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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Delamination recycling of multilayer plastic films: Solvent-assisted separation pathways and recovery of solid polyolefins

Multilayer plastic films are excellent packaging materials due to the bound layers of multiple polymers, with each different polymer contributing to the film properties. Desirable properties lead to continuously growing demand for multilayer films. Multilayer plastic films are typically single-use and, as such, their increased production and disposal have led to waste management problems. Multilayer films are not currently recyclable primarily due to the multiple bound polymers. This work advances delamination as a recycling process to separate and sequester valuable polymers from multilayer films, facilitating the incorporation of these polymers into the circular economy. We effect delamination in a physical process that preserves targeted polymers as solid film, hence retaining their embodied energy. This work documents three different pathways of inducing delamination of multilayer films, with appropriate solvents disrupting adhesion between adjacent layers or dissolving a minor component of the film in less than an hour and at temperatures below 90°C, and where all initial polyethylene is maintained in its solid form throughout the process and is recovered at high purity. Solvent-based delamination is exemplified on commercial multilayer films with majority polyethylene (PE), and polyethylene terephthalate (PET) or ethylene vinyl alcohol copolymer (EVOH) or nylon also present. Solvent-based delamination is an energy-efficient and environmentally friendly recycling process that improves upon dissolution-precipitation recycling, since delamination involves very little dissolution and no precipitation, and is superior to pyrolysis which breaks down the polymers, while delamination keeps polymer chains intact. Furthermore, delamination recycling can contribute to the sustainable use of multilayer films as they continue to protect our food and medicine.

Chemical Recycling↗
DOE OSTI2025

Developing affordable and efficient heating devices for enhanced live cell imaging in confocal microscopy

Temperature control is crucial for live cell imaging, particularly in studies involving plant responses to high ambient temperatures and thermal stress. This study presents the design, development, and testing of two cost-effective heating devices tailored for confocal microscopy applications: an aluminum heat plate and a wireless mini-heater. The aluminum heat plate, engineered to integrate seamlessly with the standard 160 mm × 110 mm microscope stage, supports temperatures up to 36°C, suitable for studies in the range of non-stressful warm temperatures (e.g., 25-27°C forArabidopsis thaliana) and moderate heat stress (e.g., 30-36°C forA. thaliana). We also developed a wireless mini-heater that offers rapid, precise heating directly at the sample slide, with a temperature increase rate over 30 times faster than the heat plate. The wireless heater effectively maintained target temperatures up to 50°C, ideal for investigating severe heat stress and heat shock responses in plants. Both devices performed well in controlled studies, including the real-time analysis of heat shock protein accumulation and stress granule formation inA. thaliana. Our designs are effective and affordable, with total construction costs lower than $300. This accessibility makes them particularly valuable for small laboratories with limited funding. Future improvements could include enhanced heat uniformity, humidity control to mitigate evaporation, and more robust thermal management to minimize focus drift during extended imaging sessions. These modifications would further solidify the utility of our heating devices in live cell imaging, offering researchers reliable, budget-friendly tools for exploring plant thermal biology.

Plant Sciences↗
DOE OSTI2025

Structure–Property Linkage in Alloys Using Graph Neural Network and Explainable Artificial Intelligence

Deep learning tools have recently shown significant potential for accelerating the prediction of microstructure–property linkage in materials. While deep neural networks like convolution neural networks (CNNs) can extract physics information from 3D microstructure images, they often require a large network architecture and substantial training time. In this research, we trained a graph neural network (GNN) using phase field generated microstructures of Ni-Al alloys to predict the evolution of mechanical properties. We found that a single GNN is capable of accurately predicting the strengthening of Ni-Al alloys with microstructures of varying sizes and dimensions, which cannot otherwise be done with a CNN. Additionally, GNN requires significantly less GPU utilization than CNN and offers more interpretable explanation of predictions using saliency analysis as features are manually defined in the graph. We also utilize explainable artificial intelligence tool Bayesian Inference to determine the coefficients in the power law equation that governs coarsening of precipitates. Overall, our work demonstrates the ability of the GNN to accurately and efficiently extract relevant information from material microstructures without having restrictions on microstructure size or dimension and offers an interpretable explanation.

