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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Blowing Snow at McMurdo Station, Antarctica During the AWARE Field Campaign: Surface and Ceilometer Observations

Blowing snow (BLSN) is an impactful process in cold climates, affecting regional thermodynamics, radiation properties, and the surface mass balance of snow. Though it has significant climatic impacts, the process is still poorly understood and not widely included in weather and climate models. In 2016, the AWARE Field Campaign saw the deployment of a large suite of in situ and remote sensing instruments to McMurdo Station, Antarctica allowing for investigation of BLSN. A ceilometer–based BLSN detection algorithm used elsewhere in Antarctica is applied to data from AWARE, yielding a BLSN frequency of 14.1% compared to 8.2% as detected by human observers. To increase confidence in detections, the algorithm is updated to have shorter temporal averaging and to include a variety of meteorological thresholds to limit false detections due to fog. Efforts to incorporate a laser disdrometer into the algorithm were unsuccessful. An unphysical dependence of particle size distributions on wind speed is found suggesting observations are problematic at wind speeds greater than 10 m s –1 . The revised algorithm detected a BLSN frequency of 7.4%, increasing agreement with human observations and confidence that the process is actively occurring at the observation site. Furthermore, these observations are put into context of a climatology of human observations of BLSN at McMurdo station from 2002–2018. An annual average of 8.0%–14.0% is estimated, with a total annual range of 3.4%–21.3%. Regardless of whether BLSN is observed by humans or instrument, the majority of cases at this location are associated with ongoing precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Novel Protection Scheme for Unbalanced Faults in Inverter Dominated Networks: A Computationally Efficient Algorithm for Entry-Level Relays

Microgrids are now a common practice in distribution systems to increase resilience and reliability. However, microgrid protection remains a critical challenge, considering its requirement to operate in both grid connected and islanded, and the variability in fault characteristics under each mode of operation. This paper presents unbalanced power (S unb ) based fault detection algorithm, which considers local voltage and current unbalances to determine faults in the system. S unb is a computationally efficient fault detection algorithm that is suitable for implementation in the programmable logic of entry level protective relays. In addition, the difference in current and voltage unbalance (D n ) is used to determine the fault type. The proposed method demonstrates high sensitivity and selectivity for line-to-ground (LG), line-to-line (LL), and double line-to-ground (LLG) faults, representing the most common faults in distribution systems. It also allows relay coordination with upstream and downstream protection devices in both island and grid connected operation, while preserving grading margins. The same pickup and time multiplier settings of a particular relay for both modes of operation eliminates the need for adaptive settings, which rely on communication networks. Validation was performed with a hardware-in-the-loop (HIL) setup using Typhoon HIL real time simulator interfaced with three entry-level, SEL 751 relays. Results confirmed the algorithm’s ability to discriminate fault conditions, and determine the fault type under both operating modes, maintain fast detection times, and ensure proper protection coordination.

fault classification↗
DOE OSTI2023

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images

Ultrasound imaging is a critical tool for triaging and diagnosing subjects but only if images can be properly interpreted. Unfortunately, in remote or military medicine situations, the expertise to interpret images can be lacking. Machine-learning image interpretation models that are explainable to the end user and deployable in real time with ultrasound equipment have the potential to solve this problem. We have previously shown how a YOLOv3 (You Only Look Once) object detection algorithm can be used for tracking shrapnel, artery, vein, and nerve fiber bundle features in a tissue phantom. However, real-time implementation of an object detection model requires optimizing model inference time. Here, we compare the performance of five different object detection deep-learning models with varying architectures and trainable parameters to determine which model is most suitable for this shrapnel-tracking ultrasound image application. We used a dataset of more than 16,000 ultrasound images from gelatin tissue phantoms containing artery, vein, nerve fiber, and shrapnel features for training and evaluating each model. Every object detection model surpassed 0.85 mean average precision except for the detection transformer model. Overall, the YOLOv7tiny model had the higher mean average precision and quickest inference time, making it the obvious model choice for this ultrasound imaging application. Other object detection models were overfitting the data as was determined by lower testing performance compared with higher training performance. In summary, the YOLOv7tiny object detection model had the best mean average precision and inference time and was selected as optimal for this application. Next steps will implement this object detection algorithm for real-time applications, an important next step in translating AI models for emergency and military medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Infrared intrusion detection system (IRIDS)

