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DOE OSTI2015

A number of upgrades on RHIC power supply system

This year marks the 15th run for the Relativistic Heavy Ion Collider (RHIC). Operation of a reliable superconducting magnet power supply system is a key factor of an accelerator’s performance. Over the past 15 years, the RHIC power supply group has made many improvements to increase the machine availability and reduce failures. During these past 15 years of operating RHIC a lot of problems have been solved or addressed. In this paper some of the essential upgrades/improvements are discussed.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Deciphering Degradation: Machine Learning on Real-World Performance Data

The solar investment community is in need of up-to-date and data-derived system degradation rates to use in financial models that calculate the risk and expectation of energy revenue. To date, financial models choose degradation rates from past studies that are limited and may not be representative of solar projects in development today. As the solar industry expands in all sectors (residential, commercial, and utility), there is a growing amount of energy generation data available to analyze. With the help of the open source degradation analysis code (RdTools) it is now possible to derive degradation on an ongoing basis and continuously provide up-to-date durability statistics to the investment community. Ongoing and accurate statistics of fielded photovoltaic systems allows financial stakeholders to constrain energy revenue projections, lower financial risk, and increase the bankability of solar. This presentation will provide a mid-term progress update on a 2 year SETO-funded degradation project that involves applying RdTools analysis on kWh Analytics’ industry database. With results from the RdTools analysis in hand, machine learning methods are employed to quantify how much variance of system performance degradation can be explained by predictor variables such as environmental factors, equipment bill of materials characteristics, and system design information. The goal of this presentation is to promote the usage of RdTools on an ongoing to data owners and to solicit feedback from researchers and stakeholders on how to maximize impact of the SETO-funded kWh Analytics degradation project.

14 SOLAR ENERGY↗

DOE OSTI2026

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

Rebooting Computers as Learning Machines

Artificial neural networks could become the technological driver that replaces Moore's law, boosting computers' utlity through a process akin to automatic programming--although physics and computer architecture would are also a factor.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Delineating Variabilities of Groundwater Level Prediction Across the Agriculturally Intensive Transboundary Aquifers of South Asia

We report the groundwater depletion in South Asia’s Himalayan, transboundary Indus-Ganges-Brahmaputra-Meghna (IGBM) rivers basin is among the highest globally. Given the high irrigation demand and population, groundwater sustainability requires an improved understanding of groundwater systems for the accurate prediction of groundwater levels (GWLs). However, the prediction of groundwater system behaviors is a significant challenge since it is dominated by spatiotemporal and subsurface depth-dependent drivers. Earlier studies that address the challenges are mainly based on the short spatial and temporal extent and/or do not separate the renewable (i.e., shallow) vs nonrenewable (i.e., deeper) groundwater signals. Here, we first identified the variable importance of spatial and depth-dependent drivers on GWL in the IGBM basin. Our results indicate a greater influence of anthropogenic factors (i.e., widespread pumping and increased population) in most parts of the IGBM basin, except in the precipitation-dominated basin of the Brahmaputra. Our next purpose was to delineate a multifactorial approach for GWL prediction using the two most used machine learning models (i.e., support vector machine and feed-forward neural network) in the literature. In general, the machine learning model outputs show a good match in comparison to the GWL from the observation wells (n = 2303 distributed across India and Bangladesh) with some limitations in areas with increased groundwater irrigation. We separately compared the results from shallow (<35 m) and deep (>35 m) observation wells, emphasizing the significance of deep groundwater pumping. Our approach highlights the importance of spatiotemporal to multidepth factors in GWL prediction and can be adopted in other parts of the globe to predict GWLs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Assessing high fidelity multi-component models to facilitate safeguards at Gas Centrifuge Enrichment Plants