Chemistry↗
DOE OSTI2024

Detection and characterization of detached tidal dwarf galaxies

Tidal interactions between galaxies often give rise to tidal tails, which can harbor concentrations of stars and interstellar gas resembling dwarf galaxies. Some of these tidal dwarf galaxies (TDGs) have the potential to detach from their parent galaxies and become independent entities, but their long-term survival is uncertain. In this study, we conducted a search for detached TDGs associated with a sample of 39 interacting galaxy pairs in the local Universe using infrared, ultraviolet, and optical images. We employed IR colors and UV/optical/IR spectral energy distributions to identify potential interlopers, such as foreground stars or background quasars. Through spectroscopic observations using the Boller and Chivens spectrograph at San Pedro Mártir Observatory, we confirmed that six candidate TDGs are at the same redshift as their putative parent galaxy pairs. We identified and measured emission lines in the optical spectra and calculated nebular oxygen abundances, which range from log(O/H) = 8.10 ± 0.01 to 8.51 ± 0.02. We have serendipitously discovered an additional detached TDG candidate in Arp72 using available spectra from SDSS. Utilizing the photometric data and the CIGALE code for stellar population and dust emission fitting, we derived the stellar masses, stellar population ages, and stellar metallicities for these detached TDGs. Compared to standard mass-metallicity relations for dwarf galaxies, five of the seven candidates have higher than expected metallicities, confirming their tidal origins. One of the seven candidates remains unclear due to large uncertainties in metallicity, and another has stellar and nebular metallicities compatible with those of a preexisting dwarf galaxy. The latter object is relatively compact in the optical relative to its stellar mass, in contrast to the other candidate TDGs, which have large diameters for their stellar masses compared to most dwarf galaxies. The derived stellar population ages range from 100 Myr to 900 Myr, while the inferred stellar masses are between 2 × 10 6 M ⊙ and 8 × 10 7 M ⊙ . Four of the six TDGs are associated with the gas-rich M51-like pair Arp 72, one TDG is associated with a second M51-like pair Arp 86, and another is associated with Arp 65, an approximately equal mass pair. In spite of the relatively low stellar masses of these TDGs, they have survived for at least 100–900 Myrs, suggesting that they are stable and in dynamical equilibrium. We conclude that encounters with a relatively low-mass companion (1/10th–1/4th of the mass of the primary) can also produce long-lasting TDGs.

Astronomy & Astrophysics↗
DOE OSTI2026

A comparison of wrought and powder metallurgical FeCrAl claddings under simulated LWR accident transients

Iron-chromium-aluminum (FeCrAl) alloys are potential accident tolerant fuel (ATF) cladding candidates for light-water reactors but are difficult to fabricate as thin-walled tubes via conventional cast-and-wrought routes. Powder metallurgy (PM) offers a manufacturing alternative with improved compositional control, but its transient accident performance has not been directly benchmarked against wrought variants. This study evaluates the burst behavior of commercially developed PM-processed FeCrAl alloys, PM-C26M (Fe-12Cr-6Al-2Mo) and the high precipitate density FA-SMT (Fe-22Cr-5Al-3Mo), under simulated light-water reactor accident transient conditions. Burst testing was conducted using the Severe Accident Test Station with heating rates of 5 °C/s and 50 °C/s and internal pressures ranging from 25 MPa to 100 MPa. PM-C26M reproduced wrought C26M burst behavior within 7–37 °C across the stress range, indicating that PM processing does not compromise transient strength. FA-SMT exhibited markedly higher burst temperatures and reduced heating-rate sensitivity, consistent with its engineered precipitate strengthening. FA-SMT rupture exhibited axial "unzipping" rather than the lateral tearing characteristic of PM- and wrought C26M. Post-test EBSD and fractography indicate that this behavior is strongly correlated with strain-gated intergranular void nucleation associated with the dense precipitate architecture of FA-SMT, a response absent in the comparatively clean PM-C26M matrix and consistent with rupture morphologies reported for oxide-dispersion strengthened (ODS) FeCrAl of similar base-matrix chemistry to PM-C26M. These findings highlight the potential of powder metallurgy as a viable fabrication route for ATF claddings from an accident performance standpoint.

