA

High-accuracy water level data are needed to resolve the temporal and spatial hydraulic response to pumping in aquifers. However, the water-level data are typically affected by physical process unrelated to the aquifer pumping such as barometric pressure changes, tides, earthquakes, {\etc}. We have developed a computational framework to correct for barometric and Earth tide effects using linear regression deconvolution, as an initial guess for a nonlinear residual minimization to account for pumping effects. The pumping effects are predicted using the Theis equation with initialization by Jacob's method. After minimizing the residuals between the observed data and Theis predicted pumping effects, the algorithm estimates storativity and transmissivity of the aquifer. The algorithm also provides information about the measurement accuracy of the collected water-level data. The computational framework CHiPBETA is developed in Python and demonstrated to analyze synthetic and real data.

Banner, Eric↗
NASA NTRS2005

Monitoring a Complex Physical System using a Hybrid Dynamic Bayes Net

The Reverse Water Gas Shift system (RWGS) is a complex physical system designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent to Mars, it would operate without human supervision, thus requiring a reliable automated system for monitoring and control. The RWGS presents many challenges typical of real-world systems, including: noisy and biased sensors, nonlinear behavior, effects that are manifested over different time granularities, and unobservability of many important quantities. In this paper we model the RWGS using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the state at each time slice contains 33 discrete and 184 continuous variables. We show how the system state can be tracked using probabilistic inference over the model. We discuss how to deal with the various challenges presented by the RWGS, providing a suite of techniques that are likely to be useful in a wide range of applications. In particular, we describe a general framework for dealing with nonlinear behavior using numerical integration techniques, extending the successful Unscented Filter. We also show how to use a fixed-point computation to deal with effects that develop at different time scales, specifically rapid changes occuring during slowly changing processes. We test our model using real data collected from the RWGS, demonstrating the feasibility of hybrid DBNs for monitoring complex real-world physical systems.

Lerner, Uri↗
NASA NTRS2008

Overview of the LaNCETS Flight Experiment and CFD Analysis. Supplemental Movies

This presentation focuses on nearfield airborne pressure signatures from the Lift and Nozzle Change Effect on Tail Shocks (LaNCETS) flight test experiment. The primary motivation for nearfield probing in the supersonic regime is to measure the shock structure of aircraft in an ongoing effort to overcome the overland sonic boom barrier for commercial supersonic transportation. LaNCETS provides the opportunity to investigate lift distribution and engine plume effects. During Phase 1 flight testing an F-15B was used to probe the F-15 LaNCETS aircraft in order to validate CFD and pre-flight prediction tools. A total of 29 probings were taken at 40,000 ft. altitude at Machs 1.2, 1.4 and 1.6. LaNCETS Phase 1 flight data are presented as a detailed pressure signature superimposed over a picture of the LaNCETS aircraft. The attenuation of the Inlet-Canard shocks with distance as well as its forward propagation and the coalescence of the noseboom shock are illustrated. A detailed CFD study on a simplified LaNCETS aircraft jet nozzle was performed providing the ability to more accurately capture the shocks from the propulsion system and emphasizing how under- and over-expanding the nozzle affects the existence of shock trains inside the jet plume. With Phase 1 being a success preparations are being made to move forward to Phase 2. Phase 2 will fly similar flight conditions, but this time changing the aircraft's lift distribution by biasing the canard positions, and changing the plume shape by under- and over-expanding the nozzle. Nearfield probing will again be completed in the same manner as in Phase 1. An additional presentation focuses on LaNCETS CFD solution methodology. Discussions highlight grid preprocessing, grid shearing and stretching, flow solving and contour plots. Efforts are underway to better capture the flow features via grid modification and flow solution methodology, which will help to achieve better agreement with flight data. An included CD-ROM provides animations of the nearfield probing procedure and of real data from one of the probings integrated with GPS positional and velocity data. An additional in-flight video from the rear seat of the probing aircraft is also provided.

Cliatt, Larry J., II↗
DOE OSTI2016

SU-F-I-08: CT Image Ring Artifact Reduction Based On Prior Image

Purpose: In computed tomography (CT) system, CT images with ring artifacts will be reconstructed when some adjacent bins of detector don’t work. The ring artifacts severely degrade CT image quality. We present a useful CT ring artifacts reduction based on projection data correction, aiming at estimating the missing data of projection data accurately, thus removing the ring artifacts of CT images. Methods: The method consists of ten steps: 1) Identification of abnormal pixel line in projection sinogram; 2) Linear interpolation within the pixel line of projection sinogram; 3) FBP reconstruction using interpolated projection data; 4) Filtering FBP image using mean filter; 5) Forwarding projection of filtered FBP image; 6) Subtraction forwarded projection from original projection; 7) Linear interpolation of abnormal pixel line area in the subtraction projection; 8) Adding the interpolated subtraction projection on the forwarded projection; 9) FBP reconstruction using corrected projection data; 10) Return to step 4 until the pre-set iteration number is reached. The method is validated on simulated and real data to restore missing projection data and reconstruct ring artifact-free CT images. Results: We have studied impact of amount of dead bins of CT detector on the accuracy of missing data estimation in projection sinogram. For the simulated case with a resolution of 256 by 256 Shepp-Logan phantom, three iterations are sufficient to restore projection data and reconstruct ring artifact-free images when the dead bins rating is under 30%. The dead-bin-induced artifacts are substantially reduced. More iteration number is needed to reconstruct satisfactory images while the rating of dead bins increases. Similar results were found for a real head phantom case. Conclusion: A practical CT image ring artifact correction scheme based on projection data is developed. This method can produce ring artifact-free CT images feasibly and effectively.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images

