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485 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Restoration Hydro: A Watershed Approach to Standard Modular New Hydropower

The objectives of FOA DE- FOA-0001836- “Standard Modular Hydropower” included designing a standardized, modular, and environmentally compatible hydropower schematic for implementation in greenfield sites that generate up to 10 MW of capacity. Utilizing funds competitively awarded under DOE’s Water Power Technologies Office, the Natel Energy team developed a concept for modular new stream reach (NSR) hydropower that incorporates multi species upstream and downstream fish passage, improved river channel connectivity, and recreational modules. The in-stream design of the collective modules minimized site specific design and maximized the opportunities for modularity. Financial data was also presented using actual costs from regional suppliers, with figures provided in 2022 dollars. While the project team did not address potential permitting process improvements, the site selection criteria did consider established barriers to hydropower development such as tribal and preserved lands, interconnection proximity, and endangered species to exclude or deprioritize. The project’s design schematic met the objectives of the FOA, and presented a unique solution that targets alluvial pockets as natural features for sustainable development. Natel’s concept also incorporated the company's fish-safe Restoration Hydro Turbine for safe downstream passage, while featuring a rock arch that integrates fish passage, water, recreation, and grade control modules (including sediment). Alignment with the Department of Energy Office of Energy Efficiency and Renewable Energy (EERE) “Innovative Design Concepts for Standard Modular Hydropower and Pumped-Storage Hydropower” Program: According to the Hydropower Vision (DOE, 2016), approximately 16 GW of hydropower growth is possible with the development of technology solutions that balance efficiency, economics, and environmental sustainability. The desired outcome of the SMH program is transformational innovation specifically in the site identification, conceptual, and detailed design phases of technology development lifecycles (DOE, 2018). In developing the SMH design schematic, the team aimed to address the opportunities outlined in the Vision through an inverted design philosophy; rather than singularly prioritizing efficiency and power production, the team focused on integrating hydropower with restoration of degraded streams to optimal ecosystem function and provision of exceptional recreation value as design criteria. To achieve this, Restoration Hydro incorporates the principles of nature-based engineering (WWAP, 2018) and biomimicry (Biomimicry NL.) to strategically deploy complementary combinations of permanent, semi-permanent, and ephemeral low-head structures - such as natural and engineered log jams - that harness geomorphological and hydrological processes at the landscape-scale. Primary applications of Restoration Hydro include: 1) restoration of degraded watersheds’ natural ecological function and enhancement of hydrological connectivity; and 2) creation of associated co-benefits to hydro production, including increased groundwater recharge, improved sediment transport and management, improved water security and water quality. Restoration Hydro projects build upon proven watershed restoration engineering techniques by integrating hydropower turbines into low-head structures using innovative and evolving civil works concepts that facilitate fish and sediment passage, and in some cases create additional revenue-generating recreational opportunities. Powering low-head structures creates a directly monetizable layer of economic value in the form of flexible, reliable, renewable energy on top of the already high-value water, environmental and recreational benefits of watershed and river restoration. The approach aims to create a virtuous, self-reinforcing cycle whereby Restoration Hydro projects support the scaling of ecosystem restoration activities, creating a water-energy-carbon multiplier effect that, through the principles of adaptive change management: 1) improves the resilience of landscapes and downstream population centers for changing hydrological cycles; 2) creates a reliable energy resource that facilitates the integration of intermittent renewable power sources into grids; and 3) supports climate change mitigation through grid decarbonization and enhanced ecosystem carbon capture and retention.

