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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2022

Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information

Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. Here, in this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2016

SPRUCE S1 Bog Porewater, Groundwater, and Stream Chemistry Data: 2011-2013

This data set reports the chemistry of S1 bog porewater, outlet stream water, and groundwater from upland wells from 2011 through 2013. These data provide information for studies of the biogeochemical status of the bog and surrounding groundwater aquifer. Porewater samples were collected from one nest of 6 piezometers installed in a hollow in the south end of the S1 bog to characterize solute chemistry depth profiles. Peat porewater was collected from piezometers that opened at 0, 0.3, 0.5, 1, 2, and 3 m depths below hollow surfaces. Samples were collected weekly (2011) or monthly (2012 & 2013) when the samplers were not frozen (typically June – November). Stream water was sampled weekly (2011-2013) from the outlet of the S1 bog (located on the southern edge of the bog). Four groundwater wells located in the uplands of the S1 watershed were sampled monthly in 2013. Bog water level data are also reported for the S1 bog. Water samples were analyzed for pH, specific conductivity, nutrient concentrations (ammonium, nitrate, soluble reactive phosphorus, total nitrogen, total phosphorus), cation concentrations (calcium, potassium, magnesium, sodium, aluminum, iron, manganese, silicon, strontium), anion concentrations (chloride, sulfate), and total organic carbon concentrations. The experimental work is to be conducted in a Picea mariana [black spruce] – Sphagnum spp. bog forest in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Accelerating Data Acquisition, Reduction, and Analysis at the Spallation Neutron Source

ORNL operates the world's brightest neutron source, the Spallation Neutron Source (SNS). Funded by the US DOE Office of Basic Energy Science, this national user facility hosts hundreds of scientists from around the world, providing a platform to enable break-through research in materials science, sustainable energy, and basic science. While the SNS provides scientists with advanced experimental instruments, the deluge of data generated from these instruments represents both a big data challenge and a big data opportunity. For example, instruments at the SNS can now generate multiple millions of neutron events per second providing unprecedented experiment fidelity but leaving the user with a dataset that cannot be processed and analyzed in a timely fashion using legacy techniques. To address this big data challenge, ORNL has developed a near real-time streaming data reduction and analysis infrastructure. The Accelerating Data Acquisition, Reduction, and Analysis (ADARA) system provides a live streaming data infrastructure based on a high-performance publish subscribe system, in situ data reduction, visualization, and analysis tools, and integration with a high-performance computing and data storage infrastructure. ADARA allows users of the SNS instruments to analyze their experiment as it is run and make changes to the experiment in real-time and visualize the results of these changes. In this paper we describe ADARA, provide a high-level architectural overview of the system, and present a set of use-cases and real-world demonstrations of the technology.

Campbell, Stuart I↗
DOE OSTI2017

Why wait? Let's start computing while data is still on the wire

In this era of Big Data, computing useful information from data is becoming increasingly complicated, particularly due to the ever increasing volumes of data that need to travel over the network to data centers to be stored and processed, all highly expensive operations in the long haul. In this paper we suggest that we can do computing and analysis of data "on the wire," i.e., while data is still in transit. The nature of these computations include analysis, visualization, pattern recognition, and prediction, or forecasting, on the streaming data. We follow a service function chaining architecture to implement this, assuming that the data packets arrive within a single network administrative domain. As a demonstration of this new computing paradigm, we present three examples. Firstly, we demonstrate pattern recognition and data visualization on streaming forex data, which can be used for lucrative trading in the forex market. In our second example, we analyze and learn user buying patterns from clickstream data streaming from multiple websites. Finally, we monitor solar sensors for a zero reading while the packets are still on their way to the data center, to schedule any maintenance and requisite repairs with no time delay.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

LHC physics dataset for unsupervised New Physics detection at 40 MHz

In the particle detectors at the Large Hadron Collider, hundreds of millions of proton-proton collisions are produced every second. If one could store the whole data stream produced in these collisions, tens of terabytes of data would be written to disk every second. The general-purpose experiments ATLAS and CMS reduce this overwhelming data volume to a sustainable level, by deciding in real-time whether each collision event should be kept for further analysis or be discarded. We introduce a dataset of proton collision events that emulates a typical data stream collected by such a real-time processing system, pre-filtered by requiring the presence of at least one electron or muon. This dataset could be used to develop novel event selection strategies and assess their sensitivity to new phenomena. In particular, we intend to stimulate a community-based effort towards the design of novel algorithms for performing unsupervised new physics detection, customized to fit the bandwidth, latency and computational resource constraints of the real-time event selection system of a typical particle detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Selecting Essential Information for Biosurveillance - A Multi-Criteria Decision Analysis

This paper proposes the use of Multi-Attribute Utility Theory to address the issue of identifying and selecting essential information for inclusion into a biosurveillance system or process. Additionally, we developed a decision support framework that can facilitate identifying data streams for use in biosurveillance systems or processes and demonstrated utility by applying the framework to the problem of evaluating data streams for use in an global infectious disease surveillance system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Stream splitting moving target defense

Systems and methods for utilizing stream splitting Moving Target Defense (MTD) to provide enhanced computer system communication system security by splitting a data stream in to a plurality of paths is described. In some implementations, Stream splitting MTD, involves splitting a single data stream (e.g., TCP stream) into a plurality of discrete units, then sending and receiving those discrete units from and to different (ideally geographically disparate) receiving servers, with the stream being reassembled on the receiving end. The plurality of discrete units of data include resequencing data. The size of each discrete unit may vary depending on the specific implementation, even down to small unit sizes (e.g., a single packet).

Evans, Nathaniel↗