Abstract The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Gauge R & R studies for angular measurements

Angular measurements lie on the circumference of a circle and have different characteristics than standard scalar measurements. For applications involving angular data, treating the measured values as scalars can lead to misinterpretation of results if its wrap-around nature is not taken into account. In this article, we propose a variance components wrapped normal model for angular measurements that is analogous to the standard normal model for continuous measurements. This model allows decomposition of contributions to the overall variance to be separated and compared to understand the drivers of the spread of the data. In this work, we analyze gauge R & R study data using Bayesian methods and illustrate the use of this wrapped normal model with simulated and real data. We also performed a small simulation study in considering the design of gauge R & R studies with angular measurements.

42 ENGINEERING↗
DOE OSTI2018

Robust extraction of the proton charge radius from electron-proton scattering data

Background: Extracting the proton charge radius from electron scattering data requires determining the slope of the charge form factor at Q 2 15 of zero. But as experimental data never reach that limit, numerous methods for making the extraction have been proposed, though often the functions are determined after seeing the data which can lead to confirmation bias. Purpose: To find functional forms that will allow for a robust extraction of the input radius for a wide variety of functional forms in order to have confidence in the extraction from upcoming low Q 2 experimental data such as the Jefferson Lab PRad experiment. Method: We create a general framework for inputting form-factor functions as well as various fitting functions. The input form factors are used to generate pseudo-data with fluctuations intended to mimic the binning and random uncertainty of a given set of real data. All combinations of input functions and fit functions can then be tested repeatedly against regenerated pseudo-data. Since the input radius is known, this allows us to find those functions that are robust for radius extractions in an objective fashion. Results: For the range and uncertainty of the PRad data, we find that a two-parameter rational function, a two-parameter continued fraction and the second order polynomial expansion of z can extract the input radius regardless of the input charge form factor function that is used. Conclusions: We have created an easily expandable framework to search for functional forms that allow for a robust extraction of the radius from a given binning and uncertainty of pseudo-data generated from a wide variety of trial functions. This method has enabled a successful search for the best functional forms to extract the radius from the upcoming PRad data, and can be used for other experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

A Data-Driven Approach to Dynamic Var Compensator Placement for Local Voltage Stabilization on Feeders with High PV Penetration

Distribution feeders with a high penetration of distributed photovoltaic (PV) systems can experience voltage variability caused by the changing load flow injections corresponding to the availability of the solar resource. The rapid, uncontrolled voltage range fluctuations can affect the system. Tap-changing voltage regulators, switched capacitors, and smart inverter functions have been used to stabilize voltage. However, in some cases, when these options are insufficient or unavailable, dedicated reactive power control devices such as dynamic var compensators (DVCs) can be used for local voltage stabilization. This paper introduces a statistical framework to characterize voltage variability and stabilization, proposes a data-driven approach to DVC placement, and presents the results of a planning study on a real feeder using real data.

Reiman, Andrew P.↗
NASA NTRS1986

Comparison of modal identification techniques using a hybrid-data approach

Modal identification of seemingly simple structures, such as the generic truss is often surprisingly difficult in practice due to high modal density, nonlinearities, and other nonideal factors. Under these circumstances, different data analysis techniques can generate substantially different results. The initial application of a new hybrid-data method for studying the performance characteristics of various identification techniques with such data is summarized. This approach offers new pieces of information for the system identification researcher. First, it allows actual experimental data to be used in the studies, while maintaining the traditional advantage of using simulated data. That is, the identification technique under study is forced to cope with the complexities of real data, yet the performance can be measured unquestionably for the artificial modes because their true parameters are known. Secondly, the accuracy achieved for the true structural modes in the data can be estimated from the accuracy achieved for the artificial modes if the results show similar characteristics. This similarity occurred in the study, for example, for a weak structural mode near 56 Hz. It may even be possible--eventually--to use the error information from the artificial modes to improve the identification accuracy for the structural modes.

Pappa, Richard S.↗
DOE OSTI2024

Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection.

