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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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Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) has created the Task 46 to undertake cooperative research in the key topic of blade erosion. Participants in the task are given in Table 1.
Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) has created the Task 46 to undertake cooperative research in the key topic of blade erosion. Participants in the task are given in Table 1.
Sierra/SD provides a massively parallel implementation of structural dynamics finite element analysis, required for high fidelity, validated models used in modal, vibration, static and shock analysis of structural systems. This manual describes the theory behind many of the constructs in Sierra/SD. For a more detailed description of how to use Sierra/SD, we refer the reader to User’s Manual. Many of the constructs in Sierra/SD are pulled directly from published material. Where possible, these materials are referenced herein. However, certain functions in Sierra/SD are specific to our implementation. We try to be far more complete in those areas. The theory manual was developed from several sources including general notes, a programmer_notes manual, the user’s notes and of course the material in the open literature.
This project focused on developing cell-free systems to directly express multi-enzyme catalysts and perform CO2 bioconversions for industrial chemical production. The use of cell-free expression (CFE) systems derived from bacterial lysates is emerging as a promising approach for biomanufacturing. CFEs are genetically programmable, permit the expression of toxic enzymes, and allow for rapid prototyping of metabolic pathways. Research Contributions: 1. Understanding the Area Investigated: This research advances the understanding of cell-free systems by demonstrating their capability to perform complex multi-enzyme reactions. By directly expressing multi-gene systems, CFEs avoid the high costs and inefficiencies associated with producing and purifying enzymes for multi-step pathways. 2. Technical Effectiveness and Economic Feasibility: The project successfully engineered a CFE-based multienzyme biocatalyst for the de novo synthesis of serine and glycine from CO2 equivalents (formate and bicarbonate) and ammonia. This method achieved a 30% conversion rate of formate into these industrially important amino acids. Additionally, an 8-gene CFE biocatalyst was developed to produce malate, conserving 43% of carbon that would otherwise be lost as CO2. This approach has the potential to reach higher carbon efficiency than microbial production. 3. Public Benefit: The cell-free production of chemicals like serine, glycine, and malate using electrochemically generated formate could significantly reduce CO2 emissions. For example, satisfying the global malate market with this method could avoid approximately 400,000 tons of CO2 emissions annually. This work demonstrates the potential of CFE systems to produce platform chemicals, contributing to environmental sustainability and reducing reliance on petrochemicals. Future Prospects: The CFE-based biocatalyst process could be extended to produce a variety of chemicals, including other industrial di-acids, aromatics, terpenes, alcohols, and polymers. This project showcases the capabilities of cell-free expression systems for prototyping carbon-conserving pathways and sustainably bioproducing platform chemicals, marking a significant step towards economically-viable industrial processes.
The objective of the project is to develop a new class of ultrafast, self-powered, programmable, autonomously operated, bidirectional solid state circuit breakers (SSCBs) using wide band gap (WBG) semiconductors with 1000X reduction in response time and 5X reduction in cost in comparison to commercial mechanical circuit breakers. In particular, we have developed two SSCB prototypes of 380VDC/220VAC/20A and 1000VDC/10A using GaN power switches for common distribution-level AC and DC voltages.
The Computational Science and AI Directorate at Fermilab is using Spack to support the development efforts of a large number of scientific programmers, in many independent projects and experiments. While independent, these projects share many dependencies. They are typically under continuous and fairly rapid development. They have to support deployment on diverse hardware. This is a different context than is typical for the management of HPC software, where Spack was born. To support our community, we have created a model that enables users to develop code with greater efficiency than is possible with Spack’s current development facilities. In this talk we will present: - a brief introduction to the science we support (particle physics) - how the code we work with is naturally organized into several layers of packages - how we are using Spack to manage those layers - how we leverage the layering to provide efficient support for developers, using our Spack extension “MPD”. - some suggestions for changes or additions to Spack to make such work easier.
