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This final technical report summarizes the development of structure wakefield accelerator (SWFA) technology to enable high-gradient, efficient particle acceleration.
A new superconducting accelerometer, capable of measuring both linear and angular accelerations, is under development at the University of Maryland. A single superconducting proof mass is magnetically levitated against gravity or any other proof force. Its relative positions and orientations with respect to the platform are monitored by six superconducting inductance bridges sharing a single amplifier, called the Superconducting Quantum Interference Device (SQUID). The six degrees of freedom, the three linear acceleration components and the three angular acceleration components, of the platform are measured simultaneously. In order to improve the linearity and the dynamic range of the instrument, the demodulated outputs of the SQUID are fed back to appropriate levitation coils so that the proof mass remains at the null position for all six inductance bridges. The expected intrinsic noise of the instrument is 4 x 10(exp -12)m s(exp -2) Hz(exp -1/2) for linear acceleration and 3 x 10(exp -11) rad s(exp -2) Hz(exp -1/2) for angular acceleration in 1-g environment. In 0-g, the linear acceleration sensitivity of the superconducting accelerometer could be improved by two orders of magnitude. The design and the operating principle of a laboratory prototype of the new instrument is discussed.
The issues of industrial productivity and economic competitiveness are of major significance in the U.S. at present. By advancing the science of design, and by creating a broad computer-based methodology for automating the design of artifacts and of industrial processes, we can attain dramatic improvements in productivity. It is our thesis that developments in computer science, especially in Artificial Intelligence (AI) and in related areas of advanced computing, provide us with a unique opportunity to push beyond the present level of computer aided automation technology and to attain substantial advances in the understanding and mechanization of design processes. To attain these goals, we need to build on top of the present state of AI, and to accelerate research and development in areas that are especially relevant to design problems of realistic complexity. We propose an approach to the special challenges in this area, which combines 'core work' in AI with the development of systems for handling significant design tasks. We discuss the general nature of design problems, the scientific issues involved in studying them with the help of AI approaches, and the methodological/technical issues that one must face in developing AI systems for handling advanced design tasks. Looking at basic work in AI from the perspective of design automation, we identify a number of research problems that need special attention. These include finding solution methods for handling multiple interacting goals, formation problems, problem decompositions, and redesign problems; choosing representations for design problems with emphasis on the concept of a design record; and developing approaches for the acquisition and structuring of domain knowledge with emphasis on finding useful approximations to domain theories. Progress in handling these research problems will have major impact both on our understanding of design processes and their automation, and also on several fundamental questions that are of intrinsic concern to AI. We present examples of current AI work on specific design tasks, and discuss new directions of research, both as extensions of current work and in the context of new design tasks where domain knowledge is either intractable or incomplete. The domains discussed include Digital Circuit Design, Mechanical Design of Rotational Transmissions, Design of Computer Architectures, Marine Design, Aircraft Design, and Design of Chemical Processes and Materials. Work in these domains is significant on technical grounds, and it is also important for economic and policy reasons.
Measuring longitudinal beam parameters is important for operation and development of high intensity linear accelerators, but it is notoriously difficult for proton and ion beams at non-relativistic energies. The Bunch Shape Monitor (BSM) is a device used for measuring the longitudinal bunch distribution in ion linacs. The existing BSM models have poor electron collection efficiency from the wire and are limited to one-dimensional measurements of the phase coordinate. In response to this problem, we have developed a new generation BSM with improved performance. The proposed design incorporates three major innovations: First, the collection efficiency was improved by adding a focusing field between the wire and the entrance slit, which will also allow measurements over a much wider dynamic range. Second, an improvement in the measurement speed was achieved by sampling longitudinal profiles of multiple energy slices simultaneously, where the BSM wire is placed at the exit of an ion spectrometer so that ions with different energies hit the wire at different horizontal coordinates along the wire. Finally, the design incorporates a motion system that can shift the wire and deflecting cavity together, enabling transverse profile measurements like a wire scanner. Here, in this paper, we will provide the design of the new BSM and report on its beam test results at the Spallation Neutron Source facility in Oak Ridge National Laboratory.
The recent development of differentiable simulation codes for particle accelerators has enabled gradient-based workflows that promise finer control and more realistic modeling of accelerator facilities. However, when using reverse-mode automatic differentiation, the memory usage continuously increases during the simulation, and can potentially exceed the available hardware memory - especially when costly space charge computation is included. To study the memory requirements for differentiable simulations, we have implemented space charge in Cheetah, a PyTorch-based beam tracking code that supports reverse-mode differentiation. We find that the memory usage for reverse-mode differentiation grows linearly with the number of macroparticles and cells, and that it is proportional to the number of space charge kicks involved in the simulation. This general scaling can be used to evaluate whether a given differentiable simulation is feasible given hardware memory constraints.