13 HYDRO ENERGY↗
DOE OSTI2025

Benders Decomposition Using Graph Modeling and Multi-Parametric Programming

Benders decomposition is a widely used method for solving large and structured optimization problems, but its performance is affected by the repeated solution of subproblems. We propose a flexible and modular algorithmic framework for accelerating Benders decomposition. Specifically, we express the problem structure by using a graph-theoretic modeling abstraction in which nodes represent optimization subproblems and edges represent connectivity between subproblems. A key innovation of our approach is that we embed multiparametric programming (mp) surrogates for node subproblems, which maps the exact analytical map of the subproblem solution space. The use of mp surrogates allows us to replace subproblem solves with fast look-ups and function evaluations for primal and dual variables during the iterative Benders process. We formally show the equivalence between classical Benders cuts and those derived from the mp solution. We implement our framework in the open-source PlasmoBenders.jl software package. To demonstrate the capabilities of the proposed framework, we apply it to a two-stage stochastic programming problem, which aims to make optimal capacity expansion decisions under market uncertainty. We evaluate both single-cut and multicut variants of Benders decomposition and show that the use of mp surrogates achieves substantial speedups in subproblem solve time, while preserving the convergence guarantees of Benders decomposition. We highlight advantages in solution analysis and interpretability that is enabled by mp critical region tracking; specifically, we show that these reveal how decisions evolve geometrically across the Benders search. Our results aim to demonstrate that combining surrogate modeling with graph modeling offers a promising and extensible foundation for structure-exploiting decomposition. In addition, by decomposing the problem into more tractable subproblems, the proposed approach also aims to overcome scalability issues of mp. Finally, the use of mp surrogates provides a unifying and modular optimization framework that enables the representation of heterogeneous node subproblems as modeling objects with a homogeneous structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Exploring scenarios for enhanced fuel compression and performance on the National Ignition Facility with machine-learning-aided design techniques

Recent fusion experiments on the National Ignition Facility (NIF) have achieved ignition, producing multi-MJ fusion yields for input laser energies of roughly 2 MJ [Abu-Shawareb et al., Phys. Rev. Lett. 132, 065102 (2024)]. Building on the success of the target designs that have achieved ignition, we explore new implosion scenarios predicted to generate significantly more compression of the dense DT ice layer and correspondingly higher yields while preserving many of the key physics characteristics of present-day ignition designs. Our main result is a novel 3-shock implosion scheme that effectively minimizes the shock-induced entropy in the dense, accelerating DT shell and maximizes the resulting fuel compression subject to a fixed leading shock strength consistent with present-day ignition experiments, which is necessary to melt the crystalline high-density carbon ablator. Compared to the first NIF experiment to fulfill Lawson's ignition criterion, shot N210808 [Abu-Shawareb et al., Phys. Rev. Lett. 129, 075001 (2022)], our design exhibits a 40% increase in simulated peak areal density (ρR) and a 5× increase in 1D fusion yield using a 4% lighter ablator and identical DT payloads. We also present a complete integrated 2D hohlraum design and laser pulse specifications capable of generating the desired 3-shock drive and maintaining control of the low-mode capsule implosion symmetry, where the increase in simulated 2D yield relative to N210808 is > 10×. This new implosion regime was discovered with help from a machine-learning-enabled capsule design optimization framework. We outline the workflow this automated tool uses to identify improved design candidates by running several rounds of capsule simulations, constructing a surrogate model mapping input variations to key physics output quantities, and querying the resulting statistical model to propose adjustments to the x-ray drive and capsule to reach a set of physics objectives prescribed by the designer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Plasma-Assisted Pre-Chamber Ignition System for Highly Dilute Stoichiometric Heavy-Duty Natural Gas Engines (Final Technical Report)

