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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Application of a Geochemically Informed Leak Detection (GILD) Model to CO 2 Injection Sites on the United States Gulf Coast

The Gulf Coast region possesses great potential for CO 2 enhanced oil recovery (EOR) and CO 2 storage. A geochemically informed leak detection (GILD) model has been applied to CO 2 injection sites on the Gulf Coast with considerations of measurement variability. The Jasper aquifer in Montgomery County, Texas, was chosen to demonstrate the method. Based on background data from wells in the area, combinations of mineral and fluid compositions were used to create 23 scenarios for the geochemical model. The output from the geochemical model was used to identify sensitive monitoring species, and response functions were generated for these as a function of the CO 2 leakage concentration. The sources of measurement variability for background conditions were characterized from the Jasper aquifer background data, and then normalized using the coefficient of variation of each species across the monitoring wells. Bayesian belief network (BBN) models were constructed, and measurement variability of different levels were added to compare leak detection probabilities. Increasing measurement variability decreased the power to detect a leak of a given size. For a moderately high CO 2 concentration of 0.2 mol/kg, the probability of detecting this leakage effect using pH as the monitoring variable in an aquifer with calcite decreases from 98% (no measurement variability) to 61% (medium variability) to 33% (high variability). The loss in power of the sampling protocol with increasing measurement variability is similar in magnitude when Ca 2+ or HCO 3 - is used as the monitoring parameter, but only for aquifers with calcite.

58 GEOSCIENCES↗
DOE OSTI2025

Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators

Abstract. Simulating low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1000–10 000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part 1, we construct an ensemble weather forecasting system based on spherical Fourier neural operators (SFNOs), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest-growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. With large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states during the rollout, and the ML ensemble passes a crucial spectral test in the literature. The IFS and ML ensembles have similar extreme forecast indices, and we show that the ML extreme weather forecasts are reliable and discriminating. These diagnostics ensure that the ensemble can reliably simulate the time evolution of the atmosphere, including low-likelihood high-impact extremes. In Part 2, we generate a huge ensemble initialized each day in summer 2023, and we characterize the simulations of extremes.

Mahesh, Ankur↗
DOE OSTI2026

Predicted aboveground biomass of Typha angustifolia in an upland brackish tidal marsh, PIE LTER, Byfield, MA (2022-2024)

This dataset contains predicted monthly aboveground Typha angustifolia biomass per sample and per square meter in a brackish tidal marsh site dominated by Typha angustifolia near the Parker River in the upper estuary of the Plum Island Sound, Massachusetts (MA) during the growing seasons (May-September) of 2022, 2023, and 2024. This site is also located within the Plum Island Ecosystems Long Term Ecological Research Station (PIE LTER). Allometric equations were developed from dry weight data and associated maximum heights collected in 2022 and 2023. The goal of this study was to investigate the difference in aboveground biomass between the site’s marsh interior (MI) and the creek bank (CB). Metadata files (Typha_biomass_predictions_dd.csv and Typha_biomass_predictions_flmd.csv) contain detailed information on variable definitions, calculations, sampling methods, and the location of the site.

DATE↗
DOE OSTI2022

Optical variability of quasars with 20-yr photometric light curves

ABSTRACT We study the optical gri photometric variability of a sample of 190 quasars within the SDSS Stripe 82 region that have long-term photometric coverage during ∼1998−2020 with SDSS, PanSTARRS-1, the Dark Energy Survey, and dedicated follow-up monitoring with Blanco 4m/DECam. With on average ∼200 nightly epochs per quasar per filter band, we improve the parameter constraints from a Damped Random Walk (DRW) model fit to the light curves over previous studies with 10–15 yr baselines and ≲ 100 epochs. We find that the average damping time-scale τDRW continues to rise with increased baseline, reaching a median value of ∼750 d (g band) in the rest frame of these quasars using the 20-yr light curves. Some quasars may have gradual, long-term trends in their light curves, suggesting that either the DRW fit requires very long baselines to converge, or that the underlying variability is more complex than a single DRW process for these quasars. Using a subset of quasars with better-constrained τDRW (less than 20 per cent of the baseline), we confirm a weak wavelength dependence of τDRW∝λ0.51 ± 0.20. We further quantify optical variability of these quasars over days to decades time-scales using structure function (SF) and power spectrum density (PSD) analyses. The SF and PSD measurements qualitatively confirm the measured (hundreds of days) damping time-scales from the DRW fits. However, the ensemble PSD is steeper than that of a DRW on time-scales less than ∼ a month for these luminous quasars, and this second break point correlates with the longer DRW damping time-scale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Modeling Changes in Measured Conductance of Thin Boron Carbide Semiconducting Films Under Irradiation

