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DOE OSTI2025

Connecting Minds: AI Use Cases to Bridge Power Systems and Large Language Models for Practical Applications

Recent advances in artificial intelligence (AI) and development of large language models (LLMs) present the opportunity to develop a new generation of power systems applications. In contrast with early power system AI applications based on structured numerical data, LLMs offer unique capabilities to perform logical reasoning using text documents, unstructured data, and application programming interface (API) calls to computational software. This paper seeks to bridge the knowledge gap between power systems engineers and LLM developers through a crosscutting explanation of use cases, characteristics, requirements, practical considerations from the perspectives of both LLM capabilities and industry needs. Specific focus is given to applications that can be realistically deployed by electric utilities. After introducing the architecture of LLMs and unique challenges of the power systems domain, this paper proposes twenty representative LLM applications grouped into categories of 1) power system operations, 2) asset management, 3) system planning and analytics, and 4) energy management and protection systems. Five use cases are presented within each category with descriptions of the motivation, objectives, approaches, example inputs / outputs, and benefits of each use case.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network

The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often require a complete dataset of internal microstructure samples. However, it is often an unbearable cost to acquire sufficient 3D digital microstructures by purely using microscopic imaging systems. Herein this paper presents a machine learning-based technique to hierarchically reconstruct 3D well-connected porous microstructures from one isotropic or several anisotropic low-cost 2D exemplar(s). To compactly characterize the large-scale microstructural features, a Gaussian image pyramid is built for each 2D exemplar. Local morphology patterns are collected from the Gaussian image pyramids, and then they serve as the training data to embed the 2D morphological statistics into feed-forward neural networks at multiple length levels. By using a specially-developed morphology integration scheme, the 3D morphological statistics at different levels can be inferred from the statistics-informed neural networks. Gibbs sampling is adopted to hierarchically reconstruct 3D microstructures by using multi-level 3D morphological statistics, where the large-scale, regional and local morphological patterns are statistically generated and successively added to the same 3D random field. The proposed method is tested on a series of porous media with distinct morphologies, and the statistical equivalence between the reconstructed and the real microstructures is systematically evaluated by comparing morphological descriptors and transport properties. The results demonstrate that the proposed 2D-to-3D microstructure reconstruction method is a universal and efficient approach to generating morphologically and physically realistic samples of porous media.

42 ENGINEERING↗

DOE OSTI2023

Earthquake-induced impact of base-isolated buildings: theory, numerical modeling, and design solutions

Earthquake shaking more intense than that used to size the horizontal clearance between a base–isolated building and near–rigid perimeter moat wall will result in hard impact, producing high–frequency, high–amplitude acceleration response in the structure and supported equipment. Herein this paper provides a design solution for the damaging effects of hard impact by installing a compliant engineered element in the load path between the base–isolated building and the moat wall, resulting in soft impact and a much smaller acceleration response. The engineered element assumed herein is a commercial–off–the–shelf marine fender with mechanical properties determined by physical testing. The attachment of a flexible engineered element, with well–defined stiffness and damping, to a near–rigid moat wall, simplifies the numerical modeling of the building–moat wall system and eliminates the need to bound the lateral stiffness of the wall for impact calculations. The simple model of the engineered element can be implemented in commercial finite element codes. Theory is developed for two–sided impact of a single–degree–of–freedom oscillator. Analytical solutions are derived for the shifted first–mode frequency of the impacted oscillator and for its free–vibration response. The shifted first–mode frequency is a function of the composite lateral stiffness of the isolator–engineered element assembly and its earthquake–induced displacement. Local peaks in the spectral response of the impacted oscillator form at odd integer multiples of the shifted first–mode frequency. The analytical solutions can be used to verify, in part, the numerical model used for impact analysis.

42 ENGINEERING↗

DOE OSTI2016

Analyzing Real-World Light Duty Vehicle Efficiency Benefits

Off-cycle technologies represent an important pathway to achieve real-world fuel savings, through which OEMs can potentially receive credit toward CAFE compliance. DOE national labs such as NREL are well positioned to provide objective input on these technologies using large, national data sets in conjunction with OEM- and technology-specific testing. This project demonstrates an approach that combines vehicle testing (dynamometer and on-road) with powertrain modeling and simulation over large, representative datasets to quantify real-world fuel economy. The approach can be applied to specific off-cycle technologies (engine encapsulation, start/stop, connected vehicle, etc.) in A/B comparisons to support calculation of realistic real-world impacts. Future work will focus on testing-based A/B technology comparisons that demonstrate the significance of this approach.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2017

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolution in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2013