A system and method for intrusion detection includes an imager directed towards an object in an interior space. The imager is in data communication with a computer. The computer is arranged to process digital three-dimensional image data received from the imager and programmed to execute a change detection algorithm in response to the processed three-dimensional data to determine movement of the object. The computer generates an alarm output in response to detecting movement of the object above a predetermined threshold. The method includes providing an imager directed towards an object in an interior space; receiving Time of Flight signals by the imager; processing digital three-dimensional image data received from the imager; and executing a change detection algorithm in response to the processed three-dimensional data to determine movement of the object.

Russell, John L.↗
DOE OSTI2024

Summertime Continental Shallow Cumulus Cloud Detection Using GOES‐16 Satellite and Ground‐Based Ceilometer at North Alabama

Abstract Accurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite‐based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground‐based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian et al., 2021, https://doi.org/10.3390/rs13122309 , 2022, https://doi.org/10.1029/2021gl097070 ). To validate this algorithm, the framework employs ground‐based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear‐sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground‐based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (∆R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a ∆R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground‐based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground‐based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land‐atmosphere interactions in North Alabama.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Detecting Short Circuits: Post Accident Electric Vehicle Battery Safety Check

Fast and accurate detection of soft short circuits (SCs) in the battery packs of damaged electric vehicles is needed by first responders and mechanics to mitigate the potential risk from battery fires that may occur hours, days, or weeks after an accident. Here, this paper presents an SC-detection algorithm for potentially damaged lithium-ion batteries that works quickly and without a priori knowledge of the battery-pack chemistry, capacity, state of charge, or state of health. The proposed universal SC-detection algorithm is designed to be implemented on an inexpensive handheld device that can connect to and monitor the voltages of all cells in a pack. Transient filtering and linear-quadratic state observation provide estimates of normalized SC current for every cell in the pack. Cells with SC-current estimates outside a sigma-based threshold are detected. Simulations, experiments, and electric vehicle (EV) crash data are used to verify the speed, sensitivity, and accuracy of the method, demonstrating 96% accurate detection of 0.0027 C SCs in under 1 h for 5S cell groups in the lab and no false positives for crashed Volkswagen, Chevrolet, and Tesla vehicles without SCs.

25 - ENERGY STORAGE↗
DOE OSTI2023

The Integration of Wi-Fi Location-Based Services to Optimize Energy Efficient Commercial Building Operations

This project investigated and demonstrated the use of Wi-Fi Location-Based Services (LBS) to perform occupancy sensing in commercial buildings. Wi-Fi LBS can be used to detect the presence of Wi-Fi enabled mobile devices and laptops that accompany occupants as they move through the building. These signals can be used to determine occupant presence, head count, and location. When integrated with the building automation system, this emerging technology approach can be used to manage other connected systems such as lighting and HVAC to reduce energy usage in the building and improve occupant comfort. An open source location detection algorithm was developed, which uses data collected from three or more Wi-Fi access points to determine the presence and estimate the location of mobile devices and laptops. Access points can detect Wi-Fi enabled devices even if they are not connected to the existing Wi-Fi network. Building occupancy is determined based on the presence, location, and movement of these devices through the space. From lab and small-scale in-situ testing, the Location Detection Algorithm (LDA) was found to be accurate to within 10 feet and could be further refined by tuning the algorithm for the specific space characteristics such as layout and obstructions (walls, furniture, etc.). An open source method to integrate the occupancy data with existing building automations systems was investigated. The Wi-Fi occupancy sensing approach was then demonstrated and validated at commercial buildings located in Saint Paul, MN; Madison, WI; New York City; and Fort Worth, TX.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Using Wi-Fi Location-Based Services (LBS) for Commercial Building Occupancy Sensing

