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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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The goal of the proposed research is to establish a computational and experimental platform that will allow reliable and efficient power and structural performance evaluation of FOWT. A secondary goal of the research is to establish an educational FOWT setup to promote renewable energy research among undergraduate and high-school students.
This project, funded by the US Department of Energy’s Solar Energy Technologies Office (SETO), focused on the operation of microgrids as a coordinated network. The primary objective, which was successfully achieved, was to develop both control strategies and hardware solutions to support the resilient and efficient operation of networked microgrids with high solar penetration. The work was structured around the following four main tasks: • Development of distributed and scalable optimization algorithms for AC-coupled networked microgrids. • Design and implementation of a novel DC interconnection hardware to enable precise power exchange between microgrids. • Laboratory operational validation of the developed technologies using 480 V testbeds and commercially available hardware. • Field operational validation of the complete solution in Adjuntas, Puerto Rico, interconnecting two kW-scale, split-phase microgrids of Casa Pueblo’s microgrids. This project addressed multiple technical challenges across the domains of optimization, control, hardware interconnection, and protection. One of its key contributions was delivering tangible, real-world solutions for networking microgrids. In contrast to purely theoretical or simulation-based work, this project included full-scale hardware operational validation both in the lab and in the field. The work conducted as part of this project—in collaboration with the University of Puerto Rico; the University of Tennessee, Knoxville; the University of Central Florida; and Casa Pueblo—has advanced the state of the art in networked microgrids. Key contributions include the development of distributed control strategies, practical solutions for real-world implementation challenges, and the introduction of a novel DC interlink approach for microgrid interconnection. The project featured both laboratory and field validation using commercial off-the-shelf components. The field deployment successfully validated that a group of microgrids can operate in a coordinated manner, enabling precise power flow between systems and mutual support during extreme events. This project resulted in 15 journal publications and 15 conference papers; 5 graduate students and 15 undergraduate students were supported. The codes of distributed optimization and forecasting were made open-source through OSTI.gov for distributed optimization and forecasting. All the publications are available in the ORNL-hosted project landing page. The DC interlink with state-of-charge balancing control was operationally validated in Adjuntas by interconnecting two real-world, 240 V split-phase microgrids. To the best knowledge of the team, this represents the first operational validation of AC microgrids interconnected via DC-interlinks. As a culmination of this project, a follow-on grant was awarded to support the technology transfer of the distributed optimization framework to a commercial microgrid controller, Stellar Edge, developed by the California-based company New Sun Road.
The primary goal of this proposal was to measure and quantify how vibronic ligand-exciton interactions at the inorganic-organic interface of semiconductor nanocrystals (NCs) govern femtosecond non-radiative relaxation and charge delocalization mechanisms as a function of nanocrystal and ligand structures – including NC size, shape, and ligand identity. Molecular-scale chemical and physical properties and interactions of surface ligands dictate nanocrystal exciton dynamics on the femto-to-microsecond timescales. Improvements in synthesis, experimental, and theoretical characterizations have enhanced our understanding of NC photophysics. However, quantitative descriptions of complex ligand-exciton interactions at the NC surface have remained elusive, which limits elucidation of relaxation mechanisms that determine overall carrier transfer dynamics at the exciton-ligand interface. Thus, the central motivation for this proposal was to fill a significant knowledge gap for judicious selection of ligand-functionalized nanocrystals for targeted isolation and capture of charge carriers to drive selective organic photocatalytic transformations in NC photocatalysts and NC-assembled photonic materials. Our unique two-pronged experimental approach combining nanocrystal synthesis and ligand-specific nonlinear spectroscopy has resulted in (i) four peer-reviewed publications; (ii) 12+ invited and contributed posters and talks at national and international scientific meetings and seminars, (iii) two completed PhD theses, (iv) training of two postdoctoral researchers, three PhD students, and two undergraduate students, and (v) two manuscripts under preparation.
