Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “streaming 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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data

We consider a continuous-valued simulation optimization (SO) problem, where a simulator is built to optimize an expected performance measure of a real-world system while parameters of the simulator are estimated from streaming data collected periodically from the system. At each period, a new batch of data is combined with the cumulative data and the parameters are re-estimated with higher precision. The system requires the decision variable to be selected in all periods. Therefore, it is sensible for the decision-maker to update the decision variable at each period by solving a more precise SO problem with the updated parameter estimate to reduce the performance loss with respect to the target system. We define this decision-making process as the multi-period SO problem and introduce a multi-period stochastic approximation (SA) framework that generates a sequence of solutions. Two algorithms are proposed: Re-start SA (ReSA) reinitializes the stepsize sequence in each period, whereas Warm-start SA (WaSA) carefully tunes the stepsizes, taking both fewer and shorter gradient-descent steps in later periods as parameter estimates become increasingly more precise. We show that under suitable strong convexity and regularity conditions, ReSA and WaSA achieve the best possible convergence rate in expected sub-optimality either when an unbiased or a simultaneous perturbation gradient estimator is employed, while WaSA accrues significantly lower computational cost as the number of periods increases. In addition, we present the regularized ReSA, which obviates the need to know the strong convexity constant and achieves the same convergence rate at the expense of additional computation.

Computer Science↗
DOE OSTI2023

Contextually aware roadside radiation measurement testbed

Here we demonstrate a contextually aware multimodal roadside radiation measurement detection testbed for traffic monitoring applications in nuclear nonproliferation. Many variables in traffic such as vehicle or cargo size, mass, speed, shape, and distance of closest approach can have significant impacts on the radiation measured from a vehicle-transported radiation source. These factors can lead to uncertainties in the analysis of the radiation source, especially for lower-strength radiation sources of interest. Our testbed, known as the Multimodal Measurement System (MMS) uses non-radiation sensors including magnetometers, geophones, radiofrequency receivers, cameras, and LiDAR to extract contextual information about vehicles passing by the system. These contextual data can then be fused with data from radiation measurements to increase the system’s sensitivity and accuracy in nuclear threat detection applications. This work describes the instrumentation of the MMS and its data acquisition pipeline. Furthermore, we describe the pre-analysis performed on the raw multimodal data streams for data fusion, and the high-level machine learning analyses for detection and characterization. The variety of sensors within the MMS provides a valuable testbed that can be used to identify the combinations of contextual sensors that provide the greatest improvements to radiation source detection and characterization within the restrictions for various proliferation detection applications. The MMS is also modular so that additional combinations of sensors can be explored in the future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

JANA2 Framework for Event Based and Triggerless Data Processing

Development of the second generation JANA2 multi-threaded event processing framework is ongoing through an LDRD initiative grant at Jefferson Lab. The framework is designed to take full advantage of all cores on modern many-core compute nodes. JANA2 efficiently handles both traditional hardware triggered event data and streaming data in online triggerless environments. Development is being done in conjunction with the Electron Ion Collider development. Anticipated to be the next large scale Nuclear Physics facility constructed. The core framework is written in modern C++ but includes an integrated Python interface. The status of development and summary of the more interesting features are presented.

Lawrence, David↗
DOE OSTI2024

Diaspora: Resilience-Enabling Services for Real-Time Distributed Workflows

The need for real-time processing to enable automated decision making and experimental steering has driven a shift from high-performance computing workflows on a centralized system to a distributed approach that integrates remote data sources, edge devices, and diverse compute facilities. Under this paradigm, data can be processed close to the source where it is generated, thus reducing latency and bandwidth usage. System resilience is thus a key challenge, requiring distributed workflows to survive component failures and to meet stringent quality-of-service requirements, which results in the need to mitigate anomalies such as congestion and low availability of resources. To address these challenges, we propose Diaspora, a unified resilience framework that is inspired by event-driven communication patterns used in public clouds. Specifically, we propose an event fabric that extends across sites, facilities, and computations to provide timely, reliable, and accurate information about data, application, and resource status. On top of the event fabric, we build resilience-enabling services that combine QoS-aware data streaming, resilient data views, resilient compute and data resources, and anomaly detection and prediction, all of which collectively enhance workflow resilience for these scientific cases.