This project explored advanced ignition technologies to significantly enhance efficiency and reduce operating costs for heavy-duty natural gas engines operating at stoichiometric conditions, while meeting ultra-low NOx emission standards. The main goal was to develop and validate a plasma-assisted pre-chamber ignition system that could deliver at least a 2% increase in brake thermal efficiency (BTE) and a 4% decrease in total cost of ownership (TCO) compared to a typical multi-cylinder engine with three-way catalyst aftertreatment, ensuring compatibility with the expected 2027 EPA/CARB regulations. In the first half of the project, the research team concentrated on developing and testing plasma-assisted pre-chamber ignition using nanosecond pulsed discharges. Extensive experiments were conducted in an optically accessible rapid-compression and expansion machine, a constant-volume chamber, and an optical single-cylinder engine. Experiments were coupled with CFD simulations. The work produced unique insights into pre-chamber flame formation, jet ignition, dilution effects, and flame quenching at pressures, temperatures, and dilution levels relevant to engines. Although plasma-assisted ignition showed promise in controlled lab settings, the research also identified fundamental and practical challenges when applying this technology to real engine conditions. Midway through the project, a crucial pivot was made, guided by three key findings. First, the power electronics required for nanosecond plasma discharges were found to be too costly for commercial use, undermining the project’s cost-of-ownership goals. Second, nanosecond plasma ignition was highly sensitive to turbulent flow in the pre-chamber, resulting in lower ignition reliability than traditional spark under engine-like conditions. Third, achieving a truly diffuse low-temperature plasma at high pressures near top dead center was not possible, reducing the anticipated chemical enhancement benefits. These results collectively suggested that continuing with plasma-assisted ignition was unlikely to meet both efficiency and cost objectives. In response, the project shifted focus to a more realistic approach: enhancing traditional spark-based pre-chamber ignition with significantly less spark energy. Using insights gained earlier in the project, the team redesigned the pre-chamber to maintain high dilution tolerance and quick combustion, even with lower ignition energy. Testing confirmed that with optimized pre-chamber design and combustion timing, a lower-energy spark could reliably ignite highly diluted stoichiometric mixtures, reduce burn time, and boost thermal efficiency. Final engine testing and techno-economic analysis verified that this revised approach successfully achieved the project goals. The optimized pre-chamber ignition system provided over a 2% increase in calculated brake thermal efficiency compared to the baseline engine. Notably, the lower ignition energy and simplified hardware reduced component stress, extended maintenance intervals, and lowered the total cost of ownership. When used with stoichiometric operation and traditional three-way aftertreatment, the system remained compatible with near-zero NOx emissions targets without increasing cost or complexity in the emissions control system. In summary, although the project deviated from its initial plasma-assisted ignition idea, the work produced a more practical and commercially viable solution. The results show that precisely optimized, low-energy pre-chamber spark ignition can significantly improve efficiency and reduce overall ownership costs for heavy-duty natural gas engines. This directly aligns with DOE goals for cleaner, more efficient, and cost-effective transportation technologies.

03 NATURAL GAS↗
DOE OSTI2023

Improved Biofuel Production through Discovery and Engineering of Terpene Metabolism in Switchgrass

Project Objectives - Of the myriad specialized metabolites that plants deploy to adapt to environmental challenges, terpenes form the largest group. In many major crops, unique terpene blends serve as key stress defenses that directly impact plant fitness and yield. In addition, terpenes, such as bisabolene and pinene, are used for producing renewable biofuels. Essential to advancing a broader use of terpenes for biofuel feedstock engineering is a system-wide knowledge of the diverse biosynthetic machinery and defensive potential of often species-specific terpene blends. The proposed project would merge genome-wide enzyme discovery with comparative –omics, protein structural and plant microbiome studies to define the biosynthesis and stress-defensive functions of the switchgrass (Panicum virgatum) terpene network. These insights would be combined with developing and applying non-transgenic genome editing tools to design plants with desirable terpene blends for higher productivity and biofuel production on marginal lands. As a dedicated lignocellulosic feedstock for U.S. biofuel production with high net energy yield, stress tolerance, and available genome resources, switchgrass is well-suited for devising new avenues for biofuel production. Project Description – The diversity of plant terpene defenses is governed by species-specific families of terpene synthase (TPS) and cytochrome P450 monooxygenase (P450) enzymes. Mining of the switchgrass genome (genotype Alamo) identified ~100 TPS and P450 candidate genes, and combinatorial biochemical analysis of synthesized TPSs and P450s revealed more than a dozen enzymes with common and novel activities. In addition, several identified terpene metabolites and the corresponding transcripts were up-regulated in response to abiotic stressors. These findings demonstrate a unique switchgrass terpene network with probable importance to abiotic stress tolerance, thus providing a large chemical portfolio for optimizing crop resistance, yield, and biofuel composition. Leveraging these preliminary data, we propose to generate a genome-wide map of the switchgrass terpene metabolic network through multi-gene co-expression analyses that allow the efficient cross-validation of TPS and P450 functions. Key enzymes would further be applied to structure-function studies via X-ray protein crystallography, homology modeling and site-directed mutagenesis to gain mechanistic insight into the catalytic specificity of switchgrass terpene metabolism and provide gene and amino acid targets for genome editing. In tandem with terpene pathway discovery, system-wide metabolomics, transcriptomics and proteomics studies in switchgrass accessions of contrasting drought tolerance would define the role of switchgrass terpene metabolism in conferring abiotic stress resilience. Metabolic changes would be assessed in a combined approach of targeted (terpenes) and untargeted metabolite profiling using a high-resolution LC-MS/MS approach, differential gene expression analyses through multiplexed Illumina RNA sequencing, and quantitative analysis of high-priority pathway enzymes using multiple reaction monitoring (MRM). Drawing on these insights, knock-down/out mutants of stress-associated pathway nodes would be generated by optimizing transient virus-induced gene silencing (VIGS) and CRISPR/Cas9 systems under control of the Tobacco Rattle Virus (TRV). The resulting mutant lines would then be analyzed for stress susceptibility and the impact on the root microbiome to define gene functions in planta. Knowledge of terpene pathways, enzyme mechanisms and bioactivities would be applied to enhance switchgrass stress resilience and to tailor-make terpene blends for biofuel production. Here, TRV-enabled CRISPR/Cas9 genome editing, including allele-specific knock-out of redundant genes, engineering of enzyme specificity via structure-guided point mutations, and overexpression of terpene genes relevant to stress-protection or biofuel production, would be used to increase metabolic flux toward desired pathways. Broader Impacts - Integrating the system-wide discovery, mechanistic analysis and non-transgenic genome engineering of the switchgrass terpene network aligns the required steps to unlock the chemical potential of this important metabolite class to generate crops that are more resistant to stress and provide advanced biofuel production in light of rising climate pressures as foreseeable challenges for bioenergy crop cultivation. The proposed project would further offer interdisciplinary student training through active involvement in the project and integration of research concepts and outcomes into newly-developed graduate and undergraduate courses on Plant Biotechnology.

