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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Production of alternate realizations of DESI fiber assignment for unbiased clustering measurement in data and simulations

A critical requirement of spectroscopic large scale structure analyses is correcting for selection of which galaxies to observe from an isotropic target list. This selection is often limited by the hardware used to perform the survey which will impose angular constraints of simultaneously observable targets, requiring multiple passes to observe all of them. In SDSS this manifested solely as the collision of physical fibers and plugs placed in plates. In DESI, there is the additional constraint of the robotic positioner which controls each fiber being limited to a finite patrol radius. A number of approximate methods have previously been proposed to correct the galaxy clustering statistics for these effects, but these generally fail on small scales. To accurately correct the clustering we need to upweight pairs of galaxies based on the inverse probability that those pairs would be observed (Bianchi & Percival 2017). This paper details an implementation of that method to correct the Dark Energy Spectroscopic Instrument (DESI) survey for incompleteness. To calculate the required probabilities, we need a set of alternate realizations of DESI where we vary the relative priority of otherwise identical targets. These realizations take the form of alternate Merged Target Ledgers (AMTL), the files that link DESI observations and targets. We present the method used to generate these alternate realizations and how they are tracked forward in time using the real observational record and hardware status, propagating the survey as though the alternate orderings had been adopted. We detail the first applications of this method to the DESI One-Percent Survey (SV3) and the DESI year 1 data. We include evaluations of the pipeline outputs, estimation of survey completeness from this and other methods, and validation of the method using mock galaxy catalogs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Towards a unified description of the hydrogen bond network of liquid water: A dynamics based approach

The definition of a hydrogen bond (H-bond) is intimately related to the topological and dynamic properties of the hydrogen bond network within liquid water. The development of a universal H-bond definition for water is an active area of research as it would remove many ambiguities in the network properties that derive from the fixed definition employed to assign whether a water dimer is hydrogen bonded. This work investigates the impact that an electronic-structure based definition, an energetic, and a geometric definition of the H-bond has upon both topological and dynamic network behavior of simulated water. In each definition, the use of a cutoff (either geometric or energetic) to assign the presence of a H-bond leads to the formation of transiently bonded or broken dimers, which have been quantified within the simulation data. The relative concentration of transient species, and their duration, results in two of the three definitions sharing similarities in either topological or dynamic features (H-bond distribution, H-bond lifetime, etc.), however no two definitions exhibit similar behavior for both classes of network properties. In fact, two networks with similar local network topology (as indicated by similar average H-bonds) can have dramatically different global network topology (as indicated by the defect state distributions) and altered H-bond lifetimes. A dynamics based correction scheme is then used to remove artificially transient H-bonds and to repair artificially broken bonds within the network such that the corrected network exhibits the same structural and dynamic properties for two H-bond definitions (the properties of the third definition being significantly improved). The algorithm described represents a significant step forward in the development of a unified hydrogen bond network whose properties are independent of the original hydrogen bond definition that is employed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2017

Launch Control System Software Development System Automation Testing

The Spaceport Command and Control System (SCCS) is the National Aeronautics and Space Administration's (NASA) launch control system for the Orion capsule and Space Launch System, the next generation manned rocket currently in development. This system requires high quality testing that will measure and test the capabilities of the system. For the past two years, the Exploration and Operations Division at Kennedy Space Center (KSC) has assigned a group including interns and full-time engineers to develop automated tests to save the project time and money. The team worked on automating the testing process for the SCCS GUI that would use streamed simulated data from the testing servers to produce data, plots, statuses, etc. to the GUI. The software used to develop automated tests included an automated testing framework and an automation library. The automated testing framework has a tabular-style syntax, which means the functionality of a line of code must have the appropriate number of tabs for the line to function as intended. The header section contains either paths to custom resources or the names of libraries being used. The automation library contains functionality to automate anything that appears on a desired screen with the use of image recognition software to detect and control GUI components. The data section contains any data values strictly created for the current testing file. The body section holds the tests that are being run. The function section can include any number of functions that may be used by the current testing file or any other file that resources it. The resources and body section are required for all test files; the data and function sections can be left empty if the data values and functions being used are from a resourced library or another file. To help equip the automation team with better tools, the Project Lead of the Automated Testing Team, Jason Kapusta, assigned the task to install and train an optical character recognition (OCR) tool to Brandon Echols, a fellow intern, and I. The purpose of the OCR tool is to analyze an image and find the coordinates of any group of text. Some issues that arose while installing the OCR tool included the absence of certain libraries needed to train the tool and an outdated software version. We eventually resolved the issues and successfully installed the OCR tool. Training the tool required many images and different fonts and sizes, but in the end the tool learned to accurately decipher the text in the images and their coordinates. The OCR tool produced a file that contained significant metadata for each section of text, but only the text and coordinates of the text was required for our purpose. The team made a script to parse the information we wanted from the OCR file to a different file that would be used by automation functions within the automated framework. Since a majority of development and testing for the automated test cases for the GUI in question has been done using live simulated data on the workstations at the Launch Control Center (LCC), a large amount of progress has been made. As of this writing, about 60% of all of automated testing has been implemented. Additionally, the OCR tool will help make our automated tests more robust due to the tool's text recognition being highly scalable to different text fonts and text sizes. Soon we will have the whole test system automated, allowing for more full-time engineers working on development projects.

