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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

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NASA NTRS1988

Cluster analysis of multiple planetary flow regimes

A modified cluster analysis method developed for the classification of quasi-stationary events into a few planetary flow regimes and for the examination of transitions between these regimes is described. The method was applied first to a simple deterministic model and then to a 500-mbar data set for Northern Hemisphere (NH), for which cluster analysis was carried out in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters were found in the low-frequency band of more than 10 days, while transient clusters were found in the band-pass frequency window between 2.5 and 6 days. In the low-frequency band, three pairs of clusters determined EOFs 1, 2, and 3, respectively; they exhibited well-known regional features, such as blocking, the Pacific/North American pattern, and wave trains. Both model and low-pass data exhibited strong bimodality.

Mo, Kingtse↗

DOE OSTI2024

Anomaly Detection and Mitigation for Dynamic Frequency Regulation in Hydropower-Battery Systems: Preprint

Hydropower operators and energy storage providers are increasingly interested in participating in frequency regulation services, driven by the incentives offered by independent system operators, such as the Pennsylvania-New Jersey-Maryland Interconnection (PJM), in a competitive electricity market. This transition, however, unfolds against the backdrop of a modernizing and rapidly digitizing power grid, exposing the integrated legacy infrastructure and vulnerable communication networks to a multitude of cybersecurity threats. These evolving threats not only endanger grid operations but also have the potential to trigger cascading disruptions across the broader grid network and influence regulation markets. This work presents an approach for developing an anomaly detection and mitigation system to address cybersecurity challenges during the participation of a hydropower-integrated battery energy storage system (BESS) in a frequency regulation market. The applied anomaly detector utilizes machine learning algorithms to provide detailed classification of cyber-physical events and provide a comprehensive situation awareness to grid operators. Later, the applied mitigation system triggers predefined corrective actions to minimize the impact of data integrity attacks on the regulation market and system stability. We evaluated the proposed approach on a fully active BESS topology using the slow regulation signal (Reg A) coming from the PJM market. Our simulation results reveal that the proposed approach performs well in detecting data integrity attacks within the allocated time frame and also minimizes the system's instability and economic loss during the participation of hydropower and BESS in the regulation market.

battery energy storage system↗

DOE OSTI2024

popclass

popclass is a lightweight python package that allows fast, probabilistic classification of the lens of a microlensing event given the event's posterior distribution and a model of the Galaxy. popclass provides the bridge between Galactic simulation and lens classification, an interface to common Bayesian inference libraries, and the ability for users to flexibly specify their own Galactic model and classification parameters.

Mcgill, Peter↗

DOE OSTI2022

Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale

A major objective of the project was to train and evaluate the effectiveness of multiple event and anomaly detection, identification and classification deep temporal learning models for processing of real-time phasor measurement unit (PMU) data streams. A vast dataset, consisting of two years of phasor measurements from all three U.S. Interconnections, was curated and released by the Department of Energy (DOE) through Pacific Northwest National Laboratory (PNNL). The dataset also included an event log that provided event times and types (e.g. generator trips, line trips, planned service events, transformer operations, etc.). Our analysis of this dataset addressed six (6) of the eleven (11) research priorities identified in Funding Opportunity Announcement (FOA) DE-FOA-0001861 “Big Data Analysis of Synchrophasor Data” (FOA 1861). Rather than being limited to pre-determined specific algorithms, this project relied on the uniquely structured, highly performant underlying time series database capabilities of the PredictiveGrid platform to assess the vast dataset utilizing a wide variety of algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Evaluating Meteorological Dust Events and Machine-Learning Based Dust Identification in Geostationary Satellite Imagery

