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DOE OSTI2025

Simulation study of high-current 7 Li 3+ beam acceleration with laser ion source and RFQ linac using direct plasma injection

Accelerator-based neutron sources (ABNS) utilizing the 7 Li(p,n) 7 Be reaction in inverse kinematics offer strong forward-directed neutron emission, making them attractive for compact and clean neutron source with low background neutrons and unwanted radiation. However, practical use of such systems requires lithium ion beam currents that exceed the capability of conventional ion accelerators by two orders of magnitude. In this study, we numerically designed and evaluated a high-current acceleration system based on a direct plasma injection scheme (DPIS), combining a laser ion source (LIS) and a radio-frequency quadrupole (RFQ) linac. The ion extraction optics and RFQ entrance were optimized using IGUN, OPERA, and GPT, demonstrating that over 1.2 A-class beam current can be injected into the RFQ. The RFQ structure was designed under realistic engineering constraints including surface field strength (Kilpatrick factor) and RF power. GPT simulations showed that a 370 mA 7 Li 3+ beam can be successfully accelerated within a ±10 % energy spread. To validate downstream compatibility, we also designed a simplified medium-energy beam transport (MEBT) section and an interdigital H-type (IH) linac, confirming successful acceleration of a 320 mA beam to the total energy of 14 MeV. These results support the feasibility of the DPIS and RFQ approach as a promising solution for compact neutron sources and other high-intensity ion beam applications.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

U.S. HL-LHC Accelerator Upgrade Project: Q1/Q3 Cryostat Assembly and Horizontal Test (Final Design Report)

The Inner Triplet (IT) quadrupoles are the magnetic system for reaching low beta functions around the Interaction Point (IP). The triplet is made of three optical elements: Q1, Q2, and Q3. The upgrade of the Inner Triplets in the high luminosity insertions is the cornerstone of the LHC upgrade. The decision for HL-LHC heavily relies on the success of the advanced Nb 3 Sn technology that provides access to magnetic fields well beyond 9 T, allowing the maximization of the aperture of the IT quadrupoles. A 15-year-long study led by the DOE in the US under the auspices of the U.S. LARP program, and lately by other EU programs, has shown the feasibility of Nb 3 Sn accelerator magnets. The HL-LHC is expected to be the first application of accelerator-quality Nb 3 Sn magnet technology in an operating particle accelerator.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Final Design of the LB650 Cryomodule for the PIP-II Linear Accelerator

The Proton Improvement Plan II (PIP-II) that will be installed at Fermilab is the first U.S. accelerator project that will have significant contributions from international partners. CEA joined the international collaboration in 2018, and its scope covers the supply of the 650 MHz low-beta cryomodule section, with the design of the cryostat (i.e the cryomodule without the cavities, the power couplers and the frequency tuning systems) and the manufacturing of its components, the assembly and tests of the pre-production cryomodule and 9 production modules. An important milestone was reached in April 2023 with the Final Design Review. This paper presents the detailed design of the 650 MHz low-beta cryomodules.

43 PARTICLE ACCELERATORS↗

NASA NTRS2016

First Flight of the Gamma-Ray Imager Polarimeter for Solar Flares (GRIPS) Instrument