Automation↗
DOE OSTI2021

Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian deep learning

The goal of generative models is to learn the intricate relations between the data to create new simulated data, but current approaches fail in very high dimensions. When the true data-generating process is based on physical processes, these impose symmetries and constraints, and the generative model can be created by learning an effective description of the underlying physics, which enables scaling of the generative model to very high dimensions. In this work, we propose Lagrangian deep learning (LDL) for this purpose, applying it to learn outputs of cosmological hydrodynamical simulations. The model uses layers of Lagrangian displacements of particles describing the observables to learn the effective physical laws. The displacements are modeled as the gradient of an effective potential, which explicitly satisfies the translational and rotational invariance. The total number of learned parameters is only of order 10, and they can be viewed as effective theory parameters. Further, we combine N-body solver fast particle mesh (FastPM) with LDL and apply it to a wide range of cosmological outputs, from the dark matter to the stellar maps, gas density, and temperature. The computational cost of LDL is nearly four orders of magnitude lower than that of the full hydrodynamical simulations, yet it outperforms them at the same resolution. We achieve this with only of order 10 layers from the initial conditions to the final output, in contrast to typical cosmological simulations with thousands of time steps. This opens up the possibility of analyzing cosmological observations entirely within this framework, without the need for large dark-matter simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SU-F-T-211: Evaluation of a Dual Focusing Magnet System for the Treatment of Small Proton Targets

Purpose: To investigate magnetic focusing for small volume proton targets using a doublet combination of quadrupole rare earth permanent magnet Halbach cylinder assemblies Methods: Monte Carlo computer simulations were performed using the Geant4 toolkit to compare dose depositions of proton beams transported through two focusing magnets or in their absence. Proton beams with energies of 127 MeV and initial diameters of 5, 8 and 10 mm were delivered through two identical focusing magnets similar to those currently in experimental use at Loma Linda University Medical Center. Analogous experiments used optimized configurations based on the simulation results. Dose was measured by a diode detector and Gafchromic EBT3 film and compared to simulation data. Based on results from the experimental data, an additional set of simulations was performed with an initial beam diameter of 18 mm and a two differing length magnets (40mm & 68mm). Results: Experimental data matched well with Monte Carlo simulations. However, under conditions necessary to produce circular beam spots at target depth, magnetically focused beams using two identical 40 mm length magnets did not meet all of our performance criteria of circular beam spots, improved peak to entrance (P/E) dose ratios and dose delivery efficiencies. The simulations using the longer 68 mm 2nd magnet yielded better results with 34% better P/E dose ratio and 20–50% better dose delivery efficiencies when compared to unfocused 10 mm beams. Conclusion: While magnetic focusing using two magnets with identical focusing power did not yield desired results, ongoing Monte Carlo simulations suggest that increasing the length of the 2nd magnet to 68 mm could improve P/E dose ratios and dose efficiencies. Future work includes additional experimental validation of the longer 2nd magnet setup as well as experiments with triplet magnet systems. This project was sponsored with funding from the Department of Defense (DOD# W81XWH-BAA-10-1).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Development of a transient signal validation technique via a modified kernel regression model - 365

The demand for robust and resilient performance has led to the use of online-monitoring and signal validation techniques to monitor the process parameters and equipment conditions using several empirical models. One of these models is kernel regression (KR). Although KR is being used and performed excellently when applied to steady state, it has limitation in time-varying data that has several repetition of the same data point of the signals, especially if those signals are used to infer the other signals. In addition, many situations are related to variation and fluctuation during normal states, such as transient - start-up and shutdown mode. However, accurate estimation of the process signal can lead to the proper understanding of the equipment behaviours as well as enhance the online monitoring applications. In the sight of foregoing, this paper proposed a modified KR model, especially for transient signal validation to resolve the setback of conventional KR by improving the measure of similarities in the conventional KR. The performance of the proposed model is verified using both experimental data and the plant simulation data, and its results are compared with that of conventional KR. The results show that the proposed model has a greater robustness in particular situations compared to conventional KR, and it can be used to improve process and equipment monitoring applications. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