From May 2019 through October 2022, this DOE-funded project investigated and demonstrated the use of Wi-Fi Location-Based Services (LBS) to perform occupancy sensing in commercial buildings. Wi-Fi LBS can be used to detect the presence of Wi-Fi enabled mobile devices and laptops that accompany occupants as they move through the building. These signals can be used to determine occupant presence, head count, and location. When integrated with the building automation system, this emerging technology approach can be used to manage other connected systems such as lighting and HVAC to reduce energy usage in the building and improve occupant comfort. An open source location detection algorithm was developed, which uses data collected from three or more Wi-Fi access points to determine the presence and estimate the location of mobile devices and laptops. Access points can detect Wi-Fi enabled devices even if they are not connected to the existing Wi-Fi network. Building occupancy is determined based on the presence, location, and movement of these devices through the space. From lab and small-scale in-situ testing, the Location Detection Algorithm (LDA) was found to be accurate to within 10 feet and could be further refined by tuning the algorithm for the specific space characteristics such as layout and obstructions (walls, furniture, etc.). An open source method to integrate the occupancy data with existing building automations systems was investigated. The Wi-Fi occupancy sensing approach was then demonstrated and validated at two commercial buildings located in Minnesota and Wisconsin.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Online Event Detection in Synchrophasor Data with Graph Signal Processing

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

Event detection↗
DOE OSTI2020

Online Event Detection in Synchrophasor Data with Graph Signal Processing

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Hybrid DC Fault Primary Protection Algorithm for Multi-Terminal HVdc Systems

Protection against dc faults is one of the main technical hurdles faced when operating converter-based HVdc systems. Protection becomes even more challenging for multi-terminal dc (MTdc) systems with more than two terminals/converter stations. In this paper, a hybrid primary fault detection algorithm for MTdc systems is proposed to detect a broad range of failures. Sensor measurements, i.e., line currents and dc reactor voltages measured at local terminals, are first processed by a top-level context clustering algorithm. For each cluster, the best fault detector is selected among a detector pool according to a rule resulting from a learning algorithm. The detector pool consists of several existing detection algorithms, each performing differently across fault scenarios. The proposed hybrid primary detection algorithm: i) offers superior performance compared to an individual detector through a data-driven approach; ii) detects all major fault types including pole-to-pole (P2P), pole-to-ground (P2G), and external dc faults; iii) identifies faults with various fault locations and impedances; iv) is more robust to noisy sensor measurements compared to existing methods; v) does not require exhaustive simulation and sampling for training the model. Performance and effectiveness of the proposed algorithm are evaluated and verified based on time-domain simulations in the PSCAD/EMTDC software environment. The results confirm satisfactory operation, accuracy, and detection speed of the proposed algorithm under various fault scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Prototype acoustic positioning system for the Pacific Ocean Neutrino Experiment

We present the design and initial performance characterization of the prototype acoustic positioning system intended for the Pacific Ocean Neutrino Experiment. It comprises novel piezo-acoustic receivers with dedicated filtering- and amplification electronics installed in P-ONE instruments and is complemented by a commercial system comprised of cabled and autonomous acoustic pingers for sub-sea installation manufactured by Sonardyne Ltd. We performed an in-depth characterization of the acoustic receiver electronics and their acoustic sensitivity when integrated into P-ONE pressure housings. These show absolute sensitivities of up to -125 dB re V2/μPa2 in a frequency range of 10–40 kHz. We furthermore conducted a positioning measurement campaign in the ocean by deploying three autonomous acoustic pingers on the seafloor, as well as a cabled acoustic interrogator and a P-ONE prototype module deployed from a ship. Using a simple peak-finding detection algorithm, we observe high accuracy in the tracking of relative ranging times at approximately 230–280 μs at distances of up to 1600 m, which is sufficient for positioning detectors in a cubic-kilometer detector and which can be further improved with more involved detection algorithms. The tracking accuracy is further confirmed by independent ranging of the Sonardyne system and closely follows the ship's drift in the wind measured by GPS. The absolute positioning shows the same tracking accuracy with its absolute precision only limited by the large uncertainties of the deployed pinger positions on the seafloor.

