Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data”

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

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

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

PFLOTRAN-E4D: A parallel open source PFLOTRAN module for simulating time-lapse electrical resistivity data

Time-lapse electrical resistivity tomography (ERT) is finding increased application for remotely monitoring processes occurring in the near subsurface in three-dimensions (i.e. 4D monitoring). However, there are few codes capable of simulating the evolution of subsurface resistivity and corresponding tomographic measurements arising from a particular process, particularly in parallel and with an open source license. Herein we describe and demonstrate an electrical resistivity tomography module for the PFLOTRAN subsurface simulation code, named PFLOTRAN-E4D. The PFLOTRAN-E4D module operates in parallel using a dedicated set of compute cores in a master-slave configuration. At each time step, the master processes receives subsurface states from PFLOTRAN, converts those states to bulk electrical conductivity, and instructs the slave processes to simulate a tomographic data set. The resulting multi-physics simulation capability enables accurate feasibility studies for ERT imaging, the identification of the ERT signatures that are unique to a given process, and facilitates the joint inversion of ERT data with hydrogeological data for subsurface characterization. PFLOTRAN-E4D is demonstrated herein using a field study of stage-driven groundwater/river water interaction ERT monitoring along the Columbia River, Washington, USA. Results demonstrate the complex nature of changes subsurface electrical conductivity, in both the saturated and unsaturated zones, arising from water table changes and from river water intrusion into the aquifer. The results also demonstrate the sensitivity of surface based ERT measurements to those changes over time. PFLOTRAN-E4D is available with the PFLOTRAN development version with an open-source license at https://bitbucket.org/pflotran/pflotran-dev .

Environmental Molecular Sciences Laboratory↗
DOE OSTI2019

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness

The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems, especially when the size of the training data is limited. To address these diculties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components of the random inputs and the analytical form. The present Data-driven Sparsity-enhancing Rotation for Arbitrary Randomness (DSRAR) framework includes a data-driven construction of multivariate polynomial basis for arbitrary mutually dependent probability measure and a sparsity enhancement rotation procedure. This sparsity-enhancing rotation method was initially proposed in our previous work [1] for Gaussian density distributions, which may not be feasible for non-Gaussian distributions due to the loss of orthogonality after the rotation. To remedy such diculties, we developed a new data-driven approach to construct orthonormal polynomials for polynomials for arbitrary mutually dependent (amdP) randomness, ensuring the constructed basis maintains the orthogonality/near-orthogonality with respect to the density of the rotated random vector, where directly applying the regular polynomial chaos including arbitrary polynomial chaos (aPC) [2] shows limitations due to the assumption of the mutual independence between the components of the random inputs. The developed DSRAR framework leads to accurate recovery, with only limited training data, of a sparse representation of the target functions. The eectiveness of our method is demonstrated in challenging problems such as PDEs and realistic molecular systems within high-dimensional conformational space (O(10)) where the underlying density is implicitly represented by a large collection of sample data, as well as systems with explicitly given non-Gaussian probabilistic measures.

Lei, Huan↗
DOE OSTI2021

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure–property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure–property relationships from the literature. Finally, models trained on these data sets will improve as they incorporate community feedback.

36 MATERIALS SCIENCE↗
DOE OSTI2015

A generalized method for high throughput in-situ experiment data analysis: An example of battery materials exploration

A generalized method is introduced to extract critical information from series of ranked correlated data. The method is generally applicable to all types of spectra evolving as a function of any arbitrary parameter. This approach is based on correlation functions and statistical scedasticity formalism. Numerous challenges in analyzing high throughput experimental data can be tackled using the herein proposed method. We applied this method to understand the reactivity pathway and formation mechanism of a Li-ion battery cathode material during high temperature synthesis using in-situ highenergy X-ray diffraction. We demonstrate that Pearson's correlation function can easily unravel all major phase transition and, more importantly, the minor structural changes which cannot be revealed by conventionally inspecting the series of diffraction patterns. Furthermore, a two-dimensional (2D) reactivity pattern calculated as the scedasticity along all measured reciprocal space of all successive diffraction pattern pairs unveils clearly the structural evolution path and the active areas of interest during the synthesis. The methods described here can be readily used for on-the-fly data analysis during various in-situ operando experiments in order to quickly evaluate and optimize experimental conditions, as well as for post data analysis and large data mining where considerable amount of data hinders the feasibility of the investigation through point-by-point inspection.

