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DOE OSTI2021

ORNL Input to GDSA Repository Systems Analysis FY21

This document satisfies the M3 milestone M3SF-21OR010304082 titled “ORNL Input to GDSA Repository Systems.” This document describes the current status of Oak Ridge National Laboratory’s (ORNL’s) efforts related to analyses of dual-purpose canister (DPC) disposal in unsaturated alluvium with a focus on thermal hydrological constraints on criticality timing and power output. This analysis updates previous analyses of thermal hydrological constraints on timing and power output of a potential criticality event in Dual-Purpose Canisters (DPCs) in a hypothetical repository in unsaturated alluvium using a more realistic representation of heat transport inside dry DPCs. PFLOTRAN was used for the simulations of multiphase thermal hydrology near a single DPC. The scenario considers a DPC failure at 9000 years, allowing water to enter and eventually fill the DPC. Once the DPC is filled to a level that could support a criticality event, different values for criticality power output were added to the decay heat. The main objective is to bound the power output that could be produced by a criticality event without driving water out of the package. For the conditions analyzed here, following a package breach, the alluvial formation could supply enough water to allow enough accumulation in the DPC to support a criticality event. However, the power output that would be generated is limited to modest values by loss of water moderator due to evaporation and vapor diffusion. In the reference case scenario, the DPC would not start to fill with water until about 16,000 years post closure and would not fill to a level that allows a criticality event until approximately 25,000 years post closure. The long-term average power output that could be sustained without driving off the water and terminating the criticality event is limited to approximately 100 W. Sensitivity to assumed conditions and parameters in the reference case, especially the deep percolation rate, need to be addressed but could not be undertaken because of numerical failures of the PFLOTRAN code running in general mode in the dry conditions of an unsaturated alluvium repository

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Generalized Cycle Benchmarking Algorithm for Characterizing Midcircuit Measurements

Midcircuit measurements (MCMs) are crucial ingredients in the development of fault-tolerant quantum computation. While there have been rapid experimental progresses in realizing MCMs, a systematic method for characterizing noisy MCMs is still under exploration. In this work, we develop a cycle benchmarking (CB)-type algorithm to characterize noisy MCMs. The key idea is to use a joint Fourier transform on the classical and quantum registers and then estimate parameters in the Fourier space, analogous to Pauli fidelities used in CB-type algorithms for characterizing the Pauli-noise channel of Clifford gates. Furthermore, we develop a theory of the noise learnability of MCMs, which determines what information can be learned about the noise model (in the presence of state preparation and terminating measurement noise) and what cannot, which shows that all learnable information can be learned using our algorithm. As an application, we show how to use the learned information to test the independence between measurement noise and state-preparation noise in an MCM. Finally, we conduct numerical simulations to illustrate the practical applicability of the algorithm. Similar to other CB-type algorithms, we expect the algorithm to provide a useful toolkit that is of experimental interest. Published by the American Physical Society 2025

Zhang, Zhihan (ORCID:0009000862907691)↗

DOE OSTI2021

CaMn 3 IV O 4 Cubane Models of the Oxygen‐Evolving Complex: Spin Ground States S <9/2 and the Effect of Oxo Protonation

Abstract We report the single crystal XRD and MicroED structure, magnetic susceptibility, and EPR data of a series of CaMn 3 IV O 4 and YMn 3 IV O 4 complexes as structural and spectroscopic models of the cuboidal subunit of the oxygen‐evolving complex (OEC). The effect of changes in heterometal identity, cluster geometry, and bridging oxo protonation on the spin‐state structure was investigated. In contrast to previous computational models, we show that the spin ground state of CaMn 3 IV O 4 complexes and variants with protonated oxo moieties need not be S =9/2. Desymmetrization of the pseudo ‐ C 3 ‐symmetric Ca(Y)Mn 3 IV O 4 core leads to a lower S =5/2 spin ground state. The magnitude of the magnetic exchange coupling is attenuated upon oxo protonation, and an S =3/2 spin ground state is observed in CaMn 3 IV O 3 (OH). Our studies complement the observation that the interconversion between the low‐spin and high‐spin forms of the S 2 state is pH‐dependent, suggesting that the (de)protonation of bridging or terminal oxygen atoms in the OEC may be connected to spin‐state changes.

