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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

ParaView Catalyst: Enabling In Situ Data Analysis and Visualization

Computer simulations are growing in sophistication and producing results of ever greater fidelity. This trend has been enabled by advances in numerical methods and increasing computing power. However these advances come with several costs including massive increases in data size, difficulties examining output data, challenges in configuring simulation runs, and difficulty debugging running codes. Interactive visualization tools, like ParaView, have been used for post-processing of simulation results. However, the increasing data sizes, and limited storage and bandwidth make high fidelity post-processing impractical. In situ analysis is recognized as one of the ways to address these challenges. In situ analysis moves some of the post-processing tasks in line with the simulation code thus short circuiting the need to communicate the data between the simulation and analysis via storage. ParaView Catalyst is a data processing and visualization library that enables in situ analysis and visualization. Built on and designed to interoperate with the standard visualization toolkit VTK and the ParaView application, Catalyst enables simulations to intelligently perform analysis, generate relevant output data, and visualize results concurrent with a running simulation. Here, we provide an overview of the Catalyst framework and some of the success stories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Investigating high-energy proton-induced reactions on spherical nuclei: Implications for the preequilibrium exciton model

A number of accelerator-based isotope production facilities utilize $100-200$ MeV proton beams due to the high production rates enabled by high-intensity beam capabilities and the greater diversity of isotope production brought on by the long range of high-energy protons. However, nuclear reaction modeling at these energies can be challenging because of the interplay between different reaction modes and a lack of existing guiding cross section data. A Tri-lab collaboration has been formed between the Lawrence Berkeley, Los Alamos, and Brookhaven National Laboratories to address these complexities by characterizing charged-particle nuclear reactions relevant to the production of established and novel radioisotopes. In the inaugural collaboration experiments, stacked-targets of niobium foils were irradiated at the Brookhaven Linac Isotope Producer ($E_p=200$ MeV) and the Los Alamos Isotope Production Facility ($E_p=100$ MeV) to measure $^{93}$Nb(p,x) cross sections between $50-200$ MeV. The results were compared with literature data as well as the default calculations of the nuclear model codes TALYS, CoH, EMPIRE, and ALICE. The default code predictions largely failed to reproduce the measurements. Therefore, we developed a standardized procedure, which determines the reaction model parameters that best reproduce the most prominent reaction channels in a physically justifiable manner. Overall, the primary focus of the procedure was to determine the best parameterization for the pre-equilibrium two-component exciton model. This modeling study revealed a trend towards a relative decrease for internal transition rates at intermediate proton energies ($E_p=20-60$ MeV) in the current exciton model as compared to the default values. The results of this work are instrumental for the planning, execution, and analysis essential to isotope production.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Temporal Analysis Of Product Computational Methods

The temporal analysis of products (TAP) reactor provides a wealth of data for transient studies of gas phase heterogeneous reactions, with the ability to provide a comprehensive picture of intrinsic reaction kinetics pertaining to complex catalytic mechanisms. A novel framework called the Y-Procedure was previously developed by Yablonsky and coworkers to extract information from this non-steady-state kinetic data. The Y-Procedure reconstructs the chemical transformation rate and gas composition in the catalytic zone with no a priori assumptions on the kinetic model and is used to relate changes in catalytic reaction rates and kinetic parameters to changes in a catalyst’s surface composition. This code provides the required steps to pre-process, analyze, and perform the Y-Procedure on experiments derived from the TAP reactor.

Kunz, Matthew R.↗
DOE OSTI2016

PROBING THE EXPANSION HISTORY OF THE UNIVERSE BY MODEL-INDEPENDENT RECONSTRUCTION FROM SUPERNOVAE AND GAMMA-RAY BURST MEASUREMENTS

To probe the late evolution history of the universe, we adopt two kinds of optimal basis systems. One of them is constructed by performing the principle component analysis, and the other is built by taking the multidimensional scaling approach. Cosmological observables such as the luminosity distance can be decomposed into these basis systems. These basis systems are optimized for different kinds of cosmological models that are based on different physical assumptions, even for a mixture model of them. Therefore, the so-called feature space that is projected from the basis systems is cosmological model independent, and it provides a parameterization for studying and reconstructing the Hubble expansion rate from the supernova luminosity distance and even gamma-ray burst (GRB) data with self-calibration. The circular problem when using GRBs as cosmological candles is naturally eliminated in this procedure. By using the Levenberg–Marquardt technique and the Markov Chain Monte Carlo method, we perform an observational constraint on this kind of parameterization. The data we used include the “joint light-curve analysis” data set that consists of 740 Type Ia supernovae and 109 long GRBs with the well-known Amati relation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Summary of the 5th IAEA technical meeting on fusion data processing, validation and analysis (FDPVA)