The Gamma-Ray Imager/Polarimeter for Solar ares (GRIPS) instrument is a balloon-borne telescope designed to study solar-flare particle acceleration and transport. We describe GRIPS's first Antarctic long-duration flight in January 2016 and report preliminary calibration and science results. Electron and ion dynamics, particle abundances and the ambient plasma conditions in solar flares can be understood by examining hard X-ray (HXR) and gamma-ray emission (20 keV to 10 MeV). Enhanced imaging, spectroscopy and polarimetry of flare emissions in this energy range are needed to study particle acceleration and transport questions. The GRIPS instrument is specifically designed to answer questions including: What causes the spatial separation between energetic electrons producing hard X-rays and energetic ions producing gamma-ray lines? How anisotropic are the relativistic electrons, and why can they dominate in the corona? How do the compositions of accelerated and ambient material vary with space and time, and why? GRIPS's key technological improvements over the current solar state of the art at HXR/gamma-ray energies, the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), include 3D position-sensitive germanium detectors (3D-GeDs) and a single-grid modulation collimator, the multi-pitch rotating modulator (MPRM). The 3D-GeDs have spectral FWHM resolution of a few hundred keV and spatial resolution less than 1cu mm. For photons that Compton scatter, usually greater or equal to 150 keV, the energy deposition sites can be tracked, providing polarization measurements as well as enhanced background reduction through Compton imaging. Each of GRIPS's detectors has 298 electrode strips read out with ASIC/FPGA electronics. In GRIPS's energy range, indirect imaging methods provide higher resolution than focusing optics or Compton imaging techniques. The MPRM grid-imaging system has a single-grid design which provides twice the throughput of a bi-grid imaging system like RHESSI. The grid is composed of 2.5 cm deep tungsten-copper slats, and quasi-continuous FWHM angular coverage from 12.5-162 arcsecs are achieved by varying the slit pitch between 1-13 mm. This angular resolution is capable of imaging the separate magnetic loop footpoint emissions in a variety of are sizes. In comparison, RHESSI's 35-arcsec resolution at similar energies makes the footpoints resolvable in only the largest ares.

Duncan, Nicole↗

DOE OSTI2026

Discovery of hydrogen storage molecules using large language models and machine learning

Accelerating the discovery of new molecules with targeted properties is a central challenge in molecular design. In this contribution, we present an AI-driven molecular discovery framework that integrates Large Language Models (LLMs) for generative molecular design with Machine Learning (ML)-based screening to identify novel Liquid Organic Hydrogen Carrier (LOHC) candidates. Using the developed framework, LOHC molecules were systematically generated, evaluated, and refined iteratively, combining LLM-guided molecular generation and ML-predicted hydrogenation enthalpies (Δ H ), under physicochemical property constraints such as optimal melting points (MP), desired hydrogen storage capacity (wt% H 2 ), and synthetic accessibility (SA) scores. This approach enabled the discovery of 42 new LOHC candidates in two distinct campaigns, one seeded with experimentally known and another with previously computationally identified LOHCs, respectively. Although we began with different numbers of starting molecules (31 vs . 7 seed molecules), both runs yielded a comparable number of viable candidates, suggesting an influence of chemically intuitive seed molecule selection for success. Selected LOHC molecules, such as 3-methyl pyridine, 1-ethylnapthalene, 1,1-diphenylethane, and benzofuran, were experimentally tested and compared with benchmark LOHCs (toluene and 9-ethylcarbazole) for hydrogenation using a series of commercial supported metal catalysts. The order of conversion into fully hydrogenated products at 200 °C was 3-methyl pyridine (100%) > 9-ethyl carbazole (86.4%) > 2,3-benzofuran (74%) > 1,1-diphenylethane (66.9%) > 1-ethylnapthalene (66.7%) > toluene (57%), further validating the AI-guided molecular design. This study demonstrates promise of LLM-driven molecular design in conjunction with ML-based screening for accelerated discovery and design of molecules.

