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DOE OSTI2022

Ramsey envelope modulation in NV diamond magnetometry

Nitrogen-vacancy (NV) spin ensembles in diamond provide an advanced magnetic sensing platform, with applications in both the physical and life sciences. The development of isotopically engineered 15 NV diamond offers advantages over naturally occurring 14 NV for magnetometry, due to its simpler hyperfine structure. However, for sensing modalities requiring a bias magnetic field not aligned with the sensing NV axis, the absence of a quadrupole moment in the 15 N nuclear spin leads to pronounced envelope modulation effects in time-dependent measurements of 15 NV spin evolution. While such behavior in spin echo experiments are well studied, analogous effects in Ramsey measurements and the implications for magnetometry remain underexplored. Here, we derive the modulated 15 NV Ramsey response to a misaligned bias field, using a simple vector description of the effective magnetic field on the nuclear spin. The predicted modulation properties are then compared to experimental results, revealing significant magnetic sensitivity loss if unaddressed. We demonstrate that double-quantum coherences of the NV S=1 electronic spin states dramatically suppress these envelope modulations, while additionally proving resilient to other parasitic effects such as strain heterogeneity and temperature shifts.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Dispatch Optimization Variable Engine

The Dispatch Optimization Variable Engine (DOVE) is software tool written in python, developed at Idaho National Laboratory (INL) that provides an easily accessible application-programming-interface (API) to performing resource dispatch optimization analysis for integrated energy system (IES) configurations. DOVE is an integral part of the Framework for Optimization of Resources and Economics (FORCE) software suite and is leveraged by codes such as the Holistic Energy Resource Optimization Network (HERON) and the Optimization of Real-Time Capacity Allocation (ORCA). The philosophy behind DOVE is to provide a modular software solution to IES planning and operation by utilizing state-of-the-art algorithms and machine learning. The goal is to accurately capture the dispatching behavior of a complex energy system given varying time-dependent signals for demand and commodity pricing.

McDowell, DylanJ. [Idaho National Laboratory (INL)↗

DOE OSTI2020

Defect cluster and nonequilibrium gas bubble associated growth in irradiated UMo fuels – a cluster dynamics and phase field model

Irradiation examination shows that gas bubble swelling kinetics is much faster after irradiation-induced recrystallization than that prior recrystallization in UMo fuels. It implies that gas bubbles in coarse grains and small recrystallized grains have different growth behavior. In this work, a phase-field model of gas bubble evolution integrating microstructure dependent cluster dynamics has been developed, for the first time, to study the gas bubble swelling behavior in the recrystallization zone of UMo fuels. Generation, diffusion, reaction, sink, emission and clustering of vacancies and interstitials are described by the cluster dynamics model while a phase-field model is used to describe the evolution of non-equilibrium gas bubbles including nucleation and growth. With the coupled model, the effect of defect generation rate, clustering rate, interstitial emission and sink rates on grain boundaries on the gas bubble evolution are systematically simulated. A set of model parameters (defect generation rate, clustering rate, interstitial emission and sink rates) is determined by comparing measured and simulated gas bubble swelling kinetics. The results demonstrate that interstitial clustering is one of the important physical mechanisms which results in a fast gas bubble swelling kinetics in the recrystallization zone. The developed model can also be extended to study the associated growth of defect and second phase precipitates often observed in irradiated materials.

Hu, Shenyang↗

DOE OSTI2022

Q-dependent collective relaxation dynamics of glass-forming liquid Ca0.4K0.6(NO3)1.4 investigated by wide-angle neutron spin-echo

Abstract The relaxation behavior of glass formers exhibits spatial heterogeneity and dramatically changes upon cooling towards the glass transition. However, the underlying mechanisms of the dynamics at different microscopic length scales are not fully understood. Employing the recently developed wide-angle neutron spin-echo spectroscopy technique, we measured the Q -dependent coherent intermediate scattering function of a prototypical ionic glass former Ca 0.4 K 0.6 (NO 3 ) 1.4 , in the highly viscous liquid state. In contrast to the structure modulated dynamics for Q < 2.4 Å −1 , i.e., at and below the structure factor main peak, for Q > 2.4 Å −1 , beyond the first minimum above the structure factor main peak, the stretching exponent exhibits no temperature dependence and concomitantly the relaxation time shows smaller deviations from Arrhenius behavior. This finding indicates a change in the dominant relaxation mechanisms around a characteristic length of 2π/(2.4 Å −1 ) ≈ 2.6 Å, below which the relaxation process exhibits a temperature independent distribution and more Arrhenius-like behavior.