Semiconducting, p-type, amorphous partially dehydrogenated boron carbide films (a-B 10 C 2+x :H y ) were deposited utilizing plasma enhanced chemical vapor deposition (PECVD) onto n-type silicon thus creating a heterojunction diode. A model was developed for the conductance of the device as a function of perturbation frequency (f) that incorporates changes of the electrical properties for both the a-B 10 C 2+x :H y film and the silicon substrate when irradiated. The virgin model has 3 independent variables (R1, C1, R3), and 1 dependent variable (f). These samples were then irradiated with 200 keV He + ions, and the conductance model was matched to the measured data. It was found that initial irradiation (0.1 displacements per atom (dpa) equivalent) resulted in a decrease in the parallel junction resistance parameter from 6032 Ω to 2705 Ω. Further irradiation drastically increased the parallel junction resistance parameter to 39000 Ω (0.2 dpa equivalent), 77440 Ω (0.3 dpa equivalent), and 190000 Ω (0.5 dpa equivalent). It is believed that the initial irradiation causes type inversion of the silicon substrate changing the original junction from a p-n to a p-p+ with a much lower barrier height leading to a lower junction resistance component between the a-B 10 C 2+x :H y and irradiated silicon. In addition, it was found that after irradiation, a second parallel resistor and capacitor component is required for the model, introducing 2 additional independent variables (R2, C2). This is interpreted as the junction between the irradiated and virgin silicon near ion end of range.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Metal(loid) bioaccessibility and inhalation risk assessment: A comparison between an urban and an industrial area

Highlights: • Metal(loid) bioaccessibility of PM{sub 10} was evaluated with simulated lung fluids. • Most studied metal(loids) were highly bioaccessible in artificial lysosomal fluid. • Metal(loid)s from the industrial site had the highest bioaccessibility. • Elevated non-carcinogenic risk associated with Mn was found at the studied area. • Carcinogenic risk was slightly exceeded for Cd exposure at the industrial location. The content of metal(loid)s in particulate matter (PM) is of special concern due to their contribution to overall (PM) toxicity. In this study, the bioaccessibility and human health risk of potentially toxic metal(loid)s associated with PM{sub 10} were investigated in two areas of the Cantabrian region (northern Spain) with different levels of exposure: an industrial area mainly influenced by a ferromanganese alloy plant; and an urban area consisting mainly of residential and commercial activities, but also affected, albeit to a lesser extent by the ferroalloy plant. Total content and bioaccessible fractions in simulated lung fluids (SLFs) of Fe, Mn, Zn, Ni, Cu, Sb, Mo, Cd and Pb were determined by ICP-MS. Gamble's solution and artificial lysosomal fluid (ALF) were used to mimic different conditions inside the human respiratory system. A health risk assessment was performed based on the United States Environmental Protection Agency's (USEPA) methodology. Most metal(loid)s showed moderate and high bioaccessibility in Gamble's solution and ALF, respectively. Despite the high variability between the samples, metal(loid) bioaccessibility was found to be higher on average at the industrial site, suggesting a greater hazard to human health in the proximity of the main metal(loid) sources. Based on the results of the risk assessment, the non-carcinogenic risk associated with Mn exposure was above the safe limit (HQ> 1) under all the studied scenarios at the industrial site and under some specific scenarios at the urban location. The estimated carcinogenic inhalation risk for Cd exposure at the industrial site was found to be within the range between 1.0 × 10{sup −6} to 1.0 × 10{sup −4} (uncertainty range) under some scenarios. The results obtained in this study indicate that Mn and Cd inhalation exposure occurring in the vicinities of the studied areas may pose a human health risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Stochastic economic dispatch of wind power under uncertainty using clustering-based extreme scenarios

