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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Distributed Outage Detection in Power Distribution Networks

Real time topology knowledge is essential for situational awareness of power distribution networks. Line outages change the topology of a distribution network. Hence, outage detection is an important task. Most of the existing outage detection algorithms are centralized, in which sensors communicate their data to a control center which performs outage detection using the received data. However, with the increasing size of the distribution network and with different areas of the network being monitored by different operators, communication is a bottleneck and scalability is a major concern. To address these issues, we propose a novel outage detection algorithm using a divide and conquer approach. First, we divide a distribution network into sub-networks, such that outage detection can be run in parallel in each sub-network independently ensuring scalability to large networks. Further, to reduce the latency, bandwidth and attenuation challenges associated with communications in a large network, we divide each sub-network into multiple control areas which communicate only with their neighbors. We employ a distributed iterative load estimation across the control areas of each sub-network and then use the load estimate for local outage detection in each control area. Here, the performance of our algorithm is evaluated for multiple feeder models and compared against traditional centralized outage detection algorithms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Kinetic Measurements in Heterogeneous Catalysis

This contribution is about the experimental determination of the rate of a heterogeneous catalytic reaction and the analysis of kinetic data. In this case, the reaction rate can be defined as the frequency at which the closed sequence of elementary steps transforming reactants into products is occurring. However, the rate of chemical reaction is not directly observed; rather, one records the rate of substance change. The rate of chemical reaction is calculated based on the rate of substance change and assumed stoichiometry of the reaction. In order to make this an intensive quantity (i.e., independent of the volume in which the reaction is performed, this frequency is divided by the latter or by a quantity proportional to the latter). As the focus of what follows is on heterogeneously catalyzed reactions this can be the catalyst mass, the catalyst surface area or the total number of active sites present in the reaction volume confined by the walls of a chemical reactor. For example, the rate may be reported as one of the following: Net rate of consumption/production of component i mol m-3 s-1 Net specific rate of consumption/production of component i mol kg-1 s-1 Rate of substance change (per unit volume of catalyst) mol m-3 s-1 Specific reaction rate (per unit mass of catalyst) mol kg-1 s-1 Net rate of consumption/production of component i mol m-2 s-1 The turnover frequency (TOF) is a characteristic originally introduced by Boudart [1] is expressed as TOF=R/G_tot , where R is the steady-state rate of reactant consumption or product generation and G_totis the total areal density of active sites [mol m-2]. The latter is typically taken from experimental chemisorption data obtained at low temperature. The advancement of a chemical reaction leads to changes in the amounts of reactants and products which, for a stoichiometric single reaction, are connected by the stoichiometric coefficients. In many cases, for the sake of simplicity, we will assume that the investigated reaction proceeds according to a single reaction pathway, so that the net production rate of a reaction component is directly proportional to the reaction rate and that the latter equals the net production rate of any of the involved components when divided by the appropriate stoichiometric coefficient. For complex stoichiometric reactions the net production rates of the involved components are linear combinations of the reaction rates. Different goals for kinetic measurements can be formulated: Catalyst testing, i.e., obtaining the kinetic dependences for the development and discrimination of efficient catalytic materials. Precise kinetic characterization of active materials via high-throughput screening is a part of such activity. Detailed kinetics, i.e., revealing the detailed mechanism of complex catalytic reaction via systematic kinetic studies, both steady-state and non-steady-state Industrial kinetics, i.e., obtaining data and relationships for describing and predicting the behaviour of catalytic reactors and processes at industrial scale Finally, mathematical modeling and analysis where kinetic measurements provide data for understanding complex kinetic phenomena, e.g., oscillations, non-linear self-organization, etc.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Phase field benchmark problems for nucleation

In this work, we present nucleation phase field model benchmark problems, expanding on our previous benchmark problems on diffusion, precipitation, dendritic growth, linear elasticity, fluid flow and electrochemistry. Nucleation is the process in which either a new thermodynamic phase or a new structure is created, such as solidification from the melt, or self-assembly of particulates. Based on where the nucleation occurs, it can be divided into two main categories: homogeneous nucleation and heterogeneous nucleation. In the first nucleation benchmark problem, we focus on homogeneous nucleation for both single seed under different initial conditions and multiple seeds. The second nucleation benchmark problem focuses on athermal heterogeneous nucleation and nucleation behavior near the free growth limit with different undercooling driving forces.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Two decades of aerosol trends over India: seasonal characteristics and urban-rural dynamics

