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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Static adhesion hysteresis in elastic structures

Adhesive interactions between elastic structures such as graphene sheets, carbon nanotubes, and microtubules have been shown to exhibit hysteresis due to irrecoverable energy loss associated with bond breakage, even in static (rate-independent) experiments. To understand this phenomenon, we start with a minimal theory for the peeling of a thin sheet from a substrate, coupling the local event of bond breaking to the nonlocal elastic relaxation of the sheet and show that this can drive static adhesion hysteresis over a bonding/debonding cycle. Using this model we quantify hysteresis in terms of the adhesion and elasticity parameters of the system. We find that this allows us to derive a scaling relation that preserves hysteresis at different levels of granularity while resolving a seeming paradox of lattice trapping in the continuum limit of a discrete fracture process. Finally, to verify our theory, we use new experiments to demonstrate and measure adhesion hysteresis in bundled microtubules.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Influence of gradation on failure mode of saturated sand at particle scale

Extensive research has been reported in the literature to characterize the failure mode of dry sand using various experimental techniques such as surface optical imaging, photo-elastic materials, three-dimensional (3D) computed tomography (CT), and 3D synchrotron micro-computed tomography (SMT). However, there is a limited literature about the behavior of saturated sand. This paper presents the results of axisymmetric triaxial compression (ATC) experiments that were conducted on saturated sand specimens. The behavior of specimens composed of a uniform sand with grain size between US sieves #40 and #50 is compared to specimens conducted on the same sand that has a wider gradation. 3D SMT technique was used to acquire 3D scans while shearing the specimens to probe localized events that are completely missed or misinterpreted when analyzing ATC measurements based on global standard measurements. The results show a higher effective principal stress ratio (EPSR) for the non-uniform specimen and a thicker shear band when compared to uniform specimen.

Elnur, Mohammed [University of Tennessee]↗
DOE OSTI2026

Phase Picking Beyond Local Distances: Where Waveform Filtering Still Matters for Deep Learning Models

Waveform filtering is a standard step in traditional seismic phase picking but often receives little attention in deep learning workflows, where models are typically trained on raw or minimally processed waveforms. Although this strategy performs well for local events, we show that performance can degrade substantially at regional distances. To address this limitation, we introduce two ways to incorporate multiband-filtered waveforms into deep learning phase pickers. The stacking approach concatenates filtered inputs along the channel dimension, while the branching approach processes each frequency band through a dedicated network branch before feature fusion. Both approaches can substantially improve performance across epicentral distances of 0° to 20°, but their effectiveness depends strongly on the selected frequency bands. Tests with multiple filter banks show that filter-bank design should be treated as part of model optimization rather than as a fixed preprocessing choice. Grad-CAM analysis of the branching model indicates that band importance varies among waveform samples and across training realizations, with only a weak overall preference for the 0.25 to 0.5 Hz band. These results show that no single filter band is consistently optimal and demonstrate that explicit feature engineering remains valuable for robust deep learning-based seismic phase picking.

58 GEOSCIENCES↗
DOE OSTI2023

Survey of Aging and Monitoring Concerns for Cables and Splices Due to Cable Repair and Replacement

