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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 OSTI2024

StOKeDMD: Streaming Occupation kernel dynamic mode decomposition

Dynamic mode decomposition (DMD) has become a common technique for constructing surrogate models for dynamical systems from observed system states. The Occupation Kernel DMD (OKDMD) method proposed in (Rosenfeld et al., 2022) and (Rosenfeld et al., 2024) is a Liouville operator based method that builds surrogate models from system state trajectories. Here, this paper proposes an extension of OKDMD to the case when the system states are observed in a streaming fashion, i.e., only a small fraction of the state trajectory is available at a given time. The developed method, Streaming Occupation Kernel DMD (StOKeDMD), accommodates the streaming data input by leveraging properties of specific choices of kernel functions and occupation kernels. We apply the StoKeDMD method as a compression method for streaming data, analyze the memory complexity, and demonstrate the performance of StoKeDMD in the compression of streaming data generated from a Lorenz system and a fluid flow simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

High Temperature Materials Interim Data Qualification Report FY 2011

Projects for the very high temperature reactor (VHTR) Technology Development Office provide data in support of Nuclear Regulatory Commission licensing of the VHTR. Fuel and materials to be used in the reactor are tested and characterized to quantify performance in high temperature and high fluence environments. The VHTR program has established the Next Generation Nuclear Plant (NGNP) Data Management and Analysis System (NDMAS) to ensure that VHTR data are qualified for use, stored in a readily accessible electronic form, and analyzed to extract useful results. This document focuses on the first NDMAS objective. It describes the High Temperature Materials characterization data stream, the processing of these data within NDMAS, and reports the interim fiscal year (FY) 2011 qualification status of the data. Data qualification activities within NDMAS for specific types of data are determined by the data qualification category assigned by the data generator. The High Temperature Materials data are being collected under the Nuclear Quality Assurance (NQA)-1 guidelines and will be qualified data. For NQA-1 qualified data, the qualification activities include: (1) capture testing to confirm that the data stored within NDMAS are identical to the raw data supplied, (2) accuracy testing to confirm that the data are an accurate representation of the system or object being measured, and (3) documenting that the data were collected under an NQA-1 or equivalent Quality Assurance program. Currently, data from seven test series within the High Temperature Materials data stream have been entered into the NDMAS vault, including tensile tests, creep tests, and cyclic tests. Of the 5,603,682 records currently in the vault, 4,480,444 have been capture passed, and capture testing is in process for the remaining 1,123,238.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Adverse Event Prediction Using Graph-Augmented Temporal Analysis (Final Report)

This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to develop a method for analyzing multiple time-series data streams to identify precursors providing advance warning of the potential occurrence of events of interest. The proposed approach combined temporal analysis of each data stream with reasoning about relationships between data streams using a geospatial-temporal semantic graph. This class of problems is relevant to several important topics of national interest. In the course of this work we developed new temporal analysis techniques, including temporal analysis using Markov Chain Monte Carlo techniques, temporal shift algorithms to refine forecasts, and a version of Ripley's K-function extended to support temporal precursor identification. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication submissions and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Adverse Event Prediction Using Graph-Augmented Temporal Analysis (Final Report)

This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to develop a method for analyzing multiple time-series data streams to identify precursors providing advance warning of the potential occurrence of events of interest. The proposed approach combined temporal analysis of each data stream with reasoning about relationships between data streams using a geospatial-temporal semantic graph. This class of problems is relevant to several important topics of national interest. In the course of this work we developed new temporal analysis techniques, including temporal analysis using Markov Chain Monte Carlo techniques, temporal shift algorithms to refine forecasts, and a version of Ripley's K-function extended to support temporal precursor identification. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication sub-missions and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication.