Lee, Heui Beom↗

DOE OSTI2021

CaMn 3 IV O 4 Cubane Models of the Oxygen‐Evolving Complex: Spin Ground States S <9/2 and the Effect of Oxo Protonation

Lee, Heui Beom↗

DOE OSTI2024

Evaluating the Accuracy of Machine Learning Forecasts

To improve the accuracy of forecasting in machine learning, we must investigate multiple machine learning models and see how accurately they can predict values after training. We used seven machine learning models to try and get more accurate predictions. The models that were used were ARIMA, SES, MLP, CART, LightGBM, and XGBoost. We used a processed dataset from a Terminal at LAX that had the number of people traveling through terminal X every hour in March from 2015-2019. We trained our models with the dates March 6 - March 19 to predict the value for March 20th and the hours 6:00 am to 6:00 pm since those are the most popular traveling hours. By using the different models, we had varying results of accuracy when estimating the amount of people traveling through terminal X on March 20th. We know that machine learning models are helpful for forecasting and by seeing how accurately these models can predict, we can see how forecasting can be helpful for other issues. Using these methods, airports can use forecasting to predict the amount of people coming in and out and can use these predictions to prepare their resource management, operational efficiency, and overall passenger experience.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems

With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Ultrafast X-ray Spectroscopy of Intersystem Crossing in Hexafluoroacetylacetone: Chromophore Photophysics and Spectral Changes in the Face of Electron-Withdrawing Groups

Intersystem crossings between singlet and triplet states represent a crucial relaxation pathway in photochemical processes. Herein, we probe the intersystem crossing in hexafluoro-acetylacetone with ultrafast X-ray transient absorption spectroscopy at the carbon K-edge. In this study, we observe the excited state dynamics following excitation with 266 nm UV light to the 1 ππ* (S 2 ) state with element and site-specificity using a broadband soft X-ray pulse produced by high harmonic generation. These results are compared to X-ray spectra computed from orbital optimized density functional theory methods. It is found that the electron-withdrawing fluorine atoms decongest the X-ray absorption spectrum by enhancing separation between features originating from different carbon atoms. This facilitates the elucidation of structural and electronic dynamics at the chromophore. The evolution of the core-to-valence resonances at the carbon K-edge reveals an ultrafast population transfer between the 1 nπ* (S 1 ) and 3 ππ* (T 1 ) states on a 1.6 ± 0.4 ps time scale, which is similar to the 1.5 ps time scale earlier observed for acetylacetone [ J. Am. Chem. Soc. 2017, 139, 16576—16583, DOI: 10.1021/jacs.7b07532]. It therefore appears that terminal fluorination has little influence on the intersystem crossing rate of the acetylacetone chromophore. In addition, the significant role of hydrogen-bond opened and twisted rotational isomers is elucidated in the excited state dynamics by comparison of the experimental transient X-ray spectra with theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Crystal Structure and C–H Bond-Cleaving Reactivity of a Mononuclear Co IV –Dinitrate Complex

High-valent Fe IV =O intermediates with a terminal metal–oxo moiety are key oxidants in many enzymatic and synthetic C–H bond oxidation reactions. While generating stable metal–oxo species for late transition metals remains synthetically challenging, notably, a number of high-valent non-oxo–metal species of late transition metals have been recently described as strong oxidants that activate C–H bonds. In this work, we obtained an unprecedented mononuclear Co IV –dinitrate complex (2) upon one-electron oxidation of its Co(III) precursor supported by a tridentate dianionic N3 ligand. 2 was structurally characterized by X-ray crystallography, showing a square pyramidal geometry with two coordinated nitrate anions. Furthermore, characterization of 2 using combined spectroscopic and computational methods revealed that 2 is a low-spin (S = 1/2) Co(IV) species with the unpaired electron located on the cobalt d z2 orbital, which is well positioned for substrate oxidations. Indeed, while having a high thermal stability, 2 is able to cleave sp 3 C–H bonds up to 87 kcal/mol to afford rate constants and kinetic isotope effects (KIEs) of 2–6 that are comparable to other high-valent metal oxidants. The ability to oxidize strong C–H bonds has yet to be observed for Co IV –O and Co III =O species previously reported. Therefore, 2 represents the first high-valent Co(IV) species that is both structurally characterized by X-ray crystallography and capable of activating strong C–H bonds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Identifying Critical Infrastructure in Imagery Data Using Explainable Convolutional Neural Networks