The purpose of this report is to survey aging and monitoring concerns for electrical cable splices in nuclear power plants (NPPs) in long term operation. As portions of existing electrical cable runs in nuclear are replaced over time due to localized events, the total number of splices in NPPs is expected to increase. Relative to cables, the body of knowledge regarding aging of splices and splices in combination with aging cables in nuclear service environments in long-term operations is low. A few reports have considered the aging of cable system components other than cables (Jacobus 1990; Nelson 1998; Villaran and Lofaro 2002), but the nuclear industry has two decades of operating experience since these were published to further enlighten this issue. Herein we discuss electrical cables and splices commonly found in U.S. nuclear power plants, their qualification in safety-related application, and methods for monitoring their health condition. Common environmental stresses that can give rise to cable and splice failure are discussed. The Nuclear Regulatory Commission (NRC) Licensee Event Reports (LER) database was used to identify documented issues of cable and splice failure. The trend in the resultant data over time is considered to see if failures are increasing as plants age. Observations and conclusions of this work include: 1. Cables and splices are highly reliable components. Occurrence rates for events of interest were low and nearly constant over the last 20 years. 2. Common-cause failure for evaluated cable events of interest was observed to primarily be associated with loose connections, which may manifest associated with workmanship issues, thermal cycling, and/or vibration. 3. Replacement of cables is more common than repair, leading to an increase in proportion of new generation cables in the plant over time. 4. Splices on degraded cables have been observed to be problematic. Due to aging NPP infrastructure, including electrical cables, it is expected that such issues will continue to increase. 5. Condition monitoring approaches, while shown to be fruitful for cables, have been shown to be insensitive to degradation of splice sleeves, which are critical to the continued performance of splices. Additional condition monitoring (CM) work is needed to evaluate methods which are sensitive to the degradation of splice components. 6. Extended Material Degradation Assessment (EMDA) knowledge gaps for electrical cables (Bernstein et al. 2014) have not been investigated for splices but may represent similar concerns such as for the accelerated aging process historically used in environmental qualification.

42 ENGINEERING↗
DOE OSTI2021

Constraints on Chondrule Generation, Disk Dynamics, and Asteroid Accretion from the Compositions of Carbonaceous Meteorites

The elemental and isotopic compositions of meteorites are expected to reflect several key processes that occurred in the early solar system, including the migration of gas and dust throughout the protoplanetary disk, the formation of chondrules, and the accretion of the first planetary bodies. However, the specific origins of the various compositions measured among these rocks are currently poorly constrained, limiting our understanding of these processes. Here, we use previously measured elemental and isotopic compositions of chondrites and iron meteorites to identify that carbonaceous (CC) meteorites are mixtures of noncarbonaceous (NC) material, calcium–aluminum-rich inclusion (CAI) material, and CI (Ivuna-like) material, in varying proportions. These trends indicate that chondrules in CO (Ornans-like), CM (Mighei-like), CV (Vigarano-like), and TL (Tagish Lake) chondrites share near-identical average proportions of CI material, arguing that they were generated through the remelting of preexisting NC chondrules all in the same disk environment. Because this proportion likely evolved over space and time throughout the disk, this similarity argues that these chondrules originate from a restricted spatial region and time interval, favoring their generation through a localized event. Moreover, the compositions of CR (Renazzo-like) chondrites indicate that their constituents formed through mechanisms different from those in CO, CM, CV, and TL chondrites. Furthermore, the recovered proportions of CI material in CC iron meteorites and chondrites together also argue for evolution in either the predominant direction of dust and gas motion in the first ~10 au of the disk or the radial distance of planetesimal accretion throughout the CC reservoir.

58 GEOSCIENCES↗
DOE OSTI2024

Flashback Studies of High-Hydrogen Flames using High-Speed OH Planar Laser-Induced Fluorescence

Flame flashback is a crucial challenge during the application of high-hydrogen fuels for the development of next-generation lean premixed combustors. The fundamentals of stable flame configuration, flashback phenomena, and spatiotemporal information of flame dynamics in a premixed, swirl-stabilized burner are investigated experimentally at atmospheric pressure conditions using high-repetition-rate nanosecond (ns)-duration hydroxyl radical planar laser-induced fluorescence (OH-PLIF) measurements. The inlet conditions are varied systematically with respect to equivalence ratio (ϕ), hydrogen percentage in a hydrogen-methane mixture (50%–90%) and pre-mixer velocities and the experiments are performed on the burner modified to provide optical access to the premixing section. Signal interferences such as saturation effects, background chemiluminescence, and laser non-uniformity issues were minimized or corrected during data acquisition and subsequent data processing steps. A stable flame characterization was performed at 20 kHz-repetition-rate for varied pre-mixer velocities, hydrogen content and ϕ in the reactant mixture and lift-off length (L) investigation showed dependence on each of these parameters. Flame dynamics such as flame flashback events, local ignition, local extinction, flame curvature as well as the main reaction zone of the combustion mechanism are well characterized. Flashback ϕ showed an expected linearly increasing trend with increasing pre-mixer velocity and decreasing hydrogen content and the conclusions drawn aligned well with detailed L investigation. Such spatially and temporally resolved kHz-rate OH-PLIF is a promising technique to observe rapidly occurring flashback dynamics and can further validate turbulence-chemistry interaction models of swirling flames.

