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DOE OSTI2025

Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference

Accurate material characterization and model calibration are essential for computationally supported high-consequence engineering decisions. Historically, characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data are collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called interlaced characterization and calibration (ICC) that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference and (4) incorporates these advancements into a quasi real-time feedback loop. The ICC framework is demonstrated here on the calibration of a material model using simulated full-field data for an aluminium cruciform specimen being deformed biaxially. The cruciform is actively driven through the myopically preferred load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain (EIG). Principal component analysis (PCA) is performed on the model predictions of full-field displacements, and fast surrogate models are built to approximate the input-output relationships of the expensive finite element model. Furthermore, the tools developed and demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modelling by enabling utilization of computational simulations at earlier stages of the design cycle.

Bayesian optimal experimental design↗

DOE OSTI2025

Machine learning for fundamental spectroscopic and thermodynamic data of actinides and lanthanides

Accurately modeling optical spectra with absolute radiometric intensities is vital for nuclear forensics applications that depend on characterizing optical emissions from energetic nuclear phenomena. This requires precise knowledge of the individual atomic transition probabilities, known as Einstein A-coefficients, for each emission line. Obtaining these values theoretically or experimentally is often impractical due to the complex electronic structures and the number of transitions involved in atoms relevant to nuclear applications. In this study, we explore the use of machine learning to predict the Einstein A coefficients for atomic transitions. Seven models were evaluated that ranged from deep learning to decision tree algorithms, and found that gradient boosting performed best, specifically the Extreme Gradient Boosting (XGB) architecture, achieving a precision of 86% across transitions of 36 elements. Furthermore, the model was cross-validated using published transition probabilities reported in the literature and applied to estimate Pu plasma temperatures from a previous experiment conducted at Savannah River National Laboratory.

Atomic spectroscopy↗

DOE OSTI2026

The U.S. Agrivoltaic Shading Tool: A National-Scale Interface for Modeling Light and Shade Patterns in Ten Common Agrivoltaic Configurations

Agrivoltaic systems are dual-use configurations that co-locate agriculture and photovoltaic (PV) infrastructure and require careful design to balance crop performance and energy generation. A critical element of agrivoltaic design is the spatial and temporal distribution of irradiance and shade within and around PV arrays. To support research, planning, and stakeholder decision-making, we introduce the U.S. Agrivoltaic Shading Tool, a novel web-based application that delivers high-resolution irradiance and photosynthetically active radiation (PAR) modeling for ten standardized PV configurations across the conterminous United States. The tool leverages the National Laboratory of the Rockies (NLR) System Advisor Model (SAM) to perform detailed irradiance simulations, using meteorological data from the National Solar Radiation Database (NSRDB). Outputs include seasonal, monthly, weekly, and diurnal patterns of available sunlight, amount of shade, irradiance, and PAR at ground level within agrivoltaic system footprints. For a user's selected location, these results are visualized through interactive visualizations, heatmaps, and time-series plots, designed to be accessible to both technical and non-technical users. In addition to facilitating rapid spatial exploration of agrivoltaic light environments, the tool will offer seamless integration with the InSPIRE Agrivoltaics Design and Analysis Model (ADAM). This optional workflow will allow users to port selected site and configuration parameters into a more advanced modeling environment for further customization of structural layouts, crop-system compatibility, power generation, and technoeconomic performance. Finally, to promote open science, the entire dataset will be hosted and available for open access through the OpenEI platform. By standardizing and disseminating high-quality irradiance data and design tools, the U.S. Agrivoltaic Shading Tool supports a wide range of users, including researchers, landowners, energy developers, and policymakers, in evaluating the agronomic and energetic feasibility of agrivoltaic systems across the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data

Understanding rare‐earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional‐materials fabrication—as a surrogate for studying mineralization with data‐driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine‐learning models—K‐nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB‐based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy‐REE systems. Overall, the study shows that functional‐materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less‐studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.