09 BIOMASS FUELS↗
DOE OSTI2020

Stochastic Analysis for Long Term Capital Structures, Systems, and Components Refurbishment and Replacement

As commercial Nuclear Power Plants (NPPs) pursue extended plant operation in the form of Second License Renewal (SLR), opportunities exist for these plants to provide capital investments to ensure long-term safe and economic performance. At the current time, several utilities have announced an intention to pursue extended operation for one or more of their NPPs via SLR . The goal of this research is to develop a risk-informed approach to evaluate and prioritize plant capital investments made in preparation for, and during the period of, extended plant operations to support decisions for NPP operations. Since the capital investments are influenced by various factors, such as markets, safety and regulatory, the decision-making process of NPP operations should take into account relevant factors for balancing risks, costs and profits. The traditional method of capital budgeting is based on the priority list of candidate projects using economic measures such as benefit-investment ratio, net present value (NPV) and internal rate of return. In the literatures, the problem of capital budgeting or the variant can be represented by an appropriate knapsack problem. The knapsack approach to capital budgeting takes as input as investment, along with the cost and profit of each project. The objective of capital budgeting is to find the combination of the binary decisions for every investment such that the overall profit is as large as possible. The output is a collection of projects to be carried out, and we refer this selected collection of projects as a project portfolio. One limitation of traditional optimization models for capital budgeting is that they do not account for risk/uncertainty in profit and cost streams associated with individual projects, they do not account for risk in resource availability in future years [1,2,3]. Projects can incur cost over-runs, especially when projects are large, performed infrequently, and when there is risk regarding technical viability, external contractors, and/or suppliers of requisite parts and materials. Occasionally, projects are performed ahead of schedule and with cost savings. Planned budgets for capital improvements can be cut and key personnel may be lost. Or, there may be surprise windfalls in budgets for maintenance activities due to decreased costs for “unplanned” maintenance. In these cases, how should we resolve capital budgeting when we have risk forecasts for costs, profits and budgets? One approach we proposed in this summary is to re-solve the optimization models based on assumed statistical distributions of given parameters. If these distributions were not available, a two-stage stochastic optimization approach can be used to provide priority lists to decision-makers to support better risk-informed decisions [4, 5]. In this summary, we will only focus on the first approach.