Parajuli, Pradeep↗
DOE OSTI2023

Real-Time Detection and Localization of Line Trip Event Via Relative Phase Angles

Line trip events widely exist in power systems. They can result in power outages and a huge economic loss if not promptly detected and localized. To provide a fast and precise solution, this paper presents a Complete Coverage of Voltage Measurement (CCVM)-based line trip event detection algorithm and a Relative Phase Angle (RPA)-based line trip event localization algorithm. First, frequency and relative phase angle features during a line trip event are calculated. Then, the CCVM-based algorithm is proposed from both frequency and rate of change of frequency estimation algorithm aspects. Additionally, the RPA-based algorithm is presented, and two cases are studied to demonstrate the uniqueness of the proposed algorithm. Various experiments are conducted, where the simulation results demonstrate that the proposed CCVM-based algorithm can detect a line trip event as short as 2.07 ms. In addition, the RPA-based algorithm has 1.26 times higher localization accuracy compared with the frequency magnitude-based and phase angle-based algorithms. In conclusion, the experiment results on the examples from two interconnected power systems in the U.S. verified the performance of the proposed algorithms in wide-area power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Power System Event Classification and Localization Using a Convolutional Neural Network

Detection and timely identification of power system disturbances are essential for situation awareness and reliable electricity grid operation. Because records of actual events in the system are limited, ensemble simulation-based events are needed to provide adequate data for building event-detection models through deep learning; e.g., a convolutional neural network (CNN). An ensemble numerical simulation-based training data set have been generated through dynamic simulations performed on the Polish system with various types of faults in different locations. Such data augmentation is proven to be able to provide adequate data for deep learning. The synchronous generators’ frequency signals are used and encoded into images for developing and evaluating CNN models for classification of fault types and locations. With a time-domain stacked image set as the benchmark, two different time-series encoding approaches, i.e., wavelet decomposition-based frequency-domain stacking and polar coordinate system-based Gramian Angular Field (GAF) stacking, are also adopted to evaluate and compare the CNN model performance and applicability. The various encoding approaches are suitable for different fault types and spatial zonation. With optimized settings of the developed CNN models, the classification and localization accuracies can go beyond 84 and 91%, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2026

Incorporating Local Extreme Weather Events into Reliability Assessments

Presentation discussing modeling extreme weather effects on the Bulk power system. Using a Production cost model to analyze weather related demand increases, generator outages, transmission outages, and fuel supply constraints. These results can be analyzed to inform on system reliability effects of extreme weather. This presentation was developed for the 2026 NERC Probabilistic Assessment Forum at EPRI in Charlotte NC.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Localization of infrasonic sources via Bayesian back projection

SUMMARY A Bayesian framework is investigated for event-specific localization of infrasonic sources using back projection ray tracing. Direction-of-arrival information from array-based detection analysis is used to initialize a back projection ray path originating from the detecting array location and quantifying propagation characteristics from hypothetical source locations. The Fisher statistic, computed from the array’s beam coherence, is mapped into uncertainty in the launch angles of the ray path. Auxiliary parameters previously introduced for solving the Transport equation to compute geometric spreading along ray paths are used to map uncertainty in the ray launch angles into spatial and temporal uncertainties in the ray path. An atmospheric ensemble approach is applied to account for atmospheric uncertainty, and the relation between uncertainties in the atmospheric state and confidence in estimated localization are evaluated using several ensembles with specified variances. The method is evaluated using a synthetic event in the western United States constructed via forward propagation simulations as well as a single-station, multi-arrival detection from a surface explosion in the western United States. Localization results using this event-specific approach are more accurate and exhibit improved precision than existing Bayesian localization methods that leverage generalized, pre-computed propagation statistics.

