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At least 433 records · Page 24

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DOE OSTI2024

Grid Edge Waveform Analytics Framework for Event Detection and Classification

This paper provides a grid edge waveform analytics framework for power system event detection and classification in the local as well as in the wide area. This framework overviews data excellence for event detection and classification. The data excellence describes the data acquisition process and requirements, data processing, data quality, and data integrity. Power system event detection in the local area based on different features such as energy-based, cyclostationary approach, template matching, and wavelet transform are also discussed. Furthermore, local area event detection and classification using approaches such as statistical, signal processing, artificial intelligence, and hybrid are also discussed. Moreover, an overview of wide-area event detection and classification along with several other aspects such as wide-area events, wide-area event detection approaches, event location and system performance, event pattern recognition, inter-area oscillation, and wide-area frequency response under variable deployment of inverter-based resources are also provided. The proposed framework is the first step toward the goal of developing appropriate tools and methodologies to detect and classify local as well as wide-area events using waveform analytics. The appropriate event detection and classification framework development is especially important now as more and more grid edge devices with communication capabilities are being deployed in the modern power grid than ever before.

Bhusal, Narayan↗

DOE OSTI2026

Low-voltage syngas synthesis via BPM electrolysis of CO 2 capture and aldehyde solution

A bipolar membrane electrolyzer coupling bicarbonate electrolysis with formaldehyde oxidation directly produces syngas (H 2 : CO = 1) at 1.7 V and 200 mA cm −2 with 200% combined Faradaic efficiency. We demonstrate an electrochemical syngas production platform that couples bicarbonate electrolysis with formaldehyde oxidation in a bipolar membrane electrode assembly. This strategy doubles syngas (CO + H 2 ) throughput compared to conventional CO 2 electrolysis by generating CO at the cathode and H 2 at the anode. The system achieves a full-cell voltage of 1.7 V while operating at an industrially relevant current density of 200 mA cm −2 . The system maintained a combined Faradaic efficiency of 200% over 8 hours. The process produces syngas with a 1 : 1 H 2 : CO ratio, aligning with downstream requirements for Fischer–Tropsch synthesis. We further investigated how Cu anode active sites and electrolyte composition affect formaldehyde oxidation activity. Our integrated electrolytic system reduces levelized energy by 45–61% relative to conventional electrochemical syngas production platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

FPGA-Based Spill Regulation System for the Muon Delivery Ring at Fermilab

The Muon to Electron Experiment (\acrshort{Mu2e}) requires a uniform beam profile from the Muon Delivery Ring to meet their experimental needs. A specialized Spill Regulation System (\acrshort{SRS}) has been developed to help achieve consistent spill uniformity. The system is based on a custom-designed carrier board featuring an Arria 10 SoC, capable of executing real-time feedback control. The FPGA processes beam pulses of approximately 200 ns every 1.695 microseconds, allowing for continuous monitoring of the extracted spill intensity through fast bunch integration. The system directly controls three quadrupole magnets, which work in conjunction with sextupole magnets to achieve third-order resonant extraction. Furthermore, the board interfaces with Fermilab s Accelerator Control Network (ACNET), enabling operators to modify spill regulation settings in real-time via the control network while providing diagnostic waveforms. These waveforms help operators monitor the process and fine-tune the feedback mechanisms. This paper presents an overview of the board's architecture and its initial progress toward regulating beam extraction. This initial version of the regulation system aims to evaluate baseline performance to inform future system improvements.

Berlioz, Jose Rene [Fermilab]↗

DOE OSTI2025

FPGA-Based Spill Regulation System for the Muon Delivery Ring at Fermilab

The Muon to Electron Experiment (Mu2e) requires a uniform beam profile from the Muon Delivery Ring to meet their experimental needs. A specialized Spill Regulation System (SRS) has been developed to help achieve consistent spill uniformity. The system is based on a custom-designed carrier board featuring an Arria 10 SoC, capable of executing real-time feedback control. The FPGA processes beam pulses of approximately 200 ns every 1.695 $μ$s, allowing for continuous monitoring of the extracted spill intensity through fast bunch integration. The system directly controls three quadrupole magnets, which work in conjunction with sextupole magnets to achieve third-order resonant extraction. Furthermore, the board interfaces with Fermilab's Accelerator Control Network (ACNET), enabling operators to modify spill regulation settings in real-time via the control network while providing diagnostic waveforms. These waveforms help operators monitor the process and fine-tune the feedback mechanisms. This paper presents an overview of the board's architecture and its initial progress toward regulating beam extraction. This initial version of the regulation system aims to evaluate baseline performance to inform future system improvements.

Berlioz, J. R. [Fermilab]↗

DOE OSTI2025

Latent space mapping: Revolutionizing predictive models for divertor plasma detachment control

The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., ~ 0.2 ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over 10 8 compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.