Many small-scale industrial radiation facilities rely on hazardous radioisotope-based gamma ray irradiators, which are being gradually replaced by alternative technologies, such as particle accelerators. RadiaBeam has developed a compact, cabinet-size, 3 MeV electron-linac-based irradiator to match the penetration of Co-60 and provide high irradiation throughput. Here the linac utilizes X-band RF power to reduce size, employs an innovative split-linac approach to lower costs, and features a novel X-ray conversion target geometry that spreads the beam out across the target, providing a low dose uniformity ratio. In this paper, we discuss the accelerator design, fabrication, and test results.
Accelerated testing must address the failure mechanisms active within the devices undergoing tests in order to assess lifetimes in a meaningful way. The assumption of constant temperature, thermally activated lifetime, based upon the Arrhenius assumptions, does not always provide the necessary understanding to interpret accelerated tests in microelectronics. Plastic encapsulants, dielectric polymers, and underfill materials are subject to delamination and cracking with thermal cycling. Crack propagation during use environment exposure, drives the potential for failure of microelectronic devices and is therefore a necessary focal point in qualification and life testing. This paper reviews the available research in the application of fracture mechanics to this class of problems in microelectronics including relevant test data. In addition, useful acceleration factor models are derived for polymer crack propagation based on principles of linear elastic fracture mechanics. Further, a simple approach to estimating the minimum temperature cycling ranges, necessary to propagate a crack, is also presented. Finally, a methodology of applying acceleration factors to develop testing plans is shown, with an example in spaceflight for a cubesat in low Earth orbit. Overall, this is a paper that shows a useful and appropriate process for creating physics of failure based life testing for delamination and cracking failures in microelectronic polymers in a temperature cycling environment.
The Chemistry Evaluation and Diagnostics Package (ChemDyg) is an open-source tool designed for the Energy Exascale Earth System Model (E3SM) developed by the U.S. Department of Energy. ChemDyg facilitates routine evaluation, tailored development, and in-depth analysis of atmospheric chemistry through its modular architecture, allowing users to compare model outputs with observational data. Version 1.1.0 introduces a robust set of diagnostic capabilities, including climatology, time evolution of key tracers, diurnal and annual cycle analyses, and extensive budget diagnostics. These features help identify model discrepancies and enhance the representation of atmospheric chemistry in E3SM. Each self-contained diagnostic set includes dedicated scripts and documentation for ease of use. The interactive HTML output improves data accessibility, accelerating chemistry model development. Additionally, ChemDyg's flexible framework allows for customization, enabling users to create unique diagnostic sets for specific scientific contributions.
A multigrid acceleration technique has been developed to solve the three-dimensional Navier-Stokes equations efficiently. An explicit multistage Runge-Kutta type of time-stepping scheme is used as the basic algorithm in conjunction with the multigrid scheme. Solutions for flow over a finite wing have been obtained on extremely fine meshes in order to achieve grid convergence of the solutions. Present solutions indicate that the number of multigrid cycles required to achieve a given level of convergence does not increase with the number of mesh points employed, making it a very attractive scheme for fine meshes.
Metallic fuels, particularly U—10Zr, are promising candidates for next-generation sodium-cooled fast reactors. Irradiation of nuclear fuels in reactors can lead to the formation of solid and gas fission product which subsequently forms microstructural pores, deteriorating fuel performance. Due to the massive amount of pores and complex phases formed, a quantitative description of fission gas pores is not yet available, preventing the development of microstructure-informed fuel performance modeling for fuel qualification. This paper applied a pre-trained deep learning model to ~10,260 high magnification scanning electron microscopy images. This method increased the accuracy of fission gas pore segmentation and allows statistical features to be extracted which cannot be achieved manually. A pre-trained decision tree model worked on the segemenation results and further classified the pores into different categories to produce a correlation between the pores, movement of lanthanides, and temperature gradient during irradiation. Finally, this paper emphasizes the potentials of machine learning models to accelerate fuel research, development, and qualification for advanced reactors.
The Space Acceleration Measurement System (SAMS) has been developed by NASA to monitor the microgravity acceleration environment aboard the space shuttle. The amount of data collected by a SAMS unit during a shuttle mission is in the several gigabytes range. Adaptive Resonance Theory 2-A (ART2-A), an unsupervised neural network, has been used to cluster these data and to develop cause and effect relationships among disturbances and the acceleration environment. Using input patterns formed on the basis of power spectral densities (psd), data collected from two missions, STS-050 and STS-057, have been clustered.
