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Dunham, Bruce

Publications and source records attributed to Dunham, Bruce.

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Development of an ERL for Coherent Electron Cooling at the Electron-Ion Collider

The Electron-Ion Collider (EIC) is currently under development of to be built at Brookhaven National Lab and requires cooling during collisions in order to preserve the quality of the hadron beam despite degradation due to intra-beam scattering and beam-beam effect. An Energy Recovery Linac (ERL) is being designed to deliver the necessary electron beam for the Coherent electron Cooling (CeC) of the hadron beam, with an electron bunch charge of 1 nC and an average current of 100 mA; two modes of operation are being developed for 150 and 55 MeV electrons, corresponding to 275 and 100 GeV protons. The injector of this SHC-ERL is shared with the Precooler ERL, which cools lower energy proton beams via bunched-beam cooling, as used in Low Energy RHIC electron Cooling (LEReC). This paper reviews the current state of the design.

Accelerator Physics↗
DOE OSTI2022

Prospects for Machine Learning and Pulse Shaping on the Scorpius Accelerator [Poster]

The Advanced Sources and Detectors (ASD) project aims to build Scorpius, a multi pulse linear induction accelerator capable of delivering a 1.4 kA electron beam at energies up to 24 MeV. One of the primary advancements of Scorpius is the use of solid state pulsed power (SSPP) to provide flexibility in pulse shaping by independently triggering 45 individual stages stacked in each of 984 line replaceable units (LRU), with 168 LRUs dedicated to the injector. By leveraging circuit modeling of each LRU stage, a machine learning model of the SSPP will be developed to allow for optimization of the pulse shape, including pulse flattening and reflection mitigation. Particle-in-cell simulations of Scorpius have, for example, demonstrated that reducing reflections during multi-pulse operation mitigates beam spill by preventing the production of off-energy electrons between pulses, thereby abating stimulated ion desorption from the wall and beam charge neutralization. This machine learning model will be validated and tuned with experimental data collected from the Scorpius injector and Integrated Test Stand

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Novel Ultrabright and Air-Stable Photocathodes Discovered from Machine Learning and Density Functional Theory Driven Screening

The high brightness, low emittance electron beams achieved in modern X-ray free-electron lasers (XFELs) have enabled powerful X-ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub-nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial-and-error-based iterative approaches, this work represents the first data-driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M 2 O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state-of-the-art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.

36 MATERIALS SCIENCE↗