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TUPMF035 | First Demonstration of the Transparent Fast-to-slow Corrector Current Shift in the NSLS-II Storage Ring | 1323 |
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To realize the full benefits of the high brightness and ultra-small beam sizes of NSLS-II, it is essential that the photon beams are exceedingly stable (a level of 10% beam size). In the circumstances of implementing local bumps, changing ID gaps, and long-term drifting, the fast orbit feedback (FOFB) requires shifting the fast corrector strengths to the slow correctors to prevent the fast corrector saturation and to make the beam orbit stable in the sub-micron level. As the result, a reliable and precise technique of fast-to-slow corrector strength shift has been developed and tested at NSLS-II. This technique is based on the fast corrector response to the slow corrector change when the FOFB is on. In this article, the shift technique is described and the result of proof-of-principle experiment carried out at NSLS-II is presented. The maximum fast corrector current was reduced from greater than 0.45 A to less than 0.04 A with the orbit perturbation within ±1 μm. Especially when the step size of the shift was below 0.012 A, the amount of noise being added to the beam was none. | ||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-TUPMF035 | |
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TUPMF036 | Top Off of NSLS-II with Inefficient Injector | 1327 |
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Funding: This manuscript has been authored by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy The NSLS-II is a 3 GeV storage with a full energy injector capable of top off injection. The injector consists of a 200 MeV linac injecting a 3 GeV booster. Recent operational events have caused us to investigate 100 MeV injection into the booster. As the booster was not designed for injection at this low energy, beam loss is observed with this low energy booster injection. This beam loss not only results of overall charge loss from the train, but a change in the overall charge distribution in the bunch train. In this paper we discuss the performance of injecting into the storage ring with the inefficient charge transfer through the injector. The changes to the top off method are discussed, as well as the achieved storage ring current stability and fill pattern. |
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DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-TUPMF036 | |
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TUPMF037 | Development of New Operational Mode for NSLS-II Injector: Low Energy 100MeV Linac-to-Booster Injection | 1330 |
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The NSLS-II injector consists of a 200 MeV linac and a 3 GeV full-energy booster synchrotron. The linac contains five traveling-wave S-band accelerating structures driven by two high-power klystrons, with a third klystron as spare. In the event that the spare klystron is not available, the failure of one klystron will prohibit the linac from injecting into the booster as the energy is too low. Therefore, we wish to develop a new operational mode that the NSLS-II injector can operate with a single klystron providing 100 MeV beam from the linac. A decremented approach with intermediate energies 170 MeV, 150 MeV, etc., takes advantages of pre-calculated booster ramps and beam based online optimization. By lowering the booster injection energy in a small step and online optimizing at each step, we were able to achieve 100 MeV booster injection. 170 MeV operation mode of the NSLS-II injector has been implemented since May 31, 2017, with a similar overall performance compared to the standard 200 MeV operation but fewer klystron trips. 100 MeV single-klystron operation has been successfully demonstrated with 20-30% overall efficiency, which is limited by booster acceptance. | ||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-TUPMF037 | |
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WEPAF022 | Application of Machine Learning to Minimize Long Term Drifts in the NSLS-II Linac | 1867 |
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Funding: This manuscript has been authored by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy Machine Learning has proven itself as a useful technique in a variety of applications from image recognition to playing Go. Artificial Neural Networks have certain advantages when used as a feedforward system, such as the predicted correction relies on a model built from data. This allows for the Artificial Neural Network to compensate for effects that are difficult to model such as low level RF adjustments to compensate for long term drifts. The NSLS-II linac suffers from long terms drifts from a number of sources including thermal drifts and klystron gain variations. These drifts have an effect on the injection efficiency into the booster, and if left unchecked, portions of the bunch train may not be injected into the booster, and the storage ring bunch pattern will ultimately suffer. In this paper, we discuss the application of Artificial Neural Networks to compensate for long term drifts in the NSLS-II linear accelerator. The Artificial Neural Network is implemented in python allowing for rapid development of the network. We discuss the design and training of the network, along with results of using the network in operation. |
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DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF022 | |
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