| Stochastic cooling is a method of using a broadband feedback system to attenuate the transverse oscillation and longitudinal energy spread. It has the advantage of cooling secondary particles of larger emittances and momentum spread, etc, and it is therefore can be complemented with electron cooling to shorten the cooling time efficiently and to obtain the beam of high quality. Stochastic cooling in the HIRFL-CSRe(Heavy Ion Research Facility in Lanzhou- experimental Cooling Storage Ring) is mainly used for cooling secondary particles produced at RIBLL2(the 2nd Radioactive Ion Beam Line in Lanzhou). Since the emittances and momentum spread of secondary particles are large( ε: 20-25 π mm.mrad, dp/p: ±0.5-1.0%), cooling must be performed to reduce the beam size rapidly, and then the beam lifetime and quality can be improved.Stochastic cooling devices are mainly distributed over Switzerland, America, Germany and Japan presently. There are big differences in the beam parameter, cooling parameter and application in different stochastic cooling devices. Therefore, it not only has the function of improving cooling rate and decreasing power requirement, but also has great significance for theoretical design and actual operation by exploring the cooling parameters.This dissertation has the purpose of exploring the cooling parameters of stochastic cooling, searching for the optimal cooling parameters and exploring the cooling nature at a deeper level. Both numerical simulation and particle tracking simulation are used in describing the stochastic cooling process in the time domain. Firstly, CSRe lattice is optimized based on the previously existing modes. The dynamic aperture, acceptance, Twiss parameters and Betatron tune are optimized in order to meet the requirements of bandwidth, phase advance and stability of stochastic cooling system. Then, based on the optimized lattice, simulation codes are developed, and then the rule of cooling time, cooling rate, microwave power, beam power, beam gain, noise to signal ratio and mixing factors by the effect of the species of particle, particle number, beam size, beam energy, Twiss parameters, number of electrodes, bandwidth, amplifier gain, noises, signal to noise ratio and power limit are analysed overall, so then the optimal cooling parameters are obtained.Simulation results show that CSRe stochastic cooling is very effective when Q/A is nearly 0.46(56Fe26+), particle number is smaller than 104, and the beam size is relatively larger, etc. There are several ways of improving the cooling rate: adding electrodes, broadening bandwidth, increasing the amplifier gain and improving the signal to noise ratio, etc. Taking the power limit into consideration, the optimal bandwidth, amplifier gain, signal to noise ratio and power limit are obtained. In addition, cooling time could be shortened and power requirement could be decreased by reducing the noises. Moreover, the heating caused by larger momentum spread in the transverse cooling process, and the non-cooling factors caused by larger particle number are analysed emphatically. The case of larger particle number could be solved by reducing the amplifier gain, while the cooling rate is decreased at the same time.Particle tracking method is innovatively used to study the stochastic cooling process of HIRFL-CSRe. By using this method, the dynamic process of stochastic cooling could be described intuitively. Cross influence of horizontal, vertical and longitudinal cooling process, and the physical mechanism are dominantly investigated. Meanwhile, the particle number, bandwidth and signal to noise ratio in the power-limit system are discussed in detail and in depth by use of the particle tracking method. |
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