The purpose of the 5th International Atomic Energy Agency technical meeting on fusion data processing, validation and analysis (FDPVA) (Ghent University, Ghent, Belgium, 12–15 June 2023) was to provide a platform during which a set of topics relevant to FDPVA were discussed with the view of meeting the needs of next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics data analysis and synthetic diagnostics reported at the meeting, including concept description of new devices; fusion databases; integrated data analysis; inverse problems; uncertainty propagation, verification and validation; probabilistic methods and machine learning. The relevant results underline trends observed in the current major fusion confinement devices.

fusion databases↗
DOE OSTI2021

Detecting optical transients using artificial neural networks and reference images from different surveys

ABSTRACT We present a technique to detect optical transients based on an artificial neural networks method. We describe the architecture of two networks capable of comparing images of the same part of the sky taken by different telescopes. One image corresponds to the epoch in which a potential transient could exist; the other is a reference image of an earlier epoch. We use data obtained by the Dr. Cristina V. Torres Memorial Astronomical Observatory and archival reference images from the Sloan Digital Sky Survey. We trained a convolutional neural network and a dense layer network on simulated source samples and then tested the trained networks on samples created from real image data. Autonomous detection methods replace the standard process of detecting transients, which is normally achieved by source extraction of a difference image followed by human inspection of the detected candidates. Replacing the human inspection component with an entirely autonomous method would allow for a rapid and automatic follow-up of interesting targets of opportunity. The toy-model pipeline that we present here is not yet able to replace human inspection, but it might provide useful hints to identify potential candidates. The method will be further expanded and tested on telescopes participating in the Transient Optical Robotic Observatory of the South Collaboration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

NEOS Data and the Origin of the 5 MeV Bump in the Reactor Antineutrino Spectrum

We perform a combined analysis of recent NEOS and Daya Bay data on the reactor antineutrino spectrum. This analysis includes approximately 1.5 million antineutrino events, which is the largest neutrino event sample analyzed to date. We use a double ratio which cancels flux model dependence and related uncertainties as well as the effects of the detector response model. We find at 3–4 standard deviation significance level, that plutonium-239 and plutonium-241 are disfavored as the single source for the so-called 5 MeV bump. This analysis method has general applicability and, in particular, with higher statistics data sets, will be able to shed significant light on the issue of the bump. With some caveats, this should also allow us to improve the sensitivity for sterile neutrino searches in NEOS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Decoding the Pair Distribution Function of Uranium in Molten Fluoride Salts from X-Ray Absorption Spectroscopy Data by Machine Learning

Thermal properties of actinides in molten salts are linked to the strongly disordered local environment of actinide ions. Here, we illustrate both the limitations of the commonly used fitting method for analysis of extended X-ray absorption fine structure (EXAFS) spectra in molten UF 4 and a possible solution using an "objective neural network - EXAFS" (ONNE) method. ONNE provides both extraction of the pair distribution function, as validated by its application to the EXAFS spectra calculated on molecular dynamics trajectory, and the EXAFS data reconstruction. The ONNE analysis of the molten UF4 has revealed reduction of the first nearest neighbor U-F coordination number, expansion of the U-F bond length and smaller contribution to the second shell compared to its crystalline counterpart. This method is therefore an attractive alternative to conventional EXAFS analysis and molecular dynamics simulations for studies of disordered environment of actinides in molten salts.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2014

Spatial density fluctuations and selection effects in galaxy redshift surveys

One of the main problems of observational cosmology is to determine the range in which a reliable measurement of galaxy correlations is possible. This corresponds to determining the shape of the correlation function, its possible evolution with redshift and the size and amplitude of large scale structures. Different selection effects, inevitably entering in any observation, introduce important constraints in the measurement of correlations. In the context of galaxy redshift surveys selection effects can be caused by observational techniques and strategies and by implicit assumptions used in the data analysis. Generally all these effects are taken into account by using pair-counting algorithms to measure two-point correlations. We review these methods stressing that they are based on the a-priori assumption that galaxy distribution is spatially homogeneous inside a given sample. We show that, when this assumption is not satisfied by the data, results of the correlation analysis are affected by finite size effects. In order to quantify these effects, we introduce a new method based on the computation of the gradient of galaxy counts along tiny cylinders. We show, by using artificial homogeneous and inhomogeneous point distributions, that this method identifies redshift dependent selection effects and disentangles them from the presence of large scale density fluctuations. We then apply this new method to several redshift catalogs and we find evidence that galaxy distribution, in those samples where selection effects are small enough, is characterized by power-law correlations with exponent γ=0.9 up to 20 Mpc/h followed by a change of slope that, in the range 20–100 Mpc/h, corresponds to a power-law exponent γ=0.25. Whether a crossover to spatial uniformity occurs at ∼ 100 Mpc/h or larger scales cannot be clarified by the present data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