The creation of a comprehensive metocean data set for offshore wind turbine simulations: Comprehensive metocean data set

A database of meteorological and ocean conditions is presented for use in offshore wind energy research and design. The original data are from 23 ocean sites around the USA and were obtained from the National Data Buoy Center run by the National Oceanic and Atmospheric Administration. The data are presented in a processed form that includes the variables of interest for offshore wind energy design: wind speed, significant wave height, wave peak-spectral period, wind direction and wave direction. For each site, a binning process is conducted to create conditional probability functions for each of these variables. The sites are then grouped according to geographic location and combined to create three representative sites, including a West Coast site, an East Coast site and a Gulf of Mexico site. Both the processed data and the probability distribution parameters for the individual and representative sites are being hosted on a publicly available domain by the National Renewable Energy Laboratory, with the intent of providing a standard basis of comparison for meteorological and ocean conditions for offshore wind energy research worldwide.

17 WIND ENERGY↗
DOE OSTI2021

High-resolution hourly surrogate modeling framework for physics-based large-scale building stock modeling

Surrogate modeling can play a key role in reducing high computational burdens for large-scale physics-based modeling and uncertainty quantification. With the rapid development of large-scale building stock energy modeling, surrogate modeling has also begun to be widely applied in this field; however, most existing surrogate models lack hourly time resolution for regional-scale modeling, which is essential for understanding building demand profiles and grid impacts. Further, there is generally a lack of necessary data and feature engineering frameworks specific to building modeling for efficiently managing large datasets and complex computations. Furthermore, this paper proposes a modeling framework for large-scale (city-/region-scale), high-resolution, high-fidelity surrogate building stock energy models. Our developed framework consists of six modules: (1) building stock energy modeling (ComStock TM and ResStock TM ), (2) data engineering for large simulation data, (3) high performance computing workflow, (4) feature engineering, (5) machine learning model development, and (6) model performance evaluation. Two case studies apply the developed framework in both residential and commercial building stock analysis to demonstrate its computational efficiency and surrogate modeling accuracies. Results show that surrogate models, when efficiently trained using the HPC workflow module, reach a high level of modeling accuracy for two case studies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce-data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal-halides spanning 3 dimensionalities and 7 space-groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross-validated accuracies for dimensionality and space-group classification of 93% and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root-causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16° 2θ, which enables an XRD pattern to be obtained and classified in 5.5 minutes or less.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Magnetic field impact on the laser heating in MagLIF

Prior to implosion in Magnetized Liner Inertial Fusion (MagLIF), the fuel is heated to temperatures on the order of several hundred eV with a multi-kJ, multi-ns laser pulse. In this paper, we present two laser heated plasma experiments, relevant to the MagLIF preheat stage, performed at Z with beryllium liners filled with deuterium and a trace amount of argon. In one experiment, there is no magnetic field and, in the other, the liner and fuel are magnetized with an 8.5 T axial magnetic field. The recorded time integrated, spatially resolved spectra of the Ar K-shell emission are sensitive to electron temperature T e . Individual analysis of the spatially resolved spectra produces electron temperature distributions T e (z) that are resolved along the axis of laser propagation. In the experiment with magnetic field, the plasma reaches higher temperatures and the heated region extends deeper within the liner than in the unmagnetized case. Radiation magnetohydrodynamics simulations of the experiments are presented and post-processed. A comparison of the results from experimental and simulated data reveals that the simulations underpredict T e in both cases but the differences are larger in the case with magnetic field.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Constructing a new predictive scaling formula for ITER's divertor heat-load width informed by a simulation-anchored machine learning

