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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 217 records · Page 12

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Performance Optimization for Relative-Error-Bounded Lossy Compression on Scientific Data

Scientific simulations in high-performance computing (HPC) environments generate vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for postanalysis. Unlike traditional data reduction schemes such as deduplication or lossless compression, not only can error-controlled lossy compression significantly reduce the data size but it also holds the promise to satisfy user demand on error control. Pointwise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications with lossy compression since error control can adapt to the error bound in the dataset automatically. Pointwise relative-error-bounded compression is complicated and time consuming. In this article, we develop efficient precomputation-based mechanisms based on the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error-bounded compression with excellent compression ratios. In addition, we reduce traversing operations for Huffman decoding, significantly accelerating the decompression process in SZ. Experiments with eight well-known real-world scientific simulation datasets show that our solution can improve the compression and decompression rates (i.e., the speed) by about 40 and 80 p, respectively, in most of cases, making our designed lossy compression strategy the best-in-class solution in most cases.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS2015

Flexible Micropost Arrays for Shear Stress Measurement

Increased fuel costs, heightened environmental protection requirements, and noise abatement continue to place drag reduction at the forefront of aerospace research priorities. Unfortunately, shortfalls still exist in the fundamental understanding of boundary-layer airflow over aerodynamic surfaces, especially regarding drag arising from skin friction. For example, there is insufficient availability of instrumentation to adequately characterize complex flows with strong pressure gradients, heat transfer, wall mass flux, three-dimensionality, separation, shock waves, and transient phenomena. One example is the acoustic liner efficacy on aircraft engine nacelle walls. Active measurement of shear stress in boundary layer airflow would enable a better understanding of how aircraft structure and flight dynamics affect skin friction. Current shear stress measurement techniques suffer from reliability, complexity, and airflow disruption, thereby compromising resultant shear stress data. The state-of-the-art for shear stress sensing uses indirect or direct measurement techniques. Indirect measurements (e.g., hot-wire, heat flux gages, oil interferometry, laser Doppler anemometry, small scale pressure drag surfaces, i.e., fences) require intricate knowledge of the studied flow, restrictive instrument arrangements, large surface areas, flow disruption, or seeding material; with smaller, higher bandwidth probes under development. Direct measurements involve strain displacement of a sensor element and require no prior knowledge of the flow. Unfortunately, conventional "floating" recessed components for direct measurements are mm to cm in size. Whispering gallery mode devices and Fiber Bragg Gratings are examples of recent additions to this type of sensor with much smaller (m) sensor components. Direct detection techniques are often single point measurements and difficult to calibrate and implement in wind tunnel experiments. In addition, the wiring, packaging, and installation of delicate micro-electromechanical devices impede the use of most direct shear sensors. Similarly, the cavity required for sensing element displacement is sensitive to particulate obstruction. This work was focused on developing a shear stress sensor for use in subsonic wind tunnel test facilities applicable to an array of test configurations. The non-displacement shear sensors described here have minimal packaging requirements resulting in minimal or no disturbance of boundary layer flow. Compared to previous concepts, device installation could be simple with reduced cost and down-time. The novelty lies in the creation of low profile (nanoscale to 100 μm) micropost arrays that stay within the viscous sub-layer of the airflow. Aerodynamic forces, which are related to the surface shear stress, cause post deflection and optical property changes. Ultimately, a reliable, accurate shear stress sensor that does not disrupt the airflow has the potential to provide high value data for flow physics researchers, aerodynamicists, and aircraft manufacturers leading to greater flight efficiency arising from more in-depth knowledge on how aircraft design impacts near surface properties.

