Publications

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Optimization of automated data collection is gaining increased interest for the purposes of enabling closed-loop self-correcting systems that inherently maximize operational efficiencies and reduce waste. Many data collection systems have several variables which influence data accuracy or consistency and which can require frequent user interaction to be monitored and maintained. Operating upon a Robo-MET.3D™ automated mechanical serial-sectioning system, a run-to-run control algorithm has been developed to accelerate data collection and reduce data inconsistency. Using historical data amassed over a decade of experiments, a linear regression model of the deterministic system dynamics is created and used to employ a run-to-run control algorithm that optimizes selected system inputs to reduce operator intervention and increase efficacy while reducing variance of system output.

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For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance-based global sensitivity analysis (GSA) using a fully coupled, ultra-low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher-resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.

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The surfaces of most metals immersed in aqueous electrolytes have a several-nanometer-thick oxide/hydroxide surface layer. This gives rise to the existence of both metal∣oxide and oxide∣liquid electrotlyte interfaces, and makes it challenging to correlate atomic length-scale structures with electrochemical properties such the potential-of-zero-charge (PZC). The PZC has been shown to be correlated the onset potential for pitting corrosion. In this work, we conduct large-scale Density Functional Theory and ab initio molecular dynamics to calculate the PZC of a Al(111)∣γ-Al2O3(110)∣ water double-interface model within the context of aluminum corrosion. By partitioning the multiple interfaces involved into binary components with additive contributions to the overall work function and voltage, we predict the PZC to be −1.53 V vs SHE for this model. We also calculate the orbital energy levels of defects like oxygen vacancies in the oxide, which are critical parameters in theories associated with pitting corrosion. We predict that the Fermi level at the PZC lies above the impurity defect levels of the oxygen vacancies, which are therefore uncharged at the PZC. From the PZC estimate, we predict the voltage needed to create oxygen vacancies with net postive charges within a flatband approximation.

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The Advanced Reactor Safeguards (ARS) program was established in 2020 as part of appropriations for the Advanced Reactor Demonstration Program (ARDP) through the Office of Nuclear Energy in the Department of Energy. The goal of this program is to help address near term challenges that advanced nuclear reactor vendors face in meeting domestic Material Control and Accountancy (MC&A) and Physical Protection System (PPS) requirements for U.S. construction. The technical work in the program is meant to (1) support nuclear reactor vendors with advanced MC&A and PPS designs for next generation reactors, (2) provide technical bases for the regulator, and (3) promote the integration of Safeguards and Security by Design early in the design process. Existing domestic regulations for safeguards and security, as outlined in the Code of Federal Regulations, were written for large light water reactors, and rule-making efforts are underway to develop regulations more suited to different reactor designs. The ARS program seeks to remove roadblocks in the deployment of new and advanced reactors by solving regulatory challenges, reducing safeguards and security costs, and utilizing the latest technologies and approaches for robust plant monitoring and protection. This roadmap discusses the goals of the ARS program, current research, and program plan for the next five years.

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The Material Protection, Accounting, and Control Technologies (MPACT) program utilizes modeling and simulation to assess Material Control and Accountability (MC&A) concerns for a variety of nuclear facilities. Single analyst tools allow for rapid design and evaluation of advanced approaches for new and existing nuclear facilities. A low enriched uranium (LEU) fuel conversion and fabrication facility simulator has been developed to assist with MC&A for existing LEU fuel fabrication for light water reactors. Simulated measurement blocks were added to the model (consistent with current best practices). Material balance calculations and statistical tests have also been added to the model.