feature importance analysis↗

DOE OSTI2026

Principles and Options for Designing Battery Energy Storage Zoning Ordinances

Deployment of battery energy storage (BESS) systems, both standalone and as part of hybrid systems paired with generation, has rapidly increased in the United States in recent years as utilities and communities have deployed storage to improve electric grid reliability and act as a cost-effective alternative to larger grid infrastructure. The modular nature of BESS technologies means systems may be built near other existing land uses, creating the potential for conflicts with neighboring landowners that can be managed and mitigated through zoning and permitting requirements established by local jurisdictions. While many cities and counties have adopted ordinances specific to BESS into their local zoning codes, these ordinances vary significantly in their requirements and level of detail. Meanwhile, many other jurisdictions, including those home to proposed or existing BESS projects, lack any specific language related to BESS in their zoning codes. Local planning and zoning officials have limited capacity and may lack the familiarity with BESS technologies needed to develop ordinances or otherwise make reasonable zoning decisions that balance safety, community impacts, and other goals. The resulting uncertainty at the local zoning level has led developers to withdraw projects in some areas and has spurred moratoria or bans on energy storage projects in others. This report intends to provide practical resources for practitioners interested in reasonable and effective local regulation of battery energy storage. It does not present a model zoning ordinance, but rather is intended to complement model ordinances developed by others by providing additional context and analysis regarding the structure of energy storage zoning ordinances and the decision points for local officials. Zoning ordinances at the city, town, and county level across the U.S. were surveyed alongside two template model ordinances to identify common elements and options for regulating the zoning and siting of BESS. Common elements identified and analyzed include definitions and general requirements, including cutoffs or tiers used to apply regulations to different system sizes and the permitted zones where jurisdictions allow BESS to be sited; visual, noise, and aesthetic requirements, including property line setbacks, fencing and visual screening, noise, and lighting requirements; and safety and planning requirements, such as site plans, decommissioning plans or funds, and requirements for access by emergency services. The report also summarizes some of the more unique regulations, including those that place additional restrictions on BESS at the local level.

25 ENERGY STORAGE↗

DOE OSTI2026

1995 San Diego Region Travel Behavior Survey

The 1995 San Diego Region Travel Behavior Survey was conducted January-June of 1995 under the auspices of the San Diego Association of Governments. The survey was an essential element in the regional study of transportation activity and travel patterns. The survey was designed to examine the relationship between characteristics of households and travel behavior, as well as to provide information state and local decision makers require when considering future regional transportation needs and investments. In total, 2,375 households were recruited to participate in the study. Of these, 2,062 households completed travel diaries, and the information was retrieved from all household members older than age five. 2,049 were successfully geocoded to California state plane coordinates for home addresses. A total of 17,060 trips were profiled in the course of this survey during the assigned 24-hour weekday.

1Hz data↗

DOE OSTI2025

Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection

This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.

Fayad, Samuel S. [University of Illinois at Urbana↗

DOE OSTI2025

Process Optimization and Modeling for Minerals Sustainability (PrOMMiS) v1.0.0

The U.S. Department of Energy's ("DOE") Process Optimization and Modeling for Minerals Sustainability project ("PrOMMiS Project") was initiated in 2023 to transform the national Critical Minerals and Rare Earth Elements (CM & REE) landscape, thereby supporting DOE's three enduring strategic objectives: security, economic competitiveness, and environmental responsibility. To address this initiative, the PrOMMiS Project will develop, demonstrate, and deploy an open-source, advanced system modeling, optimization, and analysis application ("PrOMMiS Software Application") that will support industry decision-makers by accelerating the scale-up of novel CM & REE technologies by de-risking the development and deployment of commercial scale processes and maximizing learning throughout the development cycle.

Beattie, KeithS [Lawrence Berkeley National Labora↗

DOE OSTI2025

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗

DOE OSTI2026

Study of the Protection Improvements for a Weak Grid Area With High Inverter-Based Resources (IBRs)

This project designs enhanced protection scheme for the real-world weak grid area with a high penetration of IBRs. As the existing protection schemes are originally designed for traditional synchronous machines, we first evaluate if the protection scheme will continue to operate reliably in systems with high levels of IBRs. Hardware relays are tested using a controller-hardware-in-the-loop setup. PSCAD electromagnetic transient simulation with IBR original equipment manufacturer black-box models is used to perform fault studies and generate COMTRADE data, which are replayed by a real-time digital simulator (RTDS) to feed input to the hardware relays. Three scenarios are analyzed: normal operation, an N-1 contingency, and an IBR-only scenario. The evaluation results reveal the following: 1) the protection scheme remains reliable under normal conditions and N-1 contingencies and 2) in IBR-only scenarios, differential protection (87L) continues to operate reliably, whereas local protection elements, such as distance and directional elements, fail because of the lack of regulated negative sequence current contributed by IBRs. Enhanced protection is designed to address the challenge of lack of negative sequence current from IBRs, including increased restraining factors a2 and k2 to block 32Q or using V instead QV ORDER for ground faults, enhanced mho distance element with voltage and phase angle supervision for L-L faults. The efficacy of enhanced protection logic is validated and proven to work reliably. Additionally, IEEE Std. 2800-2022 negative sequence current compliant GFL and GFM IBRs from another vendor are tested and proven to work reliably without need for enhanced logic. Therefore, this work provides valuable decision-making for utilities facing protection system challenges due to IBRs, either designing enhanced protection scheme or requesting their IBRs being IEEE Std. 2800-2022 compliant to produce regulated negative sequence current for protection relay to make correct decision.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Harvesting Reactor Pressure Vessel Beltline Material from the Decommissioned Zion Nuclear Power Plant Unit 1