Redirector: Designing Cell Factories by Reconstructing the Metabolic Objective

Advances in computational metabolic optimization are required to realize the full potential of new in vivo metabolic engineering technologies by bridging the gap between computational design and strain development. We present Redirector, a new Flux Balance Analysis-based framework for identifying engineering targets to optimize metabolite production in complex pathways. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach, modeling metabolic alterations as changes in the balance of metabolic objectives in the system. This framework iteratively selects enzyme targets, adds the associated reaction fluxes to the metabolic objective, thereby incentivizing flux towards the production of a metabolite of interest. These adjustments to the objective act in competition with cellular growth and represent up-regulation and down-regulation of enzyme mediated reactions. Using the iAF1260 E. coli metabolic network model for optimization of fatty acid production as a test case, Redirector generates designs with as many as 39 simultaneous and 111 unique engineering targets. These designs discover proven in vivo targets, novel supporting pathways and relevant interdependencies, many of which cannot be predicted by other methods. Redirector is available as open and free software, scalable to computational resources, and powerful enough to find all known enzyme targets for fatty acid production.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Crystal Structure Prediction of Binary Alloys via Deep Potential

Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg 12 Al 8 shows excellent ductility and Mg 5 Al 27 has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Development of high efficiency window air conditioner using propane under limited charge

The residential air conditioner market has a significant portion of window air conditioners (WAC). Due to the compact size and small refrigerant charge, WACs are most tolerant of flammable refrigerants. Hydrocarbon (HC) refrigerants are natural substances and have significantly better environmental impacts and lower costs than conventional refrigerants like R-410A and R-22. The paper aimed to develop a WAC prototype using propane (R-290) and verify the performance metrics in the laboratory. The key of developing the WAC using a flammable refrigerant is to control the refrigerant charge under the limit of building safety regulation with the performance meeting the Energy Star standard. To minimize the charge, compact heat exchangers were utilized. To achieve the target efficiency, we collaborated with a leading rotary compressor manufacturer to develop a compressor prototype specifically optimized for R-290, able to achieve superior energy efficiency. In addition, we conducted model-based investigations to identify further charge reduction potential. Finally, with using microchannel heat exchangers to replace the fin-and-tube condenser and evaporator, the system charge can be decreased to 150 g.

42 ENGINEERING↗

DOE OSTI2017

An Advanced One-Dimensional Finite Element Model for Incompressible Thermally Expandable Flow

Here, this paper provides an overview of a new one-dimensional finite element flow model for incompressible but thermally expandable flow. The flow model was developed for use in system analysis tools for whole-plant safety analysis of sodium fast reactors. Although the pressure-based formulation was implemented, the use of integral equations in the conservative form ensured the conservation laws of the fluid. A stabilization scheme based on streamline-upwind/Petrov-Galerkin and pressure-stabilizing/Petrov-Galerkin formulations is also introduced. The flow model and its implementation have been verified by many test problems, including density wave propagation, steep gradient problems, discharging between tanks, and the conjugate heat transfer in a heat exchanger.

42 ENGINEERING↗

DOE OSTI2018

Low-Cost Titanium Casting Technology

This is the final report of "Low-Cost Titanium Casting Technology" project. Titanium alloys have attracted increasing attentions as structural materials for high performance and weight-sensitive applications in aerospace and biomedical fields. However, in mass-production of civil applications like automotive components, titanium alloys are scarcely used due to the unaffordable costs in manufacturing compared with other metallic materials, such as aluminum, magnesium and steel. Therefore, there is a need to reduce the manufacturing cost of titanium alloys so that titanium alloys can be more competitive in terms of property and cost considerations. In this 4-year project, we started from exploring new low-cost titanium alloy systems with the guidance of Integrated Computational Materials Engineering – Calculation of Phase Diagram (ICME-CALPHAD) framework, and determined several promising alloy systems, alloying elements including Ti-Al-Fe-Mn-B-C, that can allow further development (lab-scale and industry-scale) in the future. During the new alloy system exploration, considerable amount of scientific work was completed regarding CALPHAD modeling, microstructure morphology, mechanical properties, and processing of the new alloy systems. The relevant ternary titanium alloy systems, Ti-Al-Mn and Ti-Al-Fe, were examined and reassessed both computationally and experimentally, leading to more comprehensive understanding of these alloy systems. Based on this above study, a new low-cost casting titanium alloy was developed, Ti-6Al-5Fe-0.05B-0.05C (designated as T65-0.05BC), which has competitive as-cast strength level with current commercial titanium alloys but relatively low ductility. However, in this project, since the target application, automotive engine connecting rod, is a fatigue-limited component and does not require high ductility, this alloy shall suffice as the deliverable alloy in this project. After designing and confirming the potent of the new alloy, we designed and built a lab-scale manufacturing equipment aided by computational tools at OSU labs to conceptually prove that this alloy can be manufactured with permanent mold casting, which is low-cost and potentially a mass production process. We produced a lab-scale prototype connecting rod demonstrating the capability of permanent mold casting of this new alloy.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Graph-Based Attention Mechanisms for Solving the AC Optimal Power Flow Problem in Electrical Power Networks