42 ENGINEERING↗

DOE OSTI2023

Probing the Boundary between Classical and Quantum Mechanics by Analyzing the Energy Dependence of Single-Electron Scattering Events at the Nanoscale

The relation between the energy-dependent particle and wave descriptions of electron–matter interactions on the nanoscale was analyzed by measuring the delocalization of an evanescent field from energy-filtered amplitude images of sample/vacuum interfaces with a special aberration-corrected electron microscope. The spatial field extension coincided with the energy-dependent self-coherence length of propagating wave packets that obeyed the time-dependent Schrödinger equation, and underwent a Goos–Hänchen shift. The findings support the view that wave packets are created by self-interferences during coherent–inelastic Coulomb interactions with a decoherence phase close to Δφ = 0.5 rad. Due to a strictly reciprocal dependence on energy, the wave packets shrink below atomic dimensions for electron energy losses beyond 1000 eV, and thus appear particle-like. Consequently, our observations inevitably include pulse-like wave propagations that stimulate structural dynamics in nanomaterials at any electron energy loss, which can be exploited to unravel time-dependent structure–function relationships on the nanoscale.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Overview of Base Model in Parametric Studies Specific to Performance of U-Mo Plates

This paper provides an overview of the base model specifically developed to perform parametric sensitivity studies on the U-10Mo monolithic fuel system. U-Mo monolithic fuels are being considered for the conversion of test reactors into high-performance research reactors that operate using proliferation-resistant, low-enriched uranium (LEU) fuels. These plate-type fuels contain a high-density, low-enrichment fuel sandwiched between zirconium diffusion barriers and encapsulated in aluminum claddings. All U.S. high-performance research reactors have released the designs of their LEU monolithic fuel reactor cores. These designs include nearly 50 distinct fuel plate geometries with different operational parameters. Consequently, a single generic plate geometry representing all the extreme points in this design matrix is unrealistic. To evaluate the performance for various parameters, a set of sensitivity studies was performed. These studies considered various input parameters (i.e., geometric, operational, and material property-related). The results revealed valuable information about plate performance and the sensitivity of this performance to various modeling inputs. To establish a reference state for comparing these result, base model featuring representative irradiation conditions was developed. To capture in-reactor behavior accurately, incorporation of representative constitutive models capable of evolving properties with respect to temperature, irradiation time, and burnup was needed. The behavioral models considered burnup-dependent properties, swelling, creep, and degradation. This paper introduces the base model created for the parametric sensitivity studies. The detailed description of the procedure includes the model geometry, model discretization, thermo-mechanical coupling, material properties and behavioral models. This paper also provides selected results and assesses the performance of the base model.

42 ENGINEERING↗

DOE OSTI2023

Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework

The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport, Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes. Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge. To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). Here, these are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries. We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2 %) in forecasting the discharge behavior of new unseen cathodes.

25 ENERGY STORAGE↗

DOE OSTI2022

TECHNICAL BACKGROUND FOR THE UPDATE AND EXTENSION OF THE TIME-DEPENDENT ALLOWABLE STRESSES OF TYPE 304H AND 316H STAINLESS STEELS

