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DOE OSTI2026

Protection System Validation with Machine Learning Anomaly Classification

A poster for the Early Career Poster Session. Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Development of Nano-Modified Concrete for Next Generation of Storage Systems

The goal of the research was to develop a superior concrete for the long-term storage of used nuclear fuel by engineering concrete at the nanoscale through the incorporation of nano-sized and nano-structured particles based on enhanced reactivity (not fiber reinforcement). The specific objectives were to determine for nano-modified concrete: 1. The effects of environmental weathering (i.e., wet/dry and ingress of reactive species such as chloride and sulfate from the marine environment or other sources) and exposure to radiation and thermal environments on the structural stability and microstructural evolution of the material; 2. How microstructural and morphological changes from environmental weathering and thermal and radiation damage affect the long-term, macroscale performance of the material; and, 3. Identify formulation envelopes for nanoparticle content and combinations to achieve superior concrete criteria for dry storage systems by integrating and extending existing predictive modeling approaches that couple reactive transport and thermo- chemo- and radiation induced damage. The major findings of this work are summarized as follows. The results are being processed and compiled in manuscripts for peer-reviewed publication. Due to the late access to the gamma irradiation facility, material characterization is continuing. Dispersion/agglomeration of nanoparticles in cement pastes. The dispersion/agglomeration of nanoparticles (NPs) of SiO 2 , TiO 2 , Al 2 O 3 , Fe 2 O 3 , and clay (bentonite and halloysite) in water, with and without polycarboxylate-based high range water reducer (P-HRWR), and in the environment of hydrating cement have been investigated. The intrinsic particle characteristics, surface charge, pH, monovalent and divalent ion concentrations in solution, and modes of interaction of the NPs with P-HRWR influenced the NP behavior in solution.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Application of Cyber-Informed Engineering for Protecting BESS

This white paper synthesizes an array of crucial grid services provided by BESS technology, assesses its architecture and communications, and presents a case study for analysis against the principles introduced by Cyber-Informed Engineering (CIE). Furthermore, in walking through the analysis, this paper presents a framework to evaluate risks and solutions when considering BESS components. Asset owners and buyers could perform this analysis to assess their BESS product implementations, alternative inverter-based resources (IBR), and energy management systems (EMS). Battery systems fulfill various roles contingent on the unique market demands and the specific challenges presented by regional grid infrastructures. These roles also vary due to the differing utility models for ownership and operation, which are adapted to meet regional and local capabilities and requirements. Concerns have been raised regarding the potential for adversaries to exploit knowledge of battery operational patterns to orchestrate decisive attacks. However, the security of operational data for these systems may not be the primary vulnerability, as much of this information is already well-understood within the community. Applying a modest degree of subject matter expertise can often yield valuable predictions regarding how a battery will respond under certain conditions, such as grid emergencies, high or low-temperature days, Public Safety Power Shutoff (PSPS) events, and outages. The operational characteristics of batteries are well-documented, and their capabilities, including the risks associated with misoperation and the resulting consequences, are published and understood within the industry. CIE practices represent the next step in gaining functional assurance and providing an acceptable level of risk, regardless of whether a battery vendor can support a trusted and validated supply chain. While this issue has exacerbated supply chain challenges, it is not an isolated condition. This foreign supply route is the primary source of BESS for the U.S. market. Significant efforts are underway through the Bipartisan Infrastructure Law (BIL) to change that. Still, strategic short-term operational mitigations are needed to ensure the security of our operational technology (OT) systems, which are enhanced by instilling trust and are separate from vendors implementing CIE principles.

25 ENERGY STORAGE↗

DOE OSTI2016

GraphMeta: Managing HPC Rich Metadata in Graphs

High-performance computing (HPC) systems face increasingly critical metadata management challenges, especially in the approaching exascale era. These challenges arise not only from exploding metadata volumes, but also from increasingly diverse metadata, which contains data provenance and arbitrary user-defined attributes in addition to traditional POSIX metadata. This ‘rich’ metadata is becoming critical to supporting advanced data management functionality such as data auditing and validation. In our prior work, we identified a graph-based model as a promising solution to uniformly manage HPC rich metadata due to its flexibility and generality. However, at the same time, graph-based HPC rich metadata anagement also introduces significant challenges to the underlying infrastructure. In this study, we first identify the challenges on the underlying infrastructure to support scalable, high-performance rich metadata management. Based on that, we introduce GraphMeta, a graphbased engine designed for this use case. It achieves performance scalability by introducing a new graph partitioning algorithm and a write-optimal storage engine. We evaluate GraphMeta under both synthetic and real HPC metadata workloads, compare it with other approaches, and demonstrate its advantages in terms of efficiency and usability for rich metadata management in HPC systems.