With the increasing complexity and data availability in modern power systems, learning-based approaches to AC Optimal Power Flow (AC OPF) have garnered significant attention. In particular, the structure of smart grids lends itself naturally to graph-based representations, where Graph Neural Networks (GNNs) can capture spatial and relational dependencies. This paper investigates attention-based GNN architectures tailored to heterogeneous graph representations of electric grids. We evaluate two major paradigms: relational attention, which distinguishes between edge types during message passing, and meta-path attention, which captures high-level semantics through multi-hop, typed paths. Using a large corpus of public AC OPF scenarios, we benchmark representative models of each type of attention. Our results demonstrate the benefits of heterogeneous attention-based models in accurately capturing grid dynamics; heterogeneous attention models achieve superior performance in both standard and perturbed settings. The findings highlight the importance of semantic-aware architectures for improving prediction robustness and interpretability in power system applications.

Trigui, Ali [Qubit Engineering Inc.]↗

DOE OSTI2018

Product Portfolios within a Distributed Logistical System for Aviation Components

A structural model of a distributed logistical system based on fuzzy sets is proposed. This model permits the optimal organization of product portfolios. The model assumes a central organization that produces and distributes parts in the aviation industry; and peripheral organizations within the network of the central organization.

42 ENGINEERING↗

DOE OSTI2026

Feedback and Oscillations: Constructing Feedback Systems for Root Cause Analysis of Oscillations in Power Grids

Dynamic phenomena linked to inverter-based resources (IBRs) have gained global attention. Several IBR-induced dynamics have caused bulk power system-connected wind or solar power plants to trip, and some have even led to widespread outages. In addition, many oscillations have been observed involving IBR power plants. In 2023, the IEEE Power & Energy Society (PES) IBR Subsynchronous Oscillations (SSO) task force published a journal article, “Real-World Subsynchronous Oscillation Events in Power Grids With High Penetrations of Inverter-Based Resources,” in which 19 IBR oscillation events were examined for their causation. Earlier in 2020, another PES task force article, “Definition and Classification of Power System Stability-Revisited & Extended,” authored by prominent academics, introduced converter-driven stability as a new category of stability. The international power grid industry community also took action by publishing the CIGRE Green Book, Power System Dynamic Modelling and Analysis in Evolving Networks (led by Babak Badrzadeh and Zia Emin) in 2024. In August 2024, the Energy Systems Integration Group (ESIG) released a practical guide led by Nick Miller, “Diagnosis and Mitigation of Observed Oscillations in IBR-Dominant Power System: A Practical Guide.” The goal of the guide is to assist practicing engineers in making initial judgments and conducting detailed analyses about oscillations. Finally, when addressing the classification of stability and oscillations, the guide emphasizes a causality-based taxonomy for grouping, such as voltage control-induced oscillations, synchronization-induced oscillations, and frequency or active power control-induced oscillations.

Fan, Lingling [Univ. of South Florida, Tampa, FL (↗

DOE OSTI2023

Integrated membrane material design and system synthesis

In designing membrane systems, the synergy between membrane materials and the process design is often overlooked. In this paper, we present a mixed-integer nonlinear programming (MINLP) model for synthesizing membrane systems while simultaneously designing the respective membrane materials for multicomponent gas separation. The approach considers superstructure representations for systems with: (1) same, (2) potentially different, and (3) property-targeting membrane materials. In the first two systems, the selection of membrane material is a decision, while in the final type, membrane permeances are subject to optimization. Physics-based surrogate models are used to describe permeation in crossflow and countercurrent flow permeators. We show that, through a case study of biogas upgrading, our approach obtains high quality solutions. Furthermore, we use the proposed approach while considering permeance-based production cost to find the optimal membrane.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Altering DNA-Programmable Colloidal Crystallization Paths by Modulating Particle Repulsion