42 ENGINEERING↗
DOE OSTI2014

Monte Carlo simulation of the data acquisition chain of scintillation detectors

The good performance of a detector can be strongly affected by the instrumentation used to acquire the data. The possibility of anticipating how the acquisition chain will affect the signal can help in finding the best solution among different set-ups. In this work we developed a Monte Carlo code that aims to simulate the effect of the various components of a digital Data Acquisition system (DAQ) applied to scintillation detectors. The components included in the model are: the scintillator, the photomultiplier tube (PMT), the signal cable and the digitizer. We benchmarked the code against real data acquired with a NE213 scintillator, comparing simulated and real signal pulses induced by gamma-ray interaction. Then we studied the dependence of the energy resolution of a pulse height spectrum (PHS) on the sampling frequency and the bit resolution of the digitizer. We found that exceeding some values of the sampling frequency and the bit resolution improves only marginally the performance of the system. The method can be applied for the study of various detector systems relevant for nuclear techniques, such as in fusion diagnostics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Refining Jets for CMS Run 3 using Fast Simulation

As the LHC moves into its high-luminosity phase, the CMS experiment must handle more complex data collected at much higher rates. While the Geant4-based simulation application (FullSim) provides highly accurate simulation to complement real data, FullSim’s intensive consumption of computing resources becomes an increasing liability as the rates increase, while faster tools offer an advantage. The fast MC production application (FastSim) delivers a complete simulation with a factor of 10 speedup over FullSim, but introduces inaccuracies in some observables. A specialized refinement method, Fast Perfekt, employs machine learning to improve the accuracy of FastSim. An initial report of this work focused on the refinement of jet flavor tagging observables. This article presents an update on the refinement, focusing on PUPPI jets with Run 3 data-taking conditions. Refinement is extended to include jet transverse momentum as well as its propagation to missing transverse momentum. A gridbased framework and real-time monitoring system have been developed to facilitate optimization and scaling of the refinement to a large number of target variables.

Güngördü, Açelya Deniz [Istanbul Tech. U.]↗
DOE OSTI2020

Modelling the Milky Way – I. Method and first results fitting the thick disc and halo with DES-Y3 data

ABSTRACT We present a technique to fit the stellar components of the Galaxy by comparing Hess Diagrams (HDs) generated from trilegal models to real data. We apply this technique, which we call mwfitting, to photometric data from the first 3 yr of the Dark Energy Survey (DES). After removing regions containing known resolved stellar systems such as globular clusters, dwarf galaxies, nearby galaxies, the Large Magellanic Cloud, and the Sagittarius Stream, our main sample spans a total area of ∼2300 deg2. We further explore a smaller subset (∼1300 deg2) that excludes all regions with known stellar streams and stellar overdensities. Validation tests on synthetic data possessing similar properties to the DES data show that the method is able to recover input parameters with a precision better than 3 per cent. We fit the DES data with an exponential thick disc model and an oblate double power-law halo model. We find that the best-fitting thick disc model has radial and vertical scale heights of 2.67 ± 0.09 kpc and 925 ± 40 pc, respectively. The stellar halo is fit with a broken power-law density profile with an oblateness of 0.75 ± 0.01, an inner index of 1.82 ± 0.08, an outer index of 4.14 ± 0.05, and a break at 18.52 ± 0.27 kpc from the Galactic centre. Several previously discovered stellar overdensities are recovered in the residual stellar density map, showing the reliability of mwfitting in determining the Galactic components. Simulations made with the best-fitting parameters are a promising way to predict Milky Way star counts for surveys such as the LSST and Euclid.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Active anomaly detection for time-domain discoveries

Aims. We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. Results. We show that, in the real data scenario , AAD was able to identify ~80% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Measuring the thermal and ionization state of the low- z IGM using likelihood free inference

ABSTRACT We present a new approach to measure the power-law temperature density relationship $T=T_0 (\rho/ \bar{\rho })^{\gamma -1}$ and the UV background photoionization rate $\Gamma _{{{{\rm H\, {\small I}}}}{}}$ of the intergalactic medium (IGM) based on the Voigt profile decomposition of the Ly α forest into a set of discrete absorption lines with Doppler parameter b and the neutral hydrogen column density $N_{\rm H\, {\small I}}$. Previous work demonstrated that the shape of the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution is sensitive to the IGM thermal parameters T0 and γ, whereas our new inference algorithm also takes into account the normalization of the distribution, i.e. the line-density dN/dz, and we demonstrate that precise constraints can also be obtained on $\Gamma _{{{{\rm H\, {\small I}}}}{}}$. We use density-estimation likelihood-free inference (DELFI) to emulate the dependence of the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution on IGM parameters trained on an ensemble of 624 nyx hydrodynamical simulations at z = 0.1, which we combine with a Gaussian process emulator of the normalization. To demonstrate the efficacy of this approach, we generate hundreds of realizations of realistic mock HST/COS data sets, each comprising 34 quasar sightlines, and forward model the noise and resolution to match the real data. We use this large ensemble of mocks to extensively test our inference and empirically demonstrate that our posterior distributions are robust. Our analysis shows that by applying our new approach to existing Ly α forest spectra at z ≃ 0.1, one can measure the thermal and ionization state of the IGM with very high precision ($\sigma _{\log T_0} \sim 0.08$ dex, σγ ∼ 0.06, and $\sigma _{\log \Gamma _{{{{\rm H\, {\small I}}}}{}}} \sim 0.07$ dex).

79 ASTRONOMY AND ASTROPHYSICS↗