The goal of this project is to implement real-time analysis of 2D Beam Emission Spectroscopy (BES) data to predict and control transient and high-bandwidth events at DIII-D. In essence, we wish to bring high-bandwidth fluctuation diagnostics into the realm of real-time measurements and control. The BES ML models will necessarily be deep neural networks (DNN) with a “data flow” architecture for compatibility with high-throughput, low-latency evaluation on a field-programmable gate array (FPGA) or other emerging processor technologies. The real-time output will be fed to the plasma control system (PCS) for real-time control tasks, specifically for ELM control and avoidance and for QH-mode access and sustainment. We anticipate that the real-time analysis of fluctuation diagnostics will create new enabling technologies to predict and control transient events such as confinement mode transitions, edge-localized modes, Alfven eigenmode events, and disruptions. The proposed research is aligned with ITER research needs and DIII-D programmatic goals. For instance, the prediction and avoidance of ELM events is critical for ITER machine safety. Also, H-mode access with RMP ELM suppression in ITER is an active research area due to high separatrix density, narrow SOL width, and elevated LH transition power threshold.
The Short Baseline Near Detector (SBND) serves as the near detector for Fermilab's Short Baseline Neutrino (SBN) programme. It is a 112-ton Liquid Argon Time Projection Chamber (LArTPC) designed to study neutrino-argon interactions and search for new physics phenomena such as sterile neutrinos. Situated just 110 m from the Booster Neutrino Beam (BNB), SBND just began its first physics run and is expected to record over two million neutrino interactions annually. Commissioning the time projection chambers of SBND is crucial for optimising detector performance. Each of the two anodes is composed of three charge-sensing wire planes which detect drifting ionisation electrons from charged particles traversing the detector. The wire planes, arranged with different 2D orientations, facilitate the 3D reconstruction of neutrino interactions. Electron transport induces signals on the first two planes, with the signal being collected by the rear plane. The first two induction planes require transparency to ensure all drift electrons reach the collection plane. This transparency depends on the bias voltages applied to the wires and the spacing between them. Fine-tuning these bias voltages ensures the optimisation of wire plane transparency. In this poster, I will demonstrate how transparency can be assessed by analysing the waveforms from cosmic-ray muon tracks traversing the detector at specific orientations.
This poster relates to a project that is investigating the use of the FABRIC federated testbed to scale-up research infrastructure to develop network functions that use smart (programmable) network equipment to support High Energy Physics (HEP) research. The poster describes preliminary work that involves non trivial packet parsing related to Fermilab DAQ workloads that is implemented on real hardware, and a correctness and performance evaluation.
The Offshore Code Comparison Collaboration 7 (OC7) project is organized under the International Energy Agency Wind Technology Collaboration Programme Task 56 with an objective to evaluate and enhance the predictive accuracy of engineering-level modeling tools used in the design of offshore wind energy systems. Phase I of OC7 is focused on improving the models and modeling practices of hydrodynamic viscous loads on floating offshore wind turbine platforms. In alignment with this goal, Work Package 1.1 of OC7 Phase I is formulated to investigate the modeling of hydrodynamic viscous loads on several different geometric components commonly encountered with floating offshore wind platform designs, including cylindrical columns, heave plates, and rectangular pontoons. Work Package 1.1 also explores the dependence of hydrodynamic coefficients on the sea state to drive toward practical guidance on how these coefficients can be selected or adjusted for different conditions. This report outlines the motivation and objectives behind each subphase of OC7 Phase I, along with the necessary technical specifications and load case definitions to guide the project participants. It also serves as an important part of the project documentation for future modelers who would like to reproduce this work or make use of the data and information generated from the OC7 project.
Establishing autonomy in reactor control systems has become essential for the expansion of nuclear technologies. Thermal regulation in particular remains crucial for maintaining stable operation and ensuring the integrity of fuel. To alleviate public skepticism of the safety of nuclear reactors, demonstrating control over this key factor is pivotal. Utilizing electric heat pads to simulate the heat released in a reactor core, thermocouples for temperature monitoring, and an Arduino micro programmable logic controller (PLC) for control, a hardware-based demonstration of a reactor heating system validates the efficacy of reactor control over this key parameter. To improve precision, a proportional-integral-derivative (PID) algorithm was implemented in the heating control loop to ensure meticulous control of reactor functions. In addition, the integration of this physical system with a digital simulator tool such as RELAP5-3D establishes a foundation for a comprehensive testing environment. This allows for a refinement of temperature control under various simulated reactor conditions, bringing another layer of reliability to the operation of the system. By facilitating a physical demonstration of reactor thermal management and control strategies, this project provides a foundation for expanded testing and educational outreach. Ultimately, this system advances the broader goal of demonstrating the safety and viability of autonomous reactor operations, contributing to public trust and future reactor deployment.