To date, no method utilizing satellite imagery exists for detailing the locations and functions of critical infrastructure across the United States, making response to natural disasters and other events challenging due to complex infrastructural interdependencies. This paper presents a repeatable, transferable, and explainable method for critical infrastructure analysis and implementation of a robust model for critical infrastructure detection in satellite imagery. This model consists of a DenseNet-161 convolutional neural network, pretrained with the ImageNet database. The model was provided additional training with a custom dataset, containing nine infrastructure classes. The resultant analysis achieved an overall accuracy of 90%, with the highest accuracy for airports (97%), hydroelectric dams (96%), solar farms (94%), substations (91%), potable water tanks (93%), and hospitals (93%). Critical infrastructure types with relatively low accuracy are likely influenced by data commonality between similar infrastructure components for petroleum terminals (86%), water treatment plants (78%), and natural gas generation (78%). Local interpretable model-agnostic explanations (LIME) was integrated into the overall modeling pipeline to establish trust for users in critical infrastructure applications. The results demonstrate the effectiveness of a convolutional neural network approach for critical infrastructure identification, with higher than 90% accuracy in identifying six of the critical infrastructure facility types.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

From the Starship Enterprise to Los Alamos National Laboratory: Artificial Intelligence and Machine Learning in the NSRC

Artificial Intelligence (AI) conjures up different emotions in different people. Some view it with fear, imagining a malevolent AI similar to Skynet in the Terminator movies. Others see something benevolent, such as the character Data in Star Trek: The Next Generation. Still others see it as a means to advance the frontiers of science, engineering, and technology to new levels not possible through traditional means. At the National Security Research Center (NSRC or the Center), we see AI as a tool to help us go through the monumental tasks we have in digitizing, cataloging, and searching our collections.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Blockchain Empowered Provenance Framework for Sensor Identity Management and Data Flow Security in Fossil-based Power Plants

The overall goals of this project are divided into 3 important phases. First, a peer-to-peer SCADA network is to be established where identity profile of sensors from fossil-power plants will be stored in an immutable manner in order to verify data flow integrity in real time. Second, it is aimed to develop a Blockchain-based provenance platform to audit equipment operations along with data and process integrity violations. In this phase, various operational rules will be incorporated in the ledger on behalf of the operating sensor so its abnormal use can be easily detected. In addition, it is aimed to achieve provenance when the rate of transactions is exceedingly high, thus enabling to design a scalable provenance scheme that can work in real time when number of data collecting points or sensor units increase in fossil-based power plants. Finally, a working testbed will be developed to integrate the Blockchain platform with sensor-based fossil power plant comprising of Remote Terminal Units (RTU) and SCADA devices. Using multiple Raspberry-PIs mimicking the role of RTUs that are interfaced with multiple sensors devices will be deployed to serve as a critical component of fossil power plant and the performance metrics such as throughput, latency, bootstrapping time, etc., will be evaluated on the testbed.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses

Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Data-driven optimized control of the COVID-19 epidemics

Optimizing the impact on the economy of control strategies aiming at containing the spread of COVID-19 is a critical challenge. We use daily new case counts of COVID-19 patients reported by local health administrations from different Metropolitan Statistical Areas (MSAs) within the US to parametrize a model that well describes the propagation of the disease in each area. We then introduce a time-varying control input that represents the level of social distancing imposed on the population of a given area and solve an optimal control problem with the goal of minimizing the impact of social distancing on the economy in the presence of relevant constraints, such as a desired level of suppression for the epidemics at a terminal time. We find that with the exception of the initial time and of the final time, the optimal control input is well approximated by a constant, specific to each area, which contrasts with the implemented system of reopening ‘in phases’. For all the areas considered, this optimal level corresponds to stricter social distancing than the level estimated from data. Proper selection of the time period for application of the control action optimally is important: depending on the particular MSA this period should be either short or long or intermediate. We also consider the case that the transmissibility increases in time (due e.g. to increasingly colder weather), for which we find that the optimal control solution yields progressively stricter measures of social distancing. We finally compute the optimal control solution for a model modified to incorporate the effects of vaccinations on the population and we see that depending on a number of factors, social distancing measures could be optimally reduced during the period over which vaccines are administered to the population.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