Dai, Dong↗

DOE OSTI2020

Life cycle assessment of novel heat exchanger for dry cooling of power plants based on encapsulated phase change materials

Cooling systems in power plants account for approximately 40% of total freshwater withdrawals in the U.S. Due to dwindling access to freshwater resources worldwide, continued operation of wet cooling systems poses a significant engineering challenge. To reduce water consumption, a novel air-cooled heat exchanger has been developed using encapsulated phase change material (EPCM) for dry cooling of power plants. Compared to traditional finned-tube air-cooled condensers, this novel EPCM heat exchanger improves the heat transfer coefficient and power plant efficiency while reducing the pressure drop and cooling system cost. Life cycle assessment (LCA) and techno-economic analysis (TEA) are used to evaluate the environmental and economic performance of EPCM heat exchangers from cradle-to-grave and to compare them to wet cooling and traditional air-cooled condensers. A thermodynamic model is developed to predict the EPCM heat exchanger performance for plant-scale operations. Equipment and construction costs for heat exchangers are estimated based on design parameters obtained from the thermodynamic model. Both process-LCA and economic-input–output LCA are used to simulate and test the sensitivity of EPCM alternatives with commercial wet and dry cooling technologies. We investigate options for EPCM end-of-life management upon retiring the heat exchanger and construct a process-based LCA model to estimate a greenhouse gas (GHG) emissions credit for recycling the EPCM. The life cycle GHG emission of the novel dry cooling technology is 1.16 kg CO 2 eq. /MWh compared with the 1.1–4.3 kg CO 2 eq. /MWh reported for commercial dry cooling technologies and consumes 9.5 L/MWh e of water for cradle-to-gate life cycle, which is significantly lower than that of wet cooling systems. The TEA shows many advantages of EPCM cooling technology over the state-of-art dry cooling solutions. Overall, the EPCM heat exchanger provides a better alternative compared to existing dry cooling and wet cooling technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

A Multi-Scale Inference, Estimation, and Prediction Engine for Earth System Modeling

We posit that AI methods can be leveraged to significantly enhance the predictive skill of forward Earth system modeling (ESM) activities. A hybrid framework incorporating traditional ESM modeling, inference methods, and AI techniques could make better use of both measured and computed information as well as computational resources by targeting inference tasks at program priorities, such as the predictability of precipitation extremes. This runtime pathway to closing the simulation/analysis-data/model improvement loop will streamline the traditional offline pathway to model improvement, which is based on domain science expertise, while suggesting guidance for further observations and measurements.

58 GEOSCIENCES↗

DOE OSTI2025

The Functor system: a new on-the-fly take on Material Properties based on C++ functions

In the context of solving multiphysics problems, the discretization of the partial differential equations (PDE) at hand often takes the spotlight. However, for most engineering users and even application developers, the discretization of the equations has already been performed. Instead, they are tasked with implementing specific closure relations and material properties. MOOSE has long enabled this using the Materials system. This system relied on the pre-computation of all properties before they are used in the PDE or in postprocessing. In this talk we will introduce the Functor system, which was deployed in MOOSE in 2021, then present a few applications of functors in flow modeling simulations by the NEAMS program. Functors first offer great flexibility in their evaluation. Rather than storing various arrays for material properties, they are evaluated on the fly at the location and state, e.g. current or old value, requested. Unlike regular material properties, several operations such as the time derivative, the divergence and the curl can be requested from a functor. Similar to material properties, functors can be made to depend on arbitrary combinations of variables, functions, postprocessors and other properties. However, unlike material properties, any of these can be substituted for a functor material property. Thanks to this, objects no longer need to be duplicated based on the types of their parameters.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Towards using bacterial microcompartments as a platform for spatial metabolic engineering in the industrially important and metabolically versatile Zymomonas mobilis