Autonomous thermal regulation in nuclear reactors remains crucial for maintaining stable operation and ensuring the integrity of fuel. To alleviate public skepticism of the safety of nuclear reactors, demonstrating control over this key factor is pivotal. Utilizing electric heat pads to simulate the heat released in a reactor core, thermocouples for temperature monitoring, and an Arduino micro programmable logic controller (PLC) with an embedded proportional-integral-derivative (PID) algorithm for control, a hardware-based demonstration of a reactor heating system will validate the efficacy of reactor control over this key parameter. This report covers internship project presentation as well as relevant experience with nuclear and proposal for project upgrade.
This study explores a bio-inspired approach for memristive devices by combining Keggin-type polyoxometalates (POMs)-[SiW 12 O 40 ] 4 (POM-T) and [PW 12 O 40 ] 3 (POM-P), with silk fibroin (SF) to create 2D SF–POM layers on highly ordered pyrolytic graphite (HOPG) as resistive switching layers for memristors. We propose that the ordered SF layer template 0D POMs facilitate the formation of conductive filaments, thereby enhancing the variability of the manufactured memristors. AFM analysis revealed that both SF and SF–POM layers shared similar morphologies, while SF–POM–T formed larger aggregates, likely due to the stronger acidity of POM-T, which probably caused SF to aggregate and alter its secondary structure. Scanning Kelvin probe microscopy (SKPM) revealed that POMs reduced the contact potential difference of HOPG, resulting in lower work functions. Compared to an SF device, the SF–POM–P device showed improved memristive behavior, with a larger current gap and good repeatability over multiple sweeps; whereas the SF–POM–T device did not exhibit memristor activity, likely due to acidity-induced disruption of the SF template’s order and CF formation. More importantly, SF–POM–P devices also demonstrated programmable memristive states. Finally, combining simulation-driven memristor modeling, we showcase a co-design workflow for advancing bioinspired memristors through new materials design, synthesis, and device modeling and development.
Fusion energy represents a transformative opportunity to deliver a safe, plentiful, and carbon-free source of reliable primary power. In recent years, fusion research and development have accelerated significantly, particularly within the US, driven by decades of foundational public investment. Notably, in 2024 the US Department of Energy (DOE) established a comprehensive Fusion Energy Strategy aimed at collaborating with industry partners to enable the deployment of fusion power plants and grid integration by the 2030s. This strategy is chiefly implemented through the DOE Office of Science (SC) Fusion Energy Sciences program. This project was initiated to identify potential approaches for the Office of Environment, Safety, and Health (NA-ESH-12) within DOE’s National Nuclear Security Administration to begin engagement with the fusion community on future accountable material needs. The goal of the project is to inform and influence the supply of and demand for accountable nuclear materials as fusion energy is developed and commercialized. NA-ESH-12 must proactively engage with the fusion community regarding the production and management of accountable nuclear materials. Given the complexity and scale of materials required for research, pilot projects, and eventual commercial reactors, early coordination is vital. The project’s objective is to provide insights that will shape the supply and demand landscape for critical nuclear materials, ensuring that DOE is prepared to effectively support fusion energy development and commercialization. In FY 2025, an initial limited review was conducted to identify the status of the fusion energy community’s progress toward full-scale energy production and the need for accountable nuclear material. This included communications with SC, NA-ESH-12, Savannah River National Laboratory, and Oak Ridge National Laboratory, and attending the Rutgers University–sponsored Supply Chain Workshop: Scaling the Fusion Industry and the International Atomic Energy Agency’s Ninth DEMO Programme Workshop. The review to date indicates that the amounts of tritium, lithium-6, and deuterium required by the fusion industry will be dependent on fuel type, breeding technology, blankets, and R&D improvements. One concern is that the commercial sector does not have a sufficient supply chain to meet the demand for development and commercialization for fusion energy production. The supply and demand estimates for these materials should be routinely reviewed as fusion technologies mature.