42 ENGINEERING↗
DOE OSTI2025

Game Theory Approaches for System-level Incentive Design

This report presents a generalized Stackelberg game framework for designing and evaluating financial incentives that enhance power system resilience through strategic deployment of distributed energy resources(DERs) under various contingencies. The proposed approach addresses the challenge of coordinating individual community investment decisions to meet system-wide resilience objectives. The framework is demonstrated in a three-community test system subjected to two transmission contingency scenarios: inter-community line failure (Case 1) and complete main grid disconnection (Case 2). In both cases, three incentive levels are compared: a Base case with no financial incentives, and low and high incentive cases. In Case 1, the Base case (no incentives) results in a total installed DER capacity of 217.2 MW, with no load shedding due to alternative routing, but community costs remain high. Increasing incentives raises DER deployment to 286.9 MW, lowers aggregate community costs by $22M annually, and completely avoids the need for costly new transmission line construction. In Case 2, the Base case results in 24.3 MWh of unserved load; introducing incentives eliminates all load shedding and ensures up to 89 MWh of battery storage is available for emergency reserve. These results demonstrate that targeted incentives can dramatically improve grid resilience and cost-effectiveness. The framework thus offers policymakers and system planners a robust tool to quantify and compare the effectiveness of incentive programs for multi-community transmission networks behavior, system resilience, and economic efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Load Mitigating Feedback Control of WECs

This report outlines the development of load-mitigating feedback control for wave energy converters. A simple, self-tuning multi-objective controller is demonstrated in simulation for a 3-DOF (surge, heave, pitch) point absorber. In previous work, the proposed control architecture has been shown to be effective in experiment for a variety of device archetypes for the single objective of the maximization of electrical power capture: here this architecture is extended to reduce device loading as well. In particular, PTO actuation forces and the minimization of fatigue damage (determined from the sum of wave-exerted and PTO forces) are considered as additional objectives for the self-tuning controller. This controller is demonstrated for two similar, but distinct systems: one described by the identified linear models from physical testing of the WaveBot device, and another based upon a WEC-Sim simulation that expands upon boundary element method data from the WaveBot device. In both cases, because the power surface is consistently fairly flat in the vicinity of control parameters that maximize power capture in contrasting sea-states, it is found to be generally possible to mitigate either fatigue damage or PTO load. However, PTO load is found to conflict with fatigue damage in some sea-states, limiting the efficacy of control objectives that attempt to mitigate both simultaneously. Additionally, coupling between the surge and pitch DOFs also limits the extent to which fatigue damage can be mitigated for both DOFs in some sea-states. Because control objectives can be considered a function of the sea-state (e.g., load mitigation may not be a concern until the sea is sufficiently large) a simple transition strategy is proposed and demonstrated. This transition strategy is found to be effective with some caveats: firstly, it cannot circumvent the aforementioned objective contradictions. Secondly, this objective transition is too slow to act as a system constraint, and objective thresholds must thus be considered quite conservatively. Improvement of the adjustment strategy is demonstrated through the addition of an integral term. Selection of well-performing transition parameters can be a function of sea-state. While a simple selection procedure is proposed, it is non-optimal, and a more robust selection procedure is suggested for future work.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with up 85 compatible production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Altogether, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Quantitative approaches for including equity in risk and resilience infrastructure planning analyses

Risk and resilience assessments for critical infrastructure focus on myriad objectives, from natural hazard evaluations to optimizing investments. Although research has started to characterize externalities associated with current or possible future states, incorporation of equity priorities at project inception is increasingly being recognized as critical for planning related activities. However, there is no standard methodology that guides development of equity-informed quantitative approaches for infrastructure planning activities. Here, to address this gap, we introduce a logic model that can be tailored to capture nuances about specific geographies and community priorities, effectively incorporating them into different mathematical approaches for quantitative risk assessments. Specifically, the logic model uses a graded, iterative approach to clarify specific equity objectives as well as inform the development of equations being used to support analysis. We demonstrate the utility of this framework using case studies spanning aviation fuel, produced water, and microgrid electricity infrastructures. For each case study, the use of the logic model helps clarify the ways that local priorities and infrastructure needs are used to drive the types of data and quantitative methodologies used in the respective analyses. The explicit consideration of methodological limitations (e.g., data mismatches) and stakeholder engagements serves to increase the transparency of the associated findings as well as effectively integrate community nuances (e.g., ownership of assets) into infrastructure assessments. Such integration will become increasingly important to ensure that planning activities (which occur throughout the lifecycle of the infrastructure projects) lead to long-lasting solutions to meet both energy and sustainable development goals for communities.

