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C56-33b: Fast Accurate Simulation of CSR and Space-Charge for Intense Beams

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-SC0024245
Agency Tracking Number: 0000272974
Amount: $202,045.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: C56-33b
Solicitation Number: DE-FOA-0002903
Timeline
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-07-10
Award End Date (Contract End Date): 2024-07-09
Small Business Information
6525 Gunpark Dr. STE 370-411
Boulder, CO 80301-3333
United States
DUNS: 079099850
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jonathan Edelen
 (860) 930-5457
 jedelen@radiasoft.net
Business Contact
 David Bruhwiler
Phone: (720) 502-3928
Email: bruhwiler@radiasoft.net
Research Institution
N/A
Abstract

STATEMENT OF THE PROBLEM
Though decades of computational and experimental studies have advanced our understanding of
coherent synchrotron radiation, next generation accelerators will produce beams that no longer
conform to our existing models. Moreover, high fidelity simulations of coherent synchrotron
radiation and space-charge that utilize numerical solutions to the exact point particle potentials
are computationally very expensive rendering them impractical for use in the optimization of new
accelerators.
GENERAL STATEMENT OF HOW THE PROBLEM IS BEING ADDRESSED
Our approach is to perform a systematic evaluation of different coherent synchrotron radiation
solvers used to model different regimes. We will then build robust machine learning based
integrators that will speed up the calculation of coherent synchrotron radiation wakes for used in
optimization with conventional particle tracking codes. During Phase II we will explore new
physics models that can accurately capture these effects in new regimes.
WHAT IS TO BE DONE IN PHASE I?
During Phase I we will perform a detailed simulation campaign and benchmark against archive
data collected at a representative facility. We will then develop machine learning integrators and
build in robustness by developing new domain transfer methods that utilize autoencoders. Finally
we will evaluate the efficacy of implementing our solvers with particle tracking codes for use in
the modeling and optimization of advanced concept accelerators.
COMMERCIAL APPLICATIONS AND OTHER BENEFITS
The machine learning integrators developed under this proposal will no doubt improve the ability
to optimize novel accelerators but will also extend beyond accelerator technology. Machine
learning is a fast growing field, our innovative approach to domain transfer will no doubt have far
reaching applications in scientific computing.

* Information listed above is at the time of submission. *

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