In this project, a new motor technology based on a novel design called a High Rotor Pole Switched Reluctance Motor (HRSRM) was demonstrated and evaluated. The HRSRM system combines an innovative electric motor design with a programmable variable-speed drive and a controller that provides real time monitoring and cloud-based connectivity. Unlike induction or permanent-magnet motor designs, this motor uses a switched reluctance (SR) design that purports to be simpler to manufacture and more reliable and efficient to operate. Several of the stated benefits of the HRSRM system were evaluated as part of this project. First, the technology is claimed to offer greater energy efficiency than any motor currently on the market. Second, according to the manufacturer, the motor can be dropped into an existing pump or heating, ventilation, and air-conditioning application in less than 30 minutes, and has controls that can be pre-programmed to interact with the application’s existing control structure. Third, the control system allows the HRSRM to replace a constant-speed motor with one that provides variable-speed capabilities without requiring the installation of a variable-speed drive. The evaluation of a 10 hp HRSRM system was conducted in two parts. One portion of the evaluation consisted of performance tests conducted in a tightly controlled laboratory environment in which the performance of the HRSRM system was compared with that of a premium efficiency induction motor with a variable-frequency drive. The other portion of the evaluation was conducted at a field location within a General Services Administration facility in which the HRSRM system replaced an existing motor system in a 10 hp chilled water pump. The performance of the HRSRM system was compared with that of the incumbent motor system that was supplied with the pump from the manufacturer. The overall project and performance objectives for this motor evaluation are defined in Table ES.1.
"CIE Analysis Process for Engineered Systems" outlines a comprehensive methodology for integrating Cyber-Informed Engineering (CIE) principles into both new and existing engineered systems. Sponsored by the U.S. Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (DOE CESER), the process aims to achieve cyber-informed decisions by producing functional security requirements for new systems and retrofitting existing systems to mitigate digital risks. The document details a step-by-step approach, including mission and function definition, digital asset awareness, consequence analysis, and mitigation analysis. It emphasizes the importance of documenting mechanical, electrical, programmable, and network components to protect system functions and provides examples and considerations for each step. The ultimate goal is to ensure that engineered systems remain resilient against cyber threats, maintaining safety, performance, and reliability.
With the discovery of the Higgs boson at the CERN LHC, the world's highest-energy particle accelerator complex, scientists have acquired an important tool to study the fundamental building blocks of the universe. Precision measurements of Higgs bosons produced with large momentum allow for unique insights into the structure of the interactions of the Higgs boson with other particles that may shed light on physics beyond the standard model. While experimentally challenging, exploring such interactions with novel artificial intelligence (AI) methods can advance our understanding of the Higgs sector, including the Higgs boson's self-interaction. Moreover, the LHC is undergoing a major upgrade to further increase its particle collision rate and thereby operate for an additional decade. The experimental detectors at the upgraded facility must process at least a factor of ten more data at rates of hundreds of terabytes per second all under challenging conditions. New AI techniques are required to reconstruct and select, or trigger on, the most physics-sensitive events in real-time to handle the resulting avalanche of data. The proposed research will achieve the goals of the LHC program at the CMS experiment by developing a sub-microsecond event reconstruction system using real-time AI algorithms that employ field-programmable gate array technologies. By harnessing sophisticated AI techniques, this research focuses on measuring the production of Higgs bosons at large momentum while enhancing particle reconstruction methods in the trigger and beyond. Overall, the proposed research has broader implications for the use of AI in resource-constrained, low-latency embedded applications across all fields of science.
For enhanced roadway safety, clear and immediate visual cues are essential for preventing accidents between drivers and pedestrians. At intersections, however, the line of sight to other roadway users may be obstructed by vehicles and infrastructure. Additionally, adverse conditions, including low visibility, poor weather, or inadequate lighting can increase the potential for collisions. Distracted drivers and pedestrians can further exacerbate the risk of accidents, particularly when using a smartphone, rather than focusing on roadway surroundings. These issues demonstrate the need for infrastructure upgrades that enhance visibility and awareness at crosswalks. A potential solution is through enhanced lighting signals integrated into the roadway infrastructure. One such example is the use of addressable LEDs, individually controllable lights that can change color and brightness instantaneously through programmable microcontrollers. They can be installed and integrated into crosswalks to maintain visibility in conditions where pedestrians may be difficult to see, while also offering peripheral cues to pedestrians who may be distracted by their phones or other objects rather than the road. Such a system (as one example) that is integrated into a traffic intersection digital twin that tracks all roadway users accurately, has the potential to enhance visibility of vulnerable road users, and thus enhance safety. This paper examines the potential implementation and feasibility of this technology, as well as the safety benefits it could provide. Laboratory experiments with addressable LEDs reveal the capabilities and challenges of this technology for roadway infrastructure safety. These findings could pave the way for more integrated lighting in infrastructure for vehicles and pedestrians at intersections, merge and diverge locations, and other areas where complex interactions present safety hazards. Such lighting solutions, enabled by modern computation and communications, could enhance safety and efficiency in our transportation system and improve overall mobility.