Bell, Sam [ORNL] (ORCID:0000000251905657)↗
DOE OSTI2024

Asymmetric fluctuations and self-folding of active interfaces

We study the structure and dynamics of the interface separating a passive fluid from a microtubule-based active fluid. Turbulent-like active flows power giant interfacial fluctuations, which exhibit pronounced asymmetry between regions of positive and negative curvature. Experiments, numerical simulations, and theoretical arguments reveal how the interface breaks up the spatial symmetry of the fundamental bend instability to generate local vortical flows that lead to asymmetric interface fluctuations. The magnitude of interface deformations increases with activity: In the high activity limit, the interface self-folds invaginating passive droplets and generating a foam-like phase, where active fluid is perforated with passive droplets. These results demonstrate how active stresses control the structure, dynamics, and break-up of soft, deformable, and reconfigurable liquid–liquid interfaces.

active fluid↗
DOE OSTI2026

Boron Coordination in Multicomponent Glasses: Analytical Models and Machine Learning With Uncertainty

Borosilicate glasses are extensively used in a variety of applications from kitchenware to nuclear waste immobilization due to the strong network formed by the Si-O-B bond that makes it resistant to chemical corrosion and gives it a low thermal expansion. Boron, however, exists in both trigonal BO3 and tetrahedral BO4 bonds in glass systems, which impacts the chemical durability and thermal resistance of the glass, amongst other properties. Boron coordination (N4), or the ratio of the amount of BO4 to BO3 within a glass, may aid in predicting these properties but is difficult to derive without experimental data due to the complexity of impacts from varied glass compositions and processing factors. For this reason, compositional models have been developed to predict boron coordination, but the models typically include a limited number of glass components. To help fill this gap in the models, in this work, a diverse multicomponent glass dataset of 809 glasses is compiled from a literature search, and then a number of analytical and machine learning (ML) models are trained on the dataset. Previously developed modified Bernstein and modified Du Stebbins analytical models were fitted to update parameters with the new dataset. Then, partially Bayesian neural networks, Gaussian process regressor, and heteroskedastic deterministic neural networks were evaluated. The ML models examined all have different strategies to overcome the potential for overfitting as a result of a limited training dataset, and return results that account for model uncertainty, which can be valuable for understanding model reliability. For the first time, cooling rate is introduced as an input parameter for ML models, showing consistent improvements in performance and solidifying the importance of including parameters outside of composition alone for N4 prediction. The machine learning models examined here show promise in accurate predictions of boron coordination in borosilicate glasses, all achieving R2 values of 0.91.

boron coordination↗
DOE OSTI2023

Self‐Propelling Macroscale Sheets Powered by Enzyme Pumps

Nanoscale enzymes anchored to surfaces act as chemical pumps by converting chemical energy released from enzymatic reactions into spontaneous fluid flow that propels entrained nano‐ and microparticles. Enzymatic pumps are biocompatible, highly selective, and display unique substrate specificity. Utilizing these pumps to trigger self‐propelled motion on the macroscale has, however, constituted a significant challenge and thus prevented their adaptation in macroscopic fluidic devices and soft robotics. Using experiments and simulations, we herein show that enzymatic pumps can drive centimeter‐scale polymer sheets along directed linear paths and rotational trajectories. In these studies, the sheets are confined to the air/water interface. With the addition of appropriate substrate, the asymmetric enzymatic coating on the sheets induces chemically driven, buoyancy flows that controllably propel the sheet's motion on the air/water interface. The directionality and speed of the motion can be tailored by changing the pattern of the enzymatic coating, type of enzyme, and nature and concentration of the substrate. This work highlights the utility of biocompatible enzymes for generating motion in macroscale fluidic devices and robotics and indicates their potential utility for in vivo applications.

