The FAST beamline is the injector for the planned Gamma-Ray Electron ENhanced Source (GREENS) program, which aims to achieve the demonstration and first application of a high-efficiency, high-average-power free-electron laser at 515 nm. FAST-GREENS requires high 5D peak brightness; transverse normalized projected emittances of 3 mm-mrad and a peak current of 600 A are the minimum beam requirements for the FEL to reach the 10% efficiency goal. In this work, studies of the low-energy section of the FAST beamline are presented toward these ends, including preliminary measurements of beam compression and beam emittance. An effort toward developing a high-fidelity simulation model that could be later optimized for FAST-GREENS is presented.

Machine learning (ML) has the potential for significant impact on the modeling, operation, and control of particle accelerators due to its ability to model nonlinear behavior, interpolate on complicated surfaces, and adapt to system changes over time. Anomaly detection in particular has been highlighted as an area where ML can significantly impact the operation of accelerators. These algorithms work by identifying subtle behaviors of key variables prior to negative events. Efforts to apply ML to anomaly detection have largely focused on subsystems such as RF cavities, superconducting magnets, and losses in rings. However, dedicated efforts to understand how to apply ML for anomaly detection in linear accelerators have been limited. In this paper the use of autoencoders is explored to identify anomalous behavior in measured data from the Fermilab low-energy linear accelerator.

Shaped emitters are of interest to a broad range of applications in vacuum electronic devices. In particular, thermionic energy converters (TECs) take advantage of shaped emitters to increase the local surface field, thereby extracting more current for a given cathode temperature and applied voltage. However, modeling these devices is challenging; Warp [J.-L. Vay, D. P. Grote, R. H. Cohen, and A. Friedman, Comput. Sci. Discov. 5, 014019 (2012)] is a fully 3D particle-in-cell code capable of handling a wide range of physics problems and is well suited to modeling TECs. Additionally, recent improvements to Warp have enabled the accurate modeling of emitters with arbitrary curved surfaces. Specifically, the inclusion of subgrid resolution for computing the electrostatic potential and the ability to apply mesh refinement for specific areas of interest allow for a more accurate solution to the fields on these surfaces. These improvements coupled with Warp’s ability to handle variable particle weights make it an ideal candidate for simulating these complex devices. In this paper, the authors study the applicability of different subgrid configurations for simulating shaped emission surfaces and field convergence for different mesh-refinement techniques. They then implement a custom weighting algorithm that allows for uniform sampling of emission surfaces with a large variation in the surface electric field. They then use this algorithm to study emission for curved emitters in both the field-enhancement regime and the space-charge regime.

The development, testing and use of particle accelerator modeling codes is a core competency of accelerator research laboratories around the world, and likewise for synchrotron radiation and X-ray optics codes at lightsource facilities. Such codes require time and training to learn a command-line workflow involving multiple input and configuration files, execution on a high-performance server or cluster, post-processing with specialized software and finally visualization. Such workflows are error prone and difficult to reproduce. Cloud computing and UI design are core competencies of RadiaSoft LLC, where the Sirepo framework is being developed to make state of the art codes available in the browser of any desktop, laptop or tablet. We present our initial successes as real world examples of knowledge exchange between industry and the research community. This work is leading to broader knowledge exchange throughout the community by facilitating education of students and enabling instantaneous sharing of simulation details between colleagues. Sirepo design objectives include: seamless integration with legacy codes, low barrier to entry for new users, configuration transfer to command-line mode, catalog of provenance to aid reproducibility, and simplified collaboration through multimodal sharing. The combination of intuitive browser-based GUIs and Sirepo's server-side application container technology enables simplified computational archiving and reproducibility. If embraced by the community, this could become an important asset for the design, commissioning and future upgrade of particle accelerator and X-ray beamline facilities.

Traditional finite-difference particle-in-cell methods for modeling self-consistent space charge introduce nonHamiltonian effects that make long-term tracking in storage rings unreliable. Foremost of these is so-called grid heating. Particularly for studies where the Hamiltonian invariants are critical for understanding the beam dynamics, such as nonlinear integrable optics, these spurious effects make interpreting simulation results difficult. To remedy this, we present a symplectic spectral space charge algorithm that is free of non-Hamiltonian numerical effects and, therefore, suitable for long-term tracking studies. We present initial results demonstrating the implementation of the algorithm, using a spectral representation of the fields and macro particles to preserve Hamiltonian structures. We then discuss applications to the Integrable Optics Test Accelerator (IOTA), currently under construction at Fermilab.

In an electron lens a high-current electron beam with a carefully tailored transverse profile is confined by a solenoid magnetic field. Electron lenses have been applied to, or proposed for uses such as hadron beam halo scraping, nonlinear lens elements, electron cooling and space charge compensation. in all cases a multi-amp beam at relatively low energy (normally below an MeV) must be maintained and collective effects mitigated. In the case of space charge neutralization, especially, impact ionization of a background gas must be modeled and accounted for. In this work we examine one electron lens application, a space charge neutralized electron cooling system, designed for an electron-ion collider. The particle-in-cell code Warp is used to study propagation of the electron beam through the solenoid field. Space charge neutralization is provided by impact ionization of a background hydrogen gas, which is also included in the simulations. For optimal cooling it is essential that space charge be sufficiently neutralized such that the magnetized electron trajectories along the magnetic field lines are not disturbed. We show results of simulations studying the buildup of ionized gas in the cooler and resulting space charge neutralization levels. The impact on the electron gyrocenters in the beam is quantified in terms of beam and gas conditions and the stability of neutralization levels is examined.

Thermionic energy converters (TEC) are an attractive technology for modular, efficient transfer of heat to electrical energy. The steady-state dynamics of a TEC are a function of the emission characteristics of the cathode and anode, an array of intra-gap electrodes and dielectric structures, and the self-consistent dynamics of the electrons in the gap. Proper modeling of these devices requires selfconsistent simulation of the electron interactions in the gap. We present results from simulations of these devices using the particle-in-cell codeWarp, developed at Lawrence Berkeley National Lab. We consider the role of individual energy loss mechanisms in reducing device efficiency, including kinetic losses, radiative losses, and dielectric charging. We discuss the implementation of an external circuit model to provide realistic feedback. Lastly, we illustrate the potential to use nonlinear optimization to maximize the efficiency of these devices by examining grid transparency.

Thermionic energy converters (TEC) are an attractive technology for modular, efficient transfer of heat to electric energy. TECs are comprised of narrowly-separated plates held at specified voltages. Thermionic emission at the cathode releases electrons which travel to the anode, producing a current which may generate electrical power. Simple structures are often space-charge limited, because operating temperatures produce currents exceeding the corresponding Child- Langmuir limit for the device. The steady-state dynamics of a TEC depend on the emission characteristics of the cathode and anode, an array of intra-gap electrodes and dielectric structures, and the self-consistent dynamics of the electrons in the gap. Fundamental advances in the efficiency of these devices can be achieved through optimization of the electrode arrangements along with new low-work-function materials. We present results from simulations of these devices using the particle-in-cell code Warp 1 , developed at Lawrence Berkeley National Lab. We evaluate the total efficiency of these devices through the quantification of individual energy loss mechanisms, including kinetic losses, radiative losses, and dielectric charging. Using nonlinear optimization schemes including evolutionary algorithms, we find peak operating points for realistic conditions. We then discuss the potential for TECs to operate as individual power sources or in conjunction with conventional power plants.