FY 2016 Status Report: CIRFT Testing Data Analyses and Updated Curvature Measurements

This report provides a detailed description of FY15 test result corrections/analysis based on the FY16 Cyclic Integrated Reversible-Bending Fatigue Tester (CIRFT) test program methodology update used to evaluate the vibration integrity of spent nuclear fuel (SNF) under normal transportation conditions. The CIRFT consists of a U-frame testing setup and a real-time curvature measurement method. The three-component U-frame setup of the CIRFT has two rigid arms and linkages to a universal testing machine. The curvature of rod bending is obtained through a three-point deflection measurement method. Three linear variable differential transformers (LVDTs) are used and clamped to the side connecting plates of the U-frame to capture the deformation of the rod. The contact-based measurement, or three-LVDT-based curvature measurement system, on SNF rods has been proven to be quite reliable in CIRFT testing. However, how the LVDT head contacts the SNF rod may have a significant effect on the curvature measurement, depending on the magnitude and direction of rod curvature. It has been demonstrated that the contact/curvature issues can be corrected by using a correction on the sensor spacing. The sensor spacing defines the separation of the three LVDT probes and is a critical quantity in calculating the rod curvature once the deflections are obtained. The sensor spacing correction can be determined by using chisel-type probes. The method has been critically examined this year and has been shown to be difficult to implement in a hot cell environment, and thus cannot be implemented effectively. A correction based on the proposed equivalent gauge-length has the required flexibility and accuracy and can be appropriately used as a correction factor. The correction method based on the equivalent gauge length has been successfully demonstrated in CIRFT data analysis for the dynamic tests conducted on Limerick (LMK) (17 tests), North Anna (NA) (6 tests), and Catawba mixed oxide (MOX) (10 tests) SNF samples. These CIRFT tests were completed in FY14 and FY15. Specifically, the data sets obtained from measurement and monitoring were processed and analyzed. The fatigue life of rods has been characterized in terms of moment, curvature, and equivalent stress and strain..

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Automated Analysis, Reporting, and Archiving for Robotic Nondestructive Assay of Holdup Deposits - 19508

To decommission deactivated gaseous diffusion enrichment facilities, miles of contaminated pipe must be measured. The current method requires thousands of manual measurements, repeated manual data transcription, and months of manual analysis. The Pipe Crawling Activity Measurement System (PCAMS), developed by Carnegie Mellon University and in commissioning for use at the DOE Portsmouth Gaseous Diffusion Enrichment Facility, uses a robot to measure Uranium-235 from inside pipes and automatically log the data. Radiation measurements, as well as imagery, geometric modeling, and precise measurement positioning data are digitally transferred to the PCAMS server. On the server, data can be automatically processed in minutes and summarized for analyst review. Measurement reports are auto-generated with the push of a button. A database specially-configured to hold heterogeneous data such as spectra, images, and robot trajectories serves as archive. This paper outlines the features and design of the PCAMS Post-Processing Software, currently in commissioning for use at the Portsmouth Gaseous Diffusion Enrichment Facility. The analysis process, the analyst interface to the system, and the content of auto-generated reports are each described. Example pipe-interior geometric surface models, illustration of how key report features apply in operational runs, and user feedback are discussed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

On signals faint and sparse: The ACICA algorithm for blind de-trending of exoplanetary transits with low signal-to-noise

Independent component analysis (ICA) has recently been shown to be a promising new path in data analysis and de-trending of exoplanetary time series signals. Such approaches do not require or assume any prior or auxiliary knowledge about the data or instrument in order to de-convolve the astrophysical light curve signal from instrument or stellar systematic noise. These methods are often known as 'blind-source separation' (BSS) algorithms. Unfortunately, all BSS methods suffer from an amplitude and sign ambiguity of their de-convolved components, which severely limits these methods in low signal-to-noise (S/N) observations where their scalings cannot be determined otherwise. Here we present a novel approach to calibrate ICA using sparse wavelet calibrators. The Amplitude Calibrated Independent Component Analysis (ACICA) allows for the direct retrieval of the independent components' scalings and the robust de-trending of low S/N data. Such an approach gives us an unique and unprecedented insight in the underlying morphology of a data set, which makes this method a powerful tool for exoplanetary data de-trending and signal diagnostics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification

We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with high forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

In this work, we present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system’s governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting map because it often adds auxiliary variables to the system state. The lifting map is applied to data obtained by evaluating a model for the original nonlinear system. This lifted data is projected onto its leading principal components, and low-dimensional linear and quadratic matrix operators are fit to the lifted reduced data using a least-squares operator inference procedure. Analysis of our method shows that the Lift & Learn models are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches. This preservation of system physics makes the Lift & Learn models robust to changes in inputs. Numerical experiments on the FitzHugh–Nagumo neuron activation model and the compressible Euler equations demonstrate the generalizability of our model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Dispersion of magnetic fields in molecular clouds. IV. Analysis of interferometry data

We expand on the dispersion analysis of polarimetry maps toward applications to interferometry data. We show how the filtering of low spatial frequencies can be accounted for within the idealized Gaussian turbulence model, initially introduced for single-dish data analysis, to recover reliable estimates for correlation lengths of magnetized turbulence, as well as magnetic field strengths (plane-of-the-sky component) using the Davis–Chandrasekhar–Fermi method. We apply our updated technique to TADPOL/CARMA data obtained on W3(OH), W3 Main, and DR21(OH). For W3(OH), our analysis yields a turbulence correlation length δ≃19 mpc, a ratio of turbulent-to-total magnetic energy 〈B{sub t}{sup 2}〉/〈B{sup 2}〉≃0.58, and a magnetic field strength B{sub 0}∼1.1 mG; for W3 Main δ≃22 mpc, 〈B{sub t}{sup 2}〉/〈B{sup 2}〉≃0.74, and B{sub 0}∼0.7 mG; while for DR21(OH) δ≃12 mpc, 〈B{sub t}{sup 2}〉/〈B{sup 2}〉≃0.70, and B{sub 0}∼1.2 mG.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The DESI N -body Simulation Project – II. Suppressing sample variance with fast simulations

ABSTRACT Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches $\sim 20\, h^{-3}\, \mathrm{Gpc}^{3}$. It is a great challenge to prepare high-resolution simulations with a much larger volume for validating the DESI analysis pipelines. AbacusSummit is a suite of high-resolution dark-matter-only simulations designed for this purpose, with $200\, h^{-3}\, \mathrm{Gpc}^{3}$ (10 times DESI volume) for the base cosmology. However, further efforts need to be done to provide a more precise analysis of the data and to cover also other cosmologies. Recently, the CARPool method was proposed to use paired accurate and approximate simulations to achieve high statistical precision with a limited number of high-resolution simulations. Relying on this technique, we propose to use fast quasi-N-body solvers combined with accurate simulations to produce accurate summary statistics. This enables us to obtain 100 times smaller variance than the expected DESI statistical variance at the scales we are interested in, e.g. $k \lt 0.3\, h\, \mathrm{Mpc}^{-1}$ for the halo power spectrum. In addition, it can significantly suppress the sample variance of the halo bispectrum. We further generalize the method for other cosmologies with only one realization in AbacusSummit suite to extend the effective volume ∼20 times. In summary, our proposed strategy of combining high-fidelity simulations with fast approximate gravity solvers and a series of variance suppression techniques sets the path for a robust cosmological analysis of galaxy survey data.

cosmology: theory↗
DOE OSTI2025

Ring Pull Strain Analysis Version 1.1

This report details an analysis package, Ring Pull Strain Analysis (RPSA), that can be used to present and quantify digital image correlation (DIC) data as it relates to a gaugeless ring pull test. Gaugeless ring pull is a testing technique for mechanical testing of small annular samples, usually cut from a thin-walled tube. DIC data is often necessary for this kind of test because bending moments present on the ring cause a non-uniform strain distribution and localized measurements are necessary. In addition, the annular geometry of a ring lends itself to a polar representation, which is not present with typical DIC analysis methods. RPSA was made to calculate and plot the polar representation of strain from standard pre-processed DIC data of a gaugeless ring pull test. Further analysis can be done on ring pull including a quasi-uniaxial tensile analysis and coating analysis, which are also performed by RPSA. In addition, due to the universality of DIC plotting and ring pull test analysis, RPSA can accommodate a wide variety of tests, though it is tailored for ring pull testing. This report details how RPSA works, including the theory, assumptions, and logic behind the calculations and the structure of the program.

36 MATERIALS SCIENCE↗