Abstract India faces significant air quality challenges, with one of the highest air pollution levels of any country in the world. Here, we examine two decades (2001–2019) of both particulate matter (PM 2.5 ) concentration and aerosol optical depth (AOD) over the country. Increases are seen between the two decadal averages, for 2001–2010 and 2011–2019, in western India, particularly in the Indo-Gangetic Plain (IGP). IGP region, including Bihar, West Bengal, Jharkhand, and Uttar Pradesh, shows the highest increases in AOD (+0.03, 13%) and PM 2.5, s (+8 µ g m −3 ). Seasonal AOD patterns fluctuate, with the IGP experiencing the highest wintertime increase, especially in Bihar (+0.07). In summer, there are increases in AOD along the southern and eastern coastal areas. Monsoons cause a slight rise in AOD, except in Rajasthan. In the post-monsoon season, the IGP experiences a notable increase in AOD (+0.057, 25%), potentially driven by biomass burning in Bihar (+0.11) and Uttar Pradesh (+0.075). Dividing our study area into urban and peri-urban clusters ( n = 2791), AOD is found to be similar, possibly due to advective mixing. However, the differences between urban and rural areas become more noticeable, especially in the second decade. Correlations between AOD and PM 2.5, g vary across locations, with the highest found in Kanpur ( R 2 = 0.61) and weaker in Delhi ( R 2 = 0.42), highlighting the need for more ground monitoring. However, it suggests that satellite-derived AOD can generally be used to examine trends in PM 2.5 over longer time frames.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The effect of dietary supplementation of Lycium barbarum leaves on the growth performance, organ indexes and intestinal microflora of rats

This study was conducted to investigate both fruit and different levels of leaf supplementation on the growth performance, organ indices and intestinal microflora of rats. Twenty-five healthy male Sprague–Dawley rats were randomly divided into five groups. The rats in the control (NC) and positive control (PC) groups were fed by gavage a basal diet and a basal diet with 4 g/kg of L. barbarum fruit homogenate, respectively. The test (LD, MD, and HD) groups were fed basal diets with additional 2, 4, and 8 g/kg of L. barbarum leaf homogenate, respectively. The feeding period was 35 d. The result revealed that the rats in the LD group had the highest average weight gain ( p < 0.05). The cardiac and renal indexes in the LD and MD groups were significantly higher than in NC group, respectively ( p < 0.05). Diversity analysis revealed that adding low concentrations of L. barbarum leaf homogenates markedly reduced the Shannon index of the rats cecum ( p < 0.05). The relative abundance of Verrucomicrobiota was higher in the LD group than those in other groups ( p < 0.05). The relative abundance of Actinobacteriota was found significantly higher in PC group than others ( p < 0.05). The relative abundance of Akkermansia in LD group was the highest ( p < 0.05). The relative abundance of Romboutsia in the PC group was considerably higher than that in other groups. The relative abundance of Candidatus_Saccharimonas in the supplementation groups was appreciably lower than those found in other groups. The relative abundance of Alloprevotella was significantly lower in PC, LD, and MD groups than in NC and HD groups ( p < 0.05). The relative abundance of Oscillibacter was significantly higher in HD group than in other groups ( p < 0.05). Thus, L. barbarum leaf homogenate fed to rats could increase their growth performance, internal organ weights and additionally enhance the relative abundance of beneficial bacteria. Therefore, based on the obtained data in the current study, a dose of L. barbarum leaf homogenate supplemented with 2 g/kg in diet is recommended, however, further studies are required to confirm, especially in animals.

