E-2 Design, Optimization, Control
Paper Title Page
SAPAF01 Genetic Algorithm Enhanced by Machine Learning in Dynamic Aperture Optimization 8
  • Y. Li, W.X. Cheng, R.S. Rainer, L. Yu
    BNL, Upton, Long Island, New York, USA
  Funding: This work was supported by Department of Energy Contract No. DE-SC0012704
With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the NSLS-II Ring. During the evolution employed by the genetic algorithm, the population is classified into different clusters. The clusters with top average fitness are given elite status. Intervention is implemented by repopulating some potentially competitive candidates based on the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is improved while diversity is not lost. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.
slides icon Slides SAPAF01 [6.696 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICAP2018-SAPAF01  
About • paper received ※ 15 October 2018       paper accepted ※ 24 October 2018       issue date ※ 26 January 2019  
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SAPAF02 Optimization of Heavy-Ion Synchrotrons Using Nature-Inspired Algorithms and Machine Learning 15
  • S. Appel, W. Geithner, S. Reimann, M. Sapinski, R. Singh, D.M. Vilsmeier
    GSI, Darmstadt, Germany
  The application of machine learning and nature-inspired optimization methods, like for example genetic algorithms (GA) and particle swarm optimization (PSO) can be found in various scientific/technical areas. In recent years, those approaches are finding application in accelerator physics to a greater extent. In this report, nature-inspired optimization as well as the machine learning will be shortly introduced and their application to the accelerator facility at GSI/FAIR will be presented. For the heavy-ion synchrotron SIS18 at GSI, the multi-objective GA/PSO optimization resulted in a significant improvement of multi-turn injection performance and subsequent transmission for intense beams. An automated injection optimization with genetic algorithms at the CRYRING@ESR ion storage ring has been performed. The usage of machine learning for a beam diagnostic application, where reconstruction of space-charge distorted beam profiles from ionization profile monitors is performed, will also be shown. First results and the experience gained will be presented.  
slides icon Slides SAPAF02 [2.642 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICAP2018-SAPAF02  
About • paper received ※ 16 October 2018       paper accepted ※ 27 January 2019       issue date ※ 26 January 2019  
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SAPAF03 Comparison of Model-Based and Heuristic Optimization Algorithms Applied to Photoinjectors Using Libensemble 22
  • N.R. Neveu
    IIT, Chicago, Illinois, USA
  • S. T. P. Hudson, J.M. Larson
    ANL, Argonne, Illinois, USA
  • L.K. Spentzouris
    Illinois Institute of Technology, Chicago, Illinois, USA
  Funding: U.S. DOE, OS, contract DE-AC02-06CH11357 and grant DE-SC0015479.
Genetic algorithms are common and often used in the accelerator community. They require large amounts of computational resources and empirical adjustment of hyperparameters. Model based methods are significantly more efficient, but often labeled as unreliable for the nonlinear or unsmooth problems that can be found in accelerator physics. We investigate the behavior of both approaches using a photoinjector operated in the space charge dominated regime. All optimization runs are coordinated and managed by the Python library libEnsemble, which is developed at Argonne National Laboratory.
slides icon Slides SAPAF03 [0.653 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICAP2018-SAPAF03  
About • paper received ※ 11 November 2018       paper accepted ※ 19 November 2018       issue date ※ 26 January 2019  
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SAPAF04 Single Objective Genetic Optimization of an 85% Efficient Klystron 25
  • A. Jensen, J.J. Petillo
    Leidos Corp, Billerica, MA, USA
  • R.L. Ives, M.E. Read
    CCR, San Mateo, California, USA
  • J. Neilson
    SLAC, Menlo Park, California, USA
  Overall efficiency is a critical priority for the next generation of particle accelerators as they push to higher and higher energies. In a large machine, even a small increase in efficiency of any subsystem or component can lead to a significant operational cost savings. The Core Oscillation Method (COM) and Bunch-Align-Compress (BAC) method have recently emerged as a means to greatly increase the efficiency of the klystron RF source for particle accelerators. The COM and BAC methods both work by uniquely tuning klystron cavity frequencies such that more particles from the anti-bunch are swept into the bunch before power is extracted from the beam. The single objective genetic algorithm from Sandia National Laboratory’s Dakota optimization library is used to optimize both COM and BAC based klystron designs to achieve 85% efficiency. The COM and BAC methods are discussed. Use of the Dakota optimization algorithm library from Sandia National Laboratory is discussed. Scalability of the optimization approach to High Performance Computing (HPC) is discussed. The optimization approach and optimization results are presented.  