The search for new fundamental particles is one of the defining goals of the Large Hadron Collider (LHC). The discovery of the Higgs Boson by the ATLAS and CMS collaborations provided the capstone of the Standard Model of particle physics, but outstanding questions remain. Why does the Higgs boson have a mass of 125 GeV when its natural mass would be many orders of magnitude larger? Is there a universal symmetry which unites all three forces described by the Standard Model? Can that symmetry be extended to include gravity? Is dark matter, evidenced by astronomical observations, made of a particle that interacts via Standard Model forces with the rest of matter? Together, these motivations provide compelling arguments that new physical processes await discovery. This project addressed some outstanding questions about the fundamental particles and their interactions with the ATLAS experiment at the Large Hadron Collider. In particular, the project improved the discovery potential for new, long- lived particles produced via electroweak processes in proton-proton collisions and set world-leading limits on their existence for certain values of their potential mass and lifetime. To achieve this, the project developed new data analysis methods, developed new triggers to select events with new long-lived particles during data-taking of the ATLAS experiment, and analyzed the largest proton–proton collision dataset ever produced. The project also supported significant development of the data acquisition software for the upgrade to the ATLAS inner detector, the Inner TracKer (ITk). The upgrade of the ATLAS inner detector is essential to the success of the entire Phase II physics program on ATLAS. Personnel supported by the project provided support for integration, assembly, and testing of the inner two layers of the ITk pixel system during its prototype and pre-production phase. Four PhD students and two post-doctoral scholars were supported by the grant and received invaluable scientific training as part of the research endeavor. The students and postdocs gained essential professional skills in the areas of advanced data analysis techniques, statistical analysis of data and simulation, programming in C++ and Python, hardware and instrumentation development, and presentation and collaboration skills. Additionally, approximately ten undergraduate students supported through other funding sources participated in research activities synergistic with the goals of this project, receiving essential mentorship from the personnel supported by this project.
This work reports on the investigations done during FY 25 in collaboration with one 2025 Summer undergraduate intern through the DOE SULI program. These efforts were partially funded by the Nuclear Criticality Safety Program through the Technical Support Succession Plan task. This project, developed by Matthew Molnar, aimed to apply the BNL’s machine-learning code to correct spin mis-assignments in neutron resonances, the Bayesian Resonance Reclassifier (BRR), to the odd-even nucleus 143 Nd. For this, the handling of spingroup classification had to be further investigated and improved.
This award offset the registration and travel costs for researchers from U.S. institutions to attend the 32 nd Inter-American Photochemical Society meeting held on Jan 2-5, 2025, in Miramar Beach, Florida. The Inter-American Photochemical Society (I-APS) is a non-profit organization dedicated to promoting and disseminating knowledge and encouraging the development of fundamental and allied photochemistry throughout the Americas. The I-APS annual meeting is a particularly important tool for fulfilling this goal as it provides a welcoming venue to highlight research progress in theoretical, experimental, and applied aspects of photochemistry and molecular photophysics by researchers in all stages of their careers. The participation of graduate students and postdocs at this conference is particularly important as it aids in training the next generation of scientists in photochemistry. This award provided travel awards to three Assistant Professors and fourteen graduate and two undergraduate students to attend the I-APS Meeting in 2025. A majority of the science presented/discussed at the I-APS meeting is in direct alignment with the mission of the DOE and the Solar Photochemistry program, including studying fundamental phenomena, encompassing light absorption, photoinduced energy/electron transfer, charge separation, and charge and energy transport across mesoscale dimensions, as well as their application in generating electricity and chemical bonds using light.