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

Light-triggered electrochemical biosensor using singlet oxygen for self-powered operation and glucose detection

This study introduces a light-activated sensing strategy that integrates photosensitization with electrochemical detection. The sensor employs Eosin Y, a photosensitizer that generates singlet oxygen ( 1 O 2 ) via type II photosensitization. Immobilized within a thin polymer matrix on a carbon working electrode, Eosin Y produces 1 O 2 , under green light (520 nm) illumination, initiating a redox process that yields a measurable current. To incorporate biosensing capabilities and enable self-powered operation, this 1 O 2 – mediated process was coupled with glucose oxidase (GOx) to construct a fully operational glucose biosensor. The addition of glucose reverses the current flow by causing GOx to compete for electrons, with the resulting current magnitude correlating with glucose concentration providing a sensitive measure of glucose. The biosensor, as proof-of-principle, demonstrated excellent performance over a range of glucose concentrations (0–73 mM), achieving a detection limit (LOD) of 2.8 mM for steady state photocurrent under oxygen-saturated conditions. This platform leverages light and 1 O 2 as stimuli for tunable, on-demand signal control, offering a novel approach for adaptive, real-time biosensing technologies.

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

Biochemical properties of glycerol kinase from the hypersaline-adapted archaeon Haloferax volcanii

ABSTRACT Extremophilic microorganisms are promising candidates for industrial and analytical biocatalysis.Haloferax volcanii, a halophilic archaeon that prefers glycerol over glucose, channels this substrate into central metabolism through glycerol kinase (GK). Here, we report the biochemical properties ofH. volcaniiGK and its potential for biotechnological applications. An N-terminal His-tagged GK was functionalin vivoand yielded 3 mg/L culture—4.5 times more enzyme than a C-terminal StrepII-tagged version. Size exclusion chromatography revealed a glycerol-induced oligomeric shift from homodimer to a dimer-dominant state with detectable tetramer. The purified enzyme showed robust activity across broad pH and salinity ranges, with optimal activity at 100 mM NaCl and 50°C–60°C. It retained catalytic activity in 5%–10% dimethyl sulfoxide (DMSO) and crude glycerol containing methanol. His-GK was freeze-thaw stable and thermotolerant in 2 M NaCl buffers. In the absence of ligands, the enzyme’s melting temperature (T m ) was 80°C. Glycerol increased the T m to 85°C, and combinations with MgCl₂ (84°C) or ATP (88°C) provided further stabilization. The highest T m (89°C) occurred with all three ligands, suggesting a cumulative stabilizing effect. Kinetic analyses revealed positive cooperativity for glycerol, ATP, and Mg² + ; Mn² + and Co² + also supported the activity.H. volcaniiGK is the first known GK to exhibit positive cooperativity with glycerol and ATP. Its high stability and substrate flexibility support its use in biodiesel waste valorization,in vitrobiocatalysis, and biosensor development—applications demanding robust, specific, and stable enzymes. IMPORTANCE This study reveals thatH. volcaniiGK exhibits positive cooperativity for glycerol, ATP, and Mg² + , a kinetic feature not previously reported for glycerol kinases. This behavior enables steep, switch-like responses to small substrate changes, offering unique advantages for biosensor design. Importantly,H. volcaniiGK also maintains high activity under extreme salinity, temperature, broad pH, and solvent conditions that typically limit enzyme use in industrial and environmental applications. These traits make this GK an ideal candidate for enzyme-based biosensors, which often suffer from poor tolerance to pH, solvent, and thermal stress. Its robustness supports its use in cross-linked enzyme crystals, an immobilization method that enhances enzyme stability and reusability under harsh conditions. Moreover, GKs are already employed in Mg² + detection kits; however,H. volcaniiGK’s ability to tolerate and respond to diverse divalent cations (e.g., Co² + , Mn² + ) broadens their potential for pollutant detection and environmental monitoring. These features collectively positionH. volcaniiGK as a valuable biocatalyst for biosensing,in vitrodiagnostics, and biotechnological applications requiring both precision and durability.