We report that the International Atomic Energy Agency (IAEA) inspectors routinely carry out environmental sampling (ES) as a verification method. Collection of environmental swipe samples at various locations in Gas Centrifuge Enrichment Facilities (GCEPs) is an important process in detecting misuse of a declared facility and possibly the existence of undeclared nuclear material. These samples are measured for isotopic composition in uranium containing particles by Thermal Ionization Mass Spectrometry (TIMS) or Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Even though ES is highly effective in detecting the absolute value of enrichments and their deviations from the declared values, it cannot explain the cause of those changes. Several potential explanations can serve as possibilities for particles detected above or other than the declared enrichment. These include normal and non-malicious events such as the design of the enrichment cascades, unintentional failures of the machines, or overshoot during startup of a cascade. It can also be the result of deliberate misuse by the facility operators. The primary objective of this work is to understand how these factors affect the enrichments produced by a cascade and quantify anticipated multi-isotopic concentrations for each case. The following methodology is employed to determine signatures at a particular facility. 1) Utilize a new two-dimensional multi-component diffusion code to obtain centrifuge performance data and use that information to design and perform cascade analysis. Compare and contrast the results with previous 1-D radially averaged solutions from the Pancake code. 2) Design a GCEP cascade with the production goal of 19.75% 235 U for each set of machine data above. Investigate two cascade scenarios that include enrichment of natural uranium (NU) feed to 19.75% 235U in a single cascade compared to a two-step process of NU to 5% and then 5% to 19.75%. 3) Simulate the intentional vs. unintentional off-normal scenarios in the cascades to assess the differences in isotopic concentrations. A non-ideal squared-off cascade model developed at the University of Virginia is used to calculate flow rates and isotopic concentrations of the process gas. The analysis is performed using the Rome machine model operated at 600 m/s rotor speed. The upper and lower bounds of normal and abnormal enrichments in a typical facility are used in conjunction with ES results to understand the root causes of such observations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2022

Group structure selection with random forests

Choosing an appropriate group structure for multigroup transport is far from an exact science. For some applications, one blindly uses a group structure developed years ago by forgotten methods. Furthermore, one sometimes uses the same group structure for a variety of problems, even if the group structure was originally developed with a certain application in mind. In this work, we create optimized group structures with simulated annealing for critical assembly test problems and apply a random forest regressor with bagging to choose the best group structure based on parameters of the different test problems. The optimized group structures were generated using a simulated annealing optimizer for several simple, spherical, and unreflected problems. The optimization was performed to minimize a cost function that included fission rate, absorption rate, leakage, and k{sub eff}. A random forest regressor was then trained on a set of International Criticality Safety Benchmark Evaluation Project inputs and used to select one of these six group structures. The trained machine learning model chose the best group structure 65% of the time, and one of the three best 89% of the time. Furthermore, it decreased the L2 error over all test problems by a factor of 25 when compared to the standard Los Alamos 70-group structure. In other words, the model chose group structure that were far more appropriate for the test problems than the traditional LANL group structure. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Machine learning prediction on the fractional free volume of polymer membranes

Fractional free volume (FFV) characterizes the microstructural level features of polymers and affects their properties including thermal, mechanical, and separation performance. Experimental measurements and theoretical analyses have been used to quantify the FFV of polymers, but challenges remain because of their limitations. Experimental measurements are laborious and based on semi empirical equations, while Bondi’s group contribution theory involves ambiguities like the determination of van der Waals volume and the choice of factor values in the theoretical equation. To efficiently evaluate the FFV of polymers, this study utilizes high-throughput molecular dynamics (MD) simulations to build a large dataset regarding polymer’s FFV. Based on this large dataset, we further build machine learning (ML) models to establish the composition-structure relation. Inspired by group contribution theory which correlates polymer’s functional groups to FFV, our ML models correlate polymer’s substructures or physico-chemical indexes to FFV. Here, our study first benchmarks the MD simulation protocol to obtain reliable FFV of polymers and then carries out high-throughput MD simulations for more than 6,500 homopolymers and 1,400 polyamides. Such a large and diverse dataset makes the well-trained ML models more generalizable, compared with the group contribution theory. The efficiency of a feed forward neural network model is further demonstrated by applying it to a hypothetical polyimide dataset of more than 8 million chemical structures. The predicted FFVs of hypothetical polyimides are further validated by MD simulations. The obtained FFVs of the 8 million polymers, plus their previously reported gas separation performances, demonstrate the promising capability of ML virtual screening for the discovery of polymer membranes with exceptional permeability/selectivity.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Process intensification in a spinning disk absorber for absorption heat pumps