community-based↗
DOE OSTI2020

Development of a Comprehensive Two-Phase Flow Database for the Validation of NEK-2P

Three-dimensional (3-D) two-phase Computational Fluid Dynamics (CFD) codes are emerging as a powerful and potentially practical tool for applications in which detailed local flow information is needed. However, two-phase flow models and associated closure relations are not well established for CFD applications, which is partly due to the lack of high-quality validation data. The main objective of this work is to develop a comprehensive database of two-phase flows that can be used to validate two-phase CFD codes such as NEK-2P. In this project, four advanced local measurement systems, including Particle Image Velocimetry and Planar Laser-Induced Fluorescence (PIV-PLIF), high-speed imaging, x-ray densitometry, and multi-sensor conductivity probe are employed to measure the local two-phase flow parameters of both gas and liquid phases. By combining these techniques, the local void fraction, bubble velocity, interfacial area concentration, bubble frequency, liquid velocity, turbulence intensity, etc., in various two-phase flow regimes can be obtained. These local measurement techniques are first used in a 25.4 mm circular pipe test section. Seven air-water two-phase flow conditions spanning the bubbly, slug, churn-turbulent, and annular flow regimes are measured in this facility. The obtained database contains the radial profiles of both gas- and liquid-phase parameters at three axial locations along the test section. The second facility used in this work contains a 30 mm × 10 mm rectangular test section, in which three two-phase flow and two single-phase flow conditions are measured. Two-dimensional distributions of local two-phase flow parameters in the cross-sectional plane are measured in this facility at three axial locations as well. A facility featuring a 3×3 electrically heated rod bundle is also designed and being constructed in this project. This facility is specially designed for optical measurements and is expected to provide high-quality boiling data in the future. Preliminary analyses have been performed for the data taken in the 25.4 mm circular pipe. Both center-peaked and wall-peaked void fraction profiles have been observed in the data depending on the two-phase flow conditions and/or developing lengths. The 1-D drift-flux model was evaluated with the newly obtained datasets, in which both gas- and liquid-phases data were directly measured. The distribution parameter model has been optimized based on a new void-profile classification method proposed in this study. The optimized drift-flux model shows a significant improvement in predicting both gas velocity and void fraction. The measured liquid-phase turbulence was used to benchmark Sato’s turbulence model considering the bubble-induced shear stress for the three tested bubbly flows. The benchmark results showed good agreement between the PIV measurements and model predictions. In the bubbly flows tested that have low void fractions less than 3%, the effect of the bubble-induced turbulence was found not significant. However, the bubble-induced shear stress becomes important with the increase of the void fraction. The Conjugate Heat Transfer (CHT) model was developed and implemented in NEK-2P. This model allows the coupled simulation of the solid domain and two-phase fluid domain, allowing the specification of realistic boundary conditions. The CHT implementation was verified first with non-boiling simulations. The predicted temperatures in the fluid domain were shown to be identical in simulations with or without the CHT model. The CHT model was then validated through simulations of three Becker benchmark CHF tests performed under both Dryout (DO) and Departure from Nucleate Boiling (DNB) conditions. Reasonably good agreement was obtained between calculated wall temperatures and corresponding experimental data.

42 ENGINEERING↗
DOE OSTI2020

Printing polymer blends through in situ active mixing during fused filament fabrication

Fused filament fabrication (FFF) enables production of 3D objects over a range of material compositions at low-cost relative to traditional manufacturing approaches. To date, a limited but growing number of materials are able to be used with FFF, however many applications exist where specific mechanical, thermal, or chemical properties are needed that cannot currently be met with the available feedstock selection. Therefore, a need exists to tune these materials for specific chemical or mechanical properties. One common formulation strategy to address these demanding design parameters is to develop composites or polymer blend filaments. Typically, this is a time-consuming and costly optimization process. Here, we have developed hardware for reproducibly mixing two filaments of similar or dissimilar compositions at the time of printing within individual printed layers. This mixing occurs via software-controlled rotating hardware in the chamber of an extruder’s hot-end. The efficiency of mixing within the printed layers has been characterized in detail as a function of the rotational speed and geometry of the blending hardware. These parameters were exploited to program the ratio and distribution of thermoplastic-based filaments blended within printed extrudate. Example printed specimens were produced with thermoplastic polyurethane (TPU) elastomer blended with rigid polylactic acid (PLA) and Nylon blended with PLA. In addition, a conductive carbon nanotube (CNT)-PLA composite was blended as a function of mixer geometry and input feed ratios with non-conductive PLA and resistance values were measured across the resulting printed specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Minimal Energy Routing of a Leader and a Wingmate with Periodic Connectivity