Operation of power systems with high penetrations of renewable energy sources requires tools for robust decision making under uncertainty. Stochastic economic dispatch and stochastic unit commitment are effective techniques for planning and operation under uncertainty, whose effectiveness depends on the cardinality and quality of the scenario set. Here, this article proposes a machine learning method using -means clustering for capturing relevant physical information from a large population of analog scenarios. Extreme scenario samples drawn from the clusters are used in a two-stage stochastic economic dispatch computation. The effectiveness of the proposed approach is assessed on a synthetic 200-bus system with a geographic footprint over Illinois, USA for four months from each season of WIND Toolkit data. The combination of -means clustering with importance sampling is shown to reduce the total operational cost by over 43% compared to sampling from populations based on heuristic clustering-based methods. Additionally, the variability in the mean cost is about 56% lower than the variability using Monte Carlo sampling. Moreover, the operational cost with the presented approach is shown to be close to the cost calculated based on a hindsight exact wind profile, signifying a highly accurate quantification of wind uncertainty by the presented -means clustering based sampling method.

17 WIND ENERGY↗
DOE OSTI2022

Getting lost tracking the carbon footprint of hydropower

In the transition to low-carbon electricity, well-quantified estimates of carbon dynamics are needed to ensure that emissions reduction targets are achieved. Here we review the state of the science on carbon accounting for hydropower reservoirs and identify limitations and future solutions. Nearly all research on reservoir greenhouse-gas (GHG) emissions has focused on individual reservoirs in isolation without considering their position in a freshwater network draining organic matter from upstream watersheds or the coordinated operation of reservoir cascades. Second, carbon inventories have extrapolated from a small, non-probabilistic sample of highly variable measurements of GHG emissions to unsampled reservoirs. A stronger statistical foundation is needed to estimate a global inventory and its uncertainty. Third, attribution to hydropower is based on ranks assigned to reservoir purpose. Instead, the physical influence of hydropower on carbon dynamics could be directly measured. Fourth, current carbon-accounting practices neglect time. A time-varying approach would quantify variation in emissions for electricity portfolios from changes in the fuel mix at different times and account for ancillary services, i.e., the ability to support the grid when variable renewables are not available without using natural gas. Reservoirs also sequester a significant portion of inflowing carbon in sediments and slow the carbon cycle by delaying the return of carbon to the atmosphere for decades to centuries. Together, these refinements would help to illuminate pathways toward meeting energy demand with the longest-possible delay in returning carbon to the atmosphere and without adding ancient sources to the pool of carbon cycling through aquatic ecosystems.

13 HYDRO ENERGY↗
DOE OSTI2022

Comparative Analysis of Corrinoid Profiles across Host-Associated and Environmental Samples

