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DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations. To make advancements in all the aforementioned scientific areas, many technical aspects need to be more thoroughly studied and deeply understood in parallel discrete event simulation (PDES). The unique dynamics inherent in a discrete event modeling approach, by their very nature, intersect and influence the entire stack of the computing system, including (a) the unique nature of the instruction sets exercised in PDES workloads without a predominance of high-precision floating point operations, (b) virtual time-constrained multi-threaded execution of many logical processes per processor, (c) extremely variable and difficult to predict network traffic characteristics, (d) interfaces and inter-dependencies with machine learning and artificial intelligence codes at higher software layers, and (e) highly challenging load balancing needs, especially in effectively accounting for accelerated/extremely heterogeneous computing in current and future high-performance computing systems. Efficient and accurate parallel execution of PDES workloads is also dominated by challenges in dealing with their asynchronous concurrency fundamentally present at the model level. Conservative synchronization, optimistic/speculative synchronization, and their hybrid schemes open new questions in fundamental computer science with respect to reversibility of computation and prediction (lookahead) of behaviors inherent within model codes. On the implementation front, there are relatively few scalable, general-purpose parallel discrete event simulators in the world, and even fewer have been studied on emerging hardware platforms. To enable scientific advances using PDES, the research needs in computer science must also be pursued and met in the intersection of the algorithmic and hardware-aware aspects of scalable PDES engines. This report is aimed at capturing a computer science-oriented view of this important area of research in PDES, presenting a sample of important applications with their inherent discrete event technology elements. Needs are outlined in core areas of parallel discrete event research as well as cross-cutting directions in computer science research that positively impact scientific advancements across several important application areas. A selection of priority research opportunities in advanced computing for PDES is identified to serve as reference for key research topics and their order of importance for scientific advancements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

High-Performance Transmission and Distribution Co-simulation with 10,000+ Inverter-Based Resources

The inverter-based resource (IBR) has become avery important component in the distribution system. The impacts on system transient stability introduced by high IBR penetration are not fully addressed because of the lack of high-fidelity models. The aggregate IBR model at the transmission level cannot precisely reproduce the dynamics of distributed IBR at the distribution system because of the oversimplification. In this paper, we will develop a high-penetration fully-connected transmission and distribution (T&D) co-simulation platform that supports the simulation of 10,000+ dispersed IBR models. The interfacing and iterative initialization techniques for the co-simulation have been implemented to maintain stable operation and simulation of large-multitude of IBR models. The phasor-domain IBR models with grid-forming (GFM) and grid-following (GFL) control are implemented in the distribution systems simulators. The developed platform is tested on high-performance computing (HPC) resources and can be utilized to explore the hierarchical control strategies of IBRs for the large-scale T&D hybrid system.

Liu, Yuan↗

DOE OSTI2023

The Interaction between the DOCK7 Protein and the E2 Protein of Classical Swine Fever Virus Is Not Involved with Viral Replication or Pathogenicity

The classical swine fever virus (CSFV) particle consists of three glycoproteins, all of which have been shown to be important proteins involved in many virus functions, including interaction with several host proteins. One of these proteins, E2, has been shown to be directly involved with adsorption to the host cell and important for virus virulence. Using the yeast two-hybrid system, we have previously shown that CSFV E2 specifically interacts with the (DOCK7) dedicator of cytokinesis, a scaffolding protein. In this report, the interaction between E2 and DOCK7 was evaluated. To confirm the yeast two-hybrid results and to determine that DOCK7 interacts in swine cells with E2, we performed co-immunoprecipitation and proximity ligation assay (PLA). After demonstrating the protein interaction in swine cells, E2 amino acid residues Y65, V283, and T149 were determined to be critical for interaction with Dock7 by using a random mutated library of E2 and a reverse yeast two-hybrid approach. That disruption of these three residues with mutations Y65F, V283D, and T149A abrogated the Dock7-E2 protein interaction. These mutations were then introduced into a recombinant CSFV, E2DOCK7v, by a reverse genomics approach using the highly virulent CSFV Brescia isolate as a backbone. E2DOCKv was shown to have similar growth kinetics in swine primary macrophages and SK6 cell cultures to the parental Brescia strain. Similarly, E2DOCK7v demonstrated a similar level of virulence to the parental Brescia when inoculated in domestic pigs. Animals intranasally inoculated with 10 5 TCID 50 developed a lethal form of clinical disease with virological and hematological kinetics changes indistinguishable from that produced by the parental strain. Therefore, interaction between CSFV E2 and host DOCK7 is not critically involved in the process of virus replication and disease production.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Noisy Intermediate-Scale Quantum Applications on a Pathfinder System

