Speaker
Description
Next-generation particle accelerators are strongly coupled, multiscale, and nonlinear systems in which local optimization alone cannot guarantee global stability. This paper proposes a complexity-native framework that treats the accelerator as a complex adaptive system rather than a set of independently optimized subsystems. This framework integrates three coupled layers—physical complexity, information-flow network, and system cognition—to interpret three core operational challenges: hidden coupling, delayed feedback, and anomaly propagation, while enabling stability-first intelligent operation. A concise superconducting accelerator case illustrates how helium-pressure fluctuation may propagate through cavity detuning, tuner lag, RF compensation, power-module imbalance, and protection shutdown, supporting integrated diagnosis and early warning.
| I have read and accept the Privacy Policy Statement | Yes |
|---|