Understanding and predicting divertor heat-load width λq is a critically important problem for an easier and more robust operation of ITER with high fusion gain. Previous predictive simulation data for λ q using the extreme-scale edge gyrokinetic code XGC1 [S. Ku et al., Phys. Plasmas 25, 056107 (2018)] in the electrostatic limit under attached divertor plasma conditions in three major US tokamaks [C. S. Chang et al., Nucl. Fusion 57, 116023 (2017)] reproduced the Eich and Goldston attached-divertor formula results [formula #14 in T. Eich et al., Nucl. Fusion 53, 093031 (2013) and R. J. Goldston, Nucl. Fusion 52, 013009 (2012)] and furthermore predicted over six times wider λ q than the maximal Eich and Goldston formula predictions on a full-power (Q = 10) scenario ITER plasma. After adding data from further predictive simulations on a highest current JET and highest-current Alcator C-Mod, a machine learning program is used to identify a new scaling formula for λ q as a simple modification to the Eich formula #14, which reproduces the Eich scaling formula for the present tokamaks and which embraces the wide λ q XGC for the full-current Q = 10 ITER plasma. Additionally, the new formula is then successfully tested on three more ITER plasmas: two corresponding to long burning scenarios with Q = 5 and one at low plasma current to be explored in the initial phases of ITER operation. The new physics that gives rise to the wider λ q XGC is identified to be the weakly collisional, trapped-electron-mode turbulence across the magnetic separatrix, which is known to be an efficient transporter of the electron heat and mass. Electromagnetic turbulence and high-collisionality effects on the new formula are the next study topics for XGC1.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Methodology to assess “no-touch” building audit software using simulated utility data

Building audits are conducted in many commercial buildings to identify opportunities to reduce energy costs and improve building operation. Because audits require significant effort by building engineers, they are usually only affordable for larger commercial buildings. “No-touch” building audit tools have thus been developed to identify potential savings based on a simplified analysis of building energy consumption patterns via high-level energy data such as monthly utility bills. This paper presents a comprehensive and standardized methodology to evaluate the accuracy of no-touch audit tools in detecting and diagnosing building energy problems and quantifying potential energy savings. The test suite is based on output data from a well-characterized set of building energy models, and the methodology is illustrated by applying it to a representative no-touch building audit tool. Results show that the tool estimates building energy end uses with reasonable accuracy but is less accurate in identifying probably causes of high energy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Discovering Ca II absorption lines with a neural network

Quasar absorption line analysis is critical for studying gas and dust components and their physical and chemical properties as well as the evolution and formation of galaxies in the early universe. Calcium II (Ca II ) absorbers, which are one of the dustiest absorbers and are located at lower redshifts than most other absorbers, are especially valuable when studying physical processes and conditions in recent galaxies. However, the number of known quasar Ca II absorbers is relatively low due to the difficulty of detecting them with traditional methods. In this work, we developed an accurate and quick approach to search for Ca II absorption lines using deep learning. In our deep learning model, a convolutional neural network, tuned using simulated data, is used for the classification task. The simulated training data are generated by inserting artificial Ca II absorption lines into original quasar spectra from the Sloan Digital Sky Survey (SDSS), while an existing Ca II catalogue is adopted as the test set. The resulting model achieves an accuracy of 96 per cent on the real data in the test set. Our solution runs thousands of times faster than traditional methods, taking a fraction of a second to analyse thousands of quasars, while traditional methods may take days to weeks. The trained neural network is applied to quasar spectra from SDSS’s DR7 and DR12 and discovered 399 new quasar Ca II absorbers. In addition, we confirmed 409 known quasar Ca II absorbers identified previously by other research groups through traditional methods.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Jet substructure studies with CMS open data

We utilize public data from the CMS experiment to study the two-prong substructure of jets. The CMS open data are based on 31.8 pb -1 of 7 TeV proton-proton collisions recorded at the Large Hadron Collider in 2010, yielding a sample of 768,687 events containing a high-quality central jet with transverse momentum larger than 85 GeV. Using CMS’s particle flow reconstruction algorithm to obtain jet constituents, we extract the two-prong substructure of the leading jet using soft-drop declustering. We find good agreement between results obtained from the CMS open data and those obtained from parton shower generators, and we also compare to analytic jet substructure calculations performed to modified leading-logarithmic accuracy. Even though the 2010 CMS open data do not include simulated data to help estimate systematic uncertainties, we use track-only observables to validate these substructure studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults

Classification of power system event data is a growing need, particularly where non-protective relaying-based sensors are used to monitor grid performance. Given the high burden of obtaining event data with appropriate labeling, an unsupervised approach is highly valuable. This approach enables using event data without labeling, which is far easier to obtain. This paper presents an unsupervised learning method to classify and label transients observed in the distribution grid. A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data is used to train the CVAE that identifies different faults as different clusters in the latent space. The clusters are then used as the foundation model to categorize the real-world data.

Alam, Maksudul↗