This project developed a cutting-edge 5G-integrated edge computing framework to enhance operational efficiency and reliability in coal-fired power plants through real-time component monitoring and anomaly detection. The initiative focused on leveraging distributed machine learning, federated learning, and 5G-based dynamic network slicing to support scalable, fault-tolerant monitoring environments to meet the operational requirements in industrial control systems. With a Distributed Edge Computing Service (DECS) orchestration, this project enabled federated learning at edge for condition monitoring and introduced adaptive client selection strategies to minimize communication overhead. Scalable distributed training was achieved using the Horovod framework, thus enhancing performance across edge nodes. In the realm of 5G networking, the project designed and deployed reconfigurable, QoS-aware network slicing tailored for operational technology (OT) environments, integrating software-defined networks to bolster cyber-resilience and enabling dynamic slicing for federated learning workloads. A significant milestone was the development of a virtualized ICS environment with 5G core integration—which allowed elastic and fault tolerant distributed training on real-world datasets such as NASA Bearings, Hydraulic Systems, and TEP. To broaden the impact of the project, a TRL-3 virtualized ICS testbed for research and education was designed. This project engaged several graduate and undergraduate students to conduct research on the cutting-edge technology, and it resulted in one PhD dissertation, one MS thesis, and over 14 peer-reviewed publications. With the support of this project students also participated in national cybersecurity competitions to improve their professional development skills.
Liquid centrifugation is an emerging method to separate isotopes. By spinning a liquid containing target isotopes (e.g., pure chemical or solution), the heavier isotope will be enriched at the outer part of a centrifuge, and the lighter ones will be enriched at the inner part of a centrifuge. The separation capability is positively correlated with rotation speed, outer radius and mass difference between isotopes to separate. This award targets to understand whether liquid centrifugation is effective in separating isotopes important for nuclear fission technologies. The results include the following three sections: 1) Effects of solute concentration on isotope separation; 2) Preliminary progress of building a low-speed countercurrent centrifuge, and 3) Analysis on possible candidates for 7 Li and 37 Cl. The award trained one research scientist, one Ph.D. student, one master student, and one undergraduate on liquid centrifugation and isotope measurements.
This report pertains to the grant titled “Research Traineeship for Diversity in Nuclear Physics at Yale Wright Laboratory.” The award aimed to train and retain a diverse group of future scientists, including underrepresented, first-generation, and low-income undergraduates, as prospective nuclear physicists and leaders in science.
This project investigated how plasmas interact with energetic particles and solid materials under extreme conditions relevant to fusion energy, space plasmas, and planetary environments. Using experiments on the DIII-D National Fusion Facility, the research first examined how high-energy electrons move, become trapped, and are released in plasmas containing magnetic islands—structures commonly found in fusion reactors and Earth’s magnetosphere—providing new insight into particle transport and acceleration processes. The project also explored plasma-driven chemical reactions that can occur during meteoroid entry into planetary atmospheres, demonstrating that simple molecules such as ammonia can be produced and survive in high-temperature plasma conditions. Together, these results improve understanding of plasma behavior across laboratory, space, and planetary systems while informing fusion plasma control and plasma–material interaction studies. The project additionally contributed to workforce development by training graduate students, undergraduates, and early-career researchers and by disseminating results through peer-reviewed publications and international scientific conferences.
This report summarizes the technical accomplishments of the four-year research project “Multiphysics and Multiscale Simulation Methods for Electromagnetic Energy Assisted Fossil Fuel to Hydrogen Conversion” (Award No. DE-FE0032092), conducted at Howard University and the University of Houston (subawardee) from September 2021 to August 2025. The project successfully achieved all four major objectives: 1. 3D Structural Characterization – Developed 3D optical imaging and mechanical sectioning methods to characterize catalyst distribution and support morphology in nickel foam substrates. Successfully reconstructed 3D geometries and imported them into COMSOL Multiphysics for electromagnetic simulations. 2. EM Hotspot Simulation – Created all-frequency stable electromagnetic formulations and 3D nodal discontinuous Galerkin (NDG) methods for coupled electromagnetic-thermal-fluid problems in multiscale catalytic media. Demonstrated stable solutions from DC to microwave frequencies. 3. Multiphysics Coupling – Developed multiscale simulation methods coupling FEM electromagnetic solvers with thermal transport equations. Reactive molecular dynamics (ReaxFF MD) simulations were performed to investigate catalytic reaction mechanisms at the atomistic level. Demonstrated electromagnetic-thermal co-simulation capabilities for porous catalyst structures. 4. System Optimization – Designed and optimized EM-assisted catalytic systems using nickel foam and carbon foam structures, demonstrating significant temperature increases due to microwave heating. Observed and characterized plasma generation in carbon fiber catalysts. Investigated multiple reaction chamber geometries for improved microwave energy deposition. The project produced significant scientific contributions including 15+ peer-reviewed publications, trained multiple Ph.D. students and undergraduate researchers, and advanced the understanding of microwave-assisted hydrogen production from fossil fuels.
