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At least 217 records · Page 12

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2016

Multi-omic data integration enables discovery of hidden biological regularities

Rapid growth in size and complexity of biological data sets has led to the ‘Big Data to Knowledge’ challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome-scale models, based on genomic and bibliomic data, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can be formally represented in a computable format allowing for coherent interpretation and prediction of fitness and selection that underlies cellular physiology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

LANL Deliverable to the Big Sky Carbon Sequestration Partnership: Preliminary CO 2 -PENS model

The preliminary sub-surface risk assessment for the Kevin Dome includes limited risk factors based on available data. The analysis is presented in five sections. In the first section, we review the Features, Events, and Processes (FEPs) applicable to the CO 2 -PENS model based on a larger set of FEPs developed by Schlumberger. The CO 2 -PENS applicable will be expanded in future work as site-specific data become available. The FEPs addressed in this report include 1) Injectivity and the number of injection wells needed for project success; 2) CO 2 plume extent that will feed future calculations of wellbore, fault, and caprock leakage risks; 3) Performance of production wells related to the projects ability to deliver enough CO 2 to meet project goals; and 4) Caprock leakage due to uncertainty in the caprock permeability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

The Bird project: Using Big Data tools to support Search Analytics

The Bird project explored the use of big data analytics tool to improve the findability of information within the Sandia internal network. We were able to perform query classification utilizing the supervised learning algorithms in the Apache Spark library. By relying on the distributed processing capabilities provided by the Apache Hadoop framework, we successfully processed the large query log files needed to train the models in this effort. The capabilities developed in this project are being used to enhance the effectiveness of the enterprise search engine.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

Accelerating Lossy and Lossless Compression on Emerging BlueField DPU Architectures

Data compression has become a crucial technique in addressing performance bottlenecks caused by increasing data volumes in High-Performance Computing (HPC), Big Data, and Deep Learning (DL). Despite its potential to boost system performance, recent studies have identified significant challenges with existing compression methods, mainly due to their high computational demands amidst continuously growing data sizes. Concurrently, the advent of Data Processing Units (DPUs), equipped with programmable System-on-Chip (SoC) and specialized compression accelerators, offers a promising opportunity to alter the landscape of data compression. This paper explores the complexities and potential of leveraging NVIDIA BlueField DPUs to accelerate lossy and lossless compression. Towards this, we introduce PEDAL, an innovative library that leverages the hardware capabilities of DPUs to unify and optimize data compression designs. Moreover, we seamlessly co-design PEDAL with the popular MPICH MPI library, demonstrating up to 101x speedup in compression time and 88x decrease in communication latency. Drawing on these achievements, we share our experience with various research communities about accelerating data compression on DPUs in communication-oriented HPC scenarios.

Li, Yuke↗

DOE OSTI2019

Optoelectronic Properties of C60 and C70 Fullerene Derivatives: Designing and Evaluating Novel Candidates for Efficient P3HT Polymer Solar Cells

Ten novel fullerene-derivatives (FDs) of C60 and C70 had been designed as acceptor for polymer solar cell (PSC) by employing the quantitative structure-property relationship (QSPR) model, which was developed strategically with a reasonably big pool of experimental power conversion efficiency (PCE) data. The QSPR model was checked and validated with stringent parameter and reliability of predicted PCE values of all designed FDs. They were assessed by the applicability domain (AD) and process randomization test. The predicted PCE of FDs range from 7.96 to 23.01. The obtained encouraging results led us to the additional theoretical analysis of the energetics and UV-Vis spectra of isolated dyes employing Density functional theory (DFT) and Time-dependent-DFT (TD-DFT) calculations using PBE/6-31G(d,p) and CAM-B3LYP/6-311G(d,p) level calculations, respectively. The FD4 is the best C60-derivatives candidates for PSCs as it has the lowest exciton binding energy, up-shifted lowest unoccupied molecular orbital (LUMO) energy level to increase open-circuit voltage (VOC) and strong absorption in the UV region. In case of C70-derivatives, FD7 is potential candidate for future PSCs due to its strong absorption in UV-Vis region and lower exciton binding energy with higher VOC. Our optoelectronic results strongly support the developed QSPR model equation. Analyzing QSPR model and optoelectronic parameters, we concluded that the FD1, FD2, FD4, and FD10 are the most potential candidates for acceptor fragment of fullerene-based PSC. The outcomes of tactical molecular design followed by the investigation of optoelectronic features are suggested to be employed as a significant resource for the synthesis of FDs as an acceptor of PSCs.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma [Spatio-Temporal Features in Large Irregular Data: Blob-Filaments in Fusion Plasma]

