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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Accelerating the Inference of the Exa.TrkX Pipeline

Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.TrkX pipeline based on GNNs demonstrated promising performance in reconstructing particle tracks in dense environments. It includes five discrete steps: data encoding, graph building, edge filtering, GNN, and track labeling. All steps were written in Python and run on both GPUs and CPUs. In this work, we accelerate the Python implementation of the pipeline through customized and commercial GPU-enabled software libraries, and develop a C++ implementation for inferencing the pipeline. The implementation features an improved, CUDA-enabled fixed-radius nearest neighbor search for graph building and a weakly connected component graph algorithm for track labeling. GNNs and other trained deep learning models are converted to ONNX and inferenced via the ONNX Runtime C++ API. The complete C++ implementation of the pipeline allows integration with existing tracking software. We report the memory usage and average event latency tracking performance of our implementation applied to the TrackML benchmark dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Panel-Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery

The NREL Python Panel-Segmentation package is a toolkit that automates the process of extracting accurate and valuable metadata related to solar array installations, using publicly available Google Maps satellite imagery. Previously published work includes automated azimuth estimation for individual solar installations in satellite images. Our continued research focuses on automated detection and classification of solar installation mounting configuration (tracking or fixed-tilt; rooftop, ground, or carport). Specifically, a Faster-RCNN Resnet-50 feature pyramid network (FPN) model was trained and validated on 862 manually labeled satellite images. This model was used to perform object detection on satellite imagery, locating and classifying individual solar installations' mounting configuration and type. Model results showed a mean average precision score (mAP) of 77.79%, with the model strongest at detecting fixed-tilt ground mount and fixed-tilt carport installations. The object detection model and its outputs have been incorporated into the Panel-Segmentation package's automated metadata extraction pipeline, which returns the mounting configuration and azimuth for individual solar arrays in satellite imagery. The complete image data set with labels has been released on the U.S. Department of Energy (DOE) DuraMAT DataHub, to encourage further research in this area.

deep learning↗
DOE OSTI2020

Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate

Most galaxies in the nearby Universe are gravitationally bound to a cluster or group of galaxies. Their optical contents, such as optical richness, are crucial for understanding the co-evolution of galaxies and large-scale structures in modern astronomy and cosmology. The determination of optical richness can be challenging. We propose a self-supervised approach for estimating optical richness from multi-band optical images. The method uses the data properties of the multi-band optical images for pre-training, which enables learning feature representations from a large but unlabeled dataset. We apply the proposed method to the Sloan Digital Sky Survey. The result shows our estimate of optical richness lowers the mean absolute error and intrinsic scatter by 11.84% and 20.78%, respectively, while reducing the need for labeled training data by up to 60%. We believe the proposed method will benefit astronomy and cosmology, where a large number of unlabeled multi-band images are available, but acquiring image labels is costly.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2023

Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Estimating Travel Energy Consumption Uncertainty Based on Inferred Travel Mode and Sensed Travel Length

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 8%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2016

INL transient reactor restart progress

The United States Department of Energy (DOE) has authorized resumption of transient testing and the restart of the Transient Reactor Test (TREAT) facility. The TREAT reactor was used from 1959 to 1994 to conduct more than 2800 nuclear fuel transient tests prior to being placed in standby in 1994. The plant was extensively upgraded shortly before it was placed in standby. Present day assessments revealed that a sound infrastructure remains at the plant. The assessments included extensive walk-downs and subsequent functional testing. The testing was broad in scope, but ensured that any plant operability issues, including age related degradation were discovered and resolved. The primary age related degradation found was capacitors in Reactor Trip System and Automatic Reactor Control System power supplies. The majority of low voltage power supplies required replacement. Although minimal degradation was evident, in some cases elastomers such as hydraulic hoses were preemptively replaced to take advantage of the existing maintenance window and ensure future reliable operation. Configuration management has been extensively reviewed and the plant components re-labeled using modern standards. The re-labeling required extensive work including updates to plant drawings and procedures. The plant organization has been established to restart the reactor and will subsequently seamlessly transition to reactor operations. For the restart effort, the operating organization is being supplemented by subcontractors including some personnel who were involved in TREAT historical operations. During 2015 and the first half of 2016 substantial progress has been made to renew reactor systems and infrastructure, update procedures and configuration management documents, and put in place a qualified operating organization. Reactor startup is anticipated no later than 2018. (author)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

