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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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Goal if this review is to approve and improve Philipp’s design approach chosen for IH type accelerating cavity of ion linac. The cavity is proposed to be milled out of copper blocks, with irises individually brazed and cooling pipes drilled into the blocks. Then the parts of the cavity will be brazed together and loop-coupler and flanges will be welded.
Against the backdrop of negotiations at COP26, a group of internationally recognised research and technology organisations launched a collaboration, with the intention to identify the national and international technology-led opportunities to decarbonise fossil fuel basins globally. This study will evidence the opportunities available to players across the entire energy ecosystem. Governments and funders will be able to spread costs and investment risk, energy companies will develop a greater understanding of the global transition and possible opportunities to develop pilot projects worldwide, and there will be greater incentive to the supply chain once they are aware of the global applicability of certain technologies and technology solutions. Through the analysis and comparison of national energy systems, a number of Net Zero Deployment Strategies and Technology Priorities have been identified, that are shaping the direction of the construction of net zero integrated energy systems globally. These have been considered to design Recommendations for International Collaboration to accelerate the global energy transition.
Spiking Neural Networks (SNNs) are brain-inspired computing models incorporating unique temporal dynamics and event-driven processing. Rich dynamics in both space and time offer great challenges and opportunities for efficient processing of sparse spatiotemporal data compared with conventional artificial neural networks (ANNs). Under this context, the goal of this project is to develop spiking neural network based neuromorphic computing to enable energy-efficient real-time learning and processing of spatiotemporal data. This report summarizes the key results on network architecture design, training methods, and SNN hardware acceleration achieved under this project, demonstrating the promise of spiking neural networks.
We propose to create atomically precise, highly robust, nanometer scale macromolecules that organize biomimetic metal binding groups in designed three-dimensional pockets to accelerate specific polymerization reactions. These atomically precise catalysts will create atomically precise polymers with improved mechanical and environmental properties from renewable building blocks. We seek to create and study nanometer scale catalysts that mimic metallo-enzymes in their activity and selectivity because these larger catalysts will be able to make more extensive non-covalent contacts with the transition state of the growing polymer. These catalysts will have extended lifetimes because they will better protect the metals and higher activity because additional reactive groups can be brought in close contact with the active site to enhance reactivity. They will be far more tolerant to extremes of temperature and will not denature in non-aqueous solvents because they are held together by multiple, strong, covalent bonds. Our approach to catalysts could be used to develop catalysts to replace noble metal-based catalysts when targeting other reactions, because the geometry of the active site enforced by the Molecular Lego scaffolding will access new reactivity, mimicking how nature uses earth abundant metals for much of its catalysis. We will initially target Lewis acid-based catalysts that assemble aliphatic polyesters with improved stereoselectivity, tacticity and complex alternation of monomers. Aliphatic polyesters have received growing attention as attractive, environmentally benign, and sustainable alternatives to polymers developed from petroleum feedstocks. Aliphatic polyesters are environmentally friendly because they undergo facile hydrolytic degradation to benign products, and they have excellent properties and high biocompatibility. We will assemble these catalysts using unique “Spiroligomers” (aka Molecular Lego) chemistry developed in the Schafmeister group combined with the organometallic chemistry expertise of the Dobereiner group. Polymers are an attractive synthetic target of nanoscale atomically precise catalysts because their resulting structure, stereochemistry and tacticity has profound impact on their properties (crystalline vs amorphous) and provides a readout of the catalytic mechanism. Polymer synthesis also serves as an example of “atomically precise manufacturing” where atomically precise nanoscale catalysts construct polymers with atomic precision at increasing length scales.
Integrable systems possess a hidden symmetry associated with the existence of conserved quantities known as integrals of motion. These systems play an important role in understanding general dynamics in accelerators and have potential for future designs. This work will cover two automated methods for finding integrable symplectic maps of the plane. The first algorithm is based on the observation that the evolution of an integrable system in phase space is confined to a lower-dimensional submanifold of a specific type. The second algorithm relies on an analysis of dynamical variables. Both methods rediscover some of the famous McMillan-Suris integrable mappings and ultra-discrete Painlev\'e equations. Over 100 new integrable families are presented and analyzed, some of which are isolated in the space of parameters, while others are families with one parameter (or the ratio of parameters) being either continuous or discrete. In addition, the newly discovered maps are related to a general 2D symplectic map through the use of discrete perturbation theory. A method is proposed for constructing smooth near-integrable dynamical systems based on mappings with polygon invariants.
