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DOE OSTI2022

LaSDI: Parametric Latent Space Dynamics Identification

Enabling fast and accurate physical simulations with data has become an important area of computational physics to aid in inverse problems, design-optimization, uncertainty quantification, and other various decision-making applications. This paper presents a data-driven framework for parametric latent space dynamics identification procedure that enables fast and accurate simulations. The parametric model is achieved by building a set of local latent space model and designing an interaction among them. An individual local latent space dynamics model achieves accurate solution in a trust region. By letting the set of trust region to cover the whole parameter space, our model shows an increase in accuracy with an increase in training data. Herein we introduce two different types of interaction mechanisms, i.e., point-wise and region-based approach. Both linear and nonlinear data compression techniques are used. We illustrate the framework of Latent Space Dynamics Identification (LaSDI) enable a fast and accurate solution process on various partial differential equations, i.e., Burgers’ equations, radial advection problem, and nonlinear heat conduction problem, achieving 0 (100)x speed-up and 0 (1)% relative error with respect to the corresponding full order models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Sky subtraction in an era of low surface brightness astronomy

ABSTRACT The Vera C. Rubin Observatory Wide-Fast Deep sky survey will reach unprecedented surface brightness depths over tens of thousands of square degrees. Surface brightness photometry has traditionally been a challenge. Current algorithms which combine object detection with sky estimation systematically oversubtract the sky, biasing surface brightness measurements at the faint end and destroying or severely compromising low surface brightness light. While it has recently been shown that properly accounting for undetected faint galaxies and the wings of brighter objects can in principle recover a more accurate sky estimate, this has not yet been demonstrated in practice. Obtaining a consistent spatially smooth underlying sky estimate is particularly challenging in the presence of representative distributions of bright and faint objects. In this paper, we use simulations of crowded and uncrowded fields designed to mimic Hyper Suprime-Cam data to perform a series of tests on the accuracy of the recovered sky. Dependence on field density, galaxy type, and limiting flux for detection are all considered. Several photometry packages are utilized: source extractor, gnuastro, and the LSST science pipelines. Each is configured in various modes, and their performance at extreme low surface brightness analysed. We find that the combination of the source extractor software package with novel source model masking techniques consistently produce extremely faint output sky estimates, by up to an order of magnitude, as well as returning high fidelity output science catalogues.

Astronomy & Astrophysics↗

DOE OSTI2024

Graph neural network for neutrino physics event reconstruction

Liquid argon time projection chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction techniques. Here, this article describes NUGRAPH 2, a graph neural network for low-level reconstruction of simulated neutrino interactions in a LArTPC detector. Simulated neutrino interactions in the MicroBooNE detector geometry are described as heterogeneous graphs, with energy depositions on each detector plane forming nodes on planar subgraphs. The network utilizes a multihead attention message-passing mechanism to perform background filtering and semantic labeling on these graph nodes, identifying those associated with the primary physics interaction with 98.0% efficiency and labeling them according to particle type with 94.9% efficiency. The network operates directly on detector observables across multiple two-dimensional representations but utilizes a three-dimensional-context-aware mechanism to encourage consistency between these representations. Model inference takes 0.12 s / event on a CPU and 0.005 s / event batched on a GPU. This architecture is designed to be a general-purpose solution for particle reconstruction in neutrino physics, with the potential for deployment across a broad range of detector technologies, and offers a core convolution engine that can be leveraged for a variety of tasks beyond the two described in this paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Ultrasonic waves from radial mode excitation of a piezoelectric disc on the surface of an elastic solid

In this work, we discuss the ultrasonic wave propagation characteristics in an isotropic elastic solid due to the radial mode excitation of a piezoelectric disc actuator bonded to its surface. Finite element simulations using coupled electromechanical modeling is employed to investigate the wave propagation behavior. We find that the radial mode vibrations on the surface of the elastic solid generates all the three types of ultrasonic waves: longitudinal, shear, and surface waves. The waves in the solid are comprised of a central lobe and multiple side lobes based on the frequency of the radial mode excitation. The central lobe is predominantly composed of longitudinal waves and the side lobes are composed of shear waves. While the longitudinal waves have a strong central lobe and weak side lobes that are not fully developed, shear waves consists of fully developed side lobes. In addition, we observe that the longitudinal waves have fewer side lobes inside the solid compared to the shear waves. A semi-analytical approach is presented to explain the above observations. The interface traction boundary condition between the piezoelectric disc and the elastic solid is approximated as a truncated 'Bessel' excitation over the area of the piezoelectric disc in contact with the solid. A Fourier–Bessel series expansion technique is used to investigate the wave propagation behavior from the above traction boundary condition. The results obtained explain the observations from the finite element simulations with regard to the number and distribution of side lobes pertaining to the longitudinal and shear waves generated from the radial modes of the piezoelectric disc. The proposed semi-analytical approach is general and can be applied to any arbitrary axisymmetric excitations on the surface of an elastic solid.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Bridging confined phase behavior of CH 4 -CO 2 binary systems across scales