Absorption heat pumps are often an efficient machine for recovering of low-grade waste heat to cooling applications. However, they suffer from slow transfer processes and consequently large equipment size. This paper presents an experimental investigation of a novel absorber that utilizes a combination of a spinning disk and manifold-microchannel technology to intensify the absorption process. The rotation of the disk improves solution-side absorption performance, while an adaption of manifold-microchannel technology provides water-side enhancement. The enhanced spinning disk absorber was tested in an experimental setup using aqueous lithium bromide as the working fluid. Experimental results are compared with key performance metrics reported in the existing literature. The overall heat transfer increased by a factor of 4.7, and absorption rate increased by a factor of 4 to 10 folds over those in conventional tube absorbers. The results suggest that the spinning disk reactor could substantially reduce the size of absorber in the absorption machines. Better thermal and mass transfer performance can also enable air cooling with higher heat sink temperature than the one available through cooling towers.

42 ENGINEERING↗

DOE OSTI2019

Abradable Sealing Materials for Emerging IGCC-Based Turbine Systems (Final Report)

Reducing the gap between rotating and stationary parts in gas turbine engines, and mitigating gas leakage via these paths, can significantly increase the performance and attendant efficiency. One approach to reducing such gaps is to deploy abradable coatings on the stationary shroud components as seals, to reduce the blade-tip clearances under operational conditions. Abradable coatings must be able to withstand high temperature oxidation, thermal cycling, and erosion, while providing optimal controlled abrasion and associated shape retention. Existing abradable seals utilized in power generation turbines have generally been optimized for use with natural gas-fired systems; however, preliminary testing of syngas and high-hydrogen-content (HHC) fired turbines has shown that the stability of hot-section materials (and presumably abradable sealing materials) may be substantially altered due to characteristic changes in the combustion by-products (partial pressures of water vapor, etc.) as well as characteristic impurities and particulate matter entrained in the fuel. The primary focus of this research program has been to develop a mechanistic understanding of how the altered combustion environments modify the thermo-mechanical stability and performance of the sealing materials, including the requisite abradable sealing behavior. A key component of the research program carried out has been an exploration of mechanisms underpinning observed degradation processes, and an assessment of connections to the combustion environments and characteristic non-combustible constituents. The ultimate goal of the project has been to advance the goals of the Advanced Turbine Program by developing materials design protocols leading to turbine hot-section components with improved resistance to service lifetime degradation under advanced fuels exposures. This research program has evaluated the performance and degradation of ceramic abradable seals used in the high-temperature turbine sections of gas turbine engines. The focus has been two-fold: (1) to understand performance of such materials in relation to the turbines operating on coal-derived syngas and high hydrogen content (HHC) fuels, ultimately seeking an improved understanding of factors that control performance and durability of abradable seal materials for these combustion environments (and play into materials design protocols); and (2) to evaluate the potential of alternative materials as abradable seals for the higher temperatures expected with IGCC and HHC fueled turbine systems. In particular, we carried out an initial assessment of multi-layered systems of existing abradable seal materials (YSZ) and other ceramics. The research program also investigated several classes of abradable coatings under simulated exposures to syngas-based combustion environments, evaluating the relevant wear behavior, hardness, stability under cyclic oxidation, and general thermo-mechanical behavior. The research also attempted to develop alternatives to carrying out full-scale combustion-based abradability test rig exposures to evaluate materials system performance. The research has been focused on correlating the measured thermo-mechanical behavior, and controlled abrasive wear, with the intrinsic properties of the multilayer coatings and processing-controlled microstructural features that have shown attractive machinability characteristics based on weak interfacial bonding between the constituent phases at elevate temperatures (while maintaining thermochemical stability). This program has resulted in an improved mechanistic understanding of factors governing performance of high temperature abradable seals, and degradation mechanisms unique to IGCC-based power generation turbine systems and the realization of coal-derived syngas and HHC-based combustion environments – ultimately with the goal of developing a knowledge base upon which the design of coatings that retain optimal sealing characteristics and are more resistant to the observed wear/attack mechanisms, important aspects of advancing the goals of the DOE Advanced Turbine Program.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Accelerating the discovery of low-energy structure configurations: A computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning

Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing

Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O 2 ). Furthermore this review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate "Learn and Design" for strain development.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

Abstract Wetland methane (CH 4 ) emissions ( $${F}_{{{CH}}_{4}}$$ F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature using observations from the FLUXNET-CH 4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH 4 production are thus needed to improve global CH 4 budget assessments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Dissolving salt is not equivalent to applying a pressure on water

Abstract Salt water is ubiquitous, playing crucial roles in geological and physiological processes. Despite centuries of investigations, whether or not water’s structure is drastically changed by dissolved ions is still debated. Based on density functional theory, we employ machine learning based molecular dynamics to model sodium chloride, potassium chloride, and sodium bromide solutions at different concentrations. The resulting reciprocal-space structure factors agree quantitatively with neutron diffraction data. Here we provide clear evidence that the ions in salt water do not distort the structure of water in the same way as neat water responds to elevated pressure. Rather, the computed structural changes are restricted to the ionic first solvation shells intruding into the hydrogen bond network, beyond which the oxygen radial-distribution function does not undergo major change relative to neat water. Our findings suggest that the widely cited pressure-like effect on the solvent in Hofmeister series ionic solutions should be carefully revisited.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Modeling temporal and hormonal regulation of plant transcriptional response to wounding

Plants respond to wounding stress by changing gene expression patterns and inducing the production of hormones including jasmonic acid. This wounding transcriptional response activates specialized metabolism pathways such as the glucosinolate pathways in Arabidopsis thaliana. While the regulatory factors and sequences controlling a subset of wound-response genes are known, it remains unclear how wound response is regulated globally. Here, we how these responses are regulated by incorporating putative cis-regulatory elements, known transcription factor binding sites, in vitro DNA affinity purification sequencing, and DNase I hypersensitive sites to predict genes with different wound-response patterns using machine learning. We observed that regulatory sites and regions of open chromatin differed between genes upregulated at early and late wounding time-points as well as between genes induced by jasmonic acid and those not induced. Expanding on what we currently know, we identified cis-elements that improved model predictions of expression clusters over known binding sites. Using a combination of genome editing, in vitro DNA-binding assays, and transient expression assays using native and mutated cis-regulatory elements, we experimentally validated four of the predicted elements, three of which were not previously known to function in wound-response regulation. Our study provides a global model predictive of wound response and identifies new regulatory sequences important for wounding without requiring prior knowledge of the transcriptional regulators.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

SU-E-T-248: Determining Output Factor of Irregular Electron Cutouts at Extended SSD

