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<p>This paper focuses on the uncertainty of control rod drop, which involves interval uncertainty and strong nonlinear fluid-solid interactions. Traditional analysis methods have issues like poor stability and low efficiency. To handle the strong nonlinear forces on the control rod and boost numerical solution convergence, efficiency, and accuracy, a coupled dynamic equation for control rod drop is established in the Hamiltonian framework. The symplectic adaptive time step (SATS) algorithm is then used for problem-solving. Moreover, a dynamic evolution sequence-improved artificial bee colony (DES-ABC) algorithm for interval uncertainty analysis is proposed. By integrating the dynamic evolution sequence (DES) into the artificial bee colony (ABC) algorithm, it enhances population distribution uniformity in the solution space and improves algorithm efficiency. Combining the SATS and DES-ABC algorithms, an efficient computational method named SDA is presented for interval analysis of control rod drop uncertainty response. Numerical examples validate the method’s efficiency and accuracy.</p>

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Adaptive Time-Stepping Approach for Efficient Interval Uncertainty Analysis of Control Rod Drop Dynamics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月24日

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  • Chen Li,
  • Xindi Wei,
  • Changyi Chen,
  • Jinghua Tang

摘要

This paper focuses on the uncertainty of control rod drop, which involves interval uncertainty and strong nonlinear fluid-solid interactions. Traditional analysis methods have issues like poor stability and low efficiency. To handle the strong nonlinear forces on the control rod and boost numerical solution convergence, efficiency, and accuracy, a coupled dynamic equation for control rod drop is established in the Hamiltonian framework. The symplectic adaptive time step (SATS) algorithm is then used for problem-solving. Moreover, a dynamic evolution sequence-improved artificial bee colony (DES-ABC) algorithm for interval uncertainty analysis is proposed. By integrating the dynamic evolution sequence (DES) into the artificial bee colony (ABC) algorithm, it enhances population distribution uniformity in the solution space and improves algorithm efficiency. Combining the SATS and DES-ABC algorithms, an efficient computational method named SDA is presented for interval analysis of control rod drop uncertainty response. Numerical examples validate the method’s efficiency and accuracy.