The decommissioning of the Zion Nuclear Power Plant (NPP) provided a unique opportunity to harvest and study service-aged reactor pressure vessel (RPV) beltline materials. This work, conducted through the U.S. Department of Energy’s Light Water Reactor Sustainability (LWRS) Program, aims to improve the understanding of radiation-induced embrittlement to support extended nuclear plant operations. Material segments containing the Linde 80 flux, wire heat 72105 (WF-70) beltline weld and the A533B Heat B7835-1 base metal, obtained from the intermediate shell region with a peak fluence of 0.7 × 10 19 n/cm 2 (E > 1.0 MeV), were extracted, cut into blocks, and machined into test specimens for mechanical and microstructural characterization. The segmentation process involved oxy-propane torch-cutting, followed by precision machining using wire saws and electrical discharge machining (EDM). A chemical composition analysis confirmed the expected variations in alloying elements, with copper levels being notably higher in the weld metal. The harvested specimens enable a detailed evaluation of through-wall embrittlement gradients, a comparison with the existing surveillance data, and the validation of predictive embrittlement models. This study provides critical data for assessing long-term reactor vessel integrity, informing aging-management strategies, and supporting regulatory decisions to extend the life of nuclear plants. This article is a revised and expanded version of a paper entitled, “Current Status of the Characterization of RPV Materials Harvested from the Decommissioned Zion Unit 1 Nuclear Power Plant”, PVP2017-65090, which was accepted and presented at the ASME 2017 Pressure Vessels and Piping Conference, Waikoloa, HI, USA, 16–20 July 2017.

harvesting beltline material↗

DOE OSTI2026

Electron storage ring power supply system design report

In January 2020, U.S. Secretary of Energy Dan Brouillette announced the decision to build an Electron Ion Collider (EIC) at Brookhaven National Laboratory (BNL). While advancing the state of the art of particle colliders, the EIC will enable the U.S. nuclear physics community, with world wide participation, to take a giant step forward in the centuries-old quest to understand the nature of matter at its most fundamental level, providing the clearest picture yet of how the elemental quarks and gluons interact to form the basic structure of atoms and nuclei. The EIC at Brookhaven National Laboratory will be the first particle accelerator capable of colliding polarized ions with polarized electrons.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

From binary to quinary: The rationale and development of GaInAsSbBi for mid- and long-wave infrared sensing applications

Given the added complexity in flux calibration and composition evaluation inherent to quinary alloy growth, what motivates compounding the challenges of III–V-Bi growth with the goal of producing a quinary alloy of GaInAsSbBi for mid- and long-wave infrared sensing applications? Each elemental constituent provides some additional design freedom to achieve the ultimate goal of producing a lattice-matched, bulk random alloy mid-wave infrared III–V material with smooth surface morphology and high optoelectronic quality to enable high performance elevated operating temperatures. Here, this paper reviews the evolution of Bi-containing semiconductor research, focusing on mid- and long-wave infrared materials and highlighting key research findings that motivated the decisions to accept the added complexity in going from binaries like InAs or InSb, to InAsBi, to InAsSbBi, and, finally, to GaInAsSbBi to meet the performance demands of advanced infrared sensing applications.

Webster, Preston T. [Air Force Research Laboratory↗

DOE OSTI2026

Carbon ore, rare earth, and critical minerals (core-cm) assessment of san juan and raton coal basins: what is the future potential of coal in the san juan and raton basins, new mexico and colorado

This report summarizes results from the CORE-CM project in the San Juan and Raton basins of New Mexico and Colorado and ultimately begins to address what is the future of coal in the San Juan and Raton basins. The objective of this project was to determine the nonfuel carbon-based products (CBP), critical minerals, and rare earth elements (REE) potential in coal and related stratigraphic units in the San Juan and Raton basins and adjacent areas. This project delineated favorable geologic terrains and priority areas containing potential CBP, critical minerals, and REE deposits for the DOE mandate, which also is a priority of the NMBGMR and state of New Mexico. This project is important to the state because CBP, critical minerals, and REE resources must be identified before land use decisions are made by government officials. Future mining of CBP, critical minerals, and REE will directly benefit the economy of New Mexico. Furthermore, it is crucial to re-establish a domestic source of critical minerals and REE in the U.S. to help secure the nation’s clean energy future, to reduce the vulnerability of the U.S. to material shortages related to national defense, and to maintain our global technical and economic competitiveness. In addition to these benefits, students at New Mexico Institute of Mining and Technology (NMT) and San