Biotechnology & Applied Microbiology↗
DOE OSTI2026

Breaking the Energy Barrier of Heavy Metal Ion Diffusion in Micropores with Mesoporous 3D Graphene for Fast and Efficient Cu2+ Removal

Efficient removal of heavy metals from water critically depends not only on adsorption capacity but also on ion diffusion kinetics and the associated energy barriers. In conventional carbon adsorbents, severe diffusion confinement within micropores restricts ion transport, resulting in sluggish adsorption kinetics and large apparent activation energies despite high specific surface areas. Here, we demonstrate that this fundamental limitation is overcome by engineering meso/macroporous architectures in the 3D graphene materials synthesized via our discovered alkali-metal reactions with\\\\r\\\\n2\\\\r\\\\nCO. The unique 3D graphene materials possess defect-rich graphene frameworks with interconnected meso/macroporous networks, exhibiting simultaneously high surface area and greatly enhanced meso/macropore volume that enable efficient access to adsorption sites. As a result, the Cu2+ adsorption on 3D graphene proceeds with very low activation energies (4.98 kJ mol–1), which is almost 4 times smaller than on activated carbon (23.1 kJ mol–1). This finding offers a promising platform for efficient and sustainable water purification.

25 ENERGY STORAGE↗
DOE OSTI2024

Cation valency in water-in-salt electrolytes alters the short- and long-range structure of the electrical double layer

Highly concentrated aqueous electrolytes (termed water-in-salt electrolytes, WiSEs) at solid-liquid interfaces are ubiquitous in myriad applications including biological signaling, electrosynthesis, and energy storage. This interface, known as the electrical double layer (EDL), has a different structure in WiSEs than in dilute electrolytes. Here, we investigate how divalent salts [zinc bis(trifluoromethylsulfonyl)imide, Zn(TFSI) 2 ], as well as mixtures of mono- and divalent salts [lithium bis(trifluoromethylsulfonyl)imide (LiTFSI) mixed with Zn(TFSI) 2 ], affect the short- and long-range structure of the EDL under confinement using a multimodal combination of scattering, spectroscopy, and surface forces measurements. Raman spectroscopy of bulk electrolytes suggests that the cation is closely associated with the anion regardless of valency. Wide-angle X-ray scattering reveals that all bulk electrolytes form ion clusters; however, the clusters are suppressed with increasing concentration of the divalent ion. To probe the EDL under confinement, we use a Surface Forces Apparatus and demonstrate that the thickness of the adsorbed layer of ions at the interface grows with increasing divalent ion concentration. Multiple interfacial layers form following this adlayer; their thicknesses appear dependent on anion size, rather than cation. Importantly, all electrolytes exhibit very long electrostatic decay lengths that are insensitive to valency. It is likely that in the WiSE regime, electrostatic screening is mediated by the formation of ion clusters rather than individual well-solvated ions. This work contributes to understanding the structure and charge-neutralization mechanism in this class of electrolytes and the interfacial behavior of mixed-electrolyte systems encountered in electrochemistry and biology.