25 ENERGY STORAGE↗
DOE OSTI2018

Nuclear Data Sheets for A=170☆

Nuclear structure data pertaining to all nuclei with mass number A=170 have been evaluated, and incorporated into the ENSDF data file. This evaluation supersedes the previous evaluation (Coral M. Baglin), Nuclear Data Sheets 96, 611 (2002), (cutoff date 15 August 2002) and subsequent updates of the ENSDF file for Yb to Dy (cutoff 15 April 2010), Lu (cutoff 8 October 1998), Pt (cutoff 17 February 1999) and Ta, Os (cutoff 7 April 1998). Since the previous evaluation, {sup 170}Gd has been observed and the ground state T{sub 1/2} measured. New excited states have also been observed in the neutron rich {sup 170}Dy. The ground state T{sub 1/2} of {sup 170}Tb has been determined, however, there are currently still no excited states observed in this nucleus. There remains some uncertainty in the ground state and longer-lived isomer in {sup 170}Ir which could warrent further experimental investigation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

G-mode magnetic force microscopy: Separating magnetic and electrostatic interactions using big data analytics

In this work, we develop a full information capture approach for Magnetic Force Microscopy (MFM), referred to as generalized mode (G-Mode) MFM. G-Mode MFM acquires and stores the full data stream from the photodetector, captured at sampling rates approaching the intrinsic photodiode limit. The data can be subsequently compressed, denoised, and analyzed, without information loss. Here, G-Mode MFM is implemented and compared to the traditional heterodyne-based MFM on model systems, including domain structures in ferromagnetic Yttrium Iron Garnet and the electronically and magnetically inhomogeneous high entropy alloy, CoFeMnNiSn. We investigate the use of information theory to mine the G-Mode MFM data and demonstrate its usefulness for extracting information which may be hidden in traditional MFM modes, including signatures of nonlinearities and mode-coupling phenomena. Finally, we demonstrate detection and separation of magnetic and electrostatic tip-sample interactions from a single G-Mode image, by analyzing the entire frequency response of the cantilever. G-Mode MFM is immediately implementable on any atomic force microscopy platform and as such is expected to be a useful technique for probing spatiotemporal cantilever dynamics and mapping material properties, as well as their mutual interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Automated defect detection in spent nuclear fuel using combine Cerenkov and Gamma Emission Tomography data

Spent fuel monitoring and characterization has been a pillar of safeguards and nuclear facility monitoring for many years. The Digital Cerenkov Viewing Device (DCVD) has been used since the 1980s as a method of defect detection in spent fuel. In recent years, the accounting of large quantities of spent fuel before storage has renewed interest in this relatively quick and inexpensive method. This has an impact not only in safeguards, but also in for nuclear power facilities, as accounting can be a long and arduous process. Additionally, the DCVD demonstrates limited accuracy in more complex cases such as substitution of a fuel rod with steel, or a partial defect detection. A second method, Gamma Emission Tomography (GET) has been explored as an improved defect detection method, but is much more expensive and invasive than DCVD. This work identifies deficiencies in both methods, and proposes a combination of data gathered from each method to address these deficiencies for improved spent fuel characterization. Initial results are promising, showing 97% detection of a single missing fuel rod when the data types are combined, versus approximately 50% and 70%respectively for DCVD and GET data on their own. Finally, these classification results are obtained with algorithms derived from facial recognition and applied to this problem, yielding unique accuracy in near real-time while also maintaining the information barrier between output and measurement desired in safeguards.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Automated Resonance Fitting for Nuclear Data Evaluation