A second-order-in-time, explicit approach addressing the redundancy in the low-Mach, variable-density Navier-Stokes equations

A novel algorithm for explicit temporal discretization of the variable-density, low-Mach Navier-Stokes equations is presented here in this study. Recognizing there is a redundancy between the mass conservation equation, the equation of state, and the transport equation(s) for the scalar(s) which characterize the thermochemical state, and that it destabilizes explicit methods, we demonstrate how to analytically eliminate the redundancy and propose an iterative scheme to solve the resulting transformed scalar equations. The method obtains second-order accuracy in time regardless of the number of iterations, so one can terminate this subproblem once stability is achieved. Hence, flows with larger density ratios can be simulated while still retaining the efficiency, low cost, and parallelizability of an explicit scheme. The temporal discretization algorithm is used within a pseudospectral direct numerical simulation which extends the method of Kim, Moin, and Moser for incompressible flow to the variable-density, low-Mach setting, where we demonstrate stability for density ratios up to ~25.7.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Selective Chemical Looping Combustion of Terminal Alkynes in Mixtures with Alkenes

The selective combustion of terminal alkynes in mixtures with alkenes is demonstrated during anaerobic reduction half-cycles on bulk bismuth oxide (Bi 2 O 3 ) as an approach to remove alkynes, which act as inhibitors in olefin polymerization. Bi 2 O 3 combusts phenylacetylene in styrene, 3-methylphenylacetylene in 3-methylstyrene, propyne in propylene, 1-hexyne in 1-hexene, and 1-octyne in 1-octene, with alkyne combustion selectivities exceeding 96%. Near unity reaction orders for hydrocarbon consumption during reduction half-cycles are consistent with combustion pathways initiated by rate-determining initial C–H activation, which drive selective alkyne combustion through intrinsic differences in the first-order rate constants for alkyne and alkene combustion rather than preferential adsorption of alkynes on Bi 2 O 3 surfaces. Computational assessments of initial C–H activation pathways for alkynes and alkenes on (010) α-Bi 2 O 3 surfaces using density functional theory illustrate that heterolytic transition states which form proton-carbanion pairs on Bi–O sites kinetically favor the activation of alkynes rather than alkenes due to differences in C–H bond acidity, and the barrier for heterolytic C–H activation is dictated in part by the sum of the molecular deprotonation energy and the energy to bind an R – carbanion to a Bi site in its transition-state geometry. Finally, these heterolytic reactivity channels during selective chemical looping combustion present novel routes for purifying olefin gas streams containing alkyne impurities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Hybrid Symbolic-Numerical Modeling and Parametric Stability Analysis of DC–AC Power Systems

Hybrid DC-AC power systems integrating diverse inverter-based resources (IBRs) and multi-terminal high-voltage direct current (MTDC) networks represent a promising architecture for future power grids, while introducing challenges for modeling, stability analysis, and control design. This paper develops a hybrid symbolic-numerical modeling framework and tool to characterize the parametric small-signal stability of DC-AC coupled power systems. The proposed approach constructs parametric state-space models to enable efficient representation of system dynamics under varying control parameters and network configurations, with target parameters retained as symbolic variables and the remainder treated numerically. The stability analysis framework covers eigenvalue, sensitivity, and stability boundary and region characterization. Enhanced linear matrix inequality (LMI) techniques are proposed to directly certify small-signal stability over regions of parameter space while also reducing the conservativeness and computational burden. The resulting tools and frameworks enable rapid parametric model construction across diverse grid conditions, thereby facilitating stability-informed control and operation in complex DC–AC power systems.