The upcoming Proton Improvement Plan-II (PIP-II), designated for enhancements to the Fermilab accelerator complex, features a Beam Transfer Line (BTL) that channels the beam from the linac exit to the booster. In the absence of longitudinal focusing beyond the superconducting linac, the beam experiences an elevated momentum spread, primarily due to nonlinear space-charge forces, surpassing the allowable limit of 2.1e-4. This study presents a detailed examination of the space-charge-induced momentum spread and outlines mitigative strategies. The investigation includes the fine-tuning of a de-buncher cavity, analyzed in terms of operating frequency, longitudinal location, and gap voltage, under both standard and perturbed beam conditions—specifically accounting for momentum jitter and energy variation. The impact of buncher cavity misalignments on the beam's longitudinal phase space is also assessed. The paper concludes by recommending an optimized cavity configuration to effectively mitigate the observed increase in momentum spread along the BTL.
In this project, Lumileds, the University of Michigan, the University of New Mexico, Sandia National Laboratories, and Ohio State University conducted a collaborative and comprehensive research effort to investigate the most promising approaches to improve green and yellow InGaN LEDs. Advanced LED characterization methods and predictive model calculations were applied to design optimization experiments more effectively and accelerate the pace of improvements.
Over the past decade, the RISC-V Instruction Set Architecture (ISA) has emerged as a significant player in both academic and industrial processor design due to its open-source nature, modular extension system, and versatility across domains ranging from microcontrollers to high-performance computing (HPC). One of its most important recent advancements is the RISC-V Vector Extension (RVV), which enables explicit data-level parallelism through vector registers and vectorized instructions. Unlike traditional SIMD (Single Instruction, Multiple Data) architectures that fix vector lengths at design time, RVV uses the concept of VLEN (vector register length) as a hardware-independent parameter and allows software to adapt dynamically to the available vector width. This flexible approach ensures portability across implementations while enabling scalable performance. The ARA2 core is a parameterizable RISC-V vector processor developed at the Integrated Systems Lab at ETH Zürich and the University of Bologna. Designed as a tightly-coupled accelerator to a scalar RISC-V core, ARA2 implements the RVV 1.0 specification and offers tunable architectural parameters such as the number of vector lanes, VLEN, and cache sizes.
This final report summarizes the Norfolk State University portion of the project Graduate Traineeships in Accelerator Engineering and Physics. In the funded proposal, Norfolk State University participated as a collaborating institution in a broader multi-institution traineeship effort designed to prepare graduate students for work in accelerator engineering and physics, including students from Old Dominion University, Norfolk State University, and Hampton University.
This paper presents our endeavors in developing the large-scale, ultra-high-resolution E3SM Land Model (uELM), specifically designed for exascale computers furnished with accelerators such as Nvidia GPUs. The uELM is a sophisticated code that substantially relies on High-Performance Computing (HPC) environments, necessitating particular machine and software configurations. To facilitate community-based uELM developments employing GPUs, we have created a portable, standalone software environment preconfigured with uELM input datasets, simulation cases, and source code. This environment, utilizing Docker, encompasses all essential code, libraries, and system software for uELM development on GPUs. It also features a functional unit test framework and an offline model testbed for comprehensive numerical experiments. From a technical perspective, the paper discusses GPU-ready container generations, uELM code management, and input data distribution across computational platforms. Lastly, the paper demonstrates the use of environment for functional unit testing, end-to-end simulation on CPUs and GPUs, and collaborative code development.
Our understanding of Arctic sea ice and its wide-ranging influence is deeply rooted in observation. Advancing technologies have profoundly improved our ability to observe Arctic sea ice, document its processes and properties, and describe atmosphere-ice-ocean interactions with unprecedented detail. Yet, our progress toward better understanding the Arctic sea ice system is mired by the stark disparities between observations that tend to be siloed by method, scientific discipline, and application. This article presents a review and philosophical design for observing sea ice and accelerating our understanding of the Arctic sea ice system. We give a brief history of Arctic sea ice observations and showcase the 2018 melt season within the context of five observational themes: spatial heterogeneity, temporal variability, cross-disciplinary science, scalability, and retrieval uncertainty. We synthesize buoy data, optical imagery, satellite retrievals, and airborne measurements to demonstrate how disparate data sets can be woven together to transcend observational-scale issues. The results show that there are limitations to interpreting any single data set alone. However, many of these limitations can be surmounted by combining observations that cross spatial and temporal scales. We conclude the article with pathways toward coordinating across observational platforms in order to: (1) optimize the scientific, operational, and community return on observational investments, and (2) facilitate a richer understanding of Arctic sea ice and its role in the climate system.