Wohl, Christopher J.↗

DOE OSTI2015

Quantum theory as a description of robust experiments: Derivation of the Pauli equation

It is shown that the Pauli equation and the concept of spin naturally emerge from logical inference applied to experiments on a charged particle under the conditions that (i) space is homogeneous (ii) the observed events are logically independent, and (iii) the observed frequency distributions are robust with respect to small changes in the conditions under which the experiment is carried out. The derivation does not take recourse to concepts of quantum theory and is based on the same principles which have already been shown to lead to e.g. the Schrödinger equation and the probability distributions of pairs of particles in the singlet or triplet state. Application to Stern–Gerlach experiments with chargeless, magnetic particles, provides additional support for the thesis that quantum theory follows from logical inference applied to a well-defined class of experiments. - Highlights: • The Pauli equation is obtained through logical inference applied to robust experiments on a charged particle. • The concept of spin appears as an inference resulting from the treatment of two-valued data. • The same reasoning yields the quantum theoretical description of neutral magnetic particles. • Logical inference provides a framework to establish a bridge between objective knowledge gathered through experiments and their description in terms of concepts.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Benchmarking sensor fusion capabilities of an integrated instrumentation package

Quantifying and mitigating environmental risks introduced by marine energy conversion systems requires a variety of sensors (active acoustic, passive acoustic, and optical). The operation of these sensors must satisfy three directives to be effective: (1) do not alter the environment through operation of sensors; (2) capture rare events; and (3) do not accrue unmanageable volumes of low-value data. This requires integrating sensors into a single package, rather than operating them independently. The Adaptable Monitoring Package is an integrated instrumentation package that combines a multibeam sonar, acoustic camera, current profiler, optical cameras, and an array of hydrophones. The capabilities and limitations of the AMP sensors were benchmarked using cooperative targets, and real-time target tracking and detection was used to detect opportunistic targets (e.g., diving birds, seals). During an initial deployment, automatic detection of opportunistic targets achieved a 58% true positive rate and a 99% true negative rate (100% corresponding to an ideal system in both cases). In post-processing, target tracking data were used to evaluate automatic target classification capabilities using a k-nearest neighbor algorithm. Findings suggest that real-time target classification should be possible and enable integrated instrumentation systems to meet the monitoring needs of marine energy deployments.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Systematic Generation of Moment Invariant Bases for 2D and 3D Tensor Fields

Moment invariants have been successfully applied to pattern detection tasks in 2D and 3D scalar, vector, and matrix valued data. However so far no flexible basis of invariants exists, i.e., no set that is optimal in the sense that it is complete and independent for every input pattern. Here, we prove that a basis of moment invariants can be generated that consists of tensor contractions of not more than two different moment tensors each under the conjecture of the set of all possible tensor contractions to be complete. This result allows us to derive the first generator algorithm that produces flexible bases of moment invariants with respect to orthogonal transformations by selecting a single non-zero moment to pair with all others in these two-factor products. Since at least one non-zero moment can be found in every non-zero pattern, this approach always generates a complete set of descriptors.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Active Thermochemical Tables: The Adiabatic Ionization Energy of Hydrogen Peroxide

The adiabatic ionization energy of hydrogen peroxide (HOOH) is investigated, both by means of theoretical calculations and theoretically-assisted reanalysis of previous experimental data. Values obtained by three different approaches: 10.638 ± 0.012 eV (purely theoretical determination), 10.649 ± 0.005 eV (reanalysis of photoelectron spectrum) and 10.645 ± 0.010 eV (reanalysis of photoionization spectrum) are in excellent mutual agreement. Further refinement of the latter two values to account for asymmetry of the rotational profile of the photoionization origin band leads to a reduction of 0.007 ± 0.006 eV, which tends to bring them into even closer alignment with the purely theoretical value. As a result, detailed analysis of this fundamental quantity by the Active Thermochemical Tables (ATcT) approach, using the present results and extant literature, gives a final estimate of 10.641 ± 0.006 eV.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Search for a Low-Mass Neutral Higgs Boson with Suppressed Couplings to Fermions Using Events with Multiphoton Final States

A search for a Higgs boson with suppressed couplings to fermions, $h_f$, assumed to be the neutral, lower-mass partner of the Higgs boson discovered at the Large Hadron Collider, is reported. Such a Higgs boson could exist in extensions of the standard model with two Higgs doublets, and could be produced via $p\bar{p} \to H^\pm h_f \to W^* h_f h_f \to 4\gamma + X$, where $H^\pm$ is a charged Higgs boson. This analysis uses all events with at least three photons in the final state from proton-antiproton collisions at a center-of-mass energy of 1.96~TeV collected by the Collider Detector at Fermilab, corresponding to an integrated luminosity of 9.2~${\rm fb}^{-1}$. No evidence of a signal is observed in the data. Values of Higgs-boson masses between 10 and 100 GeV/$c^2$ are excluded at 95\% Bayesian credibility.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series