This work was originally motivated the need to identify and track blob filaments, a feature often associated with instability in magnetically confined fusion plasma. Understanding and mitigating such instability would improve fusion reactors and make fusion a truly inexhaustible source of clean energy. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blobs in fusion plasma. On a set of 4.3TB data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

Big Hill 2018 InSAR Analysis U.S. Strategic Petroleum Reserve

The historical subsidence surveys shot over the U.S. Strategic Petroleum Reserve Big Hill site, located in southeastern Texas, have indicated surface uplift since 2002. In order to better understand and substantiate the surface behavior inferred from annual elevation measurements, InSAR (interferometric synthetic aperture radar) data was acquired. InSAR involves the processing of multiple satellite synthetic aperture radar scenes acquired across the same location of the Earth's surface at different times to map surface deformation. The analysis of the data can detect millimeters of motion spanning days, months, year and decades, across specific sites. The InSAR analysis indicates the fastest subsidence rates are over the north central region of the site, specifically centered over caverns 104 and 103. Subsidence rates decrease towards both the west and east, with the western side subsiding at greater rate than the eastern edge. There is some uplift noted, off the site and off the dome to the east. Overall, the subsidence pattern is in line with subsidence behavior expected over a cavern field. In investigating the validity of the uplift measured during the ground surveys it was discovered that reference location can impact results. An exercise was conducted that took the current InSAR data and presented two varying results dependent on the reference location, either on or off the dome. The conclusion was that if the reference is located on the dome, as it has been for years for the ground surveys, the reference location is moving too, giving the appearance of uplift.

02 PETROLEUM↗

DOE OSTI2021

FOA 1861 Data Curation Overview

This document describes the process executed to collect, examine, and consolidate Phasor Measurement Unit (PMU) data from multiple transmission operators into a common dataset. The consolidated PMU data set was further anonymized and distributed to the Department of Energy Funding Opportunity Announcement (FOA) 1861 Big Data Analysis of Synchrophasor Data awardees.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2013

Feature Tracking and Visualization of Madden-Julian Osciallation in Climate Simulation

Madden-Julian Oscillation (MJO) is one of the less understood aspects of tropical meteorology, which plays a significant role in tropical intra-seasonal variations in rain, temperature and winds over the Indian and Pacific Oceans. In this paper, we present an integrated analysis and visualization framework for MJO episodes simulated by a high resolution regional model. To distinguish MJOs from other weather phenomena, our framework utilizes domain knowledge to track MJOs as finding the globally optimized properties in the data. In addition to enhancing the animation with feature tracking, our visualization system also integrates different visualization components such as Virtual Globe and Hovmoller Diagrams to visualize large scale events both in space and time. By linking all of these visualization components on a web-based interface, scientists can identify cloud and environmental processes associated with the initiation and eastward propagation of MJO more easily.

big-data visualization↗

DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗

DOE OSTI2014

Occupancy schedules learning process through a data mining framework

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10 minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

What can the CMB tell about the microphysics of cosmic reheating?

