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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Assessment of HRA method predictions against operating crew performance: Part II: Overall simulator data, HRA method predictions, and intra-method comparisons

This is the third in a series of four papers documenting two large-scale human reliability analysis (HRA) empirical studies – the International HRA Empirical Study and the US HRA Empirical Study. Here, the goal of the two studies was to develop an empirically-based understanding of the performance, strengths, and weaknesses of HRA methods by comparing HRA method predictions against actual operator performance in simulated accident scenarios on nuclear power plant (NPP) simulators. However, since in most cases only a single HRA team applied a given method in the International study, it was often difficult to separate analyst effects from variability in results related to the methods themselves. Since at least two HRA teams used each of the HRA methods in the US Study, intra-method comparisons were performed to identify method strengths and weaknesses independent of analyst specific effects where possible. This paper first summarizes the intra-method comparison results from the U.S. Study. Then, it discusses the reasons for the observed HRA predictive differences and the underlying methodological and guidance limitations that permitted the differences to arise. In the fourth paper, this information is combined wit h the results of the comparisons of method predictions to the actual crew data, from both the International and U.S. Studies, to develop the final conclusions about overall strengths and weaknesses of HRA methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity

Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a Buffer Modification Workflow (BMW), in which the same sample is run by LC-MS in both liquid chromatography solvent with 14 NH 3 -acetate buffer, and in solvent with the buffer modified with 15 NH 3 -formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. This relatively simple and convenient chromatography modification annotated yeast metabolomics data with similar effectiveness to growing the yeast in isotope-labeled media. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Altogether, it identified 26% of ~ 27,000 liver LC-MS features as putative metabolites, of which ~ 2600 showed HMDB or KEGG database formula match. This workflow is well-suited to biological samples that cannot be readily isotope labeled, including plants, mammalian tissues, and tumors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Metabolomic data streaming for biology-dependent data acquisition

During the last 10 years, metabolomics has emerged as a powerful technology to interrogate cellular biochemistry at the global level. Although much of the success has been driven by advances in mass spectrometry, developments in bioinformatic resources for data processing have been equally important. Additionally, the widely used metabolomic software XCMS, in particular, has undergone substantial improvements since its first introduction in 2005 (ref. 1). In addition to improved algorithms for peak picking, retention time alignment and data visualization, XCMS has transitioned from a command-line interface requiring expertise with the R programming language, to a web-based platform with a graphical user interface2. This web-based platform, which we call XCMS Online, enables thousands of users to upload their metabolomic data and perform cloud-based processing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

The evaluation of experimental data in fast range for n + 56 Fe(n,inl)

Iron is one of the five materials selected for evaluation within the pilot international evaluation project CIELO. Analysis of experimental data for n+ 56 Fe reaction is the basis for constraining theoretical calculations and eventual creation of the evaluated file. The detail analysis was performed for inelastic cross sections of neutron induced reactions with 56 Fe in the fast range up to 20 MeV where there are significant differences among the main evaluated libraries, mainly caused by the different inelastic scattering cross section measurements. Gamma-ray production cross sections provide a way to gain experimental information about the inelastic cross section. Large discrepancies between experimental data for the 847-keV gamma ray produced in the 56 Fe(n,n 1 'γ) reaction were analyzed. In addition, experimental data for elastic scattering cross section between 9.41~11 MeV were used to deduce the inelastic cross section from the unitarity constrain.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Extreme Compression for Large Scale Data Store

For the last 5 years Accelogic pioneered and perfected a radically new theory of numerical computing codenamed “Compressive Computing”, which has an extremely profound impact on real-world computer science [1]. At the core of this new theory is the discovery of one of its fundamental theorems which states that, under very general conditions, the vast majority (typically between 70% and 80%) of the bits used in modern large-scale numerical computations are absolutely irrelevant for the accuracy of the end result. This theory of Compressive Computing provides mechanisms able to identify (with high intelligence and surgical accuracy) the number of bits (i.e., the precision) that can be used to represent numbers without affecting the substance of the end results, as they are computed and vary in real time. The bottom line outcome would be to provide a state-of-the-art compression algorithm that surpasses those currently available in the ROOT framework, with the purpose of enabling substantial economic and operational gains (including speedup) for High Energy and Nuclear Physics data storage/analysis. In our initial studies, a factor of nearly x4 (3.9) compression was achieved with RHIC/STAR data where ROOT compression managed only x1.4.In this contribution, we will present our concepts of “functionally lossless compression”, have a glance at examples and achievements in other communities, present the results and outcome of our current, ongoing R&D, as well as present a high-level view of our plan to move forward with a ROOT implementation that would deliver a basic solution readily integrated into HENP applications. As a collaboration of experimental scientists, private industry, and the ROOT Team, our aim is to capitalize on the substantial success delivered by the initial effort and produce a robust technology properly packaged as an open-source tool that could be used by virtually every experiment around the world as means for improving data management and accessibility.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Validating Nuclear Data Uncertainties Obtained from a Statistical Analysis of Experimental Data with the “Physical Uncertainty Bounds” Method

