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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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At least 253 records · Page 14

Degradation science: Integrating modeling and experiments to predict localized corrosion processes (Annual Progress Report)

Additively manufactured (AM) eutectic high-entropy alloys (EHEAs), such as nano-lamellar AlCoCrFeNi 2.1 , have excellent strength, ductility, and wear resistance even at elevated temperatures, but their corrosion behavior in aggressive acids at different length scales remain poorly understood. This work investigates the corrosion behavior of laser powder bed–fused (L-PBF) AlCoCrFeNi 2.1 as a function of annealing temperatures, probing degradation mechanisms from nanoscopic to macroscopic length scales. The alloy is dual phase consisting of a ductile FCC L1 2 phase and a high-strength BCC B2 phase. Rapid solidification during L-PBF produces a far-from-equilibrium nano-lamellar structure with nearly homogeneous elemental distribution, which tends to evolve upon annealing toward Cr/Co/Fe-enriched FCC and Al/Ni-enriched B2. Three conditions were studied: as-printed, 600 °C/5 h, and 1000 °C/1 h, over which B2 lamellae coarsen, lamellar spacing increases, and elemental segregation becomes more prominent. Microstructure and chemistry were characterized by scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS), while in-situ electrochemical atomic force microscopy (EC-AFM) was used to link early (<5 h) local dissolution to microstructure after exposure in sulfuric acid. EC-AFM highlights preferential dissolution of the BCC/B2 phase where the surrounding matrix is Cr-depleted and directly quantifies the dissolution rates within individual phases, tracks the transition from early nano-scale attack to partial repassivation, to correlate height differences with current and impedance responses. To monitor longer-term behavior (up to 96 h), ex-situ AFM, SEM, and confocal imaging were combined with conventional bulk electrochemical tests, bridging nanoscale observations to micro/meso-scale damage morphologies. At the meso-scale, the deepest dissolution channels align with the build-direction lamellae and melt-pool boundaries, indicating that printing directionality guides the propagation of these localized corrosion sites. Annealing modifies corrosion by restructuring BCC/FCC phase fractions, lamellar spacing, and Cr/Al segregation, thereby changing the cathode/anode ratio and passive film stability. The results clarify how as-printed and annealed nano-lamellar architectures differ in their susceptibility to selective dissolution; how elemental segregation competes with residual stresses along the build direction. With these insights, future work will use CALPHAD-guided alloy modification to stabilize higher Cr contents in the B2 phase while retaining a dominant FCC+B2/BCC microstructure, with the goal of designing mechanically robust, corrosion-resistant EHEAs for safety-critical applications to leverage the LLNL’s broader national and global security mission.

36 MATERIALS SCIENCE↗

Design Progress for the 22 GeV CEBAF Energy Upgrade

Extending the energy reach of CEBAF up to 22 GeV within the existing tunnel is being explored. Proposed energy upgrade can be achieved by increasing the number of recirculations, while using the existing CEBAF SRF cavity system. A proposal was formulated to raise CEBAF energy by replacing the highest-energy arcs with FFA arcs. The new pair of arcs configured with FFA (Fixed Field Alternating Gradient) lattice would support simultaneous transport of additional 6 passes with energies spanning a factor of two, using the non-scaling FFA principle implemented with Halbach-derived permanent magnets - a novel magnet technology that significantly saves energy and lowers operating costs. The design involves new optics for the linacs and remaining electromagnetic arcs, as well as new electromagnetic switchyard. These feed into the permanent magnet FFA arcs. We also report on ongoing beam dynamics studies and mitigating measures to alleviate energy loss and emittance dilution due to synchrotron radiation.

Bogacz, Alex [Thomas Jefferson National Accelerato↗

Quarterly Research Performance Progress Report

The goal of this project is to develop a high temperature, high-pressure well integrity evaluation device that can operate in enhanced geothermal system (EGS) boreholes without active cooling or substantial mitigation of borehole conditions. The product must provide consistent high resolution data regarding the efficacy of wellbore casing and cement for long-term operation. This project aims to deliver a ruggedized tool that can withstand a corrosive geothermal environment with pressure ranges up to 15,000 psi and temperatures up to 250°C for a 24-hour operating period.

15 GEOTHERMAL ENERGY↗

Progress toward double-differential cross-section measurements of single charged-pion production in charged current muon neutrino interactions on argon with SBND

Resonant neutrino–nucleus processes constitute a significant portion of neutrino interactions in the few-GeV energy region. Charged-current interactions with a muon and one single charged pion in the final state (CC1$\pi$) are primarily sensitive to resonant processes, while also receiving contributions from non-resonant processes and being strongly affected by nuclear effects and final-state interactions. A precise understanding of this channel is essential for improving neutrino interaction models and reducing systematic uncertainties in oscillation measurements. The Short-Baseline Near Detector (SBND) at Fermilab has collected the largest neutrino–argon dataset to date, providing an excellent opportunity to study CC1$\pi$ interactions on argon. This analysis aims to improve existing single-differential cross-section measurements by reporting results in a variety of kinematic variables and to perform the first double-differential CC1$\pi$ cross-section measurement on argon, using novel techniques for pion energy reconstruction.