Type 304H and 316H stainless steels are codified in the ASME Boiler and Pressure Vessel Code for Section III, Division 5, Class A construction for up to 1500F (816C) and 300,000 hours. Extension of the design lifetimes to 500,000 hours has been undertaken by various research projects and by the ASME Section III, Division 5 Code Committees. Due to a long-standing issue related to non-classical creep behavior on the use of the time to the onset of tertiary creep as one of the time-dependent allowable stress criteria, little progress was made in the extension of the time-dependent allowable stresses for Type 304H and 316H stainless steels. A recent effort by Dabrow and Nestell (Impact of Tertiary Creep on Time Dependent Allowable Stresses for Type 304H and 316H Stainless Steels, Report 0300-0003-RPT-001, MPR Associates, Alexandria, VA, 2020) has provided a rational method for the treatment of the highly variable data for the time to onset of tertiary creep. In this paper, the background formulas for determining the time-dependent allowable stress values of Type 304H and 316H stainless steels, as assembled from relevant project work, are presented. These updates and extensions are being recommended to the ASME Code committees for approval, and are subject to change upon feedback from ASME Section III Code committees.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

TECHNICAL BACKGROUND FOR THE UPDATE AND EXTENSION OF THE TIME-DEPENDENT ALLOWABLE STRESSES OF TYPE 304H AND 316H STAINLESS STEELS

304/316 life extension↗

DOE OSTI2025

Unsupervised anomaly clustering via offset alignment in multivariate grid sensing data

Modern industries increasingly rely on multi-sensor technologies to acquire complex, high-dimensional data streams, enabling advanced monitoring and control systems. One critical application is online anomaly detection in electrical smart grids, where multivariate and multimodal sensing technologies play a vital role. However, detecting anomalies in such time-series data is challenging due to their inherent temporal dependencies and stochastic behavior. Traditional approaches based on supervised and semi-supervised learning methods depend on labeled datasets, which are often unavailable in real-world scenarios. While unsupervised methods have emerged as promising alternatives, these methods are highly susceptible to noise and outliers commonly present in sensing applications. Furthermore, deep learning-based anomaly detection methods, despite their performance, are often criticized for their black-box nature, limiting their applicability in safety-critical and online environments where interpretability and explainability are paramount. In this work, we propose an unsupervised anomaly clustering method leveraging a cyclic alignment-based offset detection algorithm for multivariate time-series signals. The proposed method is applied to multivariate data collected from vibrational, voltage, and magnetic field sensors deployed in a local grid substation. Our results demonstrate the robustness of the algorithm in accurately clustering various anomalies/events across different sensing modalities. Additionally, we compare the effectiveness of the proposed approach against a simple pattern-based anomaly detection method, which performs well for univariate data but fails to generalize to multivariate and multimodal time-series data.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗

DOE OSTI2026

Validating a Dynamic PWR Safety and Security Model?

Nuclear power plants (NPPs) are assessed for safety and security using separate models that cannot capture how an attacker's decisions and a plant's response unfold together in real time, leaving regulators and operators without a complete picture of true plant vulnerability. Traditional probabilistic risk assessment (PRA) methods treat adversarial events as fixed initiators with predetermined outcomes, and are structurally incapable of representing the time-dependent interplay between physical security events, safety system response, and operator mitigative actions. At Idaho National Laboratory (INL), I contributed to the development and validation of Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF). Where static PRA relies on event-tree logic that cannot evolve mid-scenario, MASS-DEF couples a time-dependent dynamic PRA tool EMRALD (Event Modeling Risk Assessment using Linked Diagrams) with attack simulation software, allowing attacker behavior, plant system states, and operator actions to interact across time. My work focused on validating a general Pressurized Water Reactor (PWR) model. I traced model logic against PWR plant to identified errors in logic and confirm accuracy. I then built and tested attack scenarios against a general PWR model to verify that the model produced expected outcomes across all logical pathways. I also contributed a section to a related technical paper applying the same EMRALD platform to radiation dose modeling. Results show that MASS-DEF can quantitatively demonstrate that many plants exceed their regulatory security thresholds. This demonstrated margin provides a technically defensible basis for reducing the number of guards without compromising regulatory compliance. Physical security costs represent roughly 10% of annual operating budgets, making such reductions directly meaningful to INL's mission of sustaining existing commercial NPPs. This internship strengthened my understanding of nuclear systems, probabilistic modeling, and technical writing, and has solidified my pursuit of a career at a national laboratory.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗

DOE OSTI2024

Quantifying the Temperature Dependence of the Multi-Species, Multi-Reaction Model. Part 1: Parameterization for a Meso-Carbon Micro-Bead Graphite

