Bibliographic Details
| Title: |
蓄能器组参数灵敏度分析及多目标优化. (Chinese) |
| Alternate Title: |
Sensitivity Analysis and Multi-objective Optimization of Accumulator Group Parameters. (English) |
| Authors: |
张立强, 丁杰, 张建强 |
| Source: |
Machine Tool & Hydraulics; Nov2024, Vol. 52 Issue 21, p185-190, 6p |
| Subject Terms: |
MULTIPLE regression analysis, PARETO optimum, HYDRAULIC models, SIMULATION software, MULTI-objective optimization |
| Abstract (English): |
Taking the bladder accumulator group in the small hydraulic lift system as the research object, the potential energy recovery and energy release of the hydraulic lift system were simulated and analyzed by using the hydraulic system simulation software. The working process of the system was introduced, the simulation model of the hydraulic lift system was built, and the simulation was carried out according to the designed orthogonal test schemes. The stepwise regression analysis method in the multiple linear regression model was used to remove the parameters those were not significant to the test results, and the regression coefficients of the significant influen-cing factors were obtained, and the linear regression equation between them and the recovery efficiency was obtained, and the sensitivity of the significant parameters was ranked according to the regression equation. The five saliency parameters were taken as the optimization objects, and the RBF neural network surrogate model was used to obtain the global results in the whole experimental range, and then the Pareto optimal solution set was obtained by NSGA-Ⅱ multi-objective genetic algorithm to meet the two objectives of recovery efficiency and flow characteristics of the cyst accumulator group. The simulation results before and after parameter optimization were compared and analyzed, and the accuracy of the surrogate model and optimization results was verified. The simulation results show that the sensitivity of the saliency parameters is ranked in the order of p0, V1, V2, V3, V4 from high to low. The overall recovery efficiency of the optimized accumulator group is slightly improved compared with that before optimization, and the maximum response flow rate is increased by 6. 76％. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
以小型液压升降机系统中的皮囊式蓄能器组为研究对象, 利用液压系统仿真软件对液压升降机系统的势能回收及能量释放进行仿真分析. 介绍系统的工作过程, 搭建液压升降机系统仿真模型, 并根据设计的正交试验方案进行仿真. 对试验结果数据采用多元线性回归模型中逐步回归分析方法, 去除对试验结果不显著的参数, 得到显著性影响因素回归系数并得到其与回收效率之间的线性回归方程, 根据回归方程得到显著性参数的灵敏度排序. 将5个显著性参数作为优化对象, 利用RBF神经网络代理模型求得在整个试验范围内的全局结果, 再通过NSGA-Ⅱ多目标遗传算法得到满足囊式蓄能器组回收效率和流量特性两个目标的Pareto最优解集. 对参数优化前后的仿真结果进行了对比分析, 验证了代理模型和优化结果的准确性. 仿真结果表明: 显著性参数的灵敏度排序由高到低依次为: p0、V1、V2、V3、V4; 优化后的蓄能器组整体回收效率较优化前略有提高, 最大响应流量提高了 6. 76％. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