Data analysis↗
DOE OSTI2020

Cycle-Consistent Adversarial Networks for Realistic Pervasive Change Generation in Remote Sensing Imagery

This paper introduces a new method of generating realistic pervasive changes in the context of evaluating the effectiveness of change detection algorithms in controlled settings. The method - a cycle-consistent adversarial network (CycleGAN) - requires low quantities of training data to generate realistic changes. Here we show an application of CycleGAN in creating realistic snow-covered scenes of multispectral Sentinel-2 imagery, and demonstrate how these images can be used as a test bed for anomalous change detection algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

On the Robustness of Network Community Structure Under Addition of Edges

Communities represent important functional modules in networked systems. A key goal in preserving such communities is understanding their robustness under perturbations. Previous research has studied the impact of node removals and edge removals on the community structure. However, the impact of edge additions on the robustness of the community structure is relatively unknown. Edge additions or false positive edges may simulate measurement errors or external exceptional events that threaten the functionality of networked systems. Here, we study the impact of edge additions on the community structure using Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks. We show that, for a fixed network size, the impact of edge additions is greater on networks with initially weak community structure than on networks with strongly clustered structures. In addition, we find that the perception of the impact is also dependent on the community detection algorithm used to uncover communities. In particular, we found that modularity-based methods such as Leiden and Louvain are less affected than information-theoretic and message passing-based methods such as Infomap and Label Propagation. Our results demonstrate that edge addition can (a) significantly impact the community structure of networks based on their initial conditions, and (b) the perception of the impact is dependent on the community detection algorithm used. We describe limitations, open challenges, and how this methodology can inform the design of resilient networked systems under edge additions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation

Abstract Detecting earthquake arrivals within seismic time series can be a challenging task. Visual, human detection has long been considered the gold standard but requires intensive manual labor that scales poorly to large data sets. In recent years, automatic detection methods based on machine learning have been developed to improve the accuracy and efficiency. However, the accuracy of those methods relies on access to a sufficient amount of high‐quality labeled training data, often tens of thousands of records or more. We aim to resolve this dilemma by answering two questions: (1) provided with a limited amount of reliable labeled data, can we use them to generate additional, realistic synthetic waveform data? and (2) can we use those synthetic data to further enrich the training set through data augmentation, thereby enhancing detection algorithms? To address these questions, we use a generative adversarial network (GAN), a type of machine learning model which has shown supreme capability in generating high‐quality synthetic samples in multiple domains. Once trained, our GAN model is capable of producing realistic seismic waveforms of multiple labels (noise and event classes). Applied to real Earth seismic data sets in Oklahoma, we show that data augmentation from our GAN‐generated synthetic waveforms can be used to improve earthquake detection algorithms in instances when only small amounts of labeled training data are available.

Wang, Tiantong↗
DOE OSTI2023

Complete Performance Comparison Between the Optimized Image Construction Algorithm (U-MBIR) and the Existing Reconstruction Algorithm for Detecting Defects and Damage in Concrete