In this work, the temperature impact on the Multi-Species, Multi-Reaction (MSMR) model is studied. This is accomplished by acquiring data from slow rate lithiation and delithiation of a meso-carbon micro-bead (MCMB) graphite. The MSMR model is used to simulate linear-sweep voltammetry data of a porous electrode composed of graphite, and because the electrode is close to a state of dynamic equilibrium, the peaks in the differential voltage spectroscopy plot can be analyzed. Through this analysis, the temperature impact on the total fraction of available host sites in a particular MSMR gallery (X j ), the impact on the reference potential (U° j ), and the impact on the parameter detailing the deviation from Nernstian behavior ( ω j ) can be found. This is the first time the temperature dependence of the MSMR parameters have been experimentally analyzed. In Part 2, the impact of the temperature dependence of the MSMR parameters on the entropy coefficient of an intercalation material will be studied.

Paul, Abigail (ORCID:0000000172892069)↗

DOE OSTI2022

Side Chain Dynamics of Poly(norbornene)- g -Poly(propylene oxide) Bottlebrush Polymers

The segmental dynamics of the side chains of poly(norbornene)- g -poly(propylene oxide) (PNB- g -PPO) bottlebrush polymer in comparison to PPO is studied by quasi-elastic neutron scattering. Having experimental time and length scale information simultaneously allows to extract spatial information in addition to relaxation time. Tethering one end of the PPO side chain onto a stiff PNB backbone slows down the segmental relaxation, over the length and time scales investigated. The power law dependence of the relaxation time on the momentum transfer, Q , indicates a more heterogeneous relaxation pattern for the bottlebrush polymer, whereas the linear PPO has less deviations from a homogenous relaxation. Similar conclusions can be drawn from the time dependent mean square displacement, $\langle$ r 2 ( t )$\rangle$, and the non-Gaussian parameter, α 2 ( t ). Herein, the bottlebrush polymer shows a more restricted dynamics, whereas the linear PPO reaches $\langle$ r 2 ( t )$\rangle$∝ t 0.5 at the highest temperature. The deviations from Gaussian behavior are evident at the α 2 ( t ). Both samples show a decaying α 2 ( t ). Finally, the non-Gaussian parameter supports the results from the power law dependence of the relaxation times, with lower α 2 ( t ) values for PPO compared to those for PNB-g-PPO, pointing to less deviations from Gaussian behavior.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Process prediction and detection of faults using probabilistic bidirectional recurrent neural networks on real plant data

Attaining Industry 4.0 for manufacturing operations requires advanced monitoring systems and real-time data analytics of plant data, among other topics. We propose a Probabilistic Bidirectional Recurrent Network (PBRN) for industrial process monitoring for the early detection of faults. The model is based on a Gated Recurrent Unit (GRU) neural network that allows the model to retain long-term dependencies between sensor data along a time horizon, hence learning the dynamic behavior of the process. To reduce the false-positive detection rate of the model, we compel the model to learn from a highly noisy sensor reading while outputting noise-free sensor outputs. The performance of the proposed model is compared to other data-driven statistical process monitoring schemes using real plant data from an industrial Air Separations Unit (ASU) containing noisy sensor readings. We show that the model can learn from noisy data without reducing its performance. Using two different fault cases, we demonstrate the model’s ability to carry out early fault detection with average false-positive rates of 2.9% and 4.9% for both fault cases. The missed detection rates are 0.1% and 0.2%, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Asymmetric Lithium Extraction and Insertion in High Voltage Spinel at Fast Rate

Spinel-structured ordered-LiNi 0.5 Mn 1.5 O 4 (o-LNMO) has experienced a resurgence of interest in the context of reducing scarce elements such as cobalt from lithium-ion battery. O-LNMO undergoes two two-phase reaction at slow rates. However, it is not known if such phenomenon also applies at fast rates. Herein, we investigate the rate-dependent phase transition behavior of o-LNMO through in operando time-resolved X-ray diffraction. The results indicate that a narrow region of solid solution reaction exists for charge and discharge at both slow and fast rate. The overall phase transition is highly asymmetric at fast rates. During fast charge, it is a particle-by-particle mechanism resulting from asynchronized reaction among the particles. Finally, during fast discharge, it is likely a core-shell mechanism involving transition from Li 0+x Ni 0.5 Mn 1.5 O 4 to Li 1+x Ni 0.5 Mn 1.5 O 4 in the outer layer of particles. The Li 0.5 Ni 0.5 Mn 1.5 O 4 phase is suppressed during fast discharge and only appears through Li redistribution upon relaxation.