We consider a route planning problem in which two unmanned vehicles are required to complete a set of tasks present at distinct locations, referred to as targets, with minimum energy consumption. The mission environment is hazardous, and to ensure a safe operation, the UVs are required to communicate with each other at every target they visit. The problem objective is to determine the allocation of the tasks to the UVs and plan tours for the UVs to visit the targets such that the weighted sum of the distances traveled by the UVs and the distances traveled by the communicating signals between them is minimized. We formulate this problem as an Integer program and show that naively solving the problem using commercially available off-the-shelf solvers is insufficient in determining scalable solutions efficiently. To address this computational challenge, we develop an approximation and a heuristic algorithm, and employ them to compute high-quality solutions to a special case of the problem where equal weights are assigned to the distances traveled by the vehicles and the communicating signals. For this special case, we show that the approximation algorithm has a fixed approximation ratio of 3.75. We also develop lower bounds to the optimal cost of the problem to evaluate the performance of these algorithms on large-scale instances. We demonstrate the performance of these algorithms on 500 randomly generated instances with the number of targets ranging from 6 to 100, and show that the algorithms provide high-quality solutions to the problem swiftly; the average computation time of the algorithmic solutions is within a fraction of a second for instances with at most 100 targets. Finally, we show that the approximation ratio has a variable ratio for the weighted case of the problem. Specifically, if ρ denotes the ratio of the weights assigned to the distances representing the communication and travel costs, the algorithm has an a posteriori ratio of $3 + \frac{3ρ}{4}$ when ρ ≥ 1, and $\frac{3}{ρ}$ + $\frac{3}{4}$ when ρ ≤ 1.

42 ENGINEERING↗
DOE OSTI2021

Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications

Polymeric materials are ubiquitous in modern society and they play an instrumental role in almost all industries, undoubtedly including the energy and environment sectors. Increased demand of energy and awareness to sustainability both necessitates the development of novel polymers with enhanced properties. Unfortunately, their structural and behavioral complexity render such discovery challenging and impeded. To address this problem, scientists are developing various computational modeling techniques and leveraging their power to depict the relationship between structural characteristics of polymers and their properties (such as rheological behaviors), and use such prediction to guide the design and syntheses of novel polymeric materials with enhanced performances. Unfortunately, predicting the relationships between polymer structure and composition with rheological properties via atomistic modeling is still a major challenge because of the extended time and length scales involved. Studying dynamic shear viscosity and linear viscoelasticity using molecular models requires capabilities that have been elusive, including representation of large molecular weight chains with an effective internal scale capable of describing entanglement, shear-rates that are in the s-1 scale with accurate quantitative stresses, and chemically-realistic combinations of both homogeneous and heterogeneous systems. Motivated by these unmet challenges, the overall technical objective of this DOE-STTR Phase II project is to develop robust molecular predictive methods for advanced polymer discovery and applications and especially for designing and demonstrating the “smart” polymer-based waterflooding enhanced oil recovery (EOR) process. In particular, we apply state-of-the-art molecular modeling methods developed by our academic partner, Materials Stimulation Center (MSC) at California Institute of Technology (Caltech), to facilitate and accelerate the experimental discovery processes. During the Phase I of this project, we had focused on development and demonstration of the molecular modeling methods to describe rheological properties of non-Newtonian polymer fluids, and to improve our fundamental understandings of shear-thickening mechanism and kinetics. In Phase II, we further apply the theoretical models to guide our experimental programs to improve our design of smart rheology modifier (SRM) polymers and their optimization for EOR. Specifically, we have three objectives in the Phase II study: (1) to further improve out computational modeling methods, coupling with the advanced machine learning algorithms; (2) to develop cost-effective and efficient SRM-flooding process suitable for EOR applications under typical reservoir conditions; and (3) to further explore the application of our molecular predictive models for innovative material discovery in other industrial applications. The recent development of our multiscale predictive framework allows the successful prediction of rheological properties from the chemical structure for polymers of experimentally relevant molecular weights, and provides an in-silico machine learning engine for screening novel compositions and structures with optimized non-Newtonian response, required for both shear-thinning and shear-thickening applications. Our framework provides: (1) procedures and tools for systematic coarsening from atomistic models and reverse mapping of coarse-grain models to atomistic, (2) unique ab initio methods to characterize the atomistic origin of colloidal and interfacial interactions and phenomena, (3) systematic structure and composition builders based on practical descriptors that drive rheological changes in polymer melts and diluted polymer mixtures, (4) a rheological properties engine capable of predicting viscosity in the zero-shear limit and under realistic dynamic conditions (for shear-rates commensurate with experiments) for large heterogeneous systems, (5) coarse-grain force fields with improved non-bond descriptions based on accurate quantum mechanics, (6) an in-silico screening machine learning engine that feeds from the systematic model builders to cover the descriptors search space, computes the rheological properties from converged trajectories spanning sub-milliseconds and ranks them for each structure/composition using an automated viscosity-vs-shear rate fitness function that can be tuned for shear-thickening, shear-thinning and other rheological responses.