Science & Technology - Other Topics↗
DOE OSTI2025

Symmetry‐Breaking Strategy Yields Dopant‐Free Small Molecule Hole Transport Materials for Inorganic Perovskite Solar Cells with 20.58% Efficiency and Outstanding Stability

Abstract Inorganic perovskites are known for their excellent photothermal stability; however, the photothermal stability of all‐inorganic n‐i‐p perovskite solar cells (PSCs) is compromised due to ion diffusion and free radical‐induced degradation caused by the use of doped spiro‐OMeTAD hole transport materials (HTMs). In this study, two isomeric donor–acceptor–donor (D–A–D) type small molecules, namely HBT and HiBT, were developed and used as dopant‐free HTMs, using 2,1,3‐benzothiadiazole or benzo[d][1,2,3]thiadiazole as acceptor moieties. The HiBT molecule, with its symmetry‐breaking features, exhibits a large dipole moment, enhanced coordination‐active sites, and a well‐aligned energy level structure, all of which contribute to passivating perovskite surface defects and improving free charge separation. As a result, inorganic CsPbI 3 PSCs with HiBT HTM achieved an impressive power conversion efficiency (PCE) of 20.58%, the highest reported for dopant‐free HTM‐based inorganic PSCs. Moreover, the enhanced hydrophobic properties of HiBT molecules, coupled with their ability to passivate perovskite surface defects, contribute to significantly improved device stability. The unencapsulated devices based on HiBT HTM retained over 83% and 80% of their initial efficiency after being stored at 85 °C for 50 days and undergoing maximum power point (MPP) tracking at 85 °C for 1100 h, respectively. These results highlight that the symmetry‐breaking strategy is an exceptionally effective approach for designing efficient, dopant‐free small molecule HTMs, significantly contributing to both the high efficiency and enhanced stability of all‐inorganic PSCs.

Chemistry↗
DOE OSTI2024

Cross-scale interaction between microturbulence and meso-scale reversed shear Alfvén eigenmodes in DIII-D plasmas

Abstract This paper reports global nonlinear gyrokinetic simulations that couple meso-scale reversed shear Alfvén eigenmodes (RSAEs) driven by energetic particles (EPs) and ion temperature gradient (ITG) microturbulence driven by thermal plasma, using equilibrium and profiles from DIII-D discharge #159243. In simulations focusing only on the ITG, electrostatic ITG drives a huge thermal ion heat transport, which is reduced by a factor of 10 to a level close to the experimental value in electromagnetic simulation due to finite β effect. In the simulations coupling the RSAE and ITG, ITG can scatter the resonant EP nonlinearly trapped by the RSAE and damp the zonal flows generated by the RSAE. The regulation of the RSAE by the ITG greatly reduces the initial saturation amplitude of the RSAE but increases the RSAE amplitude and associated EP transport to experimental levels in the quasi-steady state. The RSAE effects on the ITG, specifically the stronger zonal flows generated by the RSAE and the RSAE frequency modulation of the ITG-induced thermal ion heat transport, in turn, leads to a reduction of the thermal ion heat transport by more than a factor of 2 . For a stronger background ITG, the regulation of the RSAE by the ITG is stronger, while the RSAE effects on the ITG are weaker. This work highlights the importance of cross-scale coupling in the dynamics of the AE turbulence and EP transport.

Physics↗
DOE OSTI2024

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

Computer Science↗
DOE OSTI2026

Joint scheduling of energy, fast and primary frequency response reserves in integrated transmission–distribution networks

Inverter-based distributed energy resources (DERs) connected to distribution networks (DNs) can provide fast frequency support, but their reserve deliverability depends on feeder constraints and differs from synchronous primary frequency response (PFR). Existing transmission–distribution coordination studies usually treat reserve generically or neglect feeder-level feasibility, while frequency-security scheduling studies rarely represent distribution feeders explicitly. This paper develops a bi-level day-ahead scheduling framework for integrated transmission–distribution networks that jointly clears energy, transmission-side PFR, and distribution-side fast frequency response (FFR) under exogenous hourly inertia and largest-loss inputs from an external unit commitment (UC) schedule. The transmission problem is modeled with DC-optimal power flow (OPF) and closed-form second-order cone (SOC) frequency-security constraints, whereas each DN is represented by a reserve-aware branch-flow AC-OPF so that scheduled fast reserves remain deliverable during activation. The bi-level problem is reformulated through Karush–Kuhn–Tucker (KKT) conditions into a mixed-integer SOC program, and a penalty term is used to tighten the distribution-network relaxation. In the reduced test system, lower exogenous inertia increased the required primary response from 179.64 MW to 191.08 MW, distribution-side fast response reduced total frequency-response procurement by up to 4.9%, and neglecting distribution constraints overstated the combined distribution-side energy and reserve award by up to 18%. In the expanded study, the largest case was solved in 2.02 s with a 0.00% optimality gap. Time-domain simulations kept the frequency nadir above 59.0 Hz in all tested hours. These results demonstrate the value of fast-response modeling and distribution-feasible reserve delivery in coordinated market clearing.