A VOI Web Application for Distinct Geothermal Domains: Statistical Evaluation of Different Data Types within the Great Basin

The Great Basin region contains different domains that have different structural and hydrothermal flow patterns. Depending on the characteristics of these patterns, certain data types may be more successful at detecting hidden geothermal resources. In this paper, we quantitatively evaluate if certain data types are more successful in certain domains. Given different aquifer, strain and structural conditions, we explore which data types statistically reveal positively labeled geothermal sites. We utilize value of information (VOI) metrics to help quantify the reliability of data types to discriminate against "positive" and "negative" labeled geothermal sites. We also evaluate how kernel density estimation can help generalize the statistics that inform VOI, which is necessary given the limited data in geothermal exploration. Except for the Carbonate Aquifer, the highest ranking of the Vimperfect is the Local Structural Setting. Next, the slip and dilation tendency is first for Carbonate Aquifer and second for Central Nevada Seismic Belt and Western Great Basin. For the Carbonate Aquifer, heat flow is has the lowest Vimperfect value compared to the other three domains, which is consistent with the understanding of how heat flow measurements are masked by regional groundwater flow.

Bayesian analysis↗
DOE OSTI2019

Path of Disposal for Hazardous and Non-hazardous Waste from Cradle to Grave

Hazardous waste is a threat to human health and the environment and must be managed and disposed of properly. A Solid Waste is defined as any material (solid, liquid, containerized gas) which has been discarded, abandoned (no longer needed), or recycled. The Resource Conservation Recovery Act (RCRA) is a law that creates the framework for the proper management of solid waste. Under RCRA, EPA has developed a program which ensures hazardous waste is managed safely from the moment it is generated to its final disposal (cradle-to-grave)2. To educate about how waste is handled at the Savannah River Research Campus (SRRC) under RCRA, the path it takes from point of generation (cradle) to disposal (grave), and the responsibilities of every person involved in the process. Researcher or other laboratory personnel that generates waste as a result of their work Responsibilities: - Fill out inventory log sheet and corresponding container sheet which includes the constituents of the waste; - Properly label waste container with SAA/WSA ID no., generator's name, what it is, the hazards, and date; - Place waste in designated waste storage area. Laboratory Custodian Responsibilities: - Ensures waste is handled and stored properly; - Ensures inventory is updated; - Ensures all container sheets are completed; - Ensures only authorized personal have access to waste storage areas. Environmental Compliance Authority Responsibilities: - Develops waste procedures; - Provide waste training; - Inspects for improperly handled waste; - Establishes or closes waste storage areas as needed; - Determines if waste can be recycled; - Determines if waste is RCRA hazardous: Is it U or P listed? - Unused commercial chemical products; Is it F listed - Spent solvents; Is it characteristic? - Flammable/Oxidizer, Corrosive, Reactive, Toxic; - Weighs waste to ensure laboratory has not exceeded its monthly or total storage limits; - Maintains waste records; - Determines how waste should be disposed: Trash - Nonhazardous solids; Drain - Nonhazardous Liquids; Labpack; - Prepares waste for shipment with Vendor and Department of Transportation (DOT) personnel. Packages, Transports, and Treats Hazardous Waste Responsibilities: - Must be EPA permitted and approved by the Department of Energy - Picks up hazardous waste for transport to a treatment, storage or disposal facility depending on waste disposal requirements - Must meet all Department of Transportation regulations for transporting hazardous waste - Dispose of waste according to RCRA regulations. Due to the potential risks to human health and the environment, waste must be handled safely through the chain of possession from cradle to grave. To ensure proper handling, there are many safety checks as required by RCRA and DOT regulations. This includes proper labeling, inventory, and characterization to ensure all hazards are known, inspections by the Custodian, ECA, and the state regulator (SCDHEC), and transportation verifications and manifesting. One of the best ways to reduce the risks that waste presents is to minimize or eliminate the creation of waste. This can be accomplished through source reduction which includes: Inventory control, material substitution, process modification, improved housekeeping, and preventative maintenance and recycling.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Semi-supervised learning approach for crack detection and identification in advanced gas-cooled reactor graphite bricks - 111