Advances in synthetic biology have enabled the incorporation of novel biochemical pathways for the production of high-value products into industrially important bacterial hosts. However, attempts to redirect metabolic fluxes towards desired products often lead to the buildup of toxic or undesirable intermediates or, more generally, unwanted metabolic cross-talk. The use of shells derived from self-assembling protein-based prokaryotic organelles, referred to as bacterial microcompartments (BMCs), as a scaffold for metabolic enzymes represents a sophisticated approach that can both insulate and integrate the incorporation of challenging metabolic pathways into industrially important bacterial hosts. Here we took a synthetic biology approach and introduced the model shell system derived from the myxobacterium Haliangium ochraceum (HO shell) into the industrially relevant organism Zymomonas mobilis with the aim of constructing a BMC-based spatial scaffolding platform. SDS-PAGE, transmission electron microscopy, and dynamic light scattering analyses collectively demonstrated the ability to express and purify empty capped and uncapped HO shells from Z. mobilis . As a proof of concept to internally load or externally decorate the shell surface with enzyme cargo, we have successfully targeted fluorophores to the surfaces of the BMC shells. Overall, our results provide the foundation for incorporating enzymes and constructing BMCs with synthetic biochemical pathways for the future production of high-value products in Z. mobilis .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Dissipation-assisted steady-state entanglement engineering based on electron transfer models

Here, we propose a series of dissipation-assisted entanglement generation protocols that can be implemented on a trapped-ion quantum simulator. Our approach builds on the single-site molecular electron transfer (ET) model recently realized in experiment [So et al., Sci. Adv. 10, eads8011 (2024)]. This model leverages spin-dependent boson displacement and dissipation controlled by sympathetic cooling. We show that, when coupled to external degrees of freedom, the ET model can be used as a dissipative quantum control mechanism, enabling the precise tailoring of both spin and boson steady states of a target subsystem. We derive simplified analytical formalisms that offer intuitive insights into the dissipative dynamics. Using realistic interactions in a trapped-ion system, we develop a protocol for generating 𝑁-qubit and 𝑁-boson 𝑊 states. Additionally, we generalize this protocol to realize generic 𝑁-qubit Dicke states with tunable excitation numbers. Finally, we outline a realistic experimental setup to implement our schemes in the presence of noise sources.

chemical charge transfer↗

DOE OSTI2023

Grey-box and ANN-based building models for multistep-ahead prediction of indoor temperature to implement model predictive control

Model-based predictive control (MPC) strategies for heating, ventilation, and air-conditioning (HVAC) systems present an opportunity to lower building energy consumption and operational costs. Such approaches rely on the development of a model to precisely forecast building thermal dynamics, such as room air temperature or heating/cooling rate, and make control-related decisions. The control-oriented modeling of building energy systems should be accurate in predicting indoor conditions and present low computational complexity. These features are the key challenge of implementing advanced control methods such as MPC. Extant studies on building modeling for MPC have focused on step-ahead forecasting techniques to forecast building thermal dynamics, while multistep-ahead forecasting is essential. Moreover, machine learning model suitable in case of the domain-based engineering expertise are also not available. To this aim, we perform a comparative analysis of the grey-box model based on a resistance-capacitance (RC) thermal network and a machine learning model composed of an artificial neural network (ANN) for multistep-ahead prediction of building thermal dynamics using current and historical data. Actual experimental data obtained from the Flexible Research Platform (FRP) in Oak Ridge National Laboratory (US) are used for estimation and validation purposes. The average root mean squared error (RMSE) of the grey-box and ANN models are 0.89 °C and 1.02°C, respectively. Finally, the results indicate that the grey-box model outperforms the ANN model in the considered validation periods in terms of accuracy and prediction stability.

42 ENGINEERING↗

DOE OSTI2019

Thermodynamics of Dual Doping in Quantum Dots

Dual doping is a powerful way to tailor the properties of semiconductor quantum dots (QDs) arising out of host–dopant and dopant–dopant interactions. Nevertheless, it has seldom been explored due to a variety of thermodynamic challenges, such as the differential bonding strength and diffusion constant within the host matrix that integrates with the host in dissimilar ways. This work discusses the challenges involved in administering them within the constraints of one host under similar conditions of temperature, time, and chemical parameters such as solubility and reactivity using CoPt-doped CdS QDs as a model system. In addition, the various forces in play, such as Kirkendall diffusion, solid- and liquid-state diffusion, hard acid soft base interaction with the host, and the effect of lattice strain due to lattice mismatch, are studied to understand the feasibility of the core to doped transformation. Furthermore, these findings suggest a potential approach for manipulating the properties of semiconductors by dual doping engineering.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2023