Reinforced concrete (RC) is a composite material subjected to mechanical, thermal, and chemical loads throughout its service life. Because of these external stressors and the susceptibility of RC structural members to shrinkage and microcracking, the material degrades throughout its life cycle. This deterioration can lead to a decrease in member capacity and, ultimately, poses a threat to the structural integrity. Thus, it is crucial that the damage caused by aging and degradation be monitored and assessed at regular intervals throughout the material’s service life. Since coring of the material is typically not feasible for in-service structural systems, non-destructive evaluation (NDE) methodologies are used to assess remaining structural capacity. NDE methods enable surface and subsurface examination without damaging or degrading the medium. Moreover, RC is a critical component of nuclear power plants; thus, its safety and reliability must be thoroughly examined throughout the life cycle of the structural system. Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. To this end, Oak Ridge National Laboratory (ORNL) has researched and developed advanced image reconstruction algorithms to capture internal damage. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL. More specifically, this report presents a comparison between reconstruction images produced via a widely employed ultrasonic NDE technique—the synthetic aperture focusing technique (SAFT)—and the ORNL-developed U-MBIR algorithm. These NDE methodologies are demonstrated using ultrasonic data collected from four concrete specimens. Overall, the U-MBIR algorithm eliminates artifacts and noise that are typically present within the SAFT reconstructions, and it shows defects and anomalies more clearly than the SAFT images. In conclusion, this algorithm is suitable for identifying concrete defects, although more improvements and optimization could be done to better define internal defects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

WISP: Watching grid Infrastructure Stealthily through Proxies (Final Technical Report)

The complex interdependencies of cyber systems (sensors and communications), physical grids and associated electricity market operations make protecting electric power grids a significant challenge. The energy sector is constantly under new, targeted, advanced and dangerous cyber-attacks that have the potential to result in the loss of human life. These threats are further exacerbated by our need to modernize the grid. One focus of cyber security research in smart grids is the securing of the SCADA system through advanced intrusion detection systems (IDS) and bad data detection algorithms in state estimation. These methods either require full knowledge of the system topology and parameters or fail to understand the physical behaviors under attack. WISP (Watching grid Infrastructure Stealthily through Proxies) is designed to provide additional protection to the power grid using only publicly available data. In particular, WISP exploits the spatio-temporal nature of the real time locational marginal prices (LMPs), in conjunction with other information such as bids, weather, outages and load data to analyze anomalous power pricing behaviors and then correlate those observations to localize regions of interest and identify potential cyber events. WISP is non-intrusive as the tool is deployed as a service in the Cloud or on premise and provides reliable information to system operators for enhanced situational awareness, without impeding energy delivery functions. The WISP technology comprises three modules: the data-driven anomaly detection core, the vulnerability and risk analysis and the root cause analysis. The data-driven anomaly detection core performs the tasks of feature selection, anomaly detection and attack region localization. The vulnerability and risk analysis module provides system level information of the vulnerable variables and times, assisting the operators in selecting monitoring and protection nodes. The root cause analysis module takes the detection results and identifies potential operational conditions that contribute to the detected anomalies. In Phase I, we have demonstrated the feasibility and effectiveness of WISP. We developed a realistic electricity market simulator capable of generating normal and attack market data under various operational conditions. We developed a series of cyber-attack detection and analysis algorithms and evaluated them under multiple data sources. Finally, we integrated all modules into an end-to-end software, providing functions for data management, data analytics and visualization. Specifically, we have achieved: (i) real-time data acceptance from external utility interfaces with >99% acceptance rate; (ii) high performance anomaly detection algorithms with >98% detection accuracy and <0.1% false alarm rate; and (iii) ultra-low computing delay <50 milliseconds. Additionally, our team developed algorithms to identify the vulnerable variables in electricity market operations and root cause analysis functions to identify major contributors to the price spikes. These ancillary modules are necessary when deploying WISP in real world industry environment. In Phase II, we have demonstrated the effectiveness of WISP software on realistic largescale power systems. We performed red team testing for the Phase I WISP software and identified software vulnerabilities and implemented corresponding mitigation solutions. We adapted the electricity market simulator for the Texas synthetic 2000-bus system and generated datasets for the false data injection attacks. We created database and visualization interfaces for the Texas system and the ISO New England system. We performed software optimization in terms of operation efficiency, computing speed and detection accuracy. Finally, we tested the software on the Texas system and the ISO New England system and evaluated the detection performance. Overall, we achieved above 89% detection rate, below 3% false alarm rate and below 37 seconds of end-to-end detection delay.

24 POWER TRANSMISSION AND DISTRIBUTION↗