Work performed under this one-year LDRD was concerned with estimating resource requirements for small quantum test beds that are expected to be available in the near future. This work represents a preliminary demonstration of our ability to leverage quantum hardware for solving small quantum simulation problems in areas of interest to the DOE. The algorithms enabling such studies are hybrid quantum-classical variational algorithms, in particular the widely-used variational quantum eigensolver (VQE). Employing this hybrid algorithm, in which the quantum computer complements the classical one, we implemented an end-to-end application-level toolchain that allows the user to specify a molecule of interest and compute the ground state energy using the VQE approach. We found significant limitations attributable to the classical portion of the hybrid system, including a greater than greater-than-quartic power scaling of the classical memory requirements with the system size. Current VQE approaches would require an exascale machine for solving any molecule with size greater than 150 nuclei. Our findings include several improvements that we implemented into the VQE toolchain, including a new classical optimizer that is decades old but hadn't been considered before in the VQE ecosystem. Our findings suggest limitations to variational hybrid approaches to simulation that further motivate the need for a gate-based fault-tolerant quantum processor that can implement larger problems using the fully digital quantum phase estimation algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Planar, curved and twisted molecular nanographenes: Reduction-induced alkali metal coordination

Planar and curved polycyclic aromatic hydrocarbons (PAHs) attract significant attention as molecular models of fullerenes, carbon nanotubes, and graphene, thus stimulating broad investigation of chemical reactivity and materials applications of designed nanocarbon π-systems. Non-planar molecular nanographenes (NGs) recently emerge as advanced anode materials in energy storage, showing high reduction limits and enhanced alkali metal intercalation levels. However, the lack of direct structure–property correlations in such complex hybrid systems impedes their further development and utilization. With a focus on alkali-metal-induced reduction of selected PAHs, we herein review original metal binding and intercalation trends, site specific coordination, and distinct carbon framework responses to stepwise electron uptake. Small planar graphene fragments, like triphenylene and coronene, are compared to π-expanded hexabenzocoronenes, followed by the discussion of bowl-shaped corannulene, sumanene and other carbon bowls, as well as bent, warped, and twisted molecular nanographenes. The effect of size, symmetry, and framework topology along with the structural deformation of carbon backbones upon reduction are analyzed, using recent crystallographic examples of alkali-metal intercalated products. In conclusion, the revealed insights into the structures, binding, and metal intercalation in molecular nanographenes should stimulate their future applications as new functional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Leveraging large language models to automate the identification of healthcare access barriers for veterans

Objective: To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency. Methods: We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis. Results: The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1. Conclusion: Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.

Healthcare access barriers↗

DOE OSTI2024

Photosynthetic biohybrid systems for solar fuels catalysis

Photosynthetic reaction center (RC) proteins are finely tuned molecular systems optimized for solar energy conversion. RCs effectively capture and convert sunlight with near unity quantum efficiency utilizing light-induced directional electron transfer through a series of molecular cofactors embedded within the protein core to generate a long-lived charge separated state with a useable electrochemical potential. Of current interest are new strategies that couple RC chemistry to the direct synthesis of energy-rich compounds. This Feature Article highlights recent work from our lab on RC and RC-inspired hybrid systems that capture the Sun's energy and convert it to chemical energy in the form of H2, a carbon-neutral energy source derived from water. Further, biohybrids made from the Photosystem I (PSI) RC are among the best photocatalytic H2-producing protein hybrids to date. Targeted self-assembly strategies that couple abiotic catalysts to PSI translate to catalyst incorporation at intrinsic PSI sites within thylakoid membranes to achieve complete solar water-splitting systems. RC-inspired biohybrids interface synthetic photosensitizers and molecular catalysts with small proteins to create photocatalytic systems and enable the spectroscopic discernment of the structural features and electron transfer processes that underpin solar-driven proton reduction. In total, these studies showcase the incredible scientific opportunities photosynthetic biohybrid research provides for harnessing the optimal qualities of both artificial and natural photosynthetic systems and developing materials that capture, convert, and store solar energy as a fuel.