One of the life-limiting components of an Advanced Gas cooled Reactor (AGR) is its graphite core. The bricks present in the core undergo radiolytic oxidation throughout their lifetime which causes graphite weight loss and irradiation which can result in some of the bricks developing cracks. Understanding the nature and extent of brick cracking within the core is key to ensuring continued and extended operation of the AGR fleet. A semi-supervised machine learning classification algorithm is proposed as a method for improving the detection of cracked graphite bricks, by combining the labels derived from infrequent, detailed inspections of the core, with unlabeled, more frequent monitoring measurements taken during refueling operations. Semi-supervised machine learning, which is an emerging field in nuclear power condition monitoring, is the combination of ideas from both supervised and unsupervised machine learning whereby the data that is used to train the algorithm is a combination of labeled and unlabeled data. This paper introduces the initial research that has been undertaken in creating a semi-supervised self-training algorithm to detect the presence of graphite brick cracks and then proceeds to show that there is an improvement in the classification of graphite bricks using a semi-supervised machine learning classifier compared to supervised machine learning classifiers. This improved classification performance is encouraging as it does not require time consuming and costly human analysis to obtain extra learning information from available data. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Radiological Shielding Design for the Neutron Backscattering Spectrometer EMU

In this paper we present modelling and design of compact radiation shielding required for operation of the EMU instrument, located at Australia's OPAL nuclear reactor. The acceptance criteria for the shielding design were (i) safety - dose rate ≤ 3 μSv/h at external surfaces of the instrument's experimental area, (ii) floor load limit < 20 tons/m{sup 2}, (iii) low cost and (iv) for maintenance purposes 'easy access' to components of the instruments. The EMU instrument is a cold neutron backscattering spectrometer designed to measure inelastic neutron scattering. Its distinctive feature is that it has a tight elastic energy transfer resolution, of about 1 μeV. The instrument provides unique opportunities to study diffusion in complex materials, e.g. water molecules in confinement, polymers or biological molecules, particularly in the Asia-Oceania region. EMU is the first instrument located on the CG3 neutron beam line of the OPAL Neutron Guide Hall (NGH). A pre-existing evacuated neutron guide of dimensions 200 mm (height) x 50 mm (width) feeds into the shared EMU/KOOKABURRA pre-monochromator bunker shielding assembly, and its bottom cross section will be intercepted ∼200 mm further in air by the highly orientated pyrolytic graphite (HOPG) EMU pre-monochromator (take-off angles from 125.4 deg. to 148 deg.). The bunker shielding assembly for EMU also contains a beryllium filter and Focusing Guide Section D (FGD, 170 mm (height) x 50 mm (width)). To accommodate the range of take-off angles Pb cylindrical shielding blocks of radius 140 mm and 500 mm height have been designed. These blocks will be located at the section of the bunker where the neutron beam will exit towards the experimental area and they will reside on top of a hollow steel base (sides 10 mm thick), on which they move by sliding and are integrated with the tertiary shutter. The experimental area is enclosed by a concrete wall of 2.5 m height and 300 mm thickness, shared with the KOOKABURRA instrument, Gyprock Plasterboard panels with viewing windows, a wall from the Out-of-Pile Bunker and the CG3 shielding. The neutron beam enters the experimental area via the tertiary shutter, into the Focusing Guide Section C (FGC), which has an entrance guide cross-section of 158 mm (height) x 79 mm (width) and exit guide cross-section of 149 mm (height) x 85 mm (width) and is of length 816 mm. The vacuum chamber of FGC will be connected to the vacuum chamber of the Background Chopper (BC, disc of outer radius 350 mm). Focusing Guide Section B (FGB) is aligned with FGC and its vacuum shared with the BC vacuum. The FGB has an exit guide cross section of 50 mm (height) x 49 mm (width) and is of length 4046 mm, which will focus the neutron beam vertically and horizontally towards the Al window entrance (0.82 mm thickness, labelled N16) of the Scattering Tank assembly (constructed from 20 mm - 40 mm thickness stainless steel and will be under vacuum < 1 x 10{sup -3} mbar). Inside the Scattering Tank there is another section of Focusing Guide (FGA), with an exit guide cross-section of 30 mm (height) 30 mm (width) and of length 417 mm. The neutron beam exiting the FGA will be reflected by the HOPG crystal pieces of the Graphite Chopper (GC) towards the Backscattering Monochromator (BM). The reflected neutron beam will first pass through the other Al window (0.82 mm thickness, labelled N17) of the Scattering Tank and then enter into a flight tube (steel of 10 mm thickness). The BM is formed in an 1800 mm spherical radius array of Si crystal elements (0.87 mm thickness) and is oscillated by the linear motor Doppler Drive. The neutron beam will be reflected back at 180 deg. from, and focussed by the BM to reenter the Scattering Tank through the N17 Al window. It will then pass through the GC (HOPG crystals are rotated out of the way) and will reach the scattering sample of interest to the instrument end user. Also, inside the Scattering Tank there will be {sup 3}He linear position-sensitive detectors, and Analyser Arrays formed in an 1800 mm radius spherical arrays of Si crystal elements (0.87 mm thickness) glued on aluminium support frames (10 mm thickness) and span ∼160 deg. arc.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Automatic Segmentation of Building Envelope Point Cloud Data Using Machine Learning