Harb, Hassan [Argonne National Laboratory (ANL), A↗

DOE OSTI2024

OpenFAST Modeling of the T-Omega Wind Floating Offshore Wind Turbine System

To reduce the cost of offshore wind energy through a more efficient design of the floating support structure, T-Omega Wind developed a novel lightweight and shallow-draft platform concept that aims to achieve a wave-following behavior without resonance amplification. The rotor and generator are carried by four tower legs with each leg supported by a shallow-draft float at the base. In preparation for a more detailed analysis, a coupled aero-hydro-elastic model of the proposed concept is developed in OpenFAST. The detailed modeling approach is presented, including a novel application of the SubDyn substructure dynamics module of OpenFAST to approximate the loads on the axle tube at the tower top from the rotor hub bearings. Preliminary results obtained with the OpenFAST model by rigidifying the substructure and blades indicate that the original sizing of the design can lead to large hub acceleration in the axial/surge and pitch directions due to platform-pitch motion. The high axial acceleration also potentially leads to large bending moments in the four tower legs. To address these issues, an updated design is developed with, among other changes, increased float spacing at the tower base and improved shear-transmitting geometry of the tower legs. The new design suggests significantly reduced extreme hub accelerations under the same conditions even with structural flexibility and will be further analyzed in the future through full aero-hydro-servo-elastic simulations.

FOWT↗

DOE OSTI2024

OpenFAST Modeling of the T-Omega Wind Floating Offshore Wind Turbine System

ENGINEERING,WIND ENERGY↗

DOE OSTI2023

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

The Abisko project aims to develop an energy-efficient spiking neural network (SNN) computing architecture and software system capable of autonomous learning and operation. The SNN architecture explores novel neuromorphic devices that are based on resistive-switching materials, such as memristors and electrochemical RAM. Equally important, Abisko uses a deep codesign approach to pursue this goal by engaging experts from across the entire range of disciplines: materials, devices and circuits, architectures and integration, software, and algorithms. Here, the key objectives of our Abisko project are threefold. First, we are designing an energy-optimized high-performance neuromorphic accelerator based on SNNs. This architecture is being designed as a chiplet that can be deployed in contemporary computer architectures and we are investigating novel neuromorphic materials to improve its design. Second, we are concurrently developing a productive software stack for the neuromorphic accelerator that will also be portable to other architectures, such as field-programmable gate arrays and GPUs. Third, we are creating a new deep codesign methodology and framework for developing clear interfaces, requirements, and metrics between each level of abstraction to enable the system design to be explored and implemented interchangeably with execution, measurement, a model, or simulation. As a motivating application for this codesign effort, we target the use of SNNs for an analog event detector for a high-energy physics sensor.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1994

Microgravity isolation system design: A case study

Many acceleration-sensitive, microgravity science experiments will require active vibration isolation from manned orbiters on which they will be mounted. The isolation problem, especially in the case of a tethered payload, is a complex three-dimensional one that is best suited to modern-control design methods. In this paper, extended H(sub 2) synthesis is used to design an active isolator (i.e., controller) for a realistic single-input-multiple-output (SIMO) microgravity vibration isolation problem. Complex mu-analysis methods are used to analyze the isolation system with respect to sensor, actuator, and umbilical uncertainties. The paper fully discusses the design process employed and the insights gained. This design case study provides a practical approach for isolation problems of greater complexity. Issues addressed include a physically intuitive state-space description of the system, disturbance and noise filters, filters for frequency weighting, and uncertainty models. The controlled system satisfies all the performance specifications and is robust with respect to model uncertainties.

Hampton, R. D.↗

DOE OSTI2020

Design and Performance of the Solid-State Laser Trigger System for HERMES III

The HERMES III accelerator is an 18-20 MeV linear induction accelerator constructed at Sandia National Laboratories in the late 1980s and which continues operation to this day. As part of recent modernization efforts, the laser triggering system on the accelerator has been replaced with a newly designed solid-state system. This system consists of ten Nd:YAG lasers, each having a nominal output energy of 40-45 mJ at a wavelength of 266 nm. The beam from each laser is split such that it triggers two of the Rimfire gas switches on the accelerator. Compared to the previous laser triggering system, this arrangement makes it possible to more readily tailor the final output pulse shape, and overall reliability for the accelerator's operation with these new lasers has increased. The design of this new laser triggering system is presented in this paper, along with details pertaining to the energy budgeting, optical beam paths, and electrical triggering of the lasers. Initial operational data from the HERMES III accelerator using this new triggering system is also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Energetic Control of Redox-Active Polymers toward Safe Organic Bioelectronic Materials