Purpose: To calculate the output factor (OPF) of any irregularly shaped electron beam at extended SSD Methods: Using Cerrobend block, circular cutouts with diameter ranging from 2.0cm to the maximum possible size for 15×15 applicator of Varian 2100C and 14×14 applicator of ELEKTA Synergy LINACs were prepared. In addition, two irregular cutouts were made. For each cutout, percentage depth dose (PDD) at standard SSD and point doses at different SSD were measured in water using Scanditronix diodes and PTW 0.125 cm{sup 3} ion-chambers, respectively. In addition, the distance at which electron fluence along the central axis becomes independent of cutout size was determined using EDR2 films. On each LINAC, electron beam energies of 6, 9, 12, and 15/20 MeV were used. Results: Using the open applicator as the reference field, the lateral buildup ratio (LBR) for each circular cutout as a function of depth was calculated. From the LBR value and radius of the cutout, the corresponding lateral spread parameter (σR(z)) was determined. Taking the cutout side dependence of σR(z)) into account, PDD of the irregular cutouts at the standard SSD were calculated. From the point dose measurement at different SSDs, the effective SSD of each cutout was calculated. Taking the calculated PDD of the irregular cutout and effective SSD (SSDeff) of the circular cutout into account, OPFs of the irregular cutout at extended SSD values were calculated. Finally, the calculated output factors were compared with the measured values. Conclusion: In this research, the improved LBR method has been generalized to calculate output factor of irregular cutouts at extended SSD. In a clinically useful region, the percentage difference between the measured and the calculated output factors of the irregular cutouts was within 2%. Similar results were obtained for all available electron energies of both Varian 2100C and ELEKTA Synergy machines.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2023

Python package for machine-readable access to PDG data (PDG Python API) v0.1

This Python package implements a high-level interface to access the data published by the Particle Data Group (PDG) in the Review of Particle Physics (the "Review"). The Particle Data Group is an international collaboration led by the PDG group at LBNL. The PDG summarizes the established knowledge in the field of particle physics in a single publication, the Review of Particle Physics. The Review is published and updated online (see https://pdg.lbl.gov) each year, and published in a scientific journal every other year. It is currently licensed under a CC BY-NC 4.0 license. In 2021 PDG was designated by the Office of Science as a SC PuRe Data Resource. The Review is one of the most highly cited publications in the field of particle physics. The PDG Python API is part of PDG's efforts to make all data provided in the Review available in machine-readable format. This data includes the PDG world averages (or best limits) on particle masses, widths or lifetimes, branching fractions, magnetic moments, form factors, coupling constant ratios, and searches, as well as particle quantum numbers. It also includes detailed information on how PDG arrived at its averages, such as e.g. tables of published measurements with comments and footnotes, information on the consistency of published measurements, and detailed fit information.

Beringer, Juerg↗

DOE OSTI2015

Phased Array Ultrasonic Examination of Reactor Coolant System (Carbon Steel-to-CASS) Dissimilar Metal Weld Mockup Specimen

In the summer of 2009, Pacific Northwest National Laboratory (PNNL) staff traveled to the Electric Power Research Institute (EPRI) NDE Center in Charlotte, North Carolina, to conduct phased-array ultrasonic testing on a large bore, reactor coolant pump nozzle-to-safe-end mockup. This mockup was fabricated by FlawTech, Inc. and the configuration originated from the Port St. Lucie nuclear power plant. These plants are Combustion Engineering-designed reactors. This mockup consists of a carbon steel elbow with stainless steel cladding joined to a cast austenitic stainless steel (CASS) safe-end with a dissimilar metal weld and is owned by Florida Power & Light. The objective of this study, and the data acquisition exercise held at the EPRI NDE Center, were focused on evaluating the capabilities of advanced, low-frequency phased-array ultrasonic testing (PA-UT) examination techniques for detection and characterization of implanted circumferential flaws and machined reflectors in a thick-section CASS dissimilar metal weld component. This work was limited to PA-UT assessments using 500 kHz and 800 kHz probes on circumferential flaws only, and evaluated detection and characterization of these flaws and machined reflectors from the CASS safe-end side only. All data were obtained using spatially encoded, manual scanning techniques. The effects of such factors as line-scan versus raster-scan examination approaches were evaluated, and PA-UT detection and characterization performance as a function of inspection frequency/wavelength, were also assessed. A comparative assessment of the data is provided, using length-sizing root-mean-square-error and position/localization results (flaw start/stop information) as the key criteria for flaw characterization performance. In addition, flaw signal-to-noise ratio was identified as the key criterion for detection performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