Vitamin B 12 (the cyanated form of cobalamin cofactors) is best known for its essential role in human health. In addition to its function in human metabolism, cobalamin also plays important roles in microbial metabolism and can impact microbial community function. Cobalamin is a member of the structurally diverse family of cofactors known as cobamides that are produced exclusively by certain prokaryotes. Cobamides are considered shared nutrients in microbial communities because the majority of bacteria that possess cobamide-dependent enzymes cannot synthesize cobamides de novo. Furthermore, different microbes have evolved metabolic specificity for particular cobamides, and therefore, the availability of cobamides in the environment is important for cobamide-dependent microbes. Determining the cobamides present in an environment of interest is essential for understanding microbial metabolic interactions. By examining the abundances of different cobamides in diverse environments, including 10 obtained in this study, we find that, contrary to its preeminence in human metabolism, cobalamin is relatively rare in many microbial habitats. Comparison of cobamide profiles of mammalian gastrointestinal samples and wood-feeding insects reveals that host-associated cobamide abundances vary and that fecal cobamide profiles differ from those of their host gastrointestinal tracts. Environmental cobamide profiles obtained from aquatic, soil, and contaminated groundwater samples reveal that the cobamide compositions of environmental samples are highly variable. As the only commercially available cobamide, cobalamin is routinely supplied during microbial culturing efforts. Furthermore, these findings suggest that cobamides specific to a given microbiome may yield greater insight into nutrient utilization and physiological processes that occur in these habitats.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Controls on Barite (BaSO 4 ) Precipitation in Unconventional Reservoirs

Barite (BaSO 4 ) precipitation is one of the most ubiquitous examples of secondary sulfate mineral scaling in shale oil and gas reservoirs. Often, a suite of chemical additives is used during fracturing operations to inhibit the accumulation of mineral scales, though their efficacy is widely varied and poorly understood. This study combines experimental data and multi-component numerical reactive transport modeling to offer a more comprehensive understanding of the geochemical behavior of barite accumulation in shale matrices under conditions typical of fracturing operations. A variety of additives and conditions are individually tested in batch reactor experiments to identify the factors controlling barite precipitation. Our experimental results demonstrate a pH dependence in the rate of barite precipitation, which we use to develop a predictive model including a pH-dependent term that satisfactorily reproduces our observations. Further, this model is then extended to consider the behavior of three major shale samples of highly variable mineralogy (Eagle Ford, Marcellus, and Barnett). This data-validated model offers a reliable tool to predict and ultimately mitigate against secondary mineral accumulation in unconventional shale reservoirs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Lack of QBO–MJO Connection in CMIP6 Models

Observational analysis has indicated a strong connection between the stratospheric quasi–biennial oscillation (QBO) and tropospheric Madden–Julian oscillation (MJO), with MJO activity being stronger during the easterly phase than the westerly phase of the QBO. We assess the representation of this QBO–MJO connection in 30 models participating in the Coupled Model Intercomparison Project 6. While some models reasonably simulate the QBO during boreal winter, none of them capture a difference in MJO activity between easterly and westerly QBO that is larger than that which would be expected from the random sampling of internal variability. The weak signal of the simulated QBO–MJO connection may be due to the weaker amplitude of the QBO than observed, especially between 100 to 50 hPa. This weaker amplitude in the models is seen both in the QBO–related zonal wind and temperature, the latter of which is thought to be critical for destabilizing tropical convection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Strong Correspondence in Evapotranspiration and Carbon Dioxide Fluxes Between Different Eddy Covariance Systems Enables Quantification of Landscape Heterogeneity in Dryland Fluxes

Abstract The eddy covariance method is widely used to investigate fluxes of energy, water, and carbon dioxide at landscape scales, providing important information on how ecological systems function. Flux measurements quantify ecosystem responses to environmental perturbations and management strategies, including nature‐based climate‐change mitigation measures. However, due to the high cost of conventional instrumentation, most eddy covariance studies employ a single system, limiting spatial representation to the flux footprint. Insufficient replication may be limiting our understanding of ecosystem behavior. To address this limitation, we deployed eight lower‐cost eddy covariance systems in two clusters around two conventional eddy covariance systems in the Chihuahuan Desert of North America for a period of 2 years. These dryland settings characterized by large temperature variations and relatively low carbon dioxide fluxes represented a challenging setting for eddy covariance. We found very good closure of energy and water balance across all systems (within ±9% of unity). We found very good correspondence between the lower‐cost and conventional systems' fluxes of sensible heat (with concordance correlation coefficient (CCC) of ≥0.87), latent energy (evapotranspiration; CCC ≥ 0.89), and useful correspondence in the net ecosystem exchange ((NEE); with CCC ≥ 0.4) at the daily temporal resolution. Relative to the conventional systems, the low‐frequency systems were characterized by a higher level of random error, particularly in the NEE fluxes. Lower‐cost systems can enable wider deployment affording better replication and sampling of spatiotemporal variability at the expense of greater measurement noise that might be limiting for certain applications. Replicated eddy covariance observations may be useful when addressing gaps in the existing monitoring of critical and underrepresented ecosystems and for measuring areas larger than a single flux footprint.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Signatures of moiré trions in WSe 2 /MoSe 2 heterobilayers