14 SOLAR ENERGY↗

DOE OSTI2022

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Current status of biogas upgrading for direct biomethane use: A review

Anaerobic digestion produces biogas, a mixture of CH 4 and CO 2 , where CH 4 is a low cost, environmentally friendly, and renewable energy source. The application of biogas production is increasing rapidly as a means of reducing the pollution impact of organic biomasses. However, biogas contains unwanted elements such as hydrogen sulfide, carbon monoxide, siloxanes, and carbon dioxide. To remove these elements, several biogas upgrading technologies like water scrubbing, amine scrubbing, pressure swing adsorption, and membrane separation have been developed and are being used at various commercial scales. Problems with these methods are high energy consumption, the use of expensive chemicals, and high operating cost. Therefore, a major effort is currently underway to improve the design of existing methods as well as developing innovative new upgrading technologies such as cryogenic separation and biological upgrading. Here, this review intends to provide a comprehensive overview of the limitations with the existing upgrading technologies along with recent advances in physical, chemical, and biological biogas upgrading technologies (e.g., pressure swing adsorption, membrane separation, biochar adsorption and CO 2 conversion by biological organisms) and further into possible future solutions, such as hybrid systems. Comparative studies of process complexities and associated economic concerns are also provided, and future perspectives that may facilitate research into sustainable biogas upgrading technologies are discussed, focusing in particular on cryogenic separation, novel biological techniques, biochar based upgrading and hybrid technologies incorporating two or more different methods seamlessly integrated.

09 BIOMASS FUELS↗

DOE OSTI2022

Thermo-economic analysis of a solid oxide fuel cell-gas turbine hybrid with commercial off-the-shelf gas turbine

The thermodynamic and economic performance of a natural gas-fueled solid oxide fuel cell (SOFC)-gas turbine (GT) hybrid system with a commercial off-the-shelf GT is investigated. Until today, commercial GTs are primarily engineered for the direct use of energy dense fuels, such as natural gas, and the integration of commercial GTs into a SOFC-GT hybrid remains challenging. In this work technically feasible and economically viable GT operating modes are identified to gauge the technology’s competitiveness on the free market. Steady state, full load operating conditions were established for various GT operating modes: I) constant spool speed operation, II) variable spool speed operation and III) partially closed compressor inlet guide vanes. For each GT operating mode, the performance was investigated over a range of SOFC fuel utilization factors, while considering physical constraints inside the SOFC, such as local temperature gradients in flow direction as well as overall cell temperature differences. The results of the thermodynamic evaluation served as inputs for the economic analysis of the SOFC-GT hybrid power plant. The results show that the integration of an off-the-shelf GT not only results in a significant derating of the GT, but also substantially impacts the SOFC operation, the main power producer in this SOFC-GT hybrid. To maximize the SOFC power output it is desirable operate the GT in a region of high air mass flow rates and low pressure ratios, which increases the number of stacks that can be accommodated and reduces the SOFC cooling requirement. The lowest costs of electricity are obtained at constant spool speed operation, while the highest efficiencies are reached at variable spool speed operation. Critical for the integration of off-the-shelf GTs, which historically have been designed for natural gas, is the surge margin. The largest surge margins are obtained by closing the compressor inlet guide vanes. Furthermore, higher SOFC fuel utilization factors are shown to increase the surge margin as the turbine firing temperature is decreased.

25 ENERGY STORAGE↗

DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Symmetry-specific orientational order parameters for complex structures

A comprehensive framework of characterizing complex self-assembled structures with a set of orientational order parameters is presented. It is especially relevant in the context of using anisotropic building blocks with various symmetries. Two classes of tensor order parameters are associated with polyhedral nematic and bond orientational order. For the latter, a variation of classical bond order parameters (BOPs) is introduced, which takes advantage of the symmetry of constituent particles and/or expected crystalline phases. These symmetrized BOPs can be averaged over an entire system or assigned locally to an individual bond. By combining that with the bond percolation procedure, one is able to identify coherent domains within a self-assembled structure. As a demonstration of the proposed framework, we apply it to a simulated hybrid system that combines isotropic and patchy particles with octahedral symmetry. In conclusion, not only does the methodology allow one to identify individual crystalline domains but also it detects coherent clusters of a peculiar compact amorphous structure that is not space-filling and lacks any long-range order.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Hybridization History and Repetitive Element Content in the Genome of a Homoploid Hybrid, Yucca gloriosa (Asparagaceae)