NASA scientists in the Short-term Prediction Research and Transition Center (SPoRT) developed a physically-based machine learning approach to identify dust in satellite imagery with a focus on night-time dust detection (Berndt et al. 201; DustTracker-AI). NASA/NOAA Geostationary Environmental Operational Satellite-16 (GOES-16) imagery was used for training and model inputs. The training, testing and validation data set consists of 28 events in the Southwest United States, capturing dust and null events in the region from 2018-2020.With 83 distinct images and millions of pixels a random forest model was trained and validated, correctly labeling 85% of dust pixels.For the first time, the model was run in near-real time production during the spring of 2022 and dust probability visualizations were made available to NOAA National Weather Service (NWS) forecasters to assess its utility for dust forecasting. Results indicated the model helped increase the confidence in the presence of dust and enabled dust tracking for a longer period of time into the night-time hours. Forecaster assessment and running the model in near real-time allowed for the team to determine the types of events missed, captured, and false alarms. To gain additional context on model performance,the SPoRT team sought to gather more detailed information on the training database(e.g., meteorological characteristics and drivers). The goal of this project was to identify the meteorological drivers for the dust events and create a database which synthesized information from observations, forecaster discussions, and analyses pertaining to the dust events to understand the types of events currently used to train the model. A more detailed meteorological synopsis was created for each dust event in the training, testing, and validation datasets. Following the completion of the database and documentation, the classification details revealed that 88% of the dust events were synoptically driven while mesoscale events were less prevalent in model datasets. Meteorological conditions found such as mixing layer depth and wind velocity had mean values of 645mb and 21kt respectively.With conditions of deep mixed layers and moderate to strong surface winds a mesoscale thunderstorm outflow event was considered and subsequently added to the model training data set to test the impact of additional mesoscale training data. The model was retrained and then qualitatively tested on a sample thunderstorm outflow case that the original model was unable to identify. Preliminary results showed potential that the addition of more mesoscale events included in the training data could help to better identify indistinct and localized dust events.

Connor Welch↗

DOE OSTI2024

A Data-Driven Framework for Power System Event Type Identification via Safe Semi-Supervised Techniques

Herein this paper investigates the use of phasor measurement unit (PMU) data with deep learning techniques to construct real-time event identification models for transmission networks. Increasing penetration of distributed energy resources represents a great opportunity to achieve decarbonization, as well as challenges in systematic situational awareness. When high-resolution PMU data and sufficient manually recorded event labels are available, the power event identification problem is defined as a statistical classification problem that can be solved by numerous cutting-edge classifiers. However, in real grids, collecting tremendous high-quality event labels is quite expensive. Utilities frequently have a large number of event records without in-depth details (i.e., unlabeled events). To bridge this gap, we propose a novel semi-supervised learning-based method to improve the performance of event classifiers trained with a limited number of labeled events by exploiting the information from massive unlabeled events. In other words, compared to existing data-driven methods, our method requires only a small portion of labeled data to achieve a similar level of accuracy. Meanwhile, this work discusses and addresses the performance degradation caused by class distribution mismatch between the training set and the real applications. Based on the proposed safe learning mechanism, our model does not directly use all unlabeled events during model training, but selectively uses them through a comprehensive evaluation procedure. Numerical studies on a sizable PMU dataset have been used to validate the performance of the proposed method.

42 ENGINEERING↗

NASA NTRS2005

An Expert System for Autonomous Spacecraft Control

The Autonomous Sciencecraft Experiment (ASE), part of the New Millennium Space Technology 6 Project, is flying onboard the Earth Orbiter 1 (EO-1) mission. The ASE software enables EO-1 to autonomously detect and respond to science events such as: volcanic activity, flooding, and water freeze/thaw. ASE uses classification algorithms to analyze imagery onboard to detect chang-e and science events. Detection of these events is then used to trigger follow-up imagery. Onboard mission planning software then develops a response plan that accounts for target visibility and operations constraints. This plan is then executed using a task execution system that can deal with run-time anomalies. In this paper we describe the autonomy flight software and how it enables a new paradigm of autonomous science and mission operations. We will also describe the current experiment status and future plans.

utonomy flight software↗

DOE OSTI2026

Near-Real-Time Statistical Analysis and Visualization of Streamflow from a Deep-Learning Rainfall-Runoff Model