Artificial neural networks↗

DOE OSTI2026

Engineering Controls Database

Cyber-Informed Engineering (CIE) addresses the reality that cyber attacks on engineered systems can have consequences far beyond data loss or disruption of digital networks. When control systems are compromised, safety, reliability, and performance of the physical process itself may be threatened. This database is meant to establish clear examples and guidance for defining and applying engineered controls in CIE. It explains what engineered controls are, how they differ from information security measures, and how they are integrated into system design. The goal is to ensure that resilience is engineered into systems from the outset. Unlike cybersecurity protections that defend the digital layer, engineered controls act directly at the physical and algorithmic levels to guarantee that unacceptable consequences are prevented or limited. CIE keeps the consequences of a cyber attack from impacting the safety, reliability, and performance of engineered systems.

Source record↗

DOE OSTI2025

Cyber-Informed Engineering (CIE) – Engineered Controls Database and Use

Cyber-Informed Engineering (CIE) addresses the reality that cyber-attacks on engineered systems can have consequences far beyond data loss or disruption of digital networks. When control systems are compromised, safety, reliability, and performance of the physical process itself may be threatened. This database is meant to establish clear examples and guidance for defining and applying engineered controls in CIE. It explains what engineered controls are, how they differ from information security measures, and how they are integrated into system design. The goal is to ensure that resilience is engineered into systems from the outset. Unlike cybersecurity protections that defend the digital layer, engineered controls act directly at the physical and algorithmic levels to guarantee that unacceptable consequences are prevented or limited. CIE keeps the consequences of a cyber attack from impacting the safety, reliability, and performance of engineered systems.

42 - ENGINEERING↗

DOE OSTI2025

2025 Workshop on Envisioning Frontiers in AI and Computing for Biological Research: Position Papers

This workshop aims to identify key research directions for transforming biology using artificial intelligence (AI), machine learning (ML) and computational methods to facilitate the discovery of new behaviors, mechanisms, and designs of biological processes relevant to DOE missions, underpinning a broader U.S. bioeconomy. By developing novel AI/ML technologies to analyze and interpret complex biological data, researchers can organize and simulate biological processes at various scales as well as advance predictive understanding and manipulation of biological systems. This integration of computation, experimentation, and next-generation experimental technologies can lead to discoveries in new biological behaviors and mechanisms relevant to DOE missions. The focus is on how advanced computational and mathematical methods can impact this mission by exploring digital twins, foundation models, automated laboratory experiments, modeling of complex living systems, and data-driven approaches for the biodesign of plants and microbial systems. While data management is important, it is not the primary focus of this workshop, which will assess the current state, trends, and AI/ML challenges at the interface between biology and computational science to identify opportunities for high-impact research at their intersection. The goal is to define research needs and opportunities that align with biological sciences, computational sciences, and applied mathematics research.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Safety Review of the Hydrogen Components of a Reference Design of the Wind-H2-Green Steel/Ammonia Project

The Department of Energy Hydrogen Fuel Cell Technology Office and Wind Energy Technologies Office’s Wind-H2-Green Steel/Ammonia project is an initiative to demonstrate the feasibility and efficacy of GW-scale integrated energy systems. The team designed reference facilities that utilize wind- and solar-produced hydrogen for industrial steel and ammonia production. This novel concept warranted review of safety codes and standards as they apply to the designs and the identification of codes and standards as they apply to the designs and the identification of codes and standards gaps. This report reviews hydrogen production and storage codes and standards using reference design specifications from a Minnesota steel plant. Requirements, recommendations, and exclusions for the system were identified. Observed gaps included non-specific salt cavern storage requirements, electrolyzer capacity beyond regulated ranges, and lack of requirements for iron reduction via hydrogen. This report will aide future project design efforts and may provide a basis for safety reviews in new designs for industrial facilities with hydrogen production integration.

08 HYDROGEN↗

DOE OSTI2024

Safety Review of the Hydrogen Components of a Reference Design of the Wind-H2-Green Steel/Ammonia Project

The Department of Energy Hydrogen Fuel Cell Technology Office and Wind Energy Technologies Office's Wind-H2-Green Steel/Ammonia project is an initiative to demonstrate the feasibility and efficacy of GW-scale integrated energy systems. The team designed reference facilities that utilize wind- and solar-produced hydrogen for industrial steel and ammonia production. This novel concept warranted review of safety codes and standards as they apply to the designs and the identification of codes and standards gaps. This report reviews hydrogen production and storage codes and standards using reference design specifications from a Minnesota steel plant. Requirements, recommendations, and exclusions for the system were identified. Observed gaps included non-specific salt cavern storage requirements, electrolyzer capacity beyond regulated ranges, and lack of requirements for iron reduction via hydrogen. This report will aide future project design efforts and may provide a basis for safety reviews in new designs for industrial facilities with hydrogen production integration.