About 50% of buildings in the US were constructed before energy codes were introduced. Modular overclad panel retrofits, in which a new envelope is constructed over the existing building, are a promising solution given that it minimizes occupant disruption and shortens construction time at the jobsite. Current state-of-the-art retrofit panel layout and dimensioning consists of three steps: 1) 3D point cloud data generation of the building envelope using commonly available surveying equipment, 2) manual segmentation of 3D point cloud data by a trained professional to identify and dimension window openings, door openings, and other architectural features, and 3) modular panel layout optimization and dimensioning by an architect or engineer. Among these steps, the second one remains the most difficult and costly because it is very labor-intensive. We propose a methodology to automatically label 3D point cloud data to reduce the time and expense spent in manual segmentation. Machine learning methods were employed to classify the point cloud data into distinct groups, each of which corresponds to different features of the building envelope. After classification, a segmentation algorithm was developed to perform boundary detection and separate the components of the façade. Finally, the algorithm returns the relative positions and dimensions of the features in the building envelope. The measurements obtained with the proposed automated method were compared against the actual dimensions to determine the overall algorithm accuracy. The proposed algorithm can then be used to reduce manual efforts for 3D point cloud labeling before modular panel layout optimization is performed.

Maldonado Puente, Bryan↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

OpenPATH - Leveraging Technology to Measure Travel Behavior

Shifting transportation to more sustainable modes is a key piece of the decarbonization puzzle. However, mobility behavior and travel patterns are difficult to influence because they are difficult to measure. OpenPATH provides a tool to capture longitudinal behaviors through a smartphone application. Agencies interested in gathering data about a population's travel behavior can set up a deployment of the app customized to the needs of their community. Partners can choose between simple mode and purpose labels or surveys for each trip to balance the level of user engagement with the associated burden. The labels, trip surveys, and an initial demographic survey can all be tailored to the specific context of the deployment. The OpenPATH tool is unique in its open-source nature, ability to gather detailed longitudinal travel data, and design allowing direct engagement with travelers. A valuable technological advancement, this tool enables partners to measure the way changes in the transportation landscape impact their community. The suite of tools includes both public and administrator dashboards. The public dashboard supports continuous data analysis through charts presenting trip information updated daily. The administrator dashboard displays geospatial data and supports data export. Example applications have included e-bike programs; gathering valuable metrics on increased access to opportunities and reduction in VMT, and studies aimed at understanding existing mobility behavior to see where advancements such as electric vehicles could fit into these habits. OpenPATH collects travel data in association with an initial demographic survey, enabling detailed insight into the behavior patterns or impact of a certain program on different populations.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Intern Deliverable Poster; Arc Flash Analysis; Landon Palmer

This poster describes what arc flash is, and what causes it. The importance of arc flash analysis is discussed, including what incident energy/working distance mean. SKM Power*Tools is discussed as a means for performing an arc flash analysis and breaker coordination. Arc flash warning labels are explained, including why it is important that up to date information is present with any changes made to an electrical system. An example arc flash label is described to give readers a better understanding of key components.

99 - GENERAL AND MISCELLANEOUS↗