slides icon Slides SAPAF04 [1.476 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICAP2018-SAPAF04  
About • paper received ※ 16 October 2018       paper accepted ※ 19 November 2018       issue date ※ 26 January 2019  
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Optimization of Hadron Therapy Beamlines Using a Novel Fast Tracking Code for Beam Transport and Beam-Matter Interactions  
  • C. Hernalsteens, K. André
    CERN, Geneva, Switzerland
  • V. Collignon, Q. Flandroy, B. Herregods
    IBA, Louvain-la-Neuve, Belgium
  • R. Jungers, Z. Wang
    UCL, Louvain-la-Neuve, Belgium
  • R. Tesse
    ULB - FSA - SMN, Bruxelles, Belgium
  The optimization of proton therapy beamlines challenges the traditional approach used in beam optics due to the very strict constraints on beam quality, especially for Pencil Beam Scanning, despite the large losses induced by the emittance increase coming from the energy degrader. In order to explore the performances of proton therapy beamlines, we proceed using a new fast beam tracking Python library coupled with a genetic algorithm. Global optimization algorithms such as the genetic algorithm or basin hopping schemes require numerous evaluations of the model and their practical implementations are limited by the computation time at each iteration. To overcome this limitation, while at the same time allowing an open-box user experience, a Python library has been developed, including transport models for the typical hadron therapy beamlines elements, as well as models for the computation of multiple Coulomb scattering. The Multi-Objective Genetic Algorithm (MOGA) allows to explore the parameter space in a global sense. This multi-objective algorithm enables the simultaneous optimization of complex constraints specific to proton therapy beamlines. Results for the IBA Proteus One system are presented and discussed in detail.  
slides icon Slides SUPAF03 [9.526 MB]  
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Uncertainty Quantification for the Fundamental Mode Spectrum of the European XFEL Cavities  
  • N. G. Georg, J. Corno, H. De Gersem, U. Römer, S. Schöps
    TEMF, TU Darmstadt, Darmstadt, Germany
  • S. Gorgi Zadeh, U. van Rienen
    Rostock University, Faculty of Computer Science and Electrical Engineering, Rostock, Germany
  • A.A. Sulimov
    DESY, Hamburg, Germany
  Funding: The authors would like to acknowledge the support by the DFG (German Research Foundation) in the framework of the Scientific Network SCHM 3127/1,2 that provided the basis for this collaborative work.
The fundamental mode spectrum of superconducting cavities is sensitive to small geometry deformations introduced by the manufacturing process. In this work we consider variations in the equatorial and iris radii of the 1.3 GHz TESLA cavities used at the European XFEL. The cavities with slightly perturbed geometry are simulated using a finite element based eigenvalue solver. Employing uncertainty quantification methods such as sparse-grids, statistical information about the fundamental mode spectrum can be efficiently calculated. Moreover, using global sensitivity analysis, in particular Sobol indices, the impact of the individual geometry parameters on the quantities of interest, i.e. resonance frequencies, field-flatness and the cell-to-cell coupling coefficient, can be computed. We will explain important aspects of the uncertainty quantification methodology and give numerical results for illustration.
slides icon Slides TUPAG08 [0.672 MB]  
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TUPAG14 Constrained Multi-Objective Shape Optimization of Superconducting RF Cavities to Counteract Dangerous Higher Order Modes 293
  • M. Kranjcevic, P. Arbenz
    ETH, Zurich, Switzerland
  • A. Adelmann
    PSI, Villigen PSI, Switzerland
  • S. Gorgi Zadeh, U. van Rienen
    Rostock University, Faculty of Computer Science and Electrical Engineering, Rostock, Germany
  High current storage rings, such as the Z operating mode of the FCC-ee, require superconducting radio frequency (RF) cavities that are optimized with respect to both the fundamental mode and the dangerous higher order modes. In order to optimize the shape of the RF cavity, a constrained multi-objective optimization problem is solved using a massively parallel implementation of an evolutionary algorithm. Additionally, a frequency-fixing scheme is employed to deal with the constraint on the frequency of the fundamental mode. Finally, the computed Pareto front approximation and an RF cavity shape with desired properties are shown.  
slides icon Slides TUPAG14 [3.001 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICAP2018-TUPAG14  
About • paper received ※ 19 October 2018       paper accepted ※ 10 December 2018       issue date ※ 26 January 2019  
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WEPAF04 Longitudinal Beam Dynamics in FRIB and ReA Linacs 330
  • A.S. Plastun, P.N. Ostroumov, A.C.C. Villari, Q. Zhao
    FRIB, East Lansing, USA
  The Front-End and first three cryomodules of the Facility for Rare Isotope Beam (FRIB) at Michigan State University (MSU) commissioned in July, 2018. The paper describes the online tuning procedures of the longitudinal beam dynamics through the FRIB linac. These procedures include tuning of the accelerating field phases and amplitudes in the cavities. We developed an automated simulation-based tuning procedure for the multi-harmonic buncher. In order to tune the radio-frequency quadrupole (RFQ) we measured and calculated its threshold voltage and scanned its longitudinal acceptance. Tuning of the rebunchers and superconducting accelerating cavities is per-formed by means of the phase scans and Time-Of-Flight (TOF) beam energy measurements with beam position and phase monitors. While FRIB is being commissioned, the re-accelerator (ReA3) for rare isotope beams (RIBs) is being upgraded. We redesigned the ReA3 RFQ to improve its cooling system and provide reliable operation with 16.1 MHz prebunched ion beams with A/Q = 5. In order to provide matching of any ReA3 beam both to the following upgrade cryomodules and physics experiments’ requirements, room temperature rebuncher/debuncher is being designed. The design procedure includes the beam dynamics, electromagnetic, thermal and mechanical simulations and optimizations.  
slides icon Slides WEPAF04 [2.406 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICAP2018-WEPAF04  
About • paper received ※ 19 October 2018       paper accepted ※ 28 January 2019       issue date ※ 26 January 2019  
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