08 HYDROGEN↗

DOE OSTI2025

Micro-Mechanically Guided High-Throughput Alloy Design Exploration Towards Metastability-Induced H Embrittlement Resistance

We develop a high-throughput approach for studying H embrittlement (HE)-resistance in alloys, which is based on combinatorial compositional screening of metastability effects by in situ scanning electron microscopy H-analyses. The project objective included: (i) technique development of high-throughput screening (HTS) for HE-resistance, (ii) discovery of new metallic materials with superior HE-resistance, (iii) Multiscale verification of HE-resistance of the new alloys and H-barrier layers, from atomic scale to an engineering scale. The investigation focused on a model system that enables exploration of different metastable states in Fe-based complex-concentrated alloys. Composition spread islands with hundreds of varying compositions are fabricated on a single substrate using a combinatorial co-sputtering technique. To screen these alloys, we developed and employed a home-built SEM-based integrated analysis system capable of characterizing H-permeability, H-trapping, H-influence on mechanical properties, and other material properties, as well as atomistic to continuum simulations to study the underlying physics.

08 HYDROGEN↗

DOE OSTI2025

Impact of shading following installation of agrivoltaic systems on eco-physiological and biochemical attributes of “Howes” cranberry in Massachusetts

Agrivoltaic (AV) systems, which integrate solar energy generation with crop production on the same piece of land, offer a sustainable solution for optimizing land-use efficiency. However, the impact of shading induced by solar panels on crop growth, physiology, and metabolic activities of cranberry is not known. Understanding these interactions is essential for assessing the feasibility of AV systems in cranberry production. In this study we evaluated eco-physiological and biochemical crop responses on a commercial ‘Howes’ cranberry bog grown under an agrivoltaics system using different row spacings in the year immediately following installation. Following installation of fixed tilt solar panels at a height of 3.0 m above the plant canopy, four shading treatments were evaluated: an uncovered control area and row spacings of 9.0 m, 7.0 m, and 5.0 m, corresponding to 30%, 35%, and 37% shading respectively. The shading effect was also evaluated at three different fruit developmental stages (green, blush, and full-red stage). Using microclimate sensors installed in the treatment areas, key eco-physiological and biochemical parameters, including leaf gas exchange, chlorophyll, anthocyanin, and antioxidant content, electrolyte leakage, proline and trehalose accumulation, were assessed. While moderate shading (30% and 35%) alleviated environmental stress, as reflected by improved leaf water status and reduced electrolyte leakage, these same treatments suppressed key physiological and metabolic functions, including photosynthetic activity, carbon reserve accumulation, and the biosynthesis of stress-related osmolytes and antioxidant compounds. Under higher shading (37%), these reductions were even more pronounced, indicating a potential threshold beyond which the benefits of shading are outweighed by declines in assimilate production and secondary metabolism. These findings highlight a trade-off between the stress-mitigating benefits of shading and the alterations in key physiological and metabolic processes under agrivoltaic systems. Minimizing these trade-offs by optimizing row spacing in agrivoltaics systems is critical to maintain cranberry physiological status. Since commercial cranberry is grown as a continuous plant canopy (no interrow spaces for equipment travel), damage caused by the installation process likely impacted crop response evaluated in the first year; multi-year studies are needed to fully understand the dynamics of a dual-use system. Notably, this work provides the first direct measurements of proline, trehalose content, and electrolyte leakage in cranberry grown under AV conditions, offering new insight into their physiological adjustments to shading.

14 SOLAR ENERGY↗

DOE OSTI2025

Transforming Energy Through Computational Excellence: A View From NREL

At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

MFANS 2024 - Formally Proving Characteristics of Cyber-Physical Systems

Cyber-physical systems (CPS) are engineered systems that rely on the smooth integration of computational algorithms and physical elements. This integration presents new challenges for verifying that systems will behave as expected. The goal of this presentation is to present current challenges and potential solutions for the formal verification of cyber-physical systems. For cyber systems, formal methods refer to systematically rigorous mathematical techniques employed in the specification, development, analysis, and verification of both software and hardware systems. Recent advancements in computer science have yielded sophisticated tools specifically designed to address challenges associated with formal methods in complex systems. These tools leverage various foundational concepts such as logic, formal languages, program semantics, type systems, type theory, and automata theory. A notable achievement in the application of formal methods is the seL4 microkernel, claimed to be the first general-purpose operating-system kernel to be verified. Its proof implies the absence of bugs and guarantees that the kernel meets specifications. For physical systems, dynamic and control theory has a history of using rigorous analytic techniques to prove functional correctness. Lyapunov, optimal, classical, modern, and robust control theories all provide rigorous mathematical methods both to analyze system performance and to design controller that can be guaranteed to meet certain objectives. Recent computational techniques like level set theory and reachability analysis provide assertions that a system's state will avoid unsafe regions. Even though success has been independently achieved for cyber systems and physical systems, the integration of such systems creates new challenges. In particular, there is an obvious discrepancy between finite-state machines and infinite-state systems, resulting in different approaches for modeling and analyzing these system. While it is possible to simulate hybrid systems, this provides only a demonstration of a performance and not proof. For hybrid systems, current formal methods and system analysis approaches typically require a workarounds to work on hybrid systems like CPS. This paper will outline the state of the art and limits of current practice for formally verifying CPS and will identify possible research directions that require attention.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators

Abstract. Simulating low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1000–10 000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part 1, we construct an ensemble weather forecasting system based on spherical Fourier neural operators (SFNOs), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest-growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. With large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states during the rollout, and the ML ensemble passes a crucial spectral test in the literature. The IFS and ML ensembles have similar extreme forecast indices, and we show that the ML extreme weather forecasts are reliable and discriminating. These diagnostics ensure that the ensemble can reliably simulate the time evolution of the atmosphere, including low-likelihood high-impact extremes. In Part 2, we generate a huge ensemble initialized each day in summer 2023, and we characterize the simulations of extremes.

Mahesh, Ankur↗

DOE OSTI2024

Federated Access from DOE Labs to Distributed Storage in the EIC Era of Computing

The Electron Ion Collider (EIC) collaboration and future experiment is a unique scientific ecosystem within Nuclear Physics as the experiment starts right off as a crosscollaboration between Brookhaven National Lab (BNL) & Jefferson Lab (JLab). As a result, this muti-lab computing model tries at best to provide services accessible from anywhere by anyone who is part of the collaboration. While the computing model for the EIC is not finalized, it is anticipated that the computational and storage resources will be made accessible to a wide range of collaborators across the world. The use of federated ID seems to be a critical element to the strategy of providing such services, allowing seamless access to each lab site computing resources. However, providing Federated access to a Federated storage is not a trivial matter and has its share of technical challenges. In this contribution, we focus on the steps we took towards the deployment of a distributed object storage system that integrates with Amazon S3 and Federated ID. We will first cover for and explain the first stage storage solutions provided to the EIC during the detector design phase. Our initial test deployment consisted of Lustre storage using MinIO, hence providing an S3 interface. High Availability load balancers were added later to provide the initial scalability it lacked. Performance of that system will be shown. While this embryonic solution worked well, it had many limitations. Looking ahead, the Ceph object storage is considered a top-of-the-line solution in the storage community - since the Ceph Object Gateway is compatible with the Amazon S3 API out of the box, our next phase will use a native S3 storage. Our Ceph deployment will consist of erasure coded storage nodes to maximize storage potential along with multiple Ceph Object Gateways for redundant access. We will compare performance of our next stage implementations. Finally, we will present how to leverage OpenID Connect with the Ceph Object Gateway’s to enable Federated ID access. We hope this contribution will serve the community needs as we move forward with cross-lab collaborations and the need for Federated ID access to distributed compute facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Engineering Against Digital Risk in CIP Applications: Cyber-Informed Engineering Use Cases

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Equilibrium-gated pattern formation: How molecular dissociation thermodynamics drive emergent behavior in dissipative polymeric systems

Emergent patterns in biological systems arise through dissipative processes that balance reaction and transport phenomena, producing highly functional properties from self-regulating mechanisms. Synthetic fabrication, by contrast, often relies on user-controlled, multistep methods that lack the self-organizing capabilities of natural systems. Inspired by nature, we sought chemical systems that integrate strongly coupled reaction and transport phenomena, identifying frontal ring-opening metathesis polymerization (FROMP) as a method capable of creating diverse forms and functions through reactive processing. By employing discrete molecular initiators, FROMP allows precise control of key reaction steps—inhibition, initiation, and propagation. Using an integrated computational and experimental framework, we uncover how near-equilibrium inhibition dynamics, coupled with far-from-equilibrium reaction kinetics, drive pattern formation in frontally polymerized synthetic materials. We propose the concept of equilibrium-gated pattern formation, demonstrating how initiator chemistry can be tuned to achieve programmable macroscale properties. Our study reveals a surprising insight: Emergent behavior in FROMP systems arises from the inhibition-dominated regime of resin composition, expanding prior observations that such behavior is confined to a narrow compositional space near the boundary between front quenching and uniform front propagation. We identify a broader compositional window, far from the quenching regime, where emergent behavior reliably manifests. This expanded design space significantly enhances the operational flexibility of reactive systems and their capacity for self-organization. Furthermore, these insights provide a roadmap for designing bioinspired materials with self-organizing capabilities, unlocking possibilities in synthetic manufacturing.

Architected Polymers↗