Noh, Seung-Gil↗
DOE OSTI2023

Optimizing Renewable Ammonia Production for a Sustainable Fertilizer Supply Chain Transition

Local renewable ammonia production using electrolytic hydrogen is an emerging approach to alleviate emissions attributed to synthetic nitrogen fertilizer production while also insulating against fluctuations in fertilizer prices and mitigating transportation costs and emissions. However, replacing ammonia currently produced using fossil fuels will not be immediate. To this end, we develop a supply chain transition model, which first optimizes the design and hourly operation of new renewable ammonia facilities to minimize production costs and then optimizes the annual installation timing, production scale, and location of these new renewable facilities along with ammonia transportation to meet county resolution demands. The objective is to augment and eventually replace conventional ammonia market imports in an economically competitive manner. We performed a case study for Minnesota's ammonia supply chain and found that a full transition to in-state renewable production by 2032 is optimal. This is incentivized by the U.S. federal government's clean hydrogen production credits. This transition results in 99 % reduction in carbon intensity along with stable supply costs below $475 per metric tonne. New renewable production facilities are an order of magnitude smaller than existing conventional plants. They use both wind and solar resources and operate dynamically to minimize expensive battery and hydrogen storage capacities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

FY26 Progress on Demonstration of a Multiphysics Steady State Capability for Modeling Core Radial Expansion in SFRs

Under the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program, an integrated multiphysics approach is being developed to model the core bowing phenomena important to liquid metal-cooled fast reactors. Core bowing is an important passive safety mechanism in liquid metal-cooled fast reactors and involves multiphysics effects including radiation transport, fluid flow, heat transfer, and mechanical response to temperature and flux gradients. This report summarizes recent progress on developing a multiphysics, MOOSE-based workflow to predict core bowing and associated reactivity feedback. Significant new capabilities in the reactor physics code Griffin - sodium backfill and pin power reconstruction for deformed geometries - were applied in this effort. This year’s work included verification, code comparisons, sensitivity studies, and coupled demonstrations that advance the state of MOOSE-based core bowing workflow. Griffin’s sodium backfill capability was verified by demonstrating that its automated treatment of geometry expansion and material-density updates reproduces manual calculations exactly, confirming solid mass conservation and proper coolant backfilling in expanded geometries. Reconstructed pin powers were compared for Griffin’s ductheterogeneous and ring-heterogeneous treatments in single-, seven-, and nineteen-assembly cases, with best agreement observed in lower-leakage configurations and the duct-heterogeneous approach offering substantially lower computational cost. Thermal-hydraulic sensitivity sensitivities showed that MOOSE SCM, SAM, and CFD are expected to produce similar deformation predictions despite variances in their temperature predictions, and that explicit treatment of inter-assembly flow becomes increasingly important as gap flow rate increases. Finally, coupled demonstrations on small multi-assembly configurations using Griffin, MOOSE Solid Mechanics, MOOSE SCM, and Heat Conduction produced physically consistent reactivity feedback from thermal expansion and bowing. The coupled demonstrations simulated grid plate expansion as well as resultant core bowing at full power conditions. Simplifications were made in current workflow, namely the assumption of instantaneous full power conditions following hot zero power, and pre-expanding the Griffin geometry axially due to lack of an axial fuel pin expansion model and temperature feedback to Griffin.

Wozniak, Nicholas↗