Pelegrina-Gutiérrez, Luis [Granada U.] (ORCID:0000↗

Progress toward differential cross-section measurements of coherent charged-pion production in charged-current muon neutrino interactions on argon with SBND

Coherent charged pion production in charged-current muon neutrino–nucleus interactions provides a clean experimental signature and enables an excellent estimate of the neutrino energy, making it particularly valuable for neutrino flux constraints and oscillation analyses in future long-baseline experiments. Despite its importance, theoretical modeling of this process remains challenging, especially in the low-energy region below 1 GeV and its dependence on the nuclear target. The Short-Baseline Near Detector (SBND) offers a unique opportunity to study coherent charged pion production using neutrinos in the few-hundred-MeV energy range from the Booster Neutrino Beam (BNB) interacting with an argon target (A = 40), with unprecedented statistics expected to yield approximately three thousand signal events in SBND’s BNB data with 3.5 x 10^20 protons on target. This analysis aims to achieve the first differential cross-section measurement of charged-current coherent charged pion production on argon, as well as the first measurement of this process using BNB neutrinos.

Oh, Sungbin [Fermilab]↗

Progress Towards Synthesis of Uranium Chloride Fuel Salts Using Zinc Chloride

Reliable, scalable methods for producing high-purity actinide chloride salts are needed to support molten salt reactor fuel development and deployment. This report describes the continued development and demonstration of a bench-scale chlorination and purification apparatus using a zinc chloride-based method for synthesizing uranium chloride fuel salts. In this approach, uranium metal is chlorinated by ZnCl2 to produce LiCl-KCl-UCl3. Reaction with three aliquots of added uranium metal was used to generate a target uranium concentration of 30 wt %. While this concentration was chosen for initial testing of the apparatus and method, the final uranium concentration is not limited to 30 wt %. The zinc metal generated in the reaction forms an immiscible layer that was removed by volatilization at moderately high temperatures. Electrochemical measurements confirmed the removal of zinc and applied sensing methods indicated the uranium concentration to be approximately 25 wt %. These initial results demonstrate that the bench-scale chlorination apparatus is an effective platform for the synthesis and purification of uranium chloride salts using ZnCl2. This method shows promise for application to industry-relevant salt systems such as NaCl-UCl3. Further development is recommended to optimize reagent loading, zinc removal, and avoid possible U-Zn alloy formation.

Dulovic, Stephanie↗

Charting the state of GEMs in microalgae: progress, challenges, and innovations

Genome-scale metabolic models (GEMs) provide a systems-level framework for understanding and engineering microalgal metabolism. This review explores the evolution of GEMs in microalgae, highlighting advances in light modeling, automation, and multi-omics integration. Special emphasis is placed on Chlamydomonas reinhardtii as a model species. Limitations of current models, particularly for microalgae, are discussed, alongside promising developments in dynamic modeling and machine learning. Together, these innovations chart a path toward more predictive, adaptable GEMs that can accelerate biotechnological applications of microalgae in sustainable production systems.

Plant Sciences↗

Progress of Gas Injection EOR Surveillance in the Bakken Unconventional Play—Technical Review and Machine Learning Study

Although considerable laboratory and modeling activities were performed to investigate the enhanced oil recovery (EOR) mechanisms and potential in unconventional reservoirs, only limited research has been reported to investigate actual EOR implementations and their surveillance in fields. Eleven EOR pilot tests that used CO2, rich gas, surfactant, water, etc., have been conducted in the Bakken unconventional play since 2008. Gas injection was involved in eight of these pilots with huff ‘n’ puff, flooding, and injectivity operations. Surveillance data, including daily production/injection rates, bottomhole injection pressure, gas composition, well logs, and tracer testing, were collected from these tests to generate time-series plots or analytics that can inform operators of downhole conditions. A technical review showed that pressure buildup, conformance issues, and timely gas breakthrough detection were some of the main challenges because of the interconnected fractures between injection and offset wells. The latest operation of co-injecting gas, water, and surfactant through the same injection well showed that these challenges could be mitigated by careful EOR design and continuous reservoir monitoring. Reservoir simulation and machine learning were then conducted for operators to rapidly predict EOR performance and take control actions to improve EOR outcomes in unconventional reservoirs.

Energy & Fuels↗

Recent Progress on Surface Water Quality Models Utilizing Machine Learning Techniques

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.

machine learning (ML)↗