Near-real-time (NRT) streamflow data are critical importance for timely water resources management. Here, we developed an open-source tool, FlowStats, for NRT streamflow analysis and visualization in Germany, based on NRT meteorological data from the German Weather Service and simulated streamflow from a long short-term memory neural network (LSTM). The LSTM model achieved very good overall performance, median NSE of 0.80 for the test period across 1,479 catchments. FlowStats provides options for deriving various streamflow statistics, from normal and abnormal streamflow detection to drought and flood analyses. An example analysis from FlowStats revealed widespread below-normal to extreme low-flow conditions across Germany from March to May 2025, which weakened from June to September 2025. Drought analysis for September 2025 highlighted severe to extreme drought conditions in northwestern Germany, while flood classifications indicated that high-flow events occurred in southwestern Germany. FlowStats can be used for various hydrological assessments to support water resources management.

Hydrological modeling↗

DOE OSTI2021

Optimized allocation method of the VSC-MTDC system for frequency regulation reserves considering the cost

In recent years, the interconnection of asynchronous power grids through the VSC-MTDC system has been proposed and extensively studied in light of the potential benefits of economical bulk power exchanges and frequency regulation reserves sharing. This paper proposed an optimized allocation method for sharing frequency regulation reserves among the interconnected power systems and the corresponding frequency regulation control of the VSC-MTDC system under emergency frequency deviation events. Firstly, the frequency regulation reserve classification is proposed. In the classification, the available frequency response capacity reserves of each interconnection are divided into commercial reserves and regular reserves. While the commercial reserves are procured through long-term contracts, the regular reserves are purchased based on market prices of frequency regulation services. Secondly, based on the proposed frequency regulation reserve classification, a novel frequency regulation control is then introduced for the VSC-MTDC system. This control method could minimize the costs of the disturbed power grid for the needed frequency response supports from the other power grids. Simulation verifications are performed on a modified IEEE 39 bus system and a highly reduced power system model representing the North American grids. The simulation verification indicates that the developed frequency regulation control significantly reduced ancillary service costs of the disturbed power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2007

Terrain Portrayal for Synthetic Vision Systems Head-Down Displays Evaluation Results: Compilation of Pilot Transcripts

The Terrain Portrayal for Head-Down Displays (TP-HDD) simulation experiment addressed multiple objectives involving twelve display concepts (two baseline concepts without terrain and ten synthetic vision system (SVS) variations), four evaluation maneuvers (two en route and one approach maneuver, plus a rare-event scenario), and three pilot group classifications. The TP-HDD SVS simulation was conducted in the NASA Langley Research Center's (LaRC's) General Aviation WorkStation (GAWS) facility. The results from this simulation establish the relationship between terrain portrayal fidelity and pilot situation awareness, workload, stress, and performance and are published in the NASA TP entitled Terrain Portrayal for Synthetic Vision Systems Head-Down Displays Evaluation Results. This is a collection of pilot comments during each run of the TP-HDD simulation experiment. These comments are not the full transcripts, but a condensed version where only the salient remarks that applied to the scenario, the maneuver, or the actual research itself were compiled.

Hughes, Monica F.↗

NASA NTRS2006

Integrated System for Autonomous Science

The New Millennium Program Space Technology 6 Project Autonomous Sciencecraft software implements an integrated system for autonomous planning and execution of scientific, engineering, and spacecraft-coordination actions. A prior version of this software was reported in "The TechSat 21 Autonomous Sciencecraft Experiment" (NPO-30784), NASA Tech Briefs, Vol. 28, No. 3 (March 2004), page 33. This software is now in continuous use aboard the Earth Orbiter 1 (EO-1) spacecraft mission and is being adapted for use in the Mars Odyssey and Mars Exploration Rovers missions. This software enables EO-1 to detect and respond to such events of scientific interest as volcanic activity, flooding, and freezing and thawing of water. It uses classification algorithms to analyze imagery onboard to detect changes, including events of scientific interest. Detection of such events triggers acquisition of follow-up imagery. The mission-planning component of the software develops a response plan that accounts for visibility of targets and operational constraints. The plan is then executed under control by a task-execution component of the software that is capable of responding to anomalies.