Rao, Nageswara↗
DOE OSTI2025

Real Time Phasor Analytics (RTPA) and RTPA-SCR System Strength Online Tool

This presentation showcases the Real-Time Phasor Analytics (RTPA) framework for monitoring inertia and assessing system strength in power grids. RTPA is an open-source tool designed to standardize access to data from Power Management Units (PMUs) and Phasor Data Concentrators (PDCs). It facilitates real-time connectivity to multiple PDCs in accordance with the IEEE C37.118-2 standard and supports asynchronous data stream integration. Additionally, RTPA can simulate a PDC server streaming C37.118-2 data and provides Python bindings for seamless interaction with the framework, eliminating the need for direct Rust programming.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series: Preprint

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.

data shift↗
DOE OSTI2022

Overview and How-to Tutorial Videos for Using NEWTS Data

Overview and How-to Tutorial Videos for Using NEWTS Data Video 1: Overview of NEWTS Database Video 2: How-to tutorial for EPA Flue Gas Desulfurization (FGD) Effluent NEWTS dataset Video 3: How-to tutorial for USGS Produced Waters NEWTS dataset Video 4: How-to tutorial for EPA Ash NEWTS dataset Video 5: How-to tutorial for Quillinan, et al 2018 DOE Geothermal Technology Office REE dataset Video 6: Tutorial video on navigating the NEWTS Dashboard, with an overview of NEWTS and navigating between the NEWTS Dashboard and Datasets (https://netl-doe.maps.arcgis.com/apps/dashboards/a5fa4192f7c6478dab3d6180d9c30b84) Video 7: Additional tutorial video on navigating the NEWTS Dashboard and investigating specific data points in the Dashboard and Datasets Video 8: Re-record of recent webinar giving an overview of the NEWTS Database and Dashboard, including interacting with the NEWTS Dashboard, locating specific data points, and finding the relevant streams in the NEWTS Database and datasets on EDX. Includes overview of the datasets, case studies, and steps for taking stream data from the database and modeling stream data in OLI Studio and Geochemist's Workbench. Note: Video 3 tutorial is also applicable to the USGS Brackish Water NEWTS dataset.

Aqueous Chemistry↗
DOE OSTI2025

A Data-Agnostic, Continuous Machine Learning Framework for Application in High Energy Physics and Beyond: Phase 1 Final Scientific/Technical Report

This Phase 1 effort has focused on the development of continual learning frameworks for use in machine learning, specifically in the applied context of High Energy Physics (HEP). Machine learning (ML) is a transformative technology by which computers, typically through the use of neural networks, are able to perform tasks with proficiency that rivals or surpasses that of human users. Model Degradation & Catastrophic Forgetting are two undesired phenomena which can occur in ML where the performance of a model degrades when either deployed on novel data streams, or trained on novel data which are sufficiently different than the data the models were initially trained on. A natural example where these sorts of effects can be observed is in the performance of detectors in harsh environments, where the detector signature may change over the lifetime of the detector as it ages and deteriorates — precisely what occurs in the experiments conducted in HEP. Real world HEP data is therefore an excellent test-ground and use-case for Continual Learning paradigms, which are techniques used in ML to counteract these problems. Ensemble learning is one such technique, where multiple smaller models are trained on subsets of the overall data and are ensembled together during inference. The intuition behind this technique is that, although there are shifts in the distributions which govern the incoming data streams, these shifts are not expected to be homogeneous or global. If a sufficient diversity in solutions within the various sub-models has been achieved, then at least one sub-model is expected to retain its performance within the overall ensemble. One further strength of this approach is that the architectures of the various models do not need to be identical, and in fact even different modalities of data can naturally be combined in this way. This work focused on applying ensemble learning techniques to derive results using two main datasets, anomaly detection in HEP data & time-series forecasting in semiconductor manufacturing data. Semiconductor manufacturing involves data with surprising similarity to that of HEP (e.g. wafer maps look very similar to digi-occupancy maps) and Cerium Lab’s prominence within the semiconductor industry makes semiconductor manufacturing a natural opportunity for commercialization of this work. Our efforts have led to two strong results. The first is that we evaluated the proposed ensembling techniques using previously proposed machine learning architectures for use in anomaly detection, namely AutoEncoder based models and their derivatives. We also developed new architectures which have not been evaluated in this context before. In fact, this work marks the first use of Vision Transformers for anomaly detection in HEP. Second, we demonstrated that ensemble learning significantly improves model performance in scenarios prone to degradation, validating its effectiveness across both HEP and semiconductor datasets. These results further support ensemble learning as a powerful strategy for mitigating catastrophic forgetting and maintaining robust performance in evolving data environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 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 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 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↗