25 ENERGY STORAGE↗

DOE OSTI2021

Changes in Future Precipitation Mean and Variability across Scales

Changes in precipitation variability can have large societal consequences, whether at the short time scales of flash floods or the longer time scales of multiyear droughts. Recent studies have suggested that in future climate projections, precipitation variability rises more steeply than does its mean, leading to concerns about societal impacts. Here we evaluate changes in mean precipitation over a broad range of spatial and temporal scales using a range of models from high-resolution regional simulations to millennial-scale global simulations. Results show that changes depend on the scale of aggregation and involve strong regional differences. On local scales that resolve individual rainfall events (hours and tens of kilometers), changes in precipitation distributions are complex and variances rise substantially more than means, as is required given the well-known disproportionate rise in precipitation intensity. On scales that aggregate across many events, distributional changes become simpler and variability changes smaller. At regional scale, future precipitation distributions can be largely reproduced by a simple transformation of present-day precipitation involving a multiplicative shift and a small additive term. The “extra” broadening is negatively correlated with changes in mean precipitation: in strongly “wetting” areas, distributions broaden less than expected from a simple multiplicative mean change; in “drying” areas, distributions narrow less. Precipitation variability changes are therefore of especial concern in the subtropics, which tend to dry under climate change. Outside the tropics, variability changes are similar on time scales from days to decades (i.e., show little frequency dependence). This behavior is highly robust across models, suggesting it may stem from some fundamental constraint.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Performing Numerical Analysis of Cybersecurity Options Using Dynamic Risk Analysis Tool EMRALD

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Considering a cyber threat should involve defense-in-depth methods and a quantitative or numerical evaluation of overall effectiveness against dynamic, time-dependent attacks to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related safety is a requirement set by North American Electric Reliability and the U.S. Nuclear Regulatory Commission. They are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks may focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want to know business reliability and recovery from those threats, and that requires modeling physical behavior of the targets. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with different tools having issues such as state-base explosion. Dynamic modeling enables time and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic numerical risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies. Keywords: cyber modeling; cyber-physical systems; numerical cyber modeling

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Surface Properties of Double-Fin Generated Shock-Wave/Boundary-Layer Interactions

Shock-Wave/Boundary-Layer Interaction (SBLI) is ubiquitous in high-speed air vehicles. SBLI surface flowfield properties of crossing shocks induced by symmetric Double-Fins (DFs) at fin angles of 8° and 10° at Mach 2 are experimentally investigated; a 10° Single-Fin (SF) is also explored as a comparison. Surface visualization is used to capture distinct flowfield structures such as separation and upstream influence. Steady and unsteady Pressure-Sensitive Paints (PSPs) are used to obtain global surface pressure fields. Characteristics of DF-SBLI surface topology are discussed by comparing the corresponding SF-SBLI. Here, the effect of fin angle on DF-SBLI is characterized, and unsteady dynamics of the pressure field are examined. Spectral Proper Orthogonal Decomposition (SPOD) is performed on unsteady PSP, and traveling surface pressure waves and their frequency-dependent behavior are discussed. Dispersion relation of surface pressure waves is revealed by performing two-dimensional space–time Fourier transform on unsteady PSP, and the results are compared with the phase velocity distributions obtained from SPOD. Accompanying group velocity behavior is examined for distinct SBLI regions wherein wave propagation is found dependent on its frequency. Steady surface pressures along the centerline are compared with previous experimental data. Fin-tip spacing is found to be an essential parameter for scaling of upstream influence length.

33 ADVANCED PROPULSION SYSTEMS↗