Avoiding faradaic side reactions during the operation of electrochemical devices is important to enhance the device stability, to achieve low power consumption, and to prevent the formation of reactive side-products. This is particularly important for bioelectronic devices, which are designed to operate in biological systems. While redox-active materials based on conducting and semiconducting polymers represent an exciting class of materials for bioelectronic devices, they are susceptible to electrochemical side-reactions with molecular oxygen during device operation. In this work, electrochemical side reactions with molecular oxygen are shown to occur during organic electrochemical transistor (OECT) operation using high-performance, state-of-the-art OECT materials. Depending on the choice of the active material, such reactions yield hydrogen peroxide (H 2 O 2 ), a reactive side-product, which may be harmful to the local biological environment and may also accelerate device degradation. A design strategy is reported for the development of redox-active organic semiconductors based on donor–acceptor copolymers that prevents the formation of H 2 O 2 during device operation. This study elucidates the previously overlooked side-reactions between redox-active conjugated polymers and molecular oxygen in electrochemical devices for bioelectronics, which is critical for the operation of electrolyte-gated devices in application-relevant environments.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Accelerated Characterization of Electrode‐Electrolyte Equilibration

Operational durability is poorly characterized by traditional (photo)electrocatalyst discovery workflows, creating a barrier to scale-up and deployment. Corrosion is a prominent degradation mechanism whose thermodynamics depend on the concentration of corrosion products in electrolyte. We present an automated system for characterizing the equilibration of (photo)electrodes with dissolved metals in electrolyte for a given electrode, pH, and electrochemical potential. Automation of electrode selection, electrolyte preparation, and electrolyte aliquoting enables rapid identification of self-passivating electrodes and estimation of the equilibrium dissolved metals concentrations. The technique is demonstrated for metal oxide photoanodes in alkaline electrolyte, where BiVO 4 is found to continually corrode, in agreement the literature. An amorphous Ni−Sb−O photoanode is found to passivate with a Ni-rich coating on the order of 1 monolayer with less than 1 μM total dissolved metals in electrolyte, demonstrating its suitability for durable photoelectrochemical operation. The automation and throughput of the instrument are designed for incorporation in accelerated electrocatalyst discovery workflows so that durability can be considered on equal footing with activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗

DOE OSTI2023

Conceptual Design of a 20 T Hybrid Cos-Theta Dipole Superconducting Magnet for Future High-Energy Particle Accelerators

High energy physics research will need more and more powerful circular accelerators in the next decades. It is therefore desirable to have dipole magnets able to produce the largest possible magnetic field, in order to keep the machine diameter within a reasonable size. A 20 T dipole is considered a desired achievement since it would allow the construction of an 80 km machine, able to circulate 100 TeV proton beams. In order to reach 20 T, a hybrid Low-Temperature Superconductor (LTS) - High-Temperature Superconductor (HTS) magnet is needed, since LTS technology is presently limited to ~16 T for accelerator magnet applications. In this paper, we present the design of a 6 layers 20 T hybrid dipole magnet using Nb 3 Sn (LTS) and Bi2212 (HTS). Here we show that it is possible to achieve this magnetic field with accelerator field quality, with sufficient margin on a realistic conductor, keeping the stresses within safe limit, avoiding conductor degradation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1990