The Strategic Laboratory Assessment (SLA), a collaborative team of SRNL and ORNL personnel, has been established to advance the objectives of the Surplus Plutonium Disposition (SPD) Project, by identifying and developing technologies to accelerate disposition, reduce life cycle costs, minimize worker radiation exposure, improve worker safety, and minimize Surplus Plutonium Disposition Program risks. [1] The SLA team has identified can cutting and plutonium (Pu) oxide size reduction as two glovebox processes where technology enhancements would be valuable. The DOESTD-3013 package currently in use for Pu downblending requires cutting two nested cans before the inner convenience can that holds the Pu oxide may be accessed for further processing. A rotary tubing-style cutter is used for opening the 3013 packages within the glovebox. Collet changeouts are required between cutting of the outer and inner cans. The SLA team is currently developing and testing an adjustable-clamp can cutter design that eliminates collet changeouts and allows cutting of the outer and inner can at the same time, resulting in significant reduction of radiological dose and process time, as well as improved ergonomics. To meet the Pu oxide particle size requirement, size reduction of Pu oxide agglomerations must be performed within the process gloveboxes. The SLA team has identified jaw crushing technology as an alternative to the currently employed rotary mill. Jaw crusher advantages include reduced dust within the glovebox, increased batch sizes, and easier integration with other glovebox processes due to the flow-through nature of jaw crushing. Commercially manufactured jaw crushers are either too large and/or too heavy for implementation in the SPD gloveboxes, so the SLA team is developing and testing a custom jaw crusher to meet the needs of the SPD Project.
Abstract Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The underlying problem enforces strict constraints on both individual states and actions as well as cumulative (global) constraints on energy requirements of the beam. Using historical accelerator data, we develop a physics-based surrogate model which is differentiable and allows for back-propagation of gradients. The results are evaluated in the form of a Pareto-front with two objectives. We show that the DDRL outperforms MFRL, BO, and GA on high dimensional problems.
HgI2 is an excellent semiconductor material for a low energy, room temperature x-ray spectrometer. The high values of the atomic numbers for its constituent elements gives high x-ray and gamma ray stopping power. The band gap of HgI2 is significantly higher than other commonly used semiconductors. Owing to the large value band gap, the leakage current for HgI2 devices is smaller, thus allowing low noise performance. Devices fabricated from HgI2 crystals have demonstrated energy resolution sufficient to distinguish the x-ray emission from the neighboring elements on the periodic table. Also the power requirements of HgI2 are very low. These characteristics make a HgI2 spectrometer an ideal component in a satellite based detection system. Unfortunately, HgI2 crystals tend to deteriorate with time, even if protected by standard semiconductor encapsulants. This degradation ruins the performance of the device in terms of its energy resolution and pulse amplitude. The degrading mechanism is believed to be material loss occurring from below the electrodes, due to high vapor pressure of HgI2 at room temperature. To address this major obstacle to rapid expansion of HgI2 technology, a research program aimed at improving device stability by encapsulation with inert polymeric materials was carried out. The program focused specifically on optimizing the encapsulant materials and their deposition techniques. The principal objectives for this program were device encapsulation, device testing, and accelerated testing to ensure very long term stability of these high resolution sensors. A variety of encapsulants were investigated with the selection criteria based on their chemical diffusion barrier properties, mechanical stability, reactivity, and morphology of encapsulant films. The investigation covered different classes of encapsulants including solvent based encapsulants, vapor deposited encapsulants, and plasma polymerized encapsulants. A variety of characterization techniques were employed to examine their effectiveness in stabilizing HgI2 devices; these included permeability evaluation, vacuum and heat testing, scanning electron microscopy (SEM) as well as studying the detector performance of coated detectors. The plasma polymerized films appear to have entirely solved the HgI2 degradation problem. Another achievement of this program was the development of an accelerated testing technique which correlates extremely well with long term tesing.
An integrated computational materials engineering (ICME) method is in development for rapid thermodynamic experimental investigation and high-fidelity computational modeling of refractory multi-principal element alloys (RMPEAs). These ultra-high-temperature (UHT) alloys are of interest for structural applications in extreme environments, but deficiency of reliable data, especially melting temperatures, impedes the prediction of alloys with favorable properties. The method leverages UHT capabilities and computational expertise of LLNL’s Materials Science Division and the McCormack Lab’s UHT conical nozzle levitation (CNL) system to iteratively map the Nb-Ta-Zr phase space, with focus on the liquidus surface, through targeted experiments selected by quantifying uncertainty in the thermodynamic model fitting parameters. This method will reduce the time to map uncharted RMPEA phase space and thereby accelerate discovery and development of advanced materials for applications in extreme environments.
The data base for composite materials' properties as they are affected by the environments encountered in operating conditions, both in flight and at ground terminals is expanded. Absorbed moisture degrades the mechanical properties of graphite/epoxy laminates at elevated temperatures. Since airplane components are frequently exposed to atmospheric moisture, rain, and accumulated water, quantitative data are required to evaluate the amount of fluids absorbed under various environmental conditions and the subsequent effects on material properties. In addition, accelerated laboratory test techniques are developed are reliably capable of predicting long term behavior. An accelerated environmental exposure testing procedure is developed, and experimental results are correlated and compared with analytical results to establish the level of confidence for predicting composite material properties.