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Current TRISO Fuel Performance Capabilities and Considerations for Expanded Operational Envelopes. US-DOE TRISO Fuel Development, AGR Fuel Design and Performance Requirements, AGR Program Fuel Irradiations, UCO Fuel Performance Evaluation Results, Performance Limiting Phenomena, Coated-Particle-Fueled Reactor Concepts and Fuel Designs, Expanded Fuel Performance Envelope, and Accelerated Fuel Qualification.
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A program to build a lepton-collider Higgs factory, to precisely measure the couplings of the Higgs boson to other particles, followed by a higher energy run to establish the Higgs self-coupling and expand the new physics reach, is widely recognized as a primary focus of modern particle physics. We propose a strategy that focuses on a new technology and preliminary estimates suggest that can lead to a compact, affordable machine. New technology investigations will provide much needed enthusiasm for our field, resulting in trained workforce. This cost-effective, compact design, with technologies useful for a broad range of other accelerator applications, could be realized as a project in the US. Its technology innovations, both in the accelerator and the detector, will offer unique and exciting opportunities to young scientists. Moreover, cost effective compact designs, broadly applicable to other fields of research, are more likely to obtain financial support from our funding agencies.
Fermi National Accelerator Laboratory (FNAL) is developing the international Long- Baseline Neutrino Facility (LBNF) and Deep Underground Neutrino Experiment (DUNE) to advance neutrino science. The flagship of the DUNE project consists of a large particle detector constructed one-mile (1.6 km) beneath the surface at the Sanford Underground Research Facility (SURF) in Lead, SD. The SURF detector is the largest of its type ever built and is comprised of four cryostats totaling 70,000 tons of liquid argon (LAr) to record neutrino interactions with unprecedented precision. Each cryostat houses a detector, the first includes 150 Anode Panel Assemblies (APA) submersed within 17,500 tons of LAr. Before installing 150 APA within the SURF detector they will be cryogenically cooled to nominally 90 K at the APA Test Facility (APATF) utilizing nitrogen flows. The APATF cryogenic system is entirely located one-mile underground at the SURF facility and includes nominally 13 kW of refrigeration at 80 K, cryogenic transfer lines, APA test cryostats, a cryogenic control system, and various control and pressure safety elements to ensure performance and safety requirements are achieved. The APATF preliminary design is in progress and major considerations include an efficient, costeffective mechanism to deliver the required refrigeration to the APATF underground, support of rigid testing intervals to support the DUNE operating schedule, temperature stability of the APA and electronics within cryostats, efficient cryogenic system operation to minimize heat leak and/or liquid nitrogen consumption, thermo-mechanical stability and flexibility of components, and pressure safety of the APATF cryogenic system. Installation and integration of the APATF cryogenic system within the footprint and to adjacent sub-systems is also discussed.
In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This article proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing Field Programmable Gate Arrays (FPGA) high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on five test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 W of power, 33 W in the optical DAS system and 22 W in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let’s use fiber optic touch instead.
HEP community leads and operates cutting-edge experiments for the DOE Office of Science which have challenging sensing, data processing, and computing requirements that far surpass typical industrial applications. To make necessary progress in the energy, material, and fundamental sciences, development of novel technologies is often required to enable these advanced detector and accelerator programs. Our capabilities include efficient co-design, which is a prerequisite to enable the deployment of advanced techniques in a scientific setting where development spans from rapid prototyping to robust and reliable production scale. This applies across the design spectrum from the low level fabrication techniques to the high level software development. It underpins the requirement for a holistic approach of innovation that accelerates the cycle of technology development and deployment. The challenges set by the next generation of experiments requires a collaborative approach between academia, industry and national labs. Just a single stakeholder will be unable to deliver the technologies required for the success of the scientific goals. Tools and techniques developed for High Energy Physics (HEP) research can accelerate scientific discovery more broadly across DOE Office of Science and other federal initiatives and also benefit industry applications.