Phase behavior of confined fluids may deviate significantly from that of the bulk fluid due to the fluid-wall interactions being a significant portion of all intermolecular interactions under confinement. Despite recent advancements in understanding confined phase behavior of pure fluids, confined phase behavior of mixtures remains an understudied topic. In this work, we examine the confined phase behavior of a CH 4 -CO 2 binary system by combining Monte Carlo (MC) simulations, a cubic equation of state (EoS), and the lattice Boltzmann method (LBM). First, the effects of confinement on density and phase distribution in nano-size pores are established using Gibbs Ensemble MC calculations, which produce precise results of liquid and vapor confined pressures and account for the modification of the phase change location. By comparing the phase envelopes of bulk and confined mixtures at a fixed temperature, here it is observed that the phase envelopes shrink with reductions in pore size. Based on this observation, we extend a modified Peng-Robinson EoS, which was originally developed for pure fluids under confinement, to mixtures via van-der-Waals-type mixing rules and by accounting for shifts in the critical properties of confined CH 4 -CO 2 . The resulting phase envelopes are in good agreement with the MC data. In addition, a local density model is used in combination with the confined EoS to calculate adsorption isotherms of CH 4 -CO 2 mixtures and to characterize the behavior of confined matter in nanopores. Finally, we incorporate this EoS in a multicomponent multiphase LBM that uses a pseudopotential model to represent intermolecular forces. This workflow utilizes multiscale simulation techniques to bridge the behavior of multicomponent systems across scales and to shed light on the confined phase behavior of CH 4 -CO 2 binary systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Nonresonant particle acceleration in strong turbulence: Comparison to kinetic and MHD simulations

Collisionless, magnetized turbulence offers a promising framework for the generation of nonthermal high-energy particles in various astrophysical sites. Yet, the detailed mechanism that governs particle acceleration has remained subject to debate. By means of 2D and 3D particle-in-cell, as well as 3D (incompressible) magnetohydrodynamic (MHD) simulations, we test here a recent model of nonresonant particle acceleration in strongly magnetized turbulence, which ascribes the energization of particles to their continuous interaction with the random velocity flow of the turbulence, in the spirit of the original Fermi model. To do so, we compare, for a large number of particles that were tracked in the simulations, the predicted and the observed histories of particles momenta. The predicted history is that derived from the model, after extracting from the simulations, at each point along the particle trajectory, the three force terms that control acceleration: the acceleration of the field line velocity projected along the field line direction, its shear projected along the same direction, and its transverse compressive part. Overall, we find a clear correlation between the model predictions and the numerical experiments, indicating that this nonresonant model can successfully account for the bulk of particle energization through Fermi-type processes in strongly magnetized turbulence. Additionally we also observe that the parallel shear contribution tends to dominate the physics of energization in the particle-in-cell simulations, while in the magnetohydrodynamic incompressible simulation, both the parallel shear and the transverse compressive term provide about equal contributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Enhanced dynamic equivalent identification method of large-scale power systems using multiple events

The increasing complexity of the interconnected power system makes high-fidelity dynamic simulation models computationally more intensive. To improve computation efficiency, model reduction techniques have been investigated to only preserve the dynamics in a limited area of interest (study area), while deriving equivalent representation for the external area. For this purpose, a measurement-based reduction approach using system identification techniques has been previously proposed. Accordingly, external areas are represented by dynamic equivalent loads using transfer function estimation. In this paper, in order to enhance the accuracy of the reduced model in preserving dynamics of the study area, multiple “grid” events of different types and at different locations are considered for identifying the parameters of the equivalent loads. Additionally, case studies are carried out in the NPCC 140-bus system. Comparisons are made between multiple events training and single event training, highlighting the advantages of the proposed method in providing a better representation of the grid dynamics under different operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Electron tomography of unirradiated and irradiated nuclear graphite