Composite materials for space applications

The objectives of the program were to: generate mechanical, thermal, and physical property test data for as-fabricated advanced materials; design and fabricate an accelerated thermal cycling chamber; and determine the effect of thermal cycling on thermomechanical properties and dimensional stability of composites. In the current program, extensive mechanical and thermophysical property tests of various organic matrix, metal matrix, glass matrix, and carbon-carbon composites were conducted, and a reliable database was constructed for spacecraft material selection. Material property results for the majority of the as-fabricated composites were consistent with the predicted values, providing a measure of consolidation integrity attained during fabrication. To determine the effect of thermal cycling on mechanical properties, microcracking, and thermal expansion behavior, approximately 500 composite specimens were exposed to 10,000 cycles between -150 and +150 F. These specimens were placed in a large (18 cu ft work space) thermal cycling chamber that was specially designed and fabricated to simulate one year low earth orbital (LEO) thermal cycling in 20 days. With this rate of thermal cycling, this is the largest thermal cycling unit in the country. Material property measurements of the thermal cycled organic matrix composite laminate specimens exhibited less than 24 percent decrease in strength, whereas, the remaining materials exhibited less than 8 percent decrease in strength. The thermal expansion response of each of the thermal cycled specimens revealed significant reduction in hysteresis and residual strain, and the average CTE values were close to the predicted values.

Rawal, Suraj P.↗

DOE OSTI2020

Dephasingless Laser Wakefield Acceleration

Laser wakefield accelerators (LWFAs) produce significantly high gradients enabling compact accelerators and radiation sources, but face design limitations, such as dephasing, occurring when trapped electrons outrun the accelerating phase of the wakefield. In this report we combine spherical aberration with a novel cylindrically symmetric echelon optic to spatiotemporally structure an ultra-short, high-intensity laser pulse that can overcome dephasing by propagating at any velocity over any distance. The ponderomotive force of the spatiotemporally shaped pulse can drive a wakefield with a phase velocity equal to the speed of light in vacuum, preventing trapped electrons from outrunning the wake. Simulations in the linear regime and scaling laws in the bubble regime illustrate that this dephasingless LWFA can accelerate electrons to high energies in much shorter distances than a traditional LWFA—a single 4.5 m stage can accelerate electrons to TeV energies without the need for guiding structures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators

Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applications in cloud and HPC. Owing to their unique compute and memory characteristics that come from an interplay between dense and sparse phases of computations, the emergence of reconfigurable dataflow (aka spatial) accelerators offers promise for acceleration by mapping optimized dataflows (i.e., computation order and parallelism) for both phases. The goal of this work is to characterize and understand the design-space of dataflow choices for running GNNs on spatial accelerators in order for the compilers to optimize the dataflow based on the workload. Specifically, we propose a taxonomy to describe all possible choices for mapping the dense and sparse phases of GNNs spatially and temporally over a spatial accelerator, capturing both the intra-phase dataflow and the inter-phase (pipelined) dataflow. Using this taxonomy, we do deep-dives into the cost and benefits of several dataflows and perform case studies on implications of hardware parameters for dataflows and value of flexibility to support pipelined execution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A Collaboration Website for Muon Catalyzed Fusion and Muon Beam Production

This project sets up a website to support the nascent Muon Catalyzed Fusion collaboration including development of particle accelerators and transport beamlines for muon beams. The website is envisioned as having the general public information pages and private pages for collaboration members. Multimedia elements like images, text animations, and video lectures, covering a broad spectrum of topics will populate the educational site, covering muon facilities, to comprehensive explorations and seminal documents that define the science of Muon Catalyzed Fusion, Acceleration, Applications, Instrumentation, Beamline Design, and beam dynamics design codes. Ensuring compatibility across devices and operating systems, it also features integration with Google Docs for collaboration, a code repository (GitHub), a blog platform with comments (WordPress), the potential for ChatGPT integration and interactive graph plotting with Python Plotty to enhance data visualization. This project will maintain public and protected private pages, due to the proprietary nature of the work or research in progress. The public sections will be built to foster dissemination of information and highlight recent work within the NK Labs collaboration, including lectures, published papers, and regular blog posts with open commenting. The private section will support unpublished or nonpublic research, by facilitating collaborative efforts through integrated Google Docs and Python Plotty for shared graphing work. Ultimately, this project strives to make complex scientific knowledge more accessible to the public, foster enhanced collaboration, and serve as a platform for sharing cutting-edge research in Muon Catalyzed Fusion and Accelerators.

43 PARTICLE ACCELERATORS↗