42 ENGINEERING↗
DOE OSTI2020

Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks, Frontiers in Earth Science: Dataset

This data package contains watershed modeling inputs and outputs as well as deep neural networks training and testing results used in "Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks" (Cromwell et al., 2021). We train various deep neural network models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models. For detailed information regarding watershed model description and DNNs setup, please refer to Cromwell et al., 2021. The deep_learning.zip file contains the inputs and outputs for the different deep neural networks. The watershed_model.zip file contains the inputs and outputs for watershed simulation using ATS. The figures.zip file contains raw figures and their corresponding scripts. For a more detailed description, please see the README.md file within each compressed file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Rain Gauges Handbook

To improve the quantitative description of precipitation processes in climate models, the Atmospheric Radiation Measurement (ARM) Climate Research Facility deployed rain gauges located near disdrometers (DISD and VDIS data streams). This handbook deals specifically with the rain gauges that make the observations for the RAIN data stream. Other precipitation observations are made by the surface meteorology instrument suite (i.e., MET data stream).

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

EJFAT Scientific Perspective

Presented new computing model to the test by deploying the EJFAT system alongside a data-stream processing framework running the production-level CLAS12 event reconstruction application. In this experiment, a continuous stream of CLAS12 Level-1 identified events was processed in real-time using the EJFAT load balancer, distributing the workload across 90 computing nodes located across the U.S. This marks the first-ever large-scale, real-time distributed data stream processing experiment, demonstrating that scientific data-streaming pipelines can efficiently scale across four dimensions, thanks to EJFAT’s advanced hardware and software capabilities.

Gyurjyan, Vardan [Thomas Jefferson National Accele↗
DOE OSTI2020

The influence of environmental microseismicity on detection and interpretation of small-magnitude events in a polar glacier setting

Glacial environments exhibit temporally variable microseismicity. To investigate how microseismicity influences event detection, we implement two noise-adaptive digital power detectors to process seismic data from Taylor Glacier, Antarctica. We add scaled icequake waveforms to the original data stream, run detectors on the hybrid data stream to estimate reliable detection magnitudes and compare analytical magnitudes predicted from an ice crack source model. We find that detection capability is influenced by environmental microseismicity for seismic events with source size comparable to thermal penetration depths. When event counts and minimum detectable event sizes change in the same direction (i.e. increase in event counts and minimum detectable event size), we interpret measured seismicity changes as ‘true’ seismicity changes rather than as changes in detection. Generally, one detector (two degree of freedom (2dof)) outperforms the other: it identifies more events, a more prominent summertime diurnal signal and maintains a higher detection capability. We conclude that real physical processes are responsible for the summertime diurnal inter-detector difference. One detector (3dof) identifies this process as environmental microseismicity; the other detector (2dof) identifies it as elevated waveform activity. Our analysis provides an example for minimizing detection biases and estimating source sizes when interpreting temporal seismicity patterns to better infer glacial seismogenic processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Towards Resilient Near Real-Time Analysis Workflows in Fusion Energy Science

Nuclear fusion holds the promise of an endless source of energy. Several research experiments across the world and joint modeling and simulation efforts between the nuclear physics and high performance computing communities are actively preparing the operation of the International Thermonuclear Experimental Reactor (ITER). Both experimental reactors and their simulated counterparts generate data that must be analyzed quickly and in a resilient way to support decision making for the configuration of subsequent runs or prevent a catastrophic failure. However, the cost if the traditional techniques used to improve the resilience of analysis workflows, i.e., replicating datasets and computational tasks, becomes prohibitive with explosion of the volume of data produced by modern instruments and simulations. Therefore, we advocate in this paper for an alternate approach based on data reduction and data streaming. The rationale is that by allowing for a reasonable, controlled, and guaranteed loss of accuracy it becomes possible to transfer smaller amounts of data, shorten the execution time of analysis workflows, and lower the cost of replication to increase resilience. We develop our research and development roadmap towards resilient near real-time analysis workflows in fusion energy science and present early results showing that data streaming and data reduction is a promising way to speed up the execution and improve the resilience of analysis workflows.

