Author: Lehe, R.    [Lehé, R.]
Paper Title Page
MOPOMS012 Simulation Studies of Drive Beam Instability in a Dielectric Wakefield Accelerator 645
 
  • W.H. Tan, P. Piot
    Northern Illinois University, DeKalb, Illinois, USA
  • A. Huebl, R. Jambunathan, R. Lehé, A. Myers, T. Rheaume, J.-L. Vay, W. Zhang
    LBNL, Berkeley, California, USA
  • P. Piot
    ANL, Lemont, Illinois, USA
  
  Funding: This work is supported by the US DOE award DE-SC0018656 with NIU and DE-AC02-06CH11357 with ANL. This work used resources from NERSC, supported by DOE contract DE-AC02-05CH11231. This research used WarpX, which is supported by the US DOE Exascale Computing Project. Primary WarpX contributors are with LBNL, LLNL, CEA-LIDYL, SLAC, DESY, CERN, and Modern Electron.
Beam-driven collinear wakefield acceleration using structure wakefield accelerators promises a high gradient acceleration within a smaller physical footprint. Sustainable extraction of energy from the drive beam relies on precise understanding of its long term dynamics and the possible onset or mitigation of the beam instability. The advance of computational power and tools makes it possible to model the full physics of beam-driven wakefield acceleration. Here we report on the long-term beam dynamics studies of a drive beam considering the example of a dielectric waveguide using high fidelity particle-in-cell simulations performed with WarpX. 		 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOMS012  
About • Received ※ 08 June 2022 — Revised ※ 10 June 2022 — Accepted ※ 13 June 2022 — Issue date ※ 16 June 2022
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WEPOST030 Multitask Optimization of Laser-Plasma Accelerators Using Simulation Codes with Different Fidelities 1761
 
  • Á. Ferran Pousa, M. Kirchen, A. Martinez de la Ossa, M. Thévenet
    DESY, Hamburg, Germany
  • S.T.P. Hudson, J.M. Larson
    ANL, Lemont, Illinois, USA
  • A. Huebl, R. Lehé, J.-L. Vay
    LBNL, Berkeley, California, USA
  • S. Jalas
    University of Hamburg, Hamburg, Germany
  
  When designing a laser-plasma acceleration experiment, one commonly explores the parameter space (plasma density, laser intensity, focal position, etc.) with simulations in order to find an optimal configuration that, for example, minimizes the energy spread or emittance of the accelerated beam. However, laser-plasma acceleration is typically modeled with full particle-in-cell (PIC) codes, which can be computationally expensive. Various reduced models can approximate beam behavior at a much lower computational cost. Although such models do not capture the full physics, they could still suggest promising sets of parameters to be simulated with a full PIC code and thereby speed up the overall design optimization. In this work we automate such a workflow with a Bayesian multitask algorithm, where each task has a different fidelity. This algorithm learns from past simulation results from both full PIC codes and reduced PIC codes and dynamically chooses the next parameters to be simulated. We illustrate this workflow with a proof-of-concept optimization using the Wake-T and FBPIC codes. The libEnsemble library is used to orchestrate this workflow on a modern GPU-accelerated high-performance computing system.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOST030  
About • Received ※ 08 June 2022 — Accepted ※ 11 June 2022 — Issue date ※ 14 June 2022  
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