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Title: Innovations in optimization and control of accelerators using methods of differential geometry and genetic algorithms

Abstract

Online tuning of particle accelerators is necessary in order to achieve optimal machine performance. However, it is also a major challenge due to the large parameter space which must be searched and the fact that many of the desir- able objectives compete with one another, and so will not reach their optimal values simultaneously. In order to mitigate these issues, we have explored using dimension-reduction techniques to reduce the size of the parameter space which must be searched and multi-objective genetic algorithms to obtain the sets of tuning parameters which provide pareto-optimal values for the objectives. These methods have enabled us to obtain improved values of the vertical emittance at the Cornell Electron Storage Ring (CESR), and to do so with greater control of orbit errors. Algorithmic tuning is a multidisciplinary endeavour, requiring expertise in beam dynamics, diagnostics, control systems and computer science, and thus a key practical problem is to formulate a common language in which experts with different specialties can communicate. We developed a solution to this problem in the form of a generic accelerator software interface that allows for rapid prototyping of optimization and control algorithms. Our interface is built on the Experimental Physics and Industrial Controlmore » Systems (EPICS) and makes possible testing control code in simulation before deployment on real accelerators, as well as deployment of third-party optimization code.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Cornell University
Publication Date:
Research Org.:
Cornell University
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1530158
Report Number(s):
DOE-CORNELL-0013571
DOE Contract Number:  
SC0013571
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Bazarov, Ivan, Andorf, Matthew, Bergan, William, Duncan, Cameron, Khachatryan, Vardan, Liarte, Danilo, Rubin, David, and Sethna, James. Innovations in optimization and control of accelerators using methods of differential geometry and genetic algorithms. United States: N. p., 2019. Web. doi:10.2172/1530158.
Bazarov, Ivan, Andorf, Matthew, Bergan, William, Duncan, Cameron, Khachatryan, Vardan, Liarte, Danilo, Rubin, David, & Sethna, James. Innovations in optimization and control of accelerators using methods of differential geometry and genetic algorithms. United States. https://doi.org/10.2172/1530158
Bazarov, Ivan, Andorf, Matthew, Bergan, William, Duncan, Cameron, Khachatryan, Vardan, Liarte, Danilo, Rubin, David, and Sethna, James. Sun . "Innovations in optimization and control of accelerators using methods of differential geometry and genetic algorithms". United States. https://doi.org/10.2172/1530158. https://www.osti.gov/servlets/purl/1530158.
@article{osti_1530158,
title = {Innovations in optimization and control of accelerators using methods of differential geometry and genetic algorithms},
author = {Bazarov, Ivan and Andorf, Matthew and Bergan, William and Duncan, Cameron and Khachatryan, Vardan and Liarte, Danilo and Rubin, David and Sethna, James},
abstractNote = {Online tuning of particle accelerators is necessary in order to achieve optimal machine performance. However, it is also a major challenge due to the large parameter space which must be searched and the fact that many of the desir- able objectives compete with one another, and so will not reach their optimal values simultaneously. In order to mitigate these issues, we have explored using dimension-reduction techniques to reduce the size of the parameter space which must be searched and multi-objective genetic algorithms to obtain the sets of tuning parameters which provide pareto-optimal values for the objectives. These methods have enabled us to obtain improved values of the vertical emittance at the Cornell Electron Storage Ring (CESR), and to do so with greater control of orbit errors. Algorithmic tuning is a multidisciplinary endeavour, requiring expertise in beam dynamics, diagnostics, control systems and computer science, and thus a key practical problem is to formulate a common language in which experts with different specialties can communicate. We developed a solution to this problem in the form of a generic accelerator software interface that allows for rapid prototyping of optimization and control algorithms. Our interface is built on the Experimental Physics and Industrial Control Systems (EPICS) and makes possible testing control code in simulation before deployment on real accelerators, as well as deployment of third-party optimization code.},
doi = {10.2172/1530158},
url = {https://www.osti.gov/biblio/1530158}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {6}
}