Guo, Yindi↗
DOE OSTI2021

Size, shade, or shape? The contribution of galaxies of different types to the star formation history of the Universe from SDSS-IV MaNGA

ABSTRACT By fitting stellar populations to the fourth generation of the Sloan Digital Sky Survey (SDSS-IV) Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey observations of ∼7000 suitably weighted individual galaxies, we reconstruct the star formation history of the Universe, which we find to be in reasonable agreement with previous studies. Dividing the galaxies by their present-day stellar mass, we demonstrate the downsizing phenomenon, whereby the more massive galaxies hosted the most star formation at earlier times. Further dividing the galaxy sample by colour and morphology, we find that a galaxy’s present-day colour tells us more about its historical contribution to the cosmic star formation history than its current morphology. We show that downsizing effects are greatest among galaxies currently in the blue cloud, but that the level of downsizing in galaxies of different morphologies depends quite sensitively on the morphological classification used, due largely to the difficulty in classifying the smaller low-mass galaxies from their ground-based images. Nevertheless, we find agreement that among galaxies with stellar masses $M_{\star } \gt 6\times 10^{9}\, \mathrm{ M}_{\odot }$, downsizing is most significant in spirals. However, there are complicating factors. For example, for more massive galaxies, we find that colour and morphology are predictors of the past star formation over a longer time-scale than in less massive systems. Presumably this effect is reflecting the longer period of evolution required to alter these larger galaxies’ physical properties, but shows that conclusions based on any single property do not tell the full story.

Peterken, Thomas↗
DOE OSTI2021

Measurement of beauty and charm production in pp collisions at $\sqrt{s}$ = 5.02 TeV via non-prompt and prompt D mesons

The pT-differential production cross sections of prompt and non-prompt (produced in beauty-hadron decays) D mesons were measured by the ALICE experiment at midrapidity (|y| < 0.5) in proton-proton collisions at s√ = 5.02 TeV. The data sample used in the analysis corresponds to an integrated luminosity of (19.3 ± 0.4) nb -1 . D mesons were reconstructed from their decays D 0 → K - π + , D + → K - π + π + , and D$^+_s$→ φπ + → K - K + π + and their charge conjugates. Compared to previous measurements in the same rapidity region, the cross sections of prompt D + and D$^+_s$ mesons have an extended pT coverage and total uncertainties reduced by a factor ranging from 1.05 to 1.6, depending on pT, allowing for a more precise determination of their pT-integrated cross sections. The results are well described by perturbative QCD calculations. The fragmentation fraction of heavy quarks to strange mesons divided by the one to non-strange mesons, f s /(f u + f d ), is compatible for charm and beauty quarks and with previous measurements at different centre-of-mass energies and collision systems. The $b\bar{b}$ production cross section per rapidity unit at midrapidity, estimated from non-prompt D-meson measurements, is d$σ_{b\bar{b}}$/d$y$| |y|<0.5 = $34.5 ± 2.4(stat)^{+4.7}_{-2.9}$(tot.syst) μb. It is compatible with previous measurements at the same centre-of-mass energy and with the cross section pre- dicted by perturbative QCD calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Transcriptome analysis and immune gene expression of channel catfish ( Ictalurus punctatus ) fed diets with inclusion of frass from black soldier fly larvae

The larval waste, exoskeleton shedding, and leftover feed components of the black soldier fly and its larvae make up the by-product known as frass. In this study, we subjected channel catfish (Ictalurus punctatus) to a 10-week feeding trial to assess how different dietary amounts of frass inclusion would affect both systemic and mucosal tissue gene expression, especially in regard to growth and immune-related genes. Fish were divided in quadruplicate aquaria, and five experimental diets comprising 0, 50, 100, 200, and 300 g of frass per kilogram of feed were fed twice daily. At the end of the trial, liver, head kidney, gill, and intestine samples were collected for gene expression analyses. First, liver and intestine samples from fish fed with a no frass inclusion diet (control), low-frass (50 g/kg) inclusion diet, or a high-frass (300 g/kg) inclusion diet were subjected to Illumina RNA sequencing to determine global differential gene expression among diet groups. Differentially expressed genes (DEGs) included the upregulation of growth-related genes such as glucose-6-phosphatase and myostatin, as well as innate immune receptors and effector molecules such as toll-like receptor 5, apolipoprotein A1, C-type lectin, and lysozyme. Based on the initial screenings of low/high frass using RNA sequencing, a more thorough evaluation of immune gene expression of all tissues sampled, and all levels of frass inclusion, was further conducted. Using targeted quantitative PCR panels for both innate and adaptive immune genes from channel catfish, differential expression of genes was identified, which included innate receptors (TLR1, TLR5, TLR9, and TLR20A), proinflammatory cytokines (IL-1β type a, IL-1β type b, IL-17, IFN-γ, and TNFα), chemokines (CFC3 and CFD), and hepcidin in both systemic (liver and head kidney) and mucosal (gill and intestine) tissues. Overall, frass from black soldier fly larvae inclusion in formulated diets was found to alter global gene expression and activate innate and adaptive immunity in channel catfish, which has the potential to support disease resistance in this species in addition to demonstrated growth benefits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Efficient bifurcation and tabulation of multi-dimensional combustion manifolds using deep mixture of experts: An a priori study