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Zero-Emissions Roadmap for Oakland County

This document outlines a strategic pathway for Oakland County, Michigan to achieve zero emissions by 2050 under the Clean Energy to Communities (C2C) Program. The report encompasses analyses and high-level modeling to guide Oakland County in its emission reduction goals. Key methods include establishing an emissions inventory baseline with ongoing evaluation of future projects’ emissions impacts. Core elements include decommissioning old infrastructure, enhancing energy efficiency, deploying hybrid and ground-source heat pumps, and transitioning to electric fleets. It is proposed to structure the planning process into 5-year strategic plans to make the decarbonization process manageable. The document also emphasizes the need for reassessment of goals and periodic updates to remain adaptive to technological advancements and funding considerations. A matrixed approach for evaluating projects by cost and emissions savings is suggested to optimize decision-making given finite resources.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Update on Parallel Process Execution in the Next Generation System Analysis Model (NGSAM)

As of the end of 2022, it is estimated that over 90,000 metric tons of heavy metal (MTHM) of spent nuclear fuel (SNF) were stored at various commercial nuclear power reactor sites (both operating and shutdown) across the United States [1]. The Office of Storage and Transportation within the U.S. Department of Energy’s Office of Nuclear Energy is planning for the transportation, storage, and eventual disposal of SNF and high-level radioactive waste (HLW). To aid in this effort and inform decision-makers about the backend of the spent fuel cycle, systems analysis tools capable of analyzing the various options with respect to SNF and HLW management are being used as well as continuously improved to meet the evolving needs of the program. System analysts typically use these tools to vary underlying assumptions (shipping rates, available facilities, start dates, interim storage capacity, etc.) and study the associated system implications such as timing for clearing sites of SNF, various cost elements, transportation infrastructure acquisition needs, etc.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

End-To-End Decentralized Transmission Line Protection in IBR-Dominated Weak Grids Using Interpretable Data-Driven Methods

Traditional transmission line protection relies on predictable synchronous-based fault signatures, which frequently fail under the non-standard, current-limited fault characteristics of Inverter-Based Resources (IBRs). This study investigates how to achieve secure, communication-free fault isolation in IBR-dominated weak grids without relying on opaque, computationally heavy "black-box" machine learning algorithms. To address this, we propose a novel, standalone, and inherently interpretable data-driven protection framework. Unlike centralized methods requiring multi-terminal communication, this decentralized approach relies solely on local measurements using a hierarchical linear-kernel Support Vector Machine (SVM). The methodology decomposes the protection task into four sequential stages that mimic traditional protection elements: fault detection and fault direction identification, fault type classification, zone classification, and location estimation. This multi-stage architecture allows for specialized feature engineering at each stage, combining high computational efficiency with logic traceability. The framework's end-to-end performance was validated via C-code and PSCAD/EMTDC co-simulation, utilizing a real-world utility network and an OEM black-box IBR model. The proposed relay achieves 97.2% overall accuracy and provides a reliable trip decision within a 2.5-cycle window. The results confirm 100% accuracy in fundamental fault detection, reliable zone selectivity across low to moderate fault resistances, and robust security against non-fault transients, proving its immediate viability for integration into commercial numerical relays.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Effects of Material Selection on the Remanufacturability of a Swashplate in an Axial Piston Pump

A swashplate is a critical component in an axial piston pump for motion transformation that undergoes cyclic loading. Traditionally, swashplates are not designed to have multiple lives of operation. According to industry experts, a remanufacturability analysis of swashplates suggests that the scrap rate is high. Among the primary reasons for this high scrap rate is insufficient material on the running surface, especially for subtractive recovery. Therefore, the selection of the proper material and its amount plays a significant role in the fatigue behavior, the remanufacturability of the swashplate, as well as its cost. Analyses addressing fatigue life, remanufacturability, and cost of the swashplate were carried out in this paper through a comparative study utilizing various materials. Recommendations on the material selection that balance the failure consequences, remanufacturability, and cost are provided to guide designers in making informed decisions for alternative design options.

42 ENGINEERING↗