Concerns within the nuclear data community led to substantial increases of Neutron Data Standards (NDS) uncertainties from its previous to the current version. For example, those associated with the NDS reference cross section 239 Pu(n,f) increased from 0.6–1.6% to 1.3–1.7% from 0.1–20 MeV. These cross sections, among others, were adopted, e.g., by ENDF/B-VII.1 (previous NDS) and ENDF/B-VIII.0 (current NDS). There has been a strong desire to be able to validate these increases based on objective criteria given their impact on our understanding of various application uncertainties. Here, the “Physical Uncertainty Bounds” method (PUBs) by Vaughan et al. is applied to validate evaluated uncertainties obtained by a statistical analysis of experimental data. We investigate with PUBs whether ENDF/B-VII.1 or ENDF/B-VIII.0 239 Pu(n,f) cross-section uncertainties are more realistic given the information content used for the actual evaluation. It is shown that the associated conservative (1.5–1.8%) and minimal realistic (1.1–1.3%) uncertainty bounds obtained by PUBs enclose ENDF/B-VIII.0 uncertainties and indicate that ENDF/B-VII.1 uncertainties are underestimated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Examining thermal transport through a frequency-domain representation of time-domain thermoreflectance data

Laser-based time-domain thermoreflectance (TDTR) and frequency-domain thermoreflectance (FDTR) techniques are widely used for investigating thermal transport at micro- and nano-scales. We demonstrate that data obtained in TDTR measurements can be represented in a frequency-domain form equivalent to FDTR, i.e., in the form of a surface temperature amplitude and phase response to time-harmonic heating. Such a representation is made possible by using a large TDTR delay time window covering the entire pulse repetition interval. We demonstrate the extraction of frequency-domain data up to 1 GHz from TDTR measurements on a sapphire sample coated with a thin layer of aluminum, and show that the frequency dependencies of both the amplitude and phase responses agree well with theory. The proposed method not only allows a direct comparison of TDTR and FDTR data, but also enables measurements at high frequencies currently not accessible to FDTR. The frequency-domain representation helps uncover aspects of the measurement physics which remain obscured in a traditional TDTR measurement, such as the importance of modeling the details of the heat transport in the metal transducer film for analyzing high frequency responses.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

An Examination of Proton Charge Radius Extractions from e–p Scattering Data

A detailed examination of issues associated with proton radius extractions from elastic electron–proton scattering experiments is presented. Sources of systematic uncertainty and model dependence in the extractions are discussed, with an emphasis on how these may impact the proton charge and magnetic radii. A comparison of recent Mainz data to previous world data is presented, highlighting the difference in treatment of systematic uncertainties as well as tension between different data sets. We find several issues that suggest that larger uncertainties than previously quoted may be appropriate, but do not find any corrections which would resolve the proton radius puzzle.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

PhenoCurve: capturing dynamic phenotype-environment relationships using phenomics data

MOTIVATION: Phenomics is significant for understanding the mechanisms that regulate or influence growth, fitness, and development. Techniques have been developed to conduct high-throughput large-scale phenotyping on animals, plants and humans, aiming to bridge the gap between genomics, gene functions and traits. Although new developments in phenotyping techniques are exciting, we are limited by the tools to analyze fully the massive phenotype data, especially the dynamic relationships between phenotypes and environments. RESULTS: We present a new algorithm called PhenoCurve, a knowledge-based curve fitting algorithm, aiming to identify the complex relationships between phenotypes and environments, thus studying both values and trends of phenomics data. The findings on both real and simulated data showed that PhenoCurve has the best performance among all the six tested methods. Its application to photosynthesis hysteresis pattern identification reveals new functions of core genes that control photosynthetic efficiency in response to varying environmental conditions, which are critical for understanding plant energy storage and improving crop productivity.