58 GEOSCIENCES↗
DOE OSTI2024

Data-Driven Insights into the Structural Essence of Plasticity in High-Entropy Alloys

The heterogeneous mechanical response of a crystalline alloy with multiple principal elements was investigated using molecular dynamics simulations. The local configuration of the alloy in its quiescent state was characterized by the variables derived from the gyration tensor and the atomic electronegativity. A multivariate analysis identified the geometric and chemical factors that influenced the atomic packing variations. Further, upon straining, the non-affine displacement exhibited spatial heterogeneity. A statistical correlation was established between the local yield events and the specific features of the local configuration. Our findings, validated by the performance metrics analysis, provided a structural criterion for the instability mechanisms in high-entropy alloys (HEAs) and enhanced the understanding of their plasticity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Robust Event Classification Using Imperfect Real-world PMU Data

Here, this paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

In Situ Machine Learning for Intelligent Data Capture on Exascale Platforms. Final Report

In many dynamic systems, interesting events occur locally in time and space. Examples of such systems include ignition events in combustion simulations, material fractures in mechanics simulations, and extreme weather events in climate simulations. Due to memory constraints and data I/O costs, current simulation workflows save data at regularly spaced time-steps, at a fixed rate determined before the start of the simulation. Often this mode of operation results in missed events of interest, necessitating a simulation restart from before an event occurred with more frequent data saves. This data saving workflow is grossly inefficient and is already a bottleneck in the computing process. We propose to develop machine learning algorithms that can detect when interesting dynamical events are occurring, triggering data saves. These machine learning algorithms will perform in situ anomaly detection to flag regions with different dynamical properties than those previously recorded. The adaptive data saves would be local in time and space to match the event of interest, thereby enabling a much more efficient workflow that will reduce data I/O costs and data storage memory requirements. The algorithms will be tested on two applications: auto-ignition simulations and climate simulations. A critical component of this project will be developing machine learning algorithms that can be deployed efficiently in situ on HPC platforms with out-of-the-box functionality. The development of in situ machine learning methods to detect anomalous events would enable a more efficient and effective workflow, in which all the relevant data are saved in a single simulation run, without re-starts or scientist intervention.

42 ENGINEERING↗
DOE OSTI2020

Characterization with dense array data of seismic sources in the shallow part of the San Jacinto fault zone

SUMMARY We analyse dominant sources identified in a catalogue of more than 156 000 localizations performed using a 26-d data set recorded by a dense array set on the San Jacinto fault near Anza, in California. Events were localized using an array processing technique called Match Field Processing. As for all array processing techniques, the quality of the event position decrease when the events are outside of the array. We thus separate localizations in and outside the array using simple geometrical conditions. We compare the time distribution of the localization to additional data such as meteorological data, day of human activity as well as existing catalogues to determine the nature of the dominant events located using our method. We find that most of the events located outside of the array could be attributed to a surface structure excited by wind. On the other hand, part of the localizations under the array occur during regional earthquakes and could correspond to diffraction on the fault's heterogeneities. The rest of the localizations inside the array could be generated by the fault itself.

Geochemistry & Geophysics↗
DOE OSTI2021

Event-Based Analysis of Solar Power Distribution Feeder Using Micro-PMU Measurements

Solar distribution feeders are commonly used in solar farms that are integrated into distribution substations. In this paper, we focus on a real-world solar distribution feeder and conduct an event-based analysis by using micro-PMU measurements. The solar distribution feeder of interest is a behind-the-meter solar farm with a generation capacity of over 4 MW that has about 200 low-voltage distributed photovoltaic (PV) inverters. The event-based analysis in this study seeks to address the following practical matters. First, we conduct event detection by using an unsupervised machine learning approach. For each event, we determine the event’s source region by an impedancebased analysis, coupled with a descriptive analytic method. We segregate the events that are caused by the solar farm, i.e., locallyinduced events, versus the events that are initiated in the grid, i.e., grid-induced events, which caused a response by the solar farm. Second, for the locally-induced events, we examine the impact of solar production level and other significant parameters to make statistical conclusions. Third, for the grid-induced events, we characterize the response of the solar farm; and make comparisons with the response of an auxiliary neighboring feeder to the same events. Fourth, we scrutinize multiple specific events; such as by revealing the dynamics to the control system of the solar distribution feeder. The results and discoveries in this study are informative to utilities and solar power industry.

14 SOLAR ENERGY↗
DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