Speaker
Description
RFQ design involves strong parameter coupling, multi-physics interactions, and multiple optimization constraints. To address these challenges, this work develops a physics-informed genetic optimization framework for RFQ beam dynamics and electromagnetic design.
By incorporating evolutionary rules associated with modulation factor, synchronous phase, and focusing strength, the framework improves optimization efficiency and physical reliability. The method has been validated for several RFQ configurations, including heavy-ion, proton, compact CW, and He²⁺ RFQs. The optimized LEAF, ADS Injector-II, PAFA, and SYSU-IFCEN HeRFQs achieved transmission efficiencies of 98.7%, 99.8%, 99.8%, and 97.4%, with RFQ lengths of 575.85 cm, 409.56 cm, 351.98 cm, and 143.42 cm, respectively. Typical optimization tasks required 80–300 core-hours using 20–70 generations with 100 individuals.
The framework was also applied to RFQ electromagnetic design using surrogate-model-assisted optimization. For a C⁴⁺ RFQ cavity, the optimized design achieved an approximately 10% increase in the Q value.
These results demonstrate an efficient approach for automated RFQ optimization and accelerator design.
Funding Agency
This work was supported by the National Natural Science Foundation of China (Grant Nos. 12505173 and 12575170)
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