Chien, Steve↗

DOE OSTI2025

Weakly supervised anomaly detection with event-level variables

We introduce a new topology for weakly supervised anomaly detection searches, diobject plus X. In this topology, one looks for a resonance decaying to two standard model particles produced in association with other anomalous event activity (X). This additional activity is used for classification. We demonstrate how anomaly detection techniques which have been developed for dijet searches focusing on jet substructure anomalies can be applied to event-level anomaly detection in this topology. To robustly capture event-level features of multiparticle kinematics, we employ new physically motivated variables derived from the geometric structure of a collision’s phase space manifold. As a proof of concept, we explore the application of this approach to several benchmark signals in the di-𝜏 and di-𝜇 plus X final states. We demonstrate that our anomaly detection approach can reach discovery-level significances for signals that would be missed in a conventional bump-hunt approach.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2013

Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of manmade radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short "transient" RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation.

Doran, G.↗

NASA NTRS2013

Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of man-made radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short “transient” RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation

Doran, G.↗

NASA NTRS2020

Dynamic Scheduling: Target of Opportunity Observations of Gravitational Wave Events

The simultaneous detection of electromagnetic and gravitational waves from the coalescence of two neutron stars (GW170817 and GRB170817A) has ushered in a new era of ‘multimessenger’ astronomy, with electromagnetic detections spanning from gamma to radio. This great opportunity for new scientific investigations raises the issue of how the available multimessenger tools can best be integrated to constitute a powerful method to study the transient Universe in particular. To facilitate the classification of possible optical counterparts to gravitational wave events, it is important to optimize the scheduling of observations and the filtering of transients, both key elements of the follow-up process. In this work, we describe the existing workflow whereby telescope networks such as GRANDMA and GROWTH are currently scheduled; we then present modifications we have developed for the scheduling process specifically, so as to face the relevant challenges that have appeared during the latest observing run of Advanced LIGO and Advanced Virgo. We address issues with scheduling more than one epoch for multiple fields within a skymap, especially for large and disjointed localizations. This is done in two ways: by optimizing the maximum number of fields that can be scheduled and by splitting up the lobes within the skymap by right ascension to be scheduled individually. In addition, we implement the ability to take previously observed fields into consideration when rescheduling. We show the improvements that these modifications produce in making the search for optical counterparts more efficient, and we point to areas needing further improvement.

Gravitational waves↗

NASA NTRS2005

Correlation of Rock Spectra with Quantitative Morphologic Indices: Evidence for a Single Rock Type at the Mars Pathfinder Landing Site

The Mars Pathfinder (MPF) landing site was predicted to contain a broad sampling of rock types varying in mineralogical, physical, mechanical and geochemical characteristics. Although rocks have been divided into several spectral categories based on Imager for Mars Pathfinder (IMP) visible/near-infrared data, efforts in isolating and classifying spectral units among MPF rocks and soils have met with varying degrees of success, as many factors influencing spectral signatures cannot be quantified to a sufficient level to be removed. It has not been fully determined which spectral categories stem from intrinsic mineralogical differences between rocks or rock surfaces, and which result from factors such as physical or chemical weathering. This has made isolation of unique rock mineralogies difficult. Morphology, like composition, is a characteristic tied to the intrinsic properties and geologic and weathering history of rocks. Rock morphologies can be assessed quantitatively and compared with spectral data, to identify and classify rock types at the MPF landing site. They can also isolate actual rock spectra from spectral types that are surficial in origin, as compositions associated with mantling dust or chemical coatings would presumably not influence rock morphology during weathering events. We previously reported on an initial classification of rocks using the quantitative morphologic indices of size, roundness, sphericity and elongation. Here, we compare this database of rock characteristics with associated rock surface spectra to improve our ability to discriminate between spectra associated with rock types and those from other sources.

Yingst, R. A.↗

DOE OSTI2023

Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60- Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states but also individual load operating power levels. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy, good scaling and generalization properties.

feature extraction↗

NASA NTRS2015

Application of Data Cubes for Improving Detection of Water Cycle Extreme Events

As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case of our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using data from the Tropical Rainfall Measuring Mission (TRMM).

water cycle extreme events↗