Colloidal crystal engineering with DNA can be used to realize precise control over nanoparticle (NP) arrangement. Here, we investigate a case of DNA-based assembly where the properties of DNA as a polyelectrolyte brush are employed to alter a hybridization-driven NP crystallization pathway. Using the co-assembly of DNA-conjugated proteins and spherical gold 2 nanoparticles (AuNPs) as a model system, we explore how steric repulsion between non-complementary, neighboring DNA-NPs due to overlapping DNA shells can influence their ligand-directed behavior. Specifically, our experimental data coupled with coarse-grained molecular dynamics (MD) simulations reveal that by changing factors related to NP repulsion, two structurally distinct outcomes can be achieved. When steric repulsion between DNA-AuNPs is significantly greater than that between DNA-proteins, a lower packing density crystal lattice is favored over the structure that is predicted by design rules based on DNA-hybridization considerations alone. This is enabled by the large difference in DNA density on AuNPs versus proteins and can be tuned by modulating the flexibility, and thus conformational entropy, of the DNA on the constituent particles. At intermediate ligand flexibility, the crystallization pathways are energetically similar and the structural outcome can be adjusted using the density of DNA duplexes on DNA-AuNPs and by screening the Coulomb potential between them. Such lattices are shown to undergo dynamic reorganization upon changing salt concentration. These data help elucidate the structural considerations necessary for understanding repulsive forces in DNA-assembly and lay the groundwork for using them to increase architectural diversity in engineering colloidal crystals.

colloidal crystals↗

DOE OSTI2023

Data Driven Approach to Dislocation-Based Plasticity Models of Face-Centered Cubic Metals

Dislocation dynamics controls plastic deformation, mechanical strength, and failure of crystalline materials. It also governs fatigue resistance under cyclic loading, creep resistance at elevated-temperature, and radiation resistance for reactor applications. There is a compelling need for understanding fundamental dislocation mechanisms for deformation because virtually all structural metals used in energy systems are fabricated to desired forms and shapes by deformation processes. To date, the most outstanding problem in a physics-based multiscale model of crystal plasticity is the lack of quantitative connections between continuum plasticity (CP) models with the lower scale dislocation models. As a result, existing CP models used in engineering applications are still phenomenological, while evidence continues to mount that they can make inaccurate predictions under realistically complex scenarios. This project takes advantage of the recent advances in high-performance discrete dislocation dynamics (DDD) simulations and data science approaches to establish the first fully connected multiscale plasticity model for pure face-centered cubic (FCC) single crystals.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Proximity proteomics in a marine diatom reveals a putative cell surface-to-chloroplast iron trafficking pathway

Iron is a biochemically critical metal cofactor in enzymes involved in photosynthesis, cellular respiration, nitrate assimilation, nitrogen fixation, and reactive oxygen species defense. Marine microeukaryotes have evolved a phytotransferrin-based iron uptake system to cope with iron scarcity, a major factor limiting primary productivity in the global ocean. Diatom phytotransferrin is endocytosed; however, proteins downstream of this environmentally ubiquitous iron receptor are unknown. We applied engineered ascorbate peroxidase APEX2-based subcellular proteomics to catalog proximal proteins of phytotransferrin in the model marine diatom Phaeodactylum tricornutum . Proteins encoded by poorly characterized iron-sensitive genes were identified including three that are expressed from a chromosomal gene cluster. Two of them showed unambiguous colocalization with phytotransferrin adjacent to the chloroplast. Further phylogenetic, domain, and biochemical analyses suggest their involvement in intracellular iron processing. Proximity proteomics holds enormous potential to glean new insights into iron acquisition pathways and beyond in these evolutionarily, ecologically, and biotechnologically important microalgae.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Particle Swarm Optimisation for group structure optimization for radiotherapy shielding

Neutron transport simulations are ubiquitous in nuclear engineering because they allow one to model experimental systems and render a model platform for easy perturbation of experimental designs. In addition, simulations allow one to gain experimental insight without actually having to go through the trouble of building a physical experiment. Neutron transport simulations can be stochastic or deterministic based. Stochastic neutron transport simulations are typically simulated using the Monte Carlo method and yield very accurate solutions but are computationally expensive, while deterministic methods are typically faster but can be less accurate. Here we focus on optimizing the accuracy of deterministic neutron transport simulations for radiotherapy simulations. Deterministic neutron transport requires discretization of angle, energy, and space to appropriately analyze the system one is trying to model. Discretization of energy is challenging because of the highly variable neutron flux at certain neutron energies. Improper discretization of energy in the transport model can lead to erroneous results and therefore inaccurate interpretations of the solution. In this study, we evaluate Particle Swarm Optimization (PSO) as a mechanism for selecting optimal group structures for radiotherapy shielding. We tested the particle swarm optimization algorithm on radiotherapy shielding problems using Los Alamos National Laboratory's (LANL) main deterministic transport code PARTISN. Results show that the optimized energy group structures generated from the optimization algorithm outperformed LANL's standard energy group structures, and therefore demonstrate utility in using PSO to expedite computation times due to the increased accuracy obtained with a smaller but optimized group structure. (authors)

43 PARTICLE ACCELERATORS↗