Moiré superlattices formed by van der Waals materials can support a wide range of electronic phases, including Mott insulators superconductors and generalized Wigner crystals2. When excitons are confined by a moiré superlattice, a new class of exciton emerges, which holds promise for realizing artificial excitonic crystals and quantum optical effects. When such moiré excitons are coupled to charge carriers, correlated states may arise. However, no experimental evidence exists for charge-coupled moiré exciton states, nor have their properties been predicted by theory. In this work, we report the optical signatures of trions coupled to the moiré potential in tungsten diselenide/molybdenum diselenide heterobilayers. The moiré trions show multiple sharp emission lines with a complex charge-density dependence, in stark contrast to the behaviour of conventional trions. We infer distinct contributions to the trion emission from radiative decay in which the remaining carrier resides in different moiré minibands. Variation of the trion features is observed in different devices and sample areas, indicating high sensitivity to sample inhomogeneity and variability. The observation of these trion features motivates further theoretical and experimental studies of higher-order electron correlation effects in moiré superlattices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Application of quasimetagenomics methods to define microbial diversity and subtype Listeria monocytogenes in dairy and seafood production facilities

Microorganisms frequently colonize surfaces within food production facilities. Detection of Listeria monocytogenes in this setting relies on culture-dependent methods, but the complex dynamics of bacterial interactions within these environments and their impact on pathogen detection remain largely unexplored. To address this challenge, we applied both 16S rRNA and shotgun quasimetagenomic (enriched microbiome) sequencing of swab culture enrichments from five seafood and seven dairy production environments. Utilizing 16S rRNA amplicon sequencing, we observed variability between 355 samples taken from these 12 production facilities and a distinctive microbiome for each environment. With shotgun quasimetagenomic sequencing, we were able to assemble L. monocytogenes metagenome-assembled genomes (MAGs) from 28 of the 32 culture-positive samples. We compared these MAGs to their corresponding whole-genome sequencing assemblies, which resulted in two polyphyletic clades consisting of L. monocytogenes lineages I and II with 13,195 and 25,556 single-nucleotide polymorphism sites, respectively. The remaining four MAGs did not produce sufficient genome coverage. To understand and establish limits for pathogen detection and subtyping using shotgun quasimetagenomics, these same data sets were downsampled in slilico to produce a titration series of abundances of L. monocytogenes and analyzed. Pathogen detection was achieved for all downsampled data sets, even those with only 3× genome coverage. This study contributes to the understanding of microbial diversity within food production environments and presents insights into the level of genome coverage needed in a metagenome sequencing data set to detect, subtype, and source track a foodborne pathogen.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

NCI Pilot 3 - Synthetic Data Generation Report

Two methods have been implemented so far: Independent Marginals (IM) and Bayesian Network (BN). In the IM method, it simply estimates individual discrete probability distribution from the available real data for each variable, and then sample from them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Uncertainty Quantification in Vibro-Acoustics Diagnosis of Alkali-Silica Reaction Degradation in Medium-Sized Concrete Samples