Global and national efforts to deliver high-quality nuclear data to users have a wide-ranging impact, affecting applications in national security, reactor operations, basic science, medicine, and more. Cross section evaluation is a major part of this effort, combining theory and experimentation to produce recommended values and uncertainties for reaction probabilities. Resonance region evaluation is a specialized type of nuclear data evaluation that can require significant manual effort and months of time from expert scientists. In this article, non-convex non-linear optimization methods are combined with concepts of inferential statistics to infer a resonance model from experimental data in an automated manner that is not dependent on prior evaluation(s). This methodology aims to enhance the workflow of a resonance evaluator by minimizing time, effort, and the potential for bias from prior assumptions, while enhancing reproducibility and documentation, thereby addressing well-known challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

A Parallel EM Algorithm for Model-Based Clustering Applied to the Exploration of Large Spatio-Temporal Data

We develop a parallel EM algorithm for multivariate Gaussian mixture models and use it to perform model-based clustering of a large climate data set. Three variants of the EM algorithm are reformulated in parallel and a new variant that is faster is presented. All are implemented using the single program, multiple data (SPMD) programming model, which is able to take advantage of the combined collective memory of large distributed computer architectures to process larger data sets. Displays of the estimated mixture model rather than the data allow us to explore multivariate relationships in a way that scales to arbitrary size data. We study the performance of our methodology on simulated data and apply our methodology to a high resolution climate dataset produced by the community atmosphere model (CAM5). This article has supplementary material online.

Chen, Wei-Chen↗
DOE OSTI2017

Boosted dark matter and its implications for the features in IceCube HESE data

Here we study the implications of the premise that any new, relativistic, highly energetic neutral particle that interacts with quarks and gluons would create cascade-like events in the IceCube (IC) detector. Such events would be observationally indistinguishable from neutral current deep-inelastic (DIS) scattering events due to neutrinos. Consequently, one reason for deviations, breaks or excesses in the expected astrophysical power-law neutrino spectrum could be the flux of such a particle. Motivated by features in the recent 1347-day IceCube high energy starting event (HESE) data, we focus on particular boosted dark matter (χ) related realizations of this premise. Here, χ is assumed to be much lighter than, and the result of, the slow decay of a massive scalar (φ ) which constitutes a major fraction of the Universe's dark matter (DM) . We show that this hypothesis, coupled with a standard power-law astrophysical neutrino flux is capable of providing very good fits to the present data, along with a possible explanation of other features in the HESE sample. These features include a) the paucity of events beyond ~ 2 PeV b) a spectral feature resembling a dip or a spectral change in the 400 TeV–1 PeV region and c) an excess in the 50-100 TeV region. We consider two different boosted DM scenarios, and determine the allowed mass ranges and couplings for four different types of mediators (scalar, pseudoscalar, vector and axial-vector) which could connect the standard and dark sectors. We consider constraints from gamma-ray observations and collider searches. We find that the gamma-ray observations provide the most restrictive constraints, disfavouring the 1σ allowed parameter space from IC fits, while still being consistent with the 3σ allowed region. We also test our proposal and its implications against the (statistically independent) sample of six year through-going muon track data recently released by IceCube.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

The electronics, trigger and data acquisition system for the liquid argon time projection chamber of the DarkSide-50 search for dark matter

The DarkSide-50 experiment at the Laboratori Nazionali del Gran Sasso is a search for dark matter using a dual phase time projection chamber with 50 kg of low radioactivity argon as target. Light signals from interactions in the argon are detected by a system of 38 photo-multiplier tubes (PMTs), 19 above and 19 below the TPC volume inside the argon cryostat. We describe the electronics which processes the signals from the photo-multipliers, the trigger system which identifies events of interest, and the data-acquisition system which records the data for further analysis. The electronics include resistive voltage dividers on the PMTs, custom pre-amplifiers mounted directly on the PMT voltage dividers in the liquid argon, and custom amplifier/discriminators (at room temperature). After amplification, the PMT signals are digitized in CAEN waveform digitizers, and CAEN logic modules are used to construct the trigger, the data acquisition system for the TPC is based on the Fermilab "artdaq" software. The system has been in operation since early 2014.

Agnes, P.↗