This work describes and validates an approach for autonomously bifurcating turbulent combustion man-ifolds to divide regression tasks amongst specialized artificial neural networks (ANNs). This approach relies on the mixture of experts (MoE) framework, where each neural network is trained to be specialized in a given portion of the input space. The assignment of different input regions to the experts is determined by a gating network, which is a neural network classifier. In some previous studies, it has been demonstrated that bifurcation of a complex combustion manifold and fitting different ANNs for each part leads to better fits or faster inference speeds. However, the manner of bifurcation in these studies was based on heuristic approaches or clustering techniques. In contrast, the proposed technique enables automatic bifurcation using non-linear planes in high-dimensional turbulent combustion manifolds that are often associated with complex behavior due to different dominating physics in various zones. Furthermore, the proposed concept is validated using 4-dimensional (4D) and 5D flamelet tables, showing that the errors obtained with a given network size, or conversely the network size required to achieve a given accuracy, is considerably reduced. The effect of the number of experts on inference speed is also investigated, showing that by increasing the number of experts from 1 to 8, the inference time can be approximately reduced by a factor of two. Moreover, it is shown that the MoE approach divides the input manifold in a physically intuitive manner, suggesting that the MoE framework can elucidate high-dimensional datasets in a physically meaningful way.

42 ENGINEERING↗
DOE OSTI2021

Initial hydration process of calcium silicates in Portland cement: A comprehensive comparison from molecular dynamics simulations

As the main components of Portland cement, calcium silicates show substantial differences in their hydration reactivity which have not been fully explained. A comprehensive comparison of the initial hydration process of calcium silicates, namely dicalcium silicate (C{sub 2}S) and tricalcium silicate (C{sub 3}S), was conducted using molecular dynamics simulations. The initial hydration process was divided into three stages using cut-off times of 0.001 ns and 3 ns. The hydration of M{sub 3}-C{sub 3}S (010) was more evident than that of β-C{sub 2}S (100), supported by the hydroxylation degree, radial distribution function, atomic density profile, etc. The coordination number of the surface Ca atoms might be the underlying reason for such a difference. Interactions between cement surfaces and water molecules were mainly characterised by solid OH bonding and Ca-water O bonding. Dissolution of Ca atoms was observed, although quite scarce, while no dissolution of Si atoms was observed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

GW-PINN: A deep learning algorithm for solving groundwater flow equations

Machine learning methods provide new perspective for more convenient and efficient prediction of groundwater flow. In this study, a deep learning method “GW-PINN” without labeled data for solving groundwater flow equations with wells was proposed. GW-PINN takes the physics inform neural network (PINN) as the backbone and uses either the hard or soft constraint in the loss function for training. A locally refined sampling strategy (LRS) is adopted to generate the consistent spatial sampling points for problems with strong hydraulic head change, and then combined with an appropriate temporal sampling scheme to obtain the final spatial-temporal sampling points. A snowball-style two-stage training strategy by dividing the temporal domain into two subdomains is designed to decrease the sampling points. Five cases were designed to test the training performance of GW-PINN under different sampling strategies and two constraints. The predicted results of GW-PINN were compared with MODFLOW and the analytical solution. The results demonstrate that GW-PINN possesses strong ability in capturing the hydraulic head change for both confined and un-confined aquifers. The hard constraint owns more robust learning ability than the soft constraint. The LRS strategy can generate more accurate results with much fewer sampling points than traditional sampling strategies, and the snowball-style two-stage training strategy is significantly efficient for problems with the drastic change of hydraulic head. Additionally, the application of GW-PINN as a surrogate model for parameterized groundwater flow equations is illustrated. This study provides an option tool for efficient groundwater flow simulation, especially for those with local refinements are needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Topological current divider in a Chern insulator junction