This is a Science Undergraduate Laboratory Internship (SULI) report. This report is submitted to the DOE as a summary of the work that I have completed over the course of my internship.
Benchmarks are a cornerstone of modern machine learning practice, providing standardized eval- uations that enable reproducibility, comparison, and scientific progress. Yet, as AI systems particularly deep learning models become increasingly dynamic, traditional static benchmarking approaches are losing their relevance. Models rapidly evolve in architecture, scale, and capability; datasets shift; and deployment contexts continuously change, creating a moving target for evaluation. Without adaptive benchmarking frame- works, both scientific assessment and real-world de- ployment risk becoming misaligned with actual system behavior. Drawing on our experience from MLCommons, educa- tional initiatives, and government programs such as the DOE s Million Parameter Consortium, we identify key barriers that hinder the broader adoption and utility of benchmarking in AI. These include substantial resource demands, limited access to specialized hardware, lack of expertise in benchmark design, and uncertainty among practitioners about how to relate benchmark results to their own application domains. Moreover, current benchmarks often emphasize peak performance on leadership-class hardware, offering limited guidance for more diverse, real-world deployment scenarios. We argue that benchmarking itself must become dy- namic in order to incorporate evolving models, updated data, and heterogeneous computational platforms while maintaining transparency, reproducibility, and inter- pretability. Democratizing this process requires not only technical innovation, but also systematic educational efforts spanning undergraduate to professional levels to develop sustained expertise in benchmark design and use. Finally, benchmarks should be framed and com- municated to support application-relevant comparisons, enabling both developers and users to make informed, context-sensitive decisions. Advancing dynamic and inclusive benchmarking practices will be essential to ensure that evaluation keeps pace with the evolving AI landscape and supports responsible, reproducible, and accessible AI deployment.
This project developed a scientist-in-the-loop data analytics framework for intelligent simulation model tuning and validation, targeting the Weather Research and Forecasting (WRF) model and its solar energy variant, WRF-Solar-BNL. Domain experts, such as climate scientists, depend on large-scale numerical simulations for knowledge discovery and decision-making, yet the complexity of parameter tuning and the disconnect between automated optimization and domain expertise pose significant challenges. We extended an interactive visual analytics framework that enables domain experts to observe and intervene in the computational steering process by identifying disagreements between the simulation model, surrogate model, and the expert’s domain knowledge. Using Bayesian Optimization with Gaussian Process Regression as the surrogate model, our system allows users to probe parameter relationships, analyze correlation patterns, and adjust tuning parameters in real time. We developed use cases for solar irradiance forecasting through sustained collaboration with Brookhaven National Laboratory, resolving critical model configuration challenges and achieving meaningful reductions in prediction error. The project supported one PhD student, one MS student, and eight undergraduate students across three Data Science Capstone projects, resulting in one master’s thesis.