Suter, Fred↗
DOE OSTI2021

Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks

Subsurface permeability is a key parameter in watershed models that controls the contribution from the subsurface flow to stream flows. Since the permeability is difficult and expensive to measure directly at the spatial extent and resolution required by fully distributed watershed models, estimation through inverse modeling has had a long history in subsurface hydrology. The wide availability of stream surface flow data, compared to groundwater monitoring data, provides a new data source to infer soil and geologic properties using integrated surface and subsurface hydrologic models. As most of the existing methods have shown difficulty in dealing with highly nonlinear inverse problems, we explore the use of deep neural networks for inversion owing to their successes in mapping complex, highly nonlinear relationships. We train various deep neural network (DNN) models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

General Mode Scanning Probe Microscopy

A critical part of SPM measurements is the information transfer from the probe-sample junction to the measurement system. Current information transfer methods heavily compress the information-rich data stream by averaging the data over a time interval, or via heterodyne detection approaches such as lock-in amplifiers and phase-locked loops. As a consequence, highly valuable information at the sub-microsecond time scales or information from frequencies outside the measurement band is lost. We have developed a fundamentally new approach called General Mode (G-mode), where we can capture the complete information stream from the detectors in the microscope. The availability of the complete information allows the microscope operator to analyze the data via information-theory analysis or comprehensive physical models. Furthermore, the complete data stream enables advanced data-driven filtering algorithms, multi-resolution imaging, ultrafast spectroscropic imaging, spatial mapping of multidimensional variability in material properties, etc. Though we applied this approach to scanning probe microscopy, the general philosophy of G-mode can be applied to many other modes of microscopy. G-mode data is captured by completely custom software written in LabVIEW and Matlab. The software generates the waveforms to electrically, thermally, or mechanically excite the SPM probe. It handles real-time communications with the microscope software for operations such as moving the SPM probe position and also controls other instrumentation hardware. The software also controls multiple variants of high-speed data acquisition cards to excite the SPM probe with the excitation waveform and simultaneously measure multiple channels of information from the microscope detectors at sampling rates of 1-100 MHz. The software also saves the raw data to the computer and allows the microscope operator to visualize processed or filtered data during the experiment. The software performs all these features while offering a user-friendly interface.

Somnath, Suhas↗
DOE OSTI2023

Data Center Facility Monitoring with Physics Aware Approach

U.S. Department of Energy's National Renewable Energy Laboratory (NREL) hosts one of the world's most energy-efficient HPC data centers; this system uses component-level warm-water liquid cooling to efficiently remove heat from the data center and capture it for reuse in the building or rejection to the atmosphere. Given the complexity of this system, building data-driven tools for holistically monitoring and operating the entire data center is a priority for ensuring maximal efficiency and resiliency. In this advanced smart facility, over one million metrics are recorded per minute using state-of-the-art streaming data architecture and software to capture and process the state of the system in real time. Here we detail two efforts to effectively analyze, visualize, and interpret this large volume streaming data. We have developed a novel, flexible system for identifying and visualizing individual metric anomalies and component performance across the data center through automatic metadata extraction and physically-motivated visualization for quick interpretation. Additionally, to directly connect system maintenance to data stream processing we explore a physics informed multi-metric drift and anomaly detection application to detect scale-build up in heat exchangers.

anomaly detection↗
DOE OSTI2019

Minimum shift keying based protection mode capable wavelength division multiplexed passive optical network optical overlay system

We explored the functioning of an optical overlay of multicast and unicast data streams on a survival wavelength division multiplexed passive optical network using intensity and minimum shift keying modulation for both single and multiuser. In optical line terminal, using a sinusoidal clock, optical subcarriers are generated not only to carry the multicast and unicast data streams simultaneously but also in case of fiber failure, protection mode is enabled to carry the data streams smoothly. Here, 10 Gbps downstream multicast in normal mode and unicast signals both in normal and protection mode are transmitted error-free through 30 km dispersion shifted fiber. In both the cases 10 Gbps upstream signal is modulated and sent back to the optical line terminal. For single user nearly similar power penalty is witnessed for downstream unicast as well as re-modulated upstream data in both the conditions and also when multicast data is disabled. The power penalties between single and multiuser for both the modes are found to be approximately 0.06, 0.42 and 0.7 dB for unicast MSK, multicast and upstream respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Real-time GIS Programming and Geocomputation