Hybridization in plants results in phenotypic and genotypic perturbations that can have dramatic effects on hybrid physiology, ecology, and overall fitness. Hybridization can also perturb epigenetic control of transposable elements, resulting in their proliferation. Understanding the mechanisms that maintain genomic integrity after hybridization is often confounded by changes in ploidy that occur in hybrid plant species. Homoploid hybrid species, which have no change in chromosome number relative to their parents, offer an opportunity to study the genomic consequences of hybridization in the absence of change in ploidy. Yucca gloriosa (Asparagaceae) is a young homoploid hybrid species, resulting from a cross between Yucca aloifolia and Yucca filamentosa. Previous analyses of ~11 kb of the chloroplast genome and nuclear-encoded microsatellites implicated a single Y. aloifolia genotype as the maternal parent of Y. gloriosa. Using whole genome resequencing, we assembled chloroplast genomes from 41 accessions of all three species to re-assess the hybrid origins of Y. gloriosa. We further used re-sequencing data to annotate transposon abundance in the three species and mRNA-seq to analyze transcription of transposons. The chloroplast phylogeny and haplotype analysis suggest multiple hybridization events contributing to the origin of Y. gloriosa, with both parental species acting as the maternal donor. Transposon abundance at the superfamily level was significantly different between the three species; the hybrid was frequently intermediate to the parental species in TE superfamily abundance or appeared more similar to one or the other parent. In only one case—Copia LTR transposons—did Y. gloriosa have a significantly higher abundance relative to either parent. Expression patterns across the three species showed little increased transcriptional activity of transposons, suggesting that either no transposon release occurred in Y. gloriosa upon hybridization, or that any transposons that were activated via hybridization were rapidly silenced. The identification and quantification of transposon families paired with expression evidence paves the way for additional work seeking to link epigenetics with the important trait variation seen in this homoploid hybrid system.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Geothermal well testing pressure prediction by using a hybrid transformer model system: FORGE well use case

Geothermal has huge potential to become an indispensable component in achieving the goal of sustainable energy economy, given its capability to provide consistent baseload power to the electric grid. Injection tests are crucial in geothermal energy system as they naturally help to evaluate reservoir properties, understand fluid flow and even enhance reservoir performance. In this research, we developed a hybrid model system that integrates machine learning (ML) regression, a physics-based mathematical model, and transformer deep learning. Trained and validated using FORGE injection test dataset, this system can forecast the pressure variations both upward and downward over time. The pressure prediction achieved prediction accuracy within 3-6% variance of true pressure values. The system can significantly save time and reduce costs by testing only a few cycles and then using model predictions for further analysis, instead of conducting additional real injection cycle tests. The developed model system also holds promise for designing injection test processes and maintaining well production in geothermal energy. Presented at the IMAGE ‘25 Conference led by Shell.

FORGE↗

DOE OSTI2024

Stability-Constrained Learning for Frequency Regulation in Power Grids With Variable Inertia

The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the power system at timescales comparable to the fast converter dynamics. This paper proposes a combined linear and neural network controller for inverter-based primary frequency control that is stable at time-varying levels of inertia. We model the time-variance in inertia via a switched affine hybrid system model. We derive stability certificates for the proposed controller via a quadratic candidate Lyapunov function. We test the proposed control on a 12-bus 3-area test network, and compare its performance with a base case linear controller, optimized linear controller, and finite-horizon Linear Quadratic Regulator (LQR). Our proposed controller achieves faster mean settling time and over 50% reduction in average control cost across 100 inertia scenarios compared to the optimized linear controller. Unlike LQR which requires complete knowledge of the inertia trajectories and system dynamics over the entire control time horizon, our proposed controller is real-time tractable, and achieves comparable performance to LQR.

data-driven control↗

DOE OSTI2020

STEPS: A Portable Numerical Simulation Toolkit for Electrical Power System Dynamic Studies