Enabling Floating Offshore VAWT Design by Coupling OWENS and OpenFAST

Vertical-axis wind turbines (VAWTs) have a long history, with a wide variety of turbine archetypes that have been designed and tested since the 1970s. While few utility-scale VAWTs currently exist, the placement of the generator near the turbine base could make VAWTs advantageous over tradition horizontal-axis wind turbines for floating offshore wind applications via reduced platform costs and improved scaling potential. However, there are currently few numerical design and analysis tools available for VAWTs. One existing engineering toolset for aero-hydro-servo-elastic simulation of VAWTs is the Offshore Wind ENergy Simulator (OWENS), but its current modeling capability for floating systems is non-standard and not ideal. This article describes how OWENS has been coupled to several OpenFAST modules to update and improve modeling of floating offshore VAWTs and discusses the verification of these new capabilities and features. The results of the coupled OWENS verification test agree well with a parallel OpenFAST simulation, validating the new modeling and simulation capabilities in OWENS for floating VAWT applications. These developments will enable the design and optimization of floating offshore VAWTs in the future.

17 WIND ENERGY↗

DOE OSTI2025

Bridging Equipment Reliability Data and Risk Informed Decisions in a Plant Operation Context

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry-developed and regulatory programs. The Risk-Informed Asset Management (RIAM) project is tasked to develop tools in support of the equipment reliability and asset management programs at nuclear power plants. These tools are designed to create a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). The goal of this article is to provide a guide for specific use cases that the RIAM project is targeting. We have grouped uses cases into three main areas. The first area focuses on the analysis of equipment reliability data with a particular emphasis on condition-based data, such as test/surveillance reports and component monitoring data. The second area focuses on the integration of equipment reliability into system/plant reliability models to determine system/plant health and identify the components that are critical to maintain an operational system. Lastly, the third area manages plant resources, such as maintenance activities and replacement scheduling using optimization methods. Here the primary focus is on supporting typical system engineer decisions regarding maintenance activity scheduling and component aging management. This is performed in a risk-informed context where the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Emerging systems biology approaches in nanotoxicology: Towards a mechanism-based understanding of nanomaterial hazard and risk

Engineered nanomaterials are being developed for a variety of technological applications. However, the increasing use of nanomaterials in society has led to concerns about their potential adverse effects on human health and the environment. During the first decade of nanotoxicological research, the realization has emerged that effective risk assessment of the multitudes of new nanomaterials would benefit from a comprehensive understanding of their toxicological mechanisms, which is difficult to achieve with traditional, low-throughput, single end-point oriented approaches. Therefore, systems biology approaches are being progressively applied within the nano(eco)toxicological sciences. This novel paradigm implies that the study of biological systems should be integrative resulting in quantitative and predictive models of nanomaterial behaviour in a biological system. To this end, global ‘omics’ approaches with which to assess changes in genes, proteins, metabolites, etc. are deployed allowing for computational modelling of the biological effects of nanomaterials. Here, we highlight omics and systems biology studies in nanotoxicology, aiming towards the implementation of a systems nanotoxicology and mechanism-based risk assessment of nanomaterials. - Highlights: • Systems nanotoxicology is a multi-disciplinary approach to quantitative modelling. • Transcriptomics, proteomics and metabolomics remain the most common methods. • Global “omics” techniques should be coupled to computational modelling approaches. • The discovery of nano-specific toxicity pathways and biomarkers is a prioritized goal. • Overall, experimental nanosafety research must endeavour reproducibility and relevance.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

T-FSM: A Scalable Distributed Task-Based System for Frequent Subgraph Pattern Mining from a Big Graph

Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel and distributed systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, poor scale-out capability, and possibly inaccurate results. In this article, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. The execution engine of T-FSM supports both intra-machine parallelism and inter-machine parallelism. For intra-machine parallelism, T-FSM adopts a novel task-based execution model to ensure high multithreading concurrency, bounded memory consumption, and effective load balancing. For inter-machine parallelism, T-FSM ensures good scale-out performance with a lightweight pattern rebalancing approach that reduces workload skewness of pattern evaluations among machines. To avoid recomputing the contexts for migrated patterns, we design a novel context cache table to support concurrent and asynchronous requesting and caching of remote context data, which can timely evict and garbage collect used pattern contexts that are no longer needed to keep memory consumption bounded. Extensive experiments show that T-FSM is orders of magnitude faster than existing state-of-the-art parallel systems (more than 10×, 51×, 131×, 55× speedup over ScaleMine, DistGraph, Pangolin and Peregrine, respectively) and distributed systems (more than 42× and 88× over ScaleMine and DistGraph, respectively) for frequent subgraph pattern mining, and it scales out satisfactorily to 512 CPU cores on the Polaris supercomputer at Argonne National Laboratory.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