NASA has exciting plans for space science and Earth observations during the next decade. A broad range of advanced spacecraft and measurement technologies will be needed to support these plans within the existing budget and schedule constraints. Many of these technology needs are common to both NASA's Office of Earth Science (OES) and Office of Space Sciences (OSS). Even though some breakthrough technologies have been identified to address these needs, project managers have traditionally been reluctant to incorporate them into flight programs because their inherent development risk. To accelerate the infusion of new technologies into its OES and OSS missions, NASA established the New Millennium Program (NMP). This program analyzes the capability needs of these enterprises, identifies candidate technologies to address these needs, incorporates advanced technology suites into validation flights, validates them in the relevant space environment, and then proactively infuses the validated technologies into future missions to enhance their capabilities while reducing their life cycle cost. The NMP employs a cross-enterprise Science Working Group, the NASA Enterprise science and technology roadmaps to define the capabilities needed by future Earth and Space science missions. Additional input from the science community is gathered through open workshops and peer-reviewed NASA Research Announcement (NRAs) for advanced measurement concepts. Technology development inputs from the technology organizations within NASA, other government agencies, federally funded research and development centers (FFRDC's), U.S. industry, and academia are sought to identify breakthrough technologies that might address these needs. This approach significantly extends NASA's technology infrastructure. To complement other flight test programs that develop or validate of individual components, the NMP places its highest priority on system-level validations of technology suites in the relevant space environment. This approach is not needed for all technologies, but it is usually essential to validate advanced system architectures or new measurement concepts. The NMP has recently revised its processes for defining candidate validation flights, and selecting technologies for these flights. The NMP now employs integrated project formulation teams, 'Which include scientists, technologists, and mission planners, to incorporate technology suites into candidate validation flights. These teams develop competing concepts, which can be rigorously evaluated prior to selection for flight. The technology providers for each concept are selected through an open, competitive, process during the project formulation phase. If their concept is selected for flight, they are incorporated into the Project Implementation Team, which develops, integrates, tests, launches, and operates the technology validation flight. Throughout the project implementation phase, the Implementation Team will document and disseminate their validation results to facilitate the infusion of their validated technologies into future OSS and OES science missions. The NMP has successfully launched its first two Deep Space flights for the OSS, and is currently implementing its first two Earth Orbiting flights for the OES. The next OSS and OES flights are currently being defined. Even though these flights are focused on specific Space Science and Earth Science themes, they are designed to validate a range of technologies that could benefit both enterprises, including advanced propulsion, communications, autonomous operations and navigation, multifunctional structures, microelectronics, and advanced instruments. Specific examples of these technologies will be provided in our presentation. The processes developed by the NMP also provide benefits across the Space and Earth Science enterprises. In particular, the extensive, nation-wide technology infrastructure developed by the NMP enhances the access to breakthrough technologies for both enterprises.
Many recent efforts in developing hardware-accelerated spiking neural networks (SNNs) are characterized by deep co-design between algorithms, architectures, and devices. Architectural advances overcome device constraints by coupling together many small resistive-RAM (ReRAM) crossbars via a network-on-chip (NoC) for neuromorphic component operation. Concurrently, improved SNN training methods increase accuracy and structural sparsity in networks despite growing problem sizes. Finally, compilers leverage these attributes to minimize area and inter-crossbar communication while mapping large SNNs to sophisticated architectures. However, for compiler-driven co-design to realize increasingly complex and profitable optimizations, a compile-time view of power consumption is critical. We present PowerMappeR to express and optimize over mapping-, architecture-, and device-specific power consumption information. By modeling the dynamic power of well-established components, we develop an integer linear programming (ILP)-based, encoding-agnostic, parametric power estimation model. Using this model, we demonstrate practical improvements in area and inter-crossbar communication by 0%–9.5% and 1.4%–5.1%, respectively. We also limit hotspot formation during optimization, achieving comparable or better results in targeted metrics with up to 96.4%–97.1% restriction of hotspot magnitude. Finally, we introduce profile-guided formulations to reduce worst-case and expected-case hotspot magnitude by 40.7%–69.5% and 40.6%–56.3%, respectively. Optimizing worst-case hotspot magnitude incidentally improves expected-case magnitude by 10.85%–33.45%. Reciprocally, optimizing expected-case magnitude incidentally improves worst-case magnitude by 4.33%–39.87%. Validation against hardware simulators confirms that PowerMappeR can decrease dynamic power consumption by 12.6%–27.3%.