Numerical simulation is the key technique for large scale power system analysis. Redistribution of global renewable power via international interconnections requires new simulation tools to study the interconnected systems with different nominal frequencies as a whole. In this paper we introduce an open source simulation toolkit for electrical power systems (STEPS) which is hosted at Github. Its kernel is coded in C++ with major functions of power flow and electro-mechanical dynamic simulation. Flexible options are provided and configurable to improve power flow solution and dynamic simulation. Common devices and models are supported in STEPS for AC/DC hybrid system studies. Studies of interconnected systems with different nominal frequencies is supported in STEPS for research of international interconnection. Application program interfaces are provided and wrapped with Python to enable high-level interfaces for general applications. STEPS is thread safe and parallel computation is supported in both kernel and script levels to accelerate simulation. It is portable and works on Windows and GNU/Linux platforms. Cases from small to large scale systems are thoroughly tested to validate the toolkit with commercial packages as benchmarks.

42 ENGINEERING↗

DOE OSTI2022

The Roles and Impacts of PV-Battery Hybrids in a Decarbonized U.S. Electricity Supply

In this paper, we explore the potential impacts of growing industry interest in hybrid systems comprising PV and battery technologies on the results and findings of the Solar Futures Study (DOE 2021). We employ similar scenario definitions in the same ReEDS capacity expansion model, but we perform two versions of each scenario: one in which PV and battery technologies must be deployed separately (No Hybrids), and one in which the model has the option of deploying them together as PVB hybrids (With Hybrids). By comparing the No Hybrids and With Hybrids versions of each scenario, we isolate the impacts of hybridization on the outcomes and findings of the Solar Futures Study. We find that PVB hybrid configurations capture a sizable share of PV deployment, and the highest-net-value PVB hybrid configuration depends strongly on policy conditions. A power sector decarbonization policy generally increases the value proposition of a more forward-looking PVB hybrid configuration that involves significant oversizing of the PV arrays, a larger battery (which facilitates greater recovery and utilization of otherwise clipped energy), and a higher capacity factor. The growing deployment of PVB hybrid configurations primarily displaces standalone PV capacity, such that total installed PV capacity is largely unaffected by the availability of PVB hybrid configurations. However, the higher capacity factors associated with PVB hybrid configurations drive a modest (1-2 percentage point) increase in PV's share of U.S. electricity supply in 2050. Finally, introducing the PVB hybrid configurations influences the future role and makeup of battery storage technologies, and it reduces the required transmission expansion, particularly under scenarios that involve a power sector decarbonization policy.

14 SOLAR ENERGY↗

DOE OSTI2020

Swine Host Protein Coiled-Coil Domain-Containing 115 (CCDC115) Interacts with Classical Swine Fever Virus Structural Glycoprotein E2 during Virus Replication

Interactions between the major structural glycoprotein E2 of classical swine fever virus (CSFV) with host proteins have been identified as important factors affecting virus replication and virulence. Previously, using the yeast two-hybrid system, we identified swine host proteins specifically interacting with CSFV E2. In this report, we use a proximity ligation assay to demonstrate that swine host protein CCDC115 interacts with E2 in CSFV-infected swine cells. Using a randomly mutated E2 library in the context of a yeast two-hybrid methodology, specific amino acid mutations in the CSFV E2 protein responsible for disrupting the interaction with CCDC115 were identified. A recombinant CSFV mutant (E2ΔCCDC115v) harboring amino acid changes disrupting the E2 protein interaction with CCDC115 was produced and used as a tool to assess the role of the E2–CCDC115 interaction in viral replication and virulence in swine. CSFV E2ΔCCDC115v showed a slightly decreased ability to replicate in the SK6 swine cell line and a greater replication defect in primary swine macrophage cultures. A decreased E2–CCDC115 interaction detected by PLA is observed in cells infected with E2ΔCCDC115v. Importantly, animals intranasally infected with 105 TCID50 of E2ΔCCDC115v experienced a significantly longer survival period when compared with those infected with the parental Brescia strain. This result would indicate that the ability of CSFV E2 to bind host CCDC115 protein during infection plays an important role in virus replication in swine macrophages and in virus virulence during the infection in domestic swine.

59 BASIC BIOLOGICAL SCIENCES↗