A Chern insulator is a two-dimensional material that hosts chiral edge states produced by the combination of topology with time reversal symmetry breaking. Such edge states are perfect one-dimensional conductors, which may exist not only on sample edges, but on any boundary between two materials with distinct topological invariants (or Chern numbers). Engineering of such interfaces is highly desirable due to emerging opportunities of using topological edge states for energy-efficient information transmission. Here, we report a chiral edge-current divider based on Chern insulator junctions formed within the layered topological magnet MnBi 2 Te 4 . We find that in a device containing a boundary between regions of different thickness, topological domains with different Chern numbers can coexist. At the domain boundary, a Chern insulator junction forms, where we identify a chiral edge mode along the junction interface. We use this to construct topological circuits in which the chiral edge current can be split, rerouted, or switched off by controlling the Chern numbers of the individual domains. Our results demonstrate MnBi 2 Te 4 as an emerging platform for topological circuits design.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Modeling information flow in a computer processor with a multi-stage queuing model

In this paper, we introduce a nonlinear stochastic model to describe the propagation of information inside a computer processor. In this model, a computational task is divided into stages, and information can flow from one stage to another. The model is formulated as a spatially-extended, continuous-time Markov chain where space represents different stages. This model is equivalent to a spatially-extended version of the M/M/s queue. The main modeling feature is the throttling function which describes the processor slowdown when the amount of information falls below a certain threshold. We derive the stationary distribution for this stochastic model and develop a closure for a deterministic ODE system that approximates the evolution of the mean and variance of the stochastic model. In conclusion, we demonstrate the validity of the closure with numerical simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data

A restricted Boltzmann machine (RBM) is a generative model that could be used in effectively balancing a cybersecurity dataset because the synthetic data a RBM generates follows the probability distribution of the training data. RBM training can be performed using contrastive divergence (CD) and quantum annealing (QA). QA-based RBM training is fundamentally different from CD and requires samples from a quantum computer. We present a real-world application that uses a quantum computer. Specifically, we train a RBM using QA for cybersecurity applications. The D-Wave 2000Q has been used to implement QA. RBMs are trained on the ISCX data, which is a benchmark dataset for cybersecurity. For comparison, RBMs are also trained using CD. CD is a commonly used method for RBM training. Our analysis of the ISCX data shows that the dataset is imbalanced. We present two different schemes to balance the training dataset before feeding it to a classifier. The first scheme is based on the undersampling of benign instances. The imbalanced training dataset is divided into five sub-datasets that are trained separately. A majority voting is then performed to get the result. Our results show the majority vote increases the classification accuracy up from 90.24% to 95.68%, in the case of CD. For the case of QA, the classification accuracy increases from 74.14% to 80.04%. In the second scheme, a RBM is used to generate synthetic data to balance the training dataset. We show that both QA and CD-trained RBM can be used to generate useful synthetic data. Balanced training data is used to evaluate several classifiers. Among the classifiers investigated, K-Nearest Neighbor (KNN) and Neural Network (NN) perform better than other classifiers. They both show an accuracy of 93%. Our results show a proof-of-concept that a QA-based RBM can be trained on a 64-bit binary dataset. The illustrative example suggests the possibility to migrate many practical classification problems to QA-based techniques. Further, we show that synthetic data generated from a RBM can be used to balance the original dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Spatiotemporal Variations of Evapotranspiration in Amazonia Using the Wavelet Phase Difference Analysis

The relationships and seasonal-to-annual variations among evapotranspiration (ET), precipitation (P), terrestrial water storage anomalies (TWSA), radiation (downward shortwave radiation, DSR), and phenology (leaf area index, LAI) are complex across the Amazon basin. Here, to analyze how ET is controlled by these influencing factors, we used wavelet phase difference (WPD) to investigate the effects of P, TWSA, DSR, and LAI on ET at different spatiotemporal scales. The Amazon-scale averaged ET has strong correlations with these factors at the annual and multi-year periodicities. The patterns of WPDs have south-north and west-east divides due to the significant variation in climatic conditions. The results demonstrate that ET is mainly affected by water and energy availability while vegetation regulates both processes. The deep soil moisture/groundwater can provide strong subsidies to ET during the meteorological dry season in the water-limited area of Amazon. The WPD can well reflect the responses of ET to the variations of P, TWSA, DSR, and LAI, and the process of vegetation sustaining ET in the dry years in the water-limited area of the Amazon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Verification and Validation Approach for COMSOL Multiphysics to Support High Flux Isotope Reactor (HFIR)