Graphite is the moderator material of several Generation IV nuclear reactor concepts, as well as the British Advanced Gas-cooled Reactors (AGR). Porosity can heavily influence the material properties, mechanical irradiation response, and neutron induced shrinkage or swelling of nuclear-grade graphite. Due to the sub-micron size of several types of pores found in graphite, only a high-resolution imaging technique such as electron tomography are capable of visualizing these features in three dimensions. In this research, we used electron tomography to characterize as-received and neutron irradiated samples of IG-110 nuclear-grade graphite to show for the first time the 3D structure of both native and irradiation-induced nano-cracks. This technique also reveals unique characteristics of graphite such as the structure that surrounds pores and could be used to inform molecular dynamic simulations of irradiated graphite and experimental techniques such as gas-absorption. This work also shows the utility of this technique for the study of other nuclear porous carbon-based materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Machine-Learned Linear Structural Dynamics

The tension between accuracy and computational cost is a common thread throughout computational simulation. One such example arises in the modeling of mechanical joints. Joints are typically confined to a physically small domain and yet are computationally expensive to model with a high-resolution finite element representation. A common approach is to substitute reduced-order models that can capture important aspects of the joint response and enable the use of more computationally efficient techniques overall. Unfortunately, such reduced-order models are often difficult to use, error prone, and have a narrow range of application. In contrast, we propose a new type of reduced-order model, leveraging machine learning, that would be both user-friendly and extensible to a wide range of applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Leveraging graph clustering techniques for cyber‐physical system analysis to enhance disturbance characterisation

Abstract Cyber‐physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber‐physical systems requires improved understanding of the combined cyber‐physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber‐physical data from smart grid systems to analyse differences and similarities in behaviour during cyber‐, physical‐, and cyber‐physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber‐physical systems. Through this analysis, deeper insights can be shared with decision‐makers on what cyber and physical components are strongly or weakly linked, what cyber‐physical pathways are most traversed, and the criticality of certain cyber‐physical nodes or edges. This paper presents several types of clustering methods for cyber‐physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber‐physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber‐physical graph interdependency analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Control of machining-induced residual stress via tool geometry and process parameter modification

Distortion generated in machined, monolithic, thin-walled aerospace components due to residual stresses leads to significant material and economic waste in the manufacturing industry. Inherent residual stress (IRS) present in stock materials combines with machining-induced residual stress (MIRS) to influence the final machined part distortion. It is hypothesized that MIRS can be controlled, based on the part geometry, through deliberate cutting tool geometry and process parameter modifications to negate the effect of IRS on distortion, consequently resulting in distortion-free parts. A finite element (FE) orthogonal cutting model is developed to study how tool geometry and process parameters influence machining-induced residual stress (MIRS). Orthogonal cutting experiments are performed on Al 7075-T651 samples to measure cutting forces and MIRS. A cutting force dynamometer is used to measure forces during cutting and a novel digital image correlation (DIC) based hole drilling technique is employed to measure the near-surface residual stress (RS) in the cut samples. These data are subsequently utilized to validate the FE prediction model. Various cases of cutting simulations involving different depths of cut, tool tip radii, and rake angles are performed to study their effects on RS. Similar to prior literature, increasing the depth of cut, tool tip radius, or rake angle is found to promote the formation of near-surface tensile stresses. The competing effects of material plowing and temperature are shown to determine the type of RS at the end of the cut. Moreover, a window of variation of RS (up to ± 400 MPa) is estimated within the given range of conditions, allowing for the control of MIRS through tool and process modification.

Mathews, Ritin [ORNL] (ORCID:0000000301440828)↗

DOE OSTI2020

Assessments of data centers for provision of frequency regulation

There are numerous opportunities for data centers to participate in demand response programs considering their large energy capacities, flexible working environments and workloads, redundant design and operation, etc. As a type of demand response, frequency regulation requires fast response, and its potential is not fully explored by data centers yet. This paper proposes a synergistic control strategy for data center frequency regulation which uses both IT and cooling systems. It combines power management techniques at the server level with control of the chilled water supply temperature to track the regulation signal from the electrical market. A frequency regulation flexibility factor is also proposed to increase the IT capacity for frequency regulation. The performance of the control strategy is studied through numerical simulations using an equation based object-oriented Modelica platform designed for data centers. Simulation results show that with well-tuned control parameters, data centers can provide frequency regulation service in both regulation up and down. The performance of data centers in providing frequency regulation service is largely influenced by the regulation capacity bid, frequency regulation flexibility factor, workload condition, and cooling mode of the cooling system, and not significantly influenced by the time constant of chillers. In addition, compared with a server-only control strategy, the proposed synergistic control strategy can provide an extra regulation capacity of 3% of the design power when chillers are activated. Here, when chillers are deactivated, both strategies have a similar regulation capacity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Development of a thermal creep model for aluminum alloy 6061 cladding in U-10Mo monolithic fuel plates