The author spent nine weeks over summer 2025 working on the tracker electronics installation team for the Mu2e experiment. One of her main responsibilities was repairing high voltage (HV) and calibration (Cal) pre-amplifiers (pre-amps). During installation, the fragile wires connecting the two sockets to the pre-amp board must be bent, often leading to breakage. During production, the sockets and wires were initially soldered to the board at UC Berkely, then the whole pre-amp was coated in parylene before transport to Fermilab. The interns were able to expedite the repairs, and thus whole installation process, by using an alternative method on-site with epoxy. Another task they were responsible for, not included in the original project specifications, was attaching copper clips to specific vias on the Cals to reduce noise. The talk will give listeners insight into the daily problem-solving required by the novel technologies in the Mu2e project. The author would like to acknowledge her fellow Monmouth College undergraduate interns, Lizzie Durfee and Gianna Maughan, advisor and PI of the DOE RENEW Grant Dr. Christopher G. Fasano, and the Mu2e team lead by co-spokesperson Dr. Bob Bernstein and tracker L2 manager Dr. Brendan Kiburg.
We have provided training to students in skill sets relevant to job placement in areas of national needs. These trainings are tuned towards placing graduating students in the workforce related to sciences at national laboratories, national security, information systems, and data sciences. The support for undergraduate students will address the academic retention shortfalls by providing opportunities in mentored research experiences.
As part of a DOE RENEW Grant, the undergraduate authors interned at Fermilab. They spent nine weeks over summer 2025 working on the tracker team for the Mu2e experiment. They gained practical experience in a multitude of new skills including electronics installation, testing, and repairs. Their work particularly increased productivity by helping repair the high voltage and calibration pre-amplifiers, over 20,000 of which are needed in the tracker. These components are very fragile and often break during the installation process. The experience also taught them several soft skills such as the importance of thoughtful data storage, record- keeping, and problem-solving skills. They learned the demands of a large, international collaboration and how to work in a team. The authors would like to acknowledge their advisor and PI of the grant, Dr. Christopher G. Fasano, and the Mu2e team lead by co- spokesperson Dr. Bob Bernstein and tracker L2 manager Dr. Brendan Kiburg.
This final technical report summarizes the activities and accomplishments in the period from February 2023 thru January 2026. The objective of the proposed research is to investigate the physical mechanisms and processes underlying the formation of structures and patterns in systems with plasma-surface interactions. In the past decades, there have been extensive studies on the interaction of glow discharges, dielectric barrier discharges, and arc discharges with confining or intervening surfaces. The advancement of the understanding of these phenomena is not only of fundamental scientific interest and relevance to the knowledge of the plasma state, but also with profound implications in various technological applications. The research will integrate theoretical, computational, and experimental work within an innovative framework of data assimilation, i.e., optimally combining model predictions with measurements. The scientific merit of this research has three aspects. Firstly, it extends the studies of plasma-surface interactions to systems with insulator surfaces and multi-layer systems, while existing studies are predominantly on electrode surfaces. Secondly, it expects to develop a novel data-driven modeling approach based on data assimilation to enhance the predictive and control capabilities, which could make transformative contributions to basic plasma research. Thirdly, it will shed new light on outstanding problems related to formation of patterns interfacing plasmas. This project also aims to launch an education and outreach initiative at Texas A&M University-Kingsville, a non-R1, minority-serving institution in South Texas. The initiative is structured as a four-tier pyramid. Tier one will be a webinar series for culture and capacity building to inform broader audience in the region about the research fields of plasma science and engineering. Tier two will be the creation and offering of an upper-level undergraduate course on introductory plasma physics, which will help with the recruitment for the upper tiers. On tier three, we will engage and mentor senior design students to conduct work toward the research goal of this project. There will also be a certificate program on general plasma science for undergrad and graduate students, part of which will be lab training at Princeton University. Tier four will be the supervision and mentoring of Ph.D. students. Therefore, this project will systematically expand the talent pipeline, broaden participation from communities historically and geographically underrepresented in DOE SC research portfolio, significantly improve the research and education capacity at the PI’s institution, and contribute to developing a diverse workforce in plasma science and engineering.