In this paper, we introduce Masked Anomaly Detection (MAD), a general self-supervised learning task for multivariate time series anomaly detection. With the increasing availability of sensor data from industrial systems, being able to detecting anomalies from streams of multivariate time series data is of significant importance. Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned normal representations can empower anomaly detection as the model has learned to capture certain key underlying data regularities. A typical formulation is to learn a predictive model, i.e., use a window of time series data to predict future data values. In this paper, we propose an alternative self-supervised learning task. By randomly masking a portion of the inputs and training a model to estimate them using the remaining ones, MAD is an improvement over the traditional left-to-right next step prediction (NSP) task. Our experimental results demonstrate that MAD can achieve better anomaly detection rates over traditional NSP approaches when using exactly the same neural network (NN) base models, and can be modified to run as fast as NSP models during test time on the same hardware, thus making it an ideal upgrade for many existing NSP-based NN anomaly detection models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Elliptically-Contoured Tensor-variate Distributions with Application to Image Learning

Statistical analysis of tensor-valued data has largely used the tensor-variate normal (TVN) distribution that may be inadequate for data arising from distributions with heavier or lighter tails. We study a general family of elliptically contoured (EC) TV distributions and derive its characterizations, moments, marginal, and conditional distributions. We describe procedures for maximum likelihood estimation from data that are (1) uncorrelated draws from an EC distribution, (2) from a scale mixture of the TVN distribution, and (3) from an underlying but unknown EC distribution, for which we extend Tyler’s robust estimator. A detailed simulation study highlights the benefits of choosing an EC distribution over the TVN for heavier-tailed data. We develop TV classification rules using discriminant analysis and EC errors and show that they better predict cats and dogs from images in the Animal Faces-HQ dataset than the TVN-based rules. A novel tensor-on-tensor regression and TV analysis of variance (TANOVA) framework under EC errors is also demonstrated to better characterize gender, age, and ethnic origin than the usual TVN-based TANOVA in the celebrated labeled faces of the wild dataset.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

KVTree

KVTree is a hierarchical, tree-structured, key-value data structure library that can be used and re-used by other tools.

Moody, Adam↗

DOE OSTI2016

Parameter Estimator for Engineering Systems

This software model generates vehicle parameter sets for downstream modeling applications using real-world data. Value to users is provided through improvements to modeling activities as a result of decreases in time taken during model development and validation. Currently configured for transportation modeling activities, the software provides initial estimates for multiple fundamental vehicle parameters including mass, coefficient of drag, and tire rolling resistance using real-world drive and duty cycle information as inputs.

Source record↗

DOE OSTI2016

PEES (Parameter Estimator for Engineering Systems) [SWR-16-38]

Miller, Eric↗

DOE OSTI2023

Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics

Here, in this paper, we consider the problem of learning a regression function without assuming its functional form. This problem is referred to as symbolic regression. An expression tree is typically used to represent a solution function, which is determined by assigning operators and operands to the nodes. Cozad and Sahinidis propose a nonconvex mixed-integer nonlinear program (MINLP), in which binary variables are used to assign operators and nonlinear expressions are used to propagate data values through nonlinear operators, such as square, square root, and exponential. We extend this formulation by adding new cuts that improve the solution of this challenging MINLP. We also propose a heuristic that iteratively builds an expression tree by solving a restricted MINLP. We perform computational experiments and compare our approach with a mixed-integer program–based method and a neural network–based method from the literature.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

