Collaborative Research: Simulation of Beam-Electron Cloud Interactions in Circular Accelerators Using Plasma Models
Bibliographic record
Abstract
Final Report for grant DE-FG02-06ER54888, "Simulation of Beam-Electron Cloud Interactions in Circular Accelerators Using Plasma Models" Viktor K. Decyk, University of California, Los Angeles Los Angeles, CA 90095-1547 The primary goal of this collaborative proposal was to modify the code QuickPIC and apply it to study the long-time stability of beam propagation in low density electron clouds present in circular accelerators. The UCLA contribution to this collaborative proposal was in supporting the development of the pipelining scheme for the QuickPIC code, which extended the parallel scaling of this code by two orders of magnitude. The USC work was as described here the PhD research for Ms. Bing Feng, lead author in reference 2 below, who performed the research at USC under the guidance of the PI Tom Katsouleas and the collaboration of Dr. Decyk The QuickPIC code [1] is a multi-scale Particle-in-Cell (PIC) code. The outer 3D code contains a beam which propagates through a long region of plasma and evolves slowly. The plasma response to this beam is modeled by slices of a 2D plasma code. This plasma response then is fed back to the beam code, and the process repeats. The pipelining is based on the observation that once the beam has passed a 2D slice, its response can be fed back to the beam immediately without waiting for the beam to pass all the other slices. Thus independent blocks of 2D slices from different time steps can be running simultaneously. The major difficulty was when particles at the edges needed to communicate with other blocks. Two versions of the pipelining scheme were developed, for the the full quasi-static code and the other for the basic quasi-static code used by this e-cloud proposal. Details of the pipelining scheme were published in [2]. The new version of QuickPIC was able to run with more than 1,000 processors, and was successfully applied in modeling e-clouds by our collaborators in this proposal [3-8]. Jean-Luc Vay at Lawrence Berkeley National Lab later implemented a similar basic quasistatic scheme including pipelining in the code WARP [9] and found good to very good quantitative agreement between the two codes in modeling e-clouds. References [1] C. Huang, V. K. Decyk, C. Ren, M. Zhou, W. Lu, W. B. Mori, J. H. Cooley, T. M. Antonsen, Jr., and T. Katsouleas, "QUICKPIC: A highly efficient particle-in-cell code for modeling wakefield acceleration in plasmas," J. Computational Phys. 217, 658 (2006). [2] B. Feng, C. Huang, V. K. Decyk, W. B. Mori, P. Muggli, and T. Katsouleas, "Enhancing parallel quasi-static particle-in-cell simulations with a pipelining algorithm," J. Computational Phys, 228, 5430 (2009). [3] C. Huang, V. K. Decyk, M. Zhou, W. Lu, W. B. Mori, J. H. Cooley, T. M. Antonsen, Jr., and B. Feng, T. Katsouleas, J. Vieira, and L. O. Silva, "QUICKPIC: A highly efficient fully parallelized PIC code for plasma-based acceleration," Proc. of the SciDAC 2006 Conf., Denver, Colorado, June, 2006 [Journal of Physics: Conference Series, W. M. Tang, Editor, vol. 46, Institute of Physics, Bristol and Philadelphia, 2006], p. 190. [4] B. Feng, C. Huang, V. Decyk, W. B. Mori, T. Katsouleas, P. Muggli, "Enhancing Plasma Wakefield and E-cloud Simulation Performance Using a Pipelining Algorithm," Proc. 12th Workshop on Advanced Accelerator Concepts, Lake Geneva, WI, July, 2006, p. 201 [AIP Conf. Proceedings, vol. 877, Melville, NY, 2006]. [5] B. Feng, P. Muggli, T. Katsouleas, V. Decyk, C. Huang, and W. Mori, "Long Time Electron Cloud Instability Simulation Using QuickPIC with Pipelining Algorithm," Proc. of the 2007 Particle Accelerator Conference, Albuquerque, NM, June, 2007, p. 3615. [6] B. Feng, C. Huang, V. Decyk, W. B. Mori, G. H. Hoffstaetter, P. Muggli, T. Katsouleas, "Simulation of Electron Cloud Effects on Electron Beam at ERL with Pipelined QuickPIC," Proc. 13th Workshop on Advanced Accelerator Concepts, Santa Cruz, CA, July-August, 2008, p. 340 [AIP Conf. Proceedings, vol. 1086, Melville, NY, 2008]. [7] B. Feng, C. Huang, V. K. Decyk, W. B. Mori, P. Muggli, and T. Katsouleas, "Enhancing parallel quasi-static particle-in-cell simulations with a pipelining algorithm," J. Computational Phys, 228, 5430 (2009). [8] C. Huang, W. An, V. K. Decyk, W. Lu, W. B. Mori, F. S. Tsung, M. Tzoufras, S. Morshed, T. Antonsen, B. Feng, T. Katsouleas, R., A. Fonseca, S. F. Martins, J. Vieira, L. O. Silva, E. Esarey, C. G. R. Geddes, W. P. Leemans, E. Cormier-Michel, J.-L. Vay, D. L. Bruhwiler, B. Cowan, J. R. Cary, and K. Paul, "Recent results and future challenges for large scale particleion- cell simulations of plasma-based accelerator concepts," Proc. of the SciDAC 2009 Conf., San Diego, CA, June, 2009 [Journal of Physics: Conference Series, vol. 180, Institute of Physics, Bristol and Philadelphia, 2009], p. 012005. [9] J.-L. Vay, C. M. Celata, M. A. Furman, G. Penn, M. Venturini, D. P. Grote, and K. G. Sonnad, ?Update on Electron-Cloud Simulations Using the Package WARP-POSINST.? Proc. of the 2009 Particle Accelerator Conference PAC09, Vancouver, Canada, June, 2009, paper FR5RFP078.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".