MO-E-18C-01: Open Access Web-Based Peer-To-Peer Training and Education in Radiotherapy

Purpose: Current training and education delivery models have limitations which result in gaps in clinical proficiency with equipment, procedures, and techniques. Educational and training opportunities offered by vendors and professional societies are by their nature not available at point of need or for the life of clinical systems. The objective of this work is to leverage modern communications technology to provide peer-to-peer training and education for radiotherapy professionals, in the clinic and on demand, as they undertake their clinical duties. Methods: We have developed a free of charge web site ( https://i.treatsafely.org ) using the Google App Engine and datastore (NDB, GQL), Python with AJAX-RPC, and Javascript. The site is a radiotherapy-specific hosting service to which user-created videos illustrating clinical or physics processes and other relevant educational material can be uploaded. Efficient navigation to the material of interest is provided through several RT specific search tools and videos can be scored by users, thus providing comprehensive peer review of the site content. The site also supports multilingual narration\translation of videos, a quiz function for competence assessment and a library function allowing groups or institutions to define their standard operating procedures based on the video content. Results: The website went live in August 2013 and currently has over 680 registered users from 55 countries; 27.2% from the United States, 9.8% from India, 8.3% from the United Kingdom, 7.3% from Brazil, and 47.5% from other countries. The users include physicists (57.4%), Oncologists (12.5%), therapists (8.2%) and dosimetrists (4.8%). There are 75 videos to date including English, Portuguese, Mandarin, and Thai. Conclusion: Based on the initial acceptance of the site, we conclude that this open access web-based peer-to-peer tool is fulfilling an important need in radiotherapy training and education. Site functionality should expand in the future to include document sharing and continuing education credits.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2015

A Framework to Support Generator Ramping Uncertainty Analysis and Visualization

Power system operation requires maintaining a continuous balance between system demand and generation within certain constraints. Traditionally, the balancing processes are based on deterministic models, which do not consider possible random deviations of system generation and load from their predicted values. With the increasing penetration of the renewable generation, unexpected balancing problems can happen due to the deviations. This can result in serious risks to system reliability and efficiency. When the available balancing reserve is not enough to cover the predicted net load range with uncertainty, deficiency of balancing needs occurs. In this case, it is necessary to commit or de-commit additional conventional generators to achieve the desired confidence level for the balancing needs. The framework is built for solving this problem. The ramping tool engine is used to predict additional balancing requirements caused by the variability and uncertainty of the renewable energy, under the constraints of the generation ramping capability and interchange schedule. The web browser- based GUI is used to visualize the data in web-environment, which provides flexibility to allow user to see the ramping outputs in any platform. The GOSS structure provides strong support to allow easy communication between ramping engine, and system inputs, as well as the GUI.

Chen, Yousu↗

DOE OSTI2017

Realization of a Hole-Doped Mott Insulator on a Triangular Silicon Lattice

The physics of doped Mott insulators is at the heart of some of the most exotic physical phenomena in materials research including insulator-metal transitions, colossal magnetoresistance, and high-temperature superconductivity in layered perovskite compounds. Advances in this field would greatly benefit from the availability of new material systems with a similar richness of physical phenomena but with fewer chemical and structural complications in comparison to oxides. In this paper, using scanning tunneling microscopy and spectroscopy, we show that such a system can be realized on a silicon platform. The adsorption of one-third monolayer of Sn atoms on a Si(111) surface produces a triangular surface lattice with half filled dangling bond orbitals. Modulation hole doping of these dangling bonds unveils clear hallmarks of Mott physics, such as spectral weight transfer and the formation of quasiparticle states at the Fermi level, well-defined Fermi contour segments, and a sharp singularity in the density of states. These observations are remarkably similar to those made in complex oxide materials, including high-temperature superconductors, but highly extraordinary within the realm of conventional $sp$-bonded semiconductor materials. Finally, it suggests that exotic quantum matter phases can be realized and engineered on silicon-based materials platforms.

36 MATERIALS SCIENCE↗