Yang, Yifan↗
DOE OSTI2018

A Radiation Weather Station: Development of a Continuous Monitoring System for the Collection, Analysis, and Display of Environmental Radiation Data

Due to heightened fear surrounding the possibility of future terrorism involving nuclear weapons and radiological dispersive devices, compounded by nonroutine nuclear power plant releases such as from emergencies or accidents, interest in contamination levels of environmental radiation has spiked. This project sought to develop a continuously operational radiation-monitoring system, with graphically visualized data easily accessible to the public. Because this continuing project is housed at a university facility, it bears no connection to perceived political or commercial interests, generally increasing the credibility of the endeavor. Outdoor weather and radiation parameters were gathered by sensors installed on the rooftop of a two-story building. A display and cloud service website was used to project the live data in an understandable format. A correlation was observed between weather and visibly heightened levels of gamma radiation. Furthermore, the goal of this paper is to share and highlight the overall hardware selection and the unique software challenges encountered when developing a robust collection and analysis system, along with the challenges of displaying meteorological and radiological data in a clear and concise fashion.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2012

Experimental study of the P₁₁(1440) and D₁₃(1520) resonances from the CLAS data on ep→e ' π⁺π⁻p '

The transition helicity amplitudes from the proton ground state to the P₁₁(1440) and D₁₃(1520) excited states (γ v pN* electrocouplings) were determined from the analysis of nine independent one-fold differential π⁺π⁻p electroproduction cross sections off a proton target, taken with CLAS at photon virtualities of 0.25 v pN* electrocouplings offers clear evidence for the reliable extraction of these fundamental quantities from meson-electroproduction data. This analysis also led to the determination of the long-awaited hadronic branching ratios for the D₁₃(1520) decay into Δπ (24%–32%) and Nρ (8%–17%).

Mokeev, V. I.↗
DOE OSTI2018

A View from ORNL: Scientific Data Research Opportunities in the Big Data Age

One of the core issues across computer and computational science today is adapting to, managing, and learning from the influx of "Big Data". In the commercial space, this problem has led to a huge investment in new technologies and capabilities that are well adapted to dealing with the sorts of human-generated logs, videos, texts, and other large-data artifacts that are processed and resulted in an explosion of useful platforms and languages (Hadoop, Spark, Pandas, etc.). However, translating this work from the enterprise space to the computational science and HPC community has proven somewhat difficult, in part because of some of the fundamental differences in type and scale of data and timescales surrounding its generation and use. We describe a forward-looking research and development plan which centers around the concept of making Input/Output (I/O) intelligent for users in the scientific community, whether they are accessing scalable storage or performing in situ workflow tasks. Much of our work is based on our experience with the Adaptable I/O System (ADIOS 1.X), and our next generation version of the software ADIOS 2.X [1].

Klasky, Scott↗
DOE OSTI2015

In-Memory Data Rearrangement for Irregular, Data-Intensive Computing

The data rearrangement engine (DRE) performs in-memory data restructuring to accelerate irregular, data-intensive applications. An emulation on a field-programmable gate array demonstrates how the DRE could improve speedup, memory bandwidth, and energy consumption on three representative benchmarks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A Bloom Filter Based Scalable Data Integrity Check Tool for Large-Scale Dataset

Large scale HPC applications are becoming increasingly data intensive. At Oak Ridge Leadership Computing Facility (OLCF), we are observing the number of files curated under individual project are reaching as high as 200 millions and project data size is exceeding petabytes. These simulation datasets, once validated, often needs to be transferred to archival system for long term storage or shared with the rest of the research community. Ensuring the data integrity of the full dataset at this scale is paramount important but also a daunting task. This is especially true considering that most conventional tools are serial and file-based, unwieldy to use and/or can't scale to meet user's demand.To tackle this particular challenge, this paper presents the design, implementation and evaluation of a scalable parallel checksumming tool, fsum, which we developed at OLCF. It is built upon the principle of parallel tree walk and work-stealing pattern to maximize parallelism and is capable of generating a single, consistent signature for the entire dataset at extreme scale. We also applied a novel bloom-filter based technique in aggregating signatures to overcome the signature ordering requirement. Given the probabilistic nature of bloom filter, we provided a detailed error and trade-off analysis. Using multiple datasets from production environment, we demonstrated that our tool can efficiently handle both very large files as well as many small-file based datasets. Our preliminary test showed that on the same hardware, it outperforms conventional tool by as much as 4×. It also exhibited near-linear scaling properties when provisioned with more compute resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications

With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpretability challenges have been proposed, they typically do not scale beyond thousands of samples, nor do they provide the high-level intuition scientists are looking for. Here, we present the first scalable solution to explore and analyze high-dimensional functions often encountered in the scientific data analysis pipeline. By combining a new streaming neighborhood graph construction, the corresponding topology computation, and a novel data aggregation scheme, namely topology aware datacubes, we enable interactive exploration of both the topological and the geometric aspect of high-dimensional data. Following two use cases from high-energy-density (HED) physics and computational biology, we demonstrate how these capabilities have led to crucial new insights in both applications.

97 MATHEMATICS AND COMPUTING↗