The objectives of this ongoing research project focus on health monitoring and data analytics of concrete slabs containing reactive aggregates and thus subjected to degradation due to alkali-silica reaction (ASR). A controlled concrete slab with four pockets of reactive aggregates (pure silica, wells, placitas, and spratt) was cast at Vanderbilt University and cured in representative conditions to accelerate degradation due to ASR. A set of four concrete samples were also cast and cured at the University of Alabama for ASR testing. Of these four samples, two slabs contained reactive aggregates while the other two had the non-reactive aggregate counterparts mixed throughout the samples. Vibro-acoustic testing was used on these slabs to locate ASR damage within the reactive samples. Vibro-acoustic modulation (VAM) is a vibration-based NDE method that utilizes signatures of nonlinear dynamic interactions on contact surfaces of crack or delamination damage to detect and localize the damage. VAM analysis was conducted on both the Vanderbilt and Alabama samples using multiple variables for damage detection and localization. The results from the data analysis of the vibro-acoustic testing on concrete slabs cured at Vanderbilt University and the University of Alabama are discussed in detail in this report. Results for damage localization are dependent on multiple variables used in the vibro-acoustic modulation experiments. A major focus of this report is to quantify the uncertainty in the diagnosis due to multiple factors and uncertainty sources. The uncertainty quantification methodology presented in this report was applied to VAM-based diagnosis and prognosis. However, the methodology is general, and is capable of being applied to multiple technique that collect spatially distributed data. Future work needs to investigate the incorporation of uncertainty quantification in developing a robust Prognostics and Health Management framework. Digital image correlation is a three-dimensional, full-field, optical NDE technique to measure contour, deformation, vibration, and strain. The application of the digital image correlation technique to study ASR-related degradation on a large concrete specimen at the University of Tennessee is discussed and observations are presented in this report.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Literature Review of Generalized Two-Dimensional Correlation Spectroscopy (2D-COS)

Generalized two-dimensional correlation spectroscopy (2D-COS) is a technique that has improved on traditional spectroscopic techniques by exposing samples to a variable amount of an external perturbation before they have been probed spectroscopically through IR, UV, Raman, etc. These perturbed signals are plotted over a second independent wavenumber axis and after mathematical manipulation, a dynamic spectrum is created that shows the synchronous and asynchronous spectra. The dynamic spectrum shows the way spectral changes in the material are correlated to each other, and a mechanism of material degradation can be proposed through a set of rules that Noda proposed. This technique is important to spectroscopic studies because of improved spectral resolution and the identification of complex or overlapping bands that cannot be distinguished in traditional analytical methods. This technique has been widely utilized since its inception, and has developed towards protein studies, as well as even wine chemistry, because of the advanced capabilities of band separation. Two-dimensional hetero-spectral correlation analysis has also been developed to find correlations in spectra with different spectroscopic probes. 2D-COS as an analytical technique has increasing avenues of expansion, and can be creatively used in many other fields in the future.

2D-COS↗
DOE OSTI2022

Mach Conference, 2022.

There has been considerable interest in chemical vapor infiltration (CVI) manufactured silicon carbide fiber and silicon carbide matrix (SiC/SiC) composite tubes due to their superior mechanical properties. The SiC/SiC tubes are known to remain stable even after prolonged exposure to radiation. However, there are many variables in the manufacturing of SiC/SiC composite tubes such as elastic constants of constituent SiC fiber and SiC matrix, braiding angle, porosity, and others whose values vary over a range. The intention is to identify the distribution of elastic constants of SiC/SiC tubes which would help model the performance of the novel cladding material. To do so, a sensitivity analysis will be carried out to determine the dominant variables that influence the elastic constants of SiC/SiC tubes. A chosen number of combinations of dominant variables will be sampled through the Latin hypercube sampling (LHS) method. The elastic constants of composite tube namely Young's moduli in the circumferential and longitudinal direction, Poisson's ratio, and shear modulus will be calculated at sampled points through finite element (FE) analysis. A polynomial response surface (PRS) will be built for each of the elastic constants to be used as a surrogate to FE analysis. Once the predictive accuracy of PRS is verified at the validation set of data points, the PRS will be invoked 105 times in Monte Carlo simulations (MCS). The uncertainty can be quantified by calculating the coefficient of variation (CV) based on MCS-generated data which will be presented at the conference.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