李明昊, 牛昊, 范佳艺, 等.基于离散元法的采煤机螺旋滚筒灵敏度分析及优化设计[J].河南理工大学学报（自然科学版）,2025,44(6):165-171.

LI M H, NIU H, FAN J Y, et al. Sensitivity analysis and optimization design of shearer’s spiral drum based on discrete element method[J].Journal of Henan Polytechnic University(Natural Science) ,2025,44(6):165-171.

基于离散元法的采煤机螺旋滚筒灵敏度分析及优化设计

李明昊1, 牛昊1, 范佳艺2, 赵丽娟3,4

1.沈阳理工大学 机械工程学院，辽宁 沈阳  110159；2.沈阳航空航天大学 机电工程学院，辽宁 沈阳  110136；3.辽宁工程技术大学 机械工程学院，辽宁 阜新  123000；4.辽宁省大型工矿装备重点实验室，辽宁 阜新  123000

摘要: 目的 为实现复杂煤层赋存条件下的采煤机装煤性能和生产效率最优化，基于离散元法、灵敏度分析理论和结构进化算法对爬底板采煤机螺旋滚筒进行研究。  方法 联合建立螺旋滚筒和夹矸煤岩的物理模型，采用Hertz模型构建滚筒与夹矸煤岩的耦合模型，通过赋予滚筒和夹矸煤岩的物理特性参数和滚筒运动参数，对采煤机装煤性能进行仿真分析，通过区域统计得到爬底板采煤机螺旋滚筒的装煤率。建立滚筒不同几何参数和运动参数的离散元耦合模型，将离散元耦合模型模拟得到的数据进行拟合，基于多目标优化设计加权算法，得到螺旋滚筒多目标设计函数，基于应力-强度干涉理论，构建采煤机螺旋滚筒装煤性能的状态评价函数。结合可靠性灵敏度设计理论得到螺旋滚筒设计联合，采用改进结构进化算法进行求解，得到采煤机螺旋滚筒设计变量的最优解。  结果 结果表明，优化后螺旋滚筒装煤率得到提高，由61.35%提高至64.17%，达到了螺旋滚筒优化设计的目的。优化后的螺旋滚筒设计变量灵敏度得到降低，优化后的螺旋滚筒装煤性能得到提升，表明优化后的滚筒装煤率整体性能趋于稳健。 结论 将离散元技术、灵敏度设计理论和结构进化算法相结合，为设计含夹矸煤层赋存条件下高效截割性能的螺旋滚筒提供了一种有效的技术路线。

关键词:采煤机;螺旋滚筒;装煤性能;灵敏度设计;优化设计

doi:10.16186/j.cnki.1673-9787.2022100035

基金项目:国家自然科学基金资助项目（51674134）；辽宁省教育厅青年科技人才育苗项目（LG202029）；辽宁省博士科研启动基金计划项目（2020-BS-153）；辽宁省属本科高校基本科研业务费专项资金资助项目（LJ212410144076，LJ232410144074）

收稿日期:2022/10/11

修回日期:2023/05/10

出版日期:2025/10/14

Sensitivity analysis and optimization design of shearer’s spiral drum based on discrete element method

Li Minghao1, Niu Hao1, Fan Jiayi2, Zhao Lijuan3,4

1.School of Mechanical Engineering， Shenyang Ligong University， Shenyang  110159， Liaoning， China；2.School of Mechatronics Engineering， Shenyang Aerospace University， Shenyang  110136， Liaoning， China；3.College of Mechanical Engineering， Liaoning Technical University， Fuxin  123000， Liaoning， China；4.Liaoning Provincial Key Laboratory of Large-Scale Mining Equipment， Fuxin  123000， Liaoning， China

Abstract: Objectives In order to optimize the coal loading performance and production efficiency of shearer working in complex coal seam， the floor climbing shearer’s spiral drum was studied based on the discrete element method， sensitivity analysis theory，and structural evolution algorithm. Methods The physical model of the spiral drum and the gangue coal rock was jointly established. The Hertz model was used to construct the coupling model between roller and gangue coal rock. Based on the physical characteristics parameters and the movement parameters of the drum and the gangue coal rock， the coal-loading performance of the shearer was simulated and analyzed， and the loading rate of the spiral drum of the climbing floor shearer was obtained by regional statistics. A discrete element coupling model for different geometric parameters and motion parameters of the drum was established. The data obtained from the simulation of discrete element coupling model was fitted， and the multi-objective design function of spiral drum was obtained based on the multi-objective optimization design weighting algorithm. Based on the stress-strength interference theory， the state evaluation function of the coal loading performance of the shearer screw drum was constructed. Combined with the sensitivity design theory， the influence law of screw drum design variables on the coal loading performance of shearer was obtained. Using structural evolution algorithm， the optimal solution of shearer spiral drum design variables was obtained. Results The results showed that the coal-loading rate of the optimized spiral drum was increased from 61.35% to 64.17%， the optimal design of spiral drum was achieved. The sensitivity of the design variable was reduced and the coal-loading performance of the spiral drum was improved， it showed that the overall performance of the optimized drum loading rate tended to be stable. Conclusions Combining discrete element technology， sensitivity design theory and structural evolution algorithm， an effective technical route for designing spiral drum with efficient cutting performance under the condition of occurrence of coal seam containing gangue was provided.

Key words: shearer; spiral drum; coal loading performance; sensitivity analysis; optimization design