Streaming data generated continuously from sensor networks, mobile devices, social media platforms and other edge devices have posed significant challenges to existing computing platforms for achieving both high throughput and low latency data processing in addition to scalable computing. This entry introduces a real-time computing and programming platform for time-critical GIS (Geographic Information System) applications. In this platform, advanced streaming data processing software, such as Apache Kafka and Spark Streaming, are integrated to enable data analytics in real-time. This computing platform can also be extended to integrate GeoAI (Geospatial Artificial Intelligence) based machine learning models to leverage both historical and streaming data to achieve real-time prediction and intelligent geospatial analytics. Two real-time geospatial applications in terms of flood simulation and climate data visualization are introduced to demonstrate how real-time programming and computing can help tackle real-world problems with important societal impacts.

Liu, Yan↗
DOE OSTI2024

Continual learning in the presence of repetition

Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment. This report provides a summary of the CLVision challenge at CVPR 2023, which focused on the topic of repetition in class-incremental learning. The report initially outlines the challenge objective and then describes three solutions proposed by finalist teams that aim to effectively exploit the repetition in the stream to learn continually. The experimental results from the challenge highlight the effectiveness of ensemble-based solutions that employ multiple versions of similar modules, each trained on different but overlapping subsets of classes. This report underscores the transformative potential of taking a different perspective in CL by employing repetition in the data stream to foster innovative strategy design.

Class-incremental learning↗
DOE OSTI2014

AGR-2 Data Qualification Report for ATR Cycle 154B

This report provides the data qualification status of Advanced Gas Reactor-2 (AGR-2) fuel irradiation experimental data from Advanced Test Reactor (ATR) Cycle 154B as recorded in the Nuclear Data Management and Analysis System (NDMAS). This is the last cycle of AGR-2 irradiation, as the test train was pulled from the ATR core during the outage portion of ATR Cycle 155A. The AGR-2 data streams addressed in this report include thermocouple (TC) temperatures, sweep gas data (flow rates including new Fission Product Monitoring (FPM) downstream flows from Fission Product Monitoring System (FPMS) detectors, pressure, and moisture content), and FPMS data (release rates and release-to-birth rate ratios [R/Bs]) for each of the six capsules in the AGR-2 experiment. The final data qualification status for these data streams is determined by a Data Review Committee (DRC) comprised of AGR technical leads, Sitewide Quality Assurance (QA), and NDMAS analysts. The Data Review Committee reviewed the data acquisition process, considered whether the data met the requirements for data collection as specified in QA-approved Very High Temperature Reactor (VHTR) data collection plans, examined the results of NDMAS data testing and statistical analyses, and confirmed the qualification status of the data as given in this report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

AGR-3/4 Data Qualification Report for ATR Cycles 151A, 151B, 152A, 152B, 154A, and 154B