In inflationary cosmology, cosmic reheating after inflation sets the initial conditions for the hot big bang. We investigate how CMB data can be used to study the effective potential and couplings of the inflaton during reheating to constrain the underlying microphysics. If there is a phase of preheating that is driven by a parametric resonance or other instability, then the thermal history and expansion history during the reheating era depend on a large number of microphysical parameters in a complicated way. In this case the connection between CMB observables and microphysical parameters can only established with intense numerical studies. Such studies can help to improve CMB constraints on the effective inflaton potential in specific models, but parameter degeneracies usually make it impossible to extract meaningful best-fit values for individual microphysical parameters. If, on the other hand, reheating is driven by perturbative processes, then it can be possible to constrain the inflaton couplings and the reheating temperature from CMB data. This provides an indirect probe of fundamental microphysical parameters that most likely can never be measured directly in the laboratory, but have an immense impact on the evolution of the cosmos by setting the stage for the hot big bang.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

Results and Lessons Learned From the DOE Commercial Building Partnerships: Preprint

Over the course of 5 years, NREL worked with commercial building owners and their design teams in the DOE Commercial Building Partnerships (CBP) to cut energy consumption by 50% in new construction (versus code) and by 30% in existing building pilot projects (versus code or pre-retrofit operational energy use depending on the preference of the Partner) using strategies that could be replicated across their building portfolios. A number of different building types were addressed, including supermarket, retail merchandise, combination big box (general merchandise and food sales), high rise office space, and warehouse. The projects began in pre-design and included a year of measurement data to evaluate performance against design expectations. Focused attention was required throughout the entire process to achieve a design with the potential to hit the energy performance target and to operate the resulting building to reach this potential. This paper will report quantitative results and cover both the technical and the human sides of CBP, including the elements that were required to succeed and where stumbling blocks were encountered. It will also address the impact of energy performance goals and intensive energy modeling on the design process innovations and best practices.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

Next Generation Workload Management System For Big Data on Heterogeneous Distributed Computing

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. ATLAS and ALICE are the largest collaborations ever assembled in the sciences and are at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, both experiments rely on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System (WMS) for managing the workflow for all data processing on hundreds of data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. The scale is demonstrated by the following numbers: PanDA manages O(10 2 ) sites, O(10 5 ) cores, O(10 8 ) jobs per year, O(10 3 ) users, and ATLAS data volume is O(10 17 ) bytes. In 2013 we started an ambitious program to expand PanDA to all available computing resources, including opportunistic use of commercial and academic clouds and Leadership Computing Facilities (LCF). The project titled 'Next Generation Workload Management and Analysis System for Big Data' (BigPanDA) is funded by DOE ASCR and HEP. Extending PanDA to clouds and LCF presents new challenges in managing heterogeneity and supporting workflow. The BigPanDA project is underway to setup and tailor PanDA at the Oak Ridge Leadership Computing Facility (OLCF) and at the National Research Center "Kurchatov Institute" together with ALICE distributed computing and ORNL computing professionals. Our approach to integration of HPC platforms at the OLCF and elsewhere is to reuse, as much as possible, existing components of the PanDA system. Finally, we will present our current accomplishments with running the PanDA WMS at OLCF and other supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Where Is the Provenance? Ethical Replicability and Reproducibility in GIScience and Its Critical Applications

As replicability and reproducibility (R&R) crises develop within emerging convergent inquiry, ethical use of provenance information is central to the establishment and preservation of trust in critical applications of GIScience and geospatial technologies. Today large volumes of geospatial data are generated at high velocity from satellite sensors and unmanned aircraft systems, citizen sensors, geolocation-based data services, global navigation satellite systems, and so on. The extensive use of these data for applications such as disaster and humanitarian response raises the issue of R&R from competing perspectives of location privacy and geospatial data quality. Although geospatial data can be integrated and linked with contextual information to identify individuals’ movements, steps taken to ensure privacy can complicate the multiuser development of high-quality geospatial workflows. Provenance information as digital records of historical (retrospective) and potential future (prospective) geospatial processes is often overlooked, misunderstood, or inadequately addressed. We explore the relationship between provenance information, location privacy, and geospatial data quality in the context of R&R with a focus on disaster analytics. Here, we argue that in the era of big data and deep learning, GIScientists and associated institutions bear greater responsibility both for geospatial workflow quality and for location privacy. Given vastly heterogenous computational landscapes, we provide practical recommendations for ethically driven provenance and R&R research and development within the GIScience community and beyond.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