DC–AC power systems↗

DOE OSTI2025

An Instrumented Capsule Design to Measure Thermal Conductivity in Miniature UO2 Specimens

Numerous separate effects irradiations of miniature nuclear fuel specimens have been conducted in the High Flux Isotope Reactor (HFIR) under the experimental platform designated as MiniFuel. MiniFuel is a static irradiation capability in which microstructural evolution and fuel performance phenomena are observed during postirradiation examination thereby offering a snapshot of the terminal fuel characteristics. This approach inherently requires fielding an irradiation where the experimental conditions are determined using predictive models and the pertinent outcomes are measured at the end of the test. Static irradiations can provide useful insights to the relationships between fuel performance and the pivotal irradiation conditions, namely temperature and burnup, but the ability to monitor fuel performance in situ would further support fuel development and qualification. To this end, an instrumented experiment design is being developed at Oak Ridge National Laboratory to capture thermal conductivity degradation and fission gas release during HFIR irradiation. These phenomena will be monitored using unique capsule designs that each target a different phenomenon. This paper details the thermal conductivity capsule (TCC) design and its expected performance envelope as determined using computer models. Each TCC will contain a miniature UO2 disc specimen (~0.5 mm thick × 5 mm diameter) sandwiched between metallic slugs with embedded thermocouples. The coupling of in situ temperature measurements, known thermal conductivity of the metallic components, and heat generation rates computed using high-fidelity neutronics models make the thermal conductivity measurement possible. This paper describes the reactor physics and heat transfer models used to predict the capsule’s performance and the methodology for calculating the fuel specimen’s thermal conductivity from the thermocouple measurements.

Gorton, Jacob [ORNL] (ORCID:0000000269806083)↗

DOE OSTI2025

Comparison of Ce( iv )/Th( iv )-alkynyl complexes and observation of a trans -influence ligand series for Ce( iv )

Organometallic cerium(iv) complexes have been challenging to isolate and characterize due to the strongly oxidizing nature of the cerium(iv) cation. Herein, we report two cerium(iv) alkynyl complexes, [Ce(TriNOx)(C[triple bond, length as m-dash]C-SiMe3)] (1-CeTMS) and [Ce(TriNOx)(C[triple bond, length as m-dash]C-Ph)] (1-CePh) (TriNOx3- = tris(2-tert-butylhydroxylaminato)benzylamine), that include terminal alkyne moieties. The isostructural thorium analogue [Th(TriNOx)(C[triple bond, length as m-dash]C-SiMe3)] (1-ThTMS) was also synthesized and compared with 1-CeTMS in bond distance, 13C-NMR spectra, vibrational spectra and electronic structure. The Ce-C bond distances were 2.501(3) Å for 1-CePh and 2.513(5) Å for 1-CeTMS on the shorter end of the few reported CeIV-C single bonds (2.478(3)-2.705(2) Å), possibly indicating significant Ce 5d- and 4f-orbital involvement. 13C-NMR spectroscopy was also consistent with Ce-C covalency, with significantly deshielded resonances ranging from 185-213 ppm. Such 13C-NMR shifts demonstrate a strong influence from spin-orbit coupling (SOC) effects, corroborated by computational studies. Raman analysis showed ν C[triple bond, length as m-dash]C stretching frequencies of 2000 cm-1 (1-CeTMS) and 2052 cm-1 (1-CePh), indicating the cerium(iv)-alkynyl interaction, compared to the parent HC[triple bond, length as m-dash]CPh (IR = 2105 cm-1 and Raman = 2104 cm-1). L3-edge X-ray absorption measurements revealed a predominant Ce(iv) electronic configuration, and magnetic measurements revealed temperature-independent paramagnetism. Electrochemical studies similarly revealed the electron donating ability of the alkynyl ligands, stronger than either fluoride or imido ligands for the Ce(iv)(TriNOx)-framework, with a cerium(iv/iii) reduction potential of E pc = -1.58 to -1.66 V vs. Fc/Fc+. Evidence for a trans-influence has been observed by evaluating a series including previously reported [CeIV(TriNOx)X]+/0 complexes with axial ligands X = THF, I-, Br-, Cl-, F-, -C[triple bond, length as m-dash]C-Ph, -C[triple bond, length as m-dash]C-SiMe3, -NH(3,5-(CF3)2-Ar), -OSiPh3, -N(M(L))(3,5-(CF3)2-Ar) [M(L) = Li(TMEDA), K(DME)2 or Cs(2,2,2-crypt)]. These data stand in contrast with previous reports of an inverse trans-influence at cerium(iv) and point to differences in involvement of cerium 4f- versus 5d-orbitals in the electronic structures of the complexes.

Yang, Qiaomu↗