This data report provides the qualification status of Advanced Gas Reactor-3/4 (AGR-3/4) fuel irradiation experimental data from Advanced Test Reactor (ATR) Cycles 151A, 151B, 152A, 152B, 154A, and 154B, as recorded in the Nuclear Data Management and Analysis System (NDMAS). Of these cycles, ATR Cycle 152A is a low power cycle that occurred when the ATR core was briefly at low power. The irradiation data are not used for physics and thermal calculation, but the qualification status of these cycle data is still covered in this report. On the other hand, during ATR Cycles 153A (unplanned Outage cycle) and 153B (Power Axial Locator Mechanism [PALM] cycle), the AGR-3/4 was pulled out from the ATR core and stored in the canal to avoid being overheated. Therefore, qualification of the AGR-3/4 irradiation data from these 2 cycles was excluded in this report. By the end of ATR Cycle 154B, AGR-3/4 was irradiated for a total of 264.1 effective full power days. The AGR-3/4 data streams addressed in this report include thermocouple (TC) temperatures, sweep gas data (flow rates, pressure, and moisture content), and Fission Product Monitoring System (FPMS) data (release rates and release-to-birth rate ratios [R/Bs]) for each of the twelve capsules in the AGR-3/4 experiment. The final data qualification status for these data streams is determined by a Data Review Committee (DRC) composed of AGR technical leads, Sitewide Quality Assurance (QA), and NDMAS analysts. The DRC convened on February 12, 2014, reviewed the data acquisition process, and considered whether the data met the requirements for data collection as specified in QA-approved Very High Temperature Reactor (VHTR) Technology Development Office (TDO) data collection plans. The DRC also examined the results of NDMAS data testing and statistical analyses, and confirmed the qualification status of the data as given in this report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

AGR-3/4 Final Data Qualification Report for ATR Cycles 151A through 155B-1

This report provides the qualification status of experimental data for the entire Advanced Gas Reactor 3/4 (AGR 3/4) fuel irradiation. AGR-3/4 is the third in a series of planned irradiation experiments conducted in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) for the AGR Fuel Development and Qualification Program, which supports development of the advanced reactor technology under the INL ART Technology Development Office (TDO). The main objective of AGR-3/4 irradiation is to provide a known source of fission products for subsequent transport through compact matrix and structural graphite materials due to the presence of designed-to-fail fuel particles. Full power irradiation of the AGR 3/4 test began on December 14, 2011 (ATR Cycle 151A), and was completed on April 12, 2014 (end of ATR Cycle 155B) after 369.1 effective full power days of irradiation. The AGR-3/4 test was in the reactor core for eight of the ten ATR cycles between 151A and 155B. During the unplanned outage cycle, 153A, the experiment was removed from the ATR northeast flux trap (NEFT) location and stored in the ATR canal. This was to prevent overheating of fuel compacts due to higher than normal ATR power during the subsequent Powered Axial Locator Mechanism cycle, 153B. The AGR 3/4 test was inserted back into the ATR NEFT location during the outage of ATR Cycle 154A on April 26, 2013. Therefore, the AGR-3/4 irradiation data received during these 2 cycles (153A and 153B) are irrelevant and their qualification status isnot included in this report. Additionally, during ATR Cycle 152A the ATR core ran at low power for a short enough duration that the irradiation data are not used for physics and thermal calculations. However, the qualification status of irradiation data for this cycle is still covered in this report. As a result, this report includes data from 8 ATR Cycles: 151A, 151B, 152A, 152B, 154A, 154B, 155A, and 155B, as recorded in the Nuclear Data Management and Analysis System (NDMAS). The AGR 3/4 data streams addressed in this report include thermocouple (TC) temperatures, sweep gas data (flow rates, pressure, and moisture content), and Fission Product Monitoring System (FPMS) data (release rates, release to birth rate ratios [R/Bs], and particle failure counts) for each of the twelve capsules in the AGR 3/4 experiment. During Outage Cycle 155A, fourteen flow meters were installed downstream from fourteen FPMS monitors to measure flows from the monitors; qualification status of these data are also included in the report. The final data qualification status for these data streams is determined by a Data Review Committee (DRC) composed of AGR technical leads, Sitewide Quality Assurance (QA), and NDMAS analysts. For ATR Cycles 151A through 154B, the DRC convened on February 12, 2014, reviewed the data acquisition process, and considered whether the data met the requirements for data collection as specified in QA approved INL ART TDO data collection plans. The DRC also examined the results of NDMAS data testing and statistical analyses, and confirmed the qualification status of the data as given in this report. The qualification status of AGR-3/4 irradiation data during the first six cycles were previously reported in INL/EXT-14-31186 document. This report presents data qualification status for the entire AGR-3/4 irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