FACE-MDS Phase 2: Meteorological Data and Protocols

This dataset contains meteorological data, site parameterization data, and protocols for simulation of the five Free Air CO2 Enrichment (FACE) experiments and one Open Top Chamber (OTC) experiment that were the study sites of the FACE Model Data Synthesis (FACE-MDS) project. This dataset should contain everything necessary to simulate these six CO2 enrichment experiments -- Duke, Kennedy Space Center, NDFF, ORNL, PHACE, and Rhinelander -- following the FACE-MDS simulation protocol. Data cover 1998-2008 (1998-01-01 to 2008-12-31). File naming convention can be found in the file 'facemds-file-table-metdata.xml' and information about variable naming convention can be found in the file 'facemds-standard-name-table-metvars.xml'. Where possible, Assistance for the Land-surface Modelling Activities (ALMA) v3 convention for meteorological variables naming ontology was followed. Additional info can be found in the user guide, protocol documents, and readmes in this dataset. The original naming convention mentioned in the protocol was replaced with the one described in 'facemds-standard-name-table-modvars.xml' in the accompanying dataset (doi:10.15485/1480327). The dataset contains 21 data files that are available in comma-separate (*.csv), plain text (*.txt), and NetCDF (*.nc) format. Nine companion files and additional metadata are provided: three data dictionaries and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format. This dataset was updated on 2026-06-02 to add a version of the data files in comma separate (.csv) format, a user guide, data dictionary files, and a file-level metadata file. No previously released data values were changed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Altamaha River in Georgia, USA

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of tidally induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Altamaha River, GA, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in October 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour. This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. Note that the included hydrograph has a separate DOI that should also be cited. There is no obligation to include WHONDRS members as co-authors. Please note that this data package was originally published in November 2019. It was updated in July 2020. See the readme file in the data package for details. None of the data values were altered. The units were updated in one column of the main metadata file and in one of the methods codes descriptions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Short-Duration Simulations from Measurements.

A method is presented that ascribes proper statistical variability to simulations that are derived from longer-duration measurements. This method is applicable to simulations of either real-value or integer-value data. An example is presented that demonstrates the applicability of this technique to the synthesis of gamma-ray spectra.

Mitchell, Dean J.↗

DOE OSTI2017

Software Comparison for Renewable Energy Deployment in a Distribution Network

The main objective of this report is to evaluate different software options for performing robust distributed generation (DG) power system modeling. The features and capabilities of four simulation tools, OpenDSS, GridLAB-D, CYMDIST, and PowerWorld Simulator, are compared to analyze their effectiveness in analyzing distribution networks with DG. OpenDSS and GridLAB-D, two open source software, have the capability to simulate networks with fluctuating data values. These packages allow the running of a simulation each time instant by iterating only the main script file. CYMDIST, a commercial software, allows for time-series simulation to study variations on network controls. PowerWorld Simulator, another commercial tool, has a batch mode simulation function through the 'Time Step Simulation' tool, which obtains solutions for a list of specified time points. PowerWorld Simulator is intended for analysis of transmission-level systems, while the other three are designed for distribution systems. CYMDIST and PowerWorld Simulator feature easy-to-use graphical user interfaces (GUIs). OpenDSS and GridLAB-D, on the other hand, are based on command-line programs, which increase the time necessary to become familiar with the software packages.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

Gas Migration Project: Risk Assessment Tool and Computational Analyses to Investigate Wellbore/Mine Interactions, Secretary's Potash Area, Southeastern New Mexico

The Bureau of Land Management (BLM), US Department of the Interior has asked Sandia National Laboratories (SNL) to perform scientific studies relevant to technical issues that arise in the development of co-located resources of potash and petroleum in southeastern New Mexico in the Secretary’s Potash Area. The BLM manages resource development, issues permits and interacts with the State of New Mexico in the process of developing regulations, in an environment where many issues are disputed by industry stakeholders. The present report is a deliverable of the study of the potential for gas migration from a wellbore to a mine opening in the event of wellbore leakage, a risk scenario about which there is disagreement among stakeholders and little previous site specific analysis. One goal of this study was to develop a framework that required collaboratively developed inputs and analytical approaches in order to encourage stakeholder participation and to employ ranges of data values and scenarios. SNL presents here a description of a basic risk assessment (RA) framework that will fulfill the initial steps of meeting that goal. SNL used the gas migration problem to set up example conceptual models, parameter sets and computer models and as a foundation for future development of RA to support BLM resource development.

02 PETROLEUM↗