02 PETROLEUM↗
DOE OSTI2026

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advanced Laboratory and Field Arrays (ALFA)/Lab Collaboration Project (LCP) for Marine Energy (Final Scientific/Technical Report)

The objective of the Advanced Laboratory and Field Arrays (ALFA) project was to reduce the Levelized Cost of Energy (LCOE) of Marine and Hydrokinetic (MHK) energy by leveraging research, development, and testing capabilities at Oregon State University, University of Washington, and the University of Alaska, Fairbanks. ALFA is a project within the Pacific Marine Energy Center (PMEC; formerly NNMREC), a multi-institution entity with a diverse funding base that focuses on research and development for marine renewables. The ALFA project aimed to accelerate the development of next-generation arrays of wave energy conversion (WEC) and tidal energy conversion (TEC) devices through a suite of field-focused R&D activities spanning a broad range of strategic opportunity areas identified in the Funding Opportunity Announcement: • Device and/or array operation and maintenance (O&M) logistics development; • High-fidelity resource characterization and/or modeling technique development and validation; • Array-specific component technology development (e.g. moorings and foundations, transmission, and other offshore grid components); • Array performance testing and evaluation; and • Novel cost-effective environmental monitoring techniques and instrumentation testing and evaluation. The objective of the Lab Collaboration Project (LCP) was to accelerate the development of next-generation marine energy conversion systems. The LCP aimed to achieve these project objectives in collaboration with the national laboratories by: • Developing concept generation and assessment tools; • Improving access to existing testing resources; • Validating collision risk models between fish and turbines; and • Advancing analysis and simulation capabilities for wave-WEC interactions and PTO analysis in nonlinear ocean waves. The ALFA portion of the project was comprised of six overarching technical tasks: • Task 1: Debris Modeling, Detection and Mitigation; • Task 2: Autonomous Monitoring & Intervention; • Task 3: Resource Characterization for Extreme Conditions; • Task 4: Robust Models for Design of Offshore Anchoring and Mooring Systems; • Task 5: Performance Enhancement for Marine Energy Converter (MEC) Arrays; and • Task 6: Evaluating Sampling Techniques for MHK Biological Monitoring. The LCP was divided into four overarching technical tasks: • Task 7: Project Management and Reporting • Task 8: Novel Design and Assessment Methodologies for Wave Energy Converter Design (Wave- SPARC) • Task 9: Testing Access for Commercial Marine Renewable Energy Technology Developers • Task 10: Quantifying Collision Risk for Fish and Turbines • Task 11: Nonlinear Ocean Waves and PTO Control Strategy Each ALFA/LCP task listed above functioned as a separate and discreet project. A final Technical Report was written for each individual task and these reports were uploaded to OSTI, after receiving DOE approval. The following document is a compilation of each of these final, approved reports arranged as individual chapters.

13 HYDRO ENERGY↗