基于RSM-DEM的TBM出渣效率优化研究

Research on Optimizing TBM Slurry Discharge Efficiency Based on RSM-DEM

摘要: 本研究旨在通过结合离散元方法(DEM)和响应曲面法(RSM)对全断面隧道掘进机(TBM)的出渣效率进行多参数优化。研究以一台直径7.83 m的TBM刀盘为对象，选取了刀盘掘进速度、刀盘转速、出渣槽长度、出渣槽宽度和溜渣板长度这五个关键的结构与工况参数作为影响因子。研究首先通过单因素仿真分析确定了各因子的有效水平范围，然后基于中心复合设计(CCD)建立了二次回归模型，用以分析各因素及其交互作用对30秒内排渣效率的影响。研究结果表明，各参数间存在显著的交互耦合效应，特别是刀盘转速与其它四项参数的交互作用尤为突出。最终，研究得到了最优参数组合(掘进速度61.5 mm/min，转速7.85 rpm，出渣槽长度810 mm，宽度688 mm，溜渣板长度1852 mm)，在此条件下模型预测的排渣率为65.96%，与DEM仿真验证结果(66.34%)的偏差仅为0.57%，证明了优化方法与模型的可靠性。

Abstract: This study aims to perform multi-parameter optimization of the mucking efficiency of a tunnel boring machine (TBM) cutterhead by combining the Discrete Element Method (DEM) and Response Surface Methodology (RSM). A cutterhead with a diameter of 7.83 m was selected as the research object, and five key structural and operational parameters—cutterhead penetration rate, rotation speed, muck chute length, muck chute width, and muck guide plate length—were considered as influencing factors. Single-factor simulation analyses were first conducted to determine the effective level ranges of each factor. Based on the Central Composite Design (CCD), a quadratic regression model was then established to analyze the effects and interactions of these parameters on the mucking efficiency within 30 seconds. The results reveal significant interactive coupling effects among the parameters, with cutterhead rotation speed exhibiting the most prominent interactions with the other four variables. The optimal parameter combination was obtained as follows: penetration rate of 61.5 mm/min, rotation speed of 7.85 rpm, muck chute length of 810 mm, muck chute width of 688 mm, and muck guide plate length of 1852 mm. Under these conditions, the model predicted a mucking rate of 65.96%, which deviated by only 0.57% from the DEM simulation result (66.34%), confirming the reliability of the proposed optimization method and the accuracy of the regression model.