Plate-type fuel elements consisting of a high-density, low-enriched uranium (LEU) U–10Mo-based fuel foil encapsulated in an aluminum alloy (AA) cladding are fabricated using the hot isostatic pressing (HIP) technique. During the HIP process, the fuel plate system is heated to 560 °C, then cooled to room temperature. This heat cycle significantly affects the mechanical properties of the aluminum cladding, and experimental investigations have shown that, post-HIP bonding, the mechanical properties of the aluminum cladding transition from those of AA 6061-T6 to something closer to the O temper. More specifically, the ultimate strength of the cladding decreases while its ductility increases, making it challenging to capture the changes in mechanical behavior and material properties. Understanding the residual stresses generated during the HIP process is critical for assessing the fuel plate’s integrity under various temperature, pressure, and irradiation. To simulate the HIP bonding process, the elastic, plastic, and thermal properties of the cladding are assumed to be similar to those of AA 6061-O temper. However, the primary challenge lies in the lack of available data for the creep model of the AA 6061 cladding during this transient process of HIP. The present study focuses on developing a computational model that predicts the creep behavior of the aluminum cladding in the fuel plates during the HIP process, as cladding creep significantly influences the residual stresses generated in U-10Mo fuel plates during HIP fabrication. Furthermore, as HIP bonding occurs at high temperatures that are nearing the melting point of aluminum, the present work considered a temperature-dependent Arrhenius-type creep model. In particular, a hyperbolic sine creep model is employed to estimate the creep properties of the as-fabricated aluminum cladding. In conclusion, the residual stresses predicted in the U-10Mo fuel when using the newly calibrated creep model closely align with the experimental measurements, validating the model’s accuracy.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings

In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD. We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization. We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Edge ML for CAN bus intrusion detection in AVs

Autonomous Vehicles (AVs) are revolutionizing transportation, but their reliance on interconnected cyber-physical systems exposes them to unprecedented cybersecurity risks. This study addresses the critical challenge of detecting real-time cyber intrusions in self-driving vehicles by leveraging a dataset from the Udacity self-driving car project. We simulate four high-impact attack vectors, Denial of Service (DoS), spoofing, replay, and fuzzy attacks, by injecting noise into spatial features (e.g., bounding box coordinates) to replicate adversarial scenarios. We develop and evaluate two lightweight neural network architectures (NN-1 and NN-2) alongside a logistic regression baseline (LG-1) for intrusion detection. The models achieve exceptional performance, with NN-2 attaining an AUC score of 93.15% and 93.15% accuracy, demonstrating their suitability for edge deployment in AV environments. Through explainable AI techniques, we uncover unique forensic fingerprints of each attack type, such as spatial corruption in fuzzy attacks and temporal anomalies in replay attacks, offering actionable insights for feature engineering and proactive defense. Visual analytics, including confusion matrices, ROC curves, and feature importance plots, validate the models' robustness and interpretability. This research sets a new benchmark for AV cybersecurity, delivering a scalable, field-ready toolkit for Original Equipment Manufacturers (OEMs) and policymakers. By aligning intrusion fingerprints with SAE J3061 automotive security standards, we provide a pathway for integrating machine learning into safety-critical AV systems. Our findings underscore the urgent need for security-by-design AI, ensuring that AVs not only drive autonomously but also defend autonomously. This work bridges the gap between theoretical cybersecurity and life-preserving engineering, offering a leap toward safer, more secure autonomous transportation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Enhanced Dynamic Equivalent Identification Method of Large-Scale Power Systems Using Multiple Event

The increasing complexity of the interconnected power system makes high-fidelity dynamic simulation models computationally more intensive. To improve computation efficiency, model reduction techniques have been investigated to only preserve the dynamics in a limited area of interest (study area), while deriving equivalent representation for the external area. For this purpose, a measurement-based reduction approach using system identification techniques has been previously proposed. In that case, external areas are represented by dynamic equivalent loads using transfer function estimation. In this paper, in order to enhance the accuracy of the reduced model in preserving dynamics of the study area, multiple “grid” events of different types and at different locations are considered for identifying the parameters of the equivalent loads. Case studies are carried out in the NPCC 140-bus system. Comparison are made between multiple events training and single event training, highlighting the advantages of the proposed method in providing a better representation of the grid dynamics under different operating conditions.

Xue, Yaosuo↗

DOE OSTI2024

Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions

Methane (CH 4 ) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH 4 fluxes is examined at 14 FLUXNET- CH 4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH 4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH 4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.

54 ENVIRONMENTAL SCIENCES↗