Over the last several decades, many reactors have successfully been converted from high enriched uranium (HEU) to low enriched uranium (LEU) fuels in United States in support of its global non-proliferation objectives. Of the reactors slated for conversion, five high-performance research reactors (HPRRs) remain. The high-flux isotope reactor (HFIR) at Oak Ridge National Laboratory (ORNL) is one of the five. Conversion of HFIR requires the qualification of a new fuel. To aid in the conversion process as well as improve safety margins, COMSOL Multiphysics was chosen to support and eventually supplement the steady-state heat-transfer code (SSHTC). In order to use COMSOL as a supplementary tool, i.e. one used for safety basis calculations, the code needs to undergo a verification and validation process. Verification and validation are important processes for the use of any software; however, physics-based solvers have a unique challenge, in that the validation is much more rigorous and its domain is only applicable to the general scope of the problem. This document details the verification and validation of COMOSL for analysis of HFIR. This report is intended for two different users: (1) current and future users of COMSOL for HFIR and (2) for individuals interested in assessing the scope of the validation of COMSOL for HFIR. This report is divided into six sections. The first of which details the process of verification and validation. Section one provides an overview of key concepts from ASME V&V 10, 20, and 40 and how these concepts are used to provide a validation of any multiphysics software. Section two covers the approach needed in order to validate multiphysics software for a given HFIR analysis. Parts three through five split up the key concepts highlighted in section two. The final section provides concluding remarks which demonstrate the scope and limitations of the validation study and how these may be expanded and improved.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Medium Voltage Testbed for Comparing Advanced Power Line Sensors vs. Transformers with Electrical Grid Events

Electrical utilities have relied upon potential transformers (PTs) and current transformers (CTs) for very accurate metering and to provide reliable signals for protective relays. These devices measure phase voltages and currents and are commissioned by electrical engineers. PTs/CTs can detect and react to various electrical anomalies that could adversely affect electrical grid operations. Less expensive alternative sensing technologies offer the possibility of wider deployment, particularly in grids that employ distributed energy resources. In this work, the performance of an advanced medium-voltage sensor is compared with that of a reference PT and a CT and experimentally evaluated for different power grid scenarios on an advanced outdoor power line sensor testbed at the U.S. Department of Energy’s Oak Ridge National Laboratory. The sensor is based on a capacitive divider for voltage monitoring and a Rogowski coil with an integrator for current monitoring. This study simulated a power grid model based on a utility circuit at the Riverside EPB of Chattanooga. The simulation circuit was created with MATLAB/Simulink software and was integrated into an RT-LAB project to run with the OP4510 real-time simulator at the OPLST. During the tests, the real-time simulations were run for 40 s, and the signal to record the test event with the power meter was set at 30 s for the event-trigger circuit. The advanced outdoor power line sensor testbed has a real-time simulator that is used to generate transient scenarios (e.g., electrical faults, capacitor bank operation, and service restoration), while the analog signals are recorded by the same high-resolution power meter. The behaviors of analog signals, harmonic components, total harmonic distortion, and crest factors are assessed for this power line sensor and compared with those of the reference PT/CT because of the absence of testing standards for advanced outdoor power line sensors. The results showed that this OPLS technology responded identically to the PT and CT under all conditions.

Piesciorovsky, Emilio [ORNL]↗
DOE OSTI2022

Developing a heterogeneous ensemble learning framework to evaluate Alkali-silica reaction damage in concrete using acoustic emission signals

The monitoring and evaluation of Alkali-silica reaction (ASR) damage in concrete structures are required to ensure the serviceability and integrity of concrete infrastructures such as bridges and dams. The innovation of this paper lies in the development of an automatic ASR monitoring and evaluation approach by leveraging acoustic emission (AE) and a heterogeneous ensemble learning framework. Here, in this paper, ASR was monitored by AE sensors attached to a concrete specimen, which was placed in a chamber with high humidity and temperature. The recorded AE signals were filtered and divided by four ASR phases according to signal strength, crack width and expansion strain. A heterogeneous ensemble network including convolutional neural networks (CNN) and random forest models was employed to learn different features from AE signals and classify the AE signals into their corresponding phases. The results suggest that the proposed model has a high performance and classifies the signals into the assigned phases with high accuracy.

42 ENGINEERING↗