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| 1 | 基于遗传算法的串联明渠多参数率定方法研究显示文摘对于复杂串联明渠调水工程的多参数率定问题,传统的试算方法需要基于水动力模型进行多次试错得到最优的参数值组合,需要的计算工作量较大。此研究提出了一种基于遗传算法的串联明渠多参数率定方法,将水动力模型中的多参数率定问题转化为以待率定参数为状态量的优化问题,并通过遗传算法寻优得到最优的待率定参数值。将方法用于对南水北调中线工程中“西黑山节制闸——坟庄河节制闸”河段的三段河道糙率与3个节制闸过闸流量系数率定,结果表明本文方法可通过一次优化过程搜索得到复杂串联明渠调水工程最优的糙率与过闸流量系数组合,且采用率定后参数值进行仿真计算得到的水位与实测水位较为接近,平均偏差在2 cm以内。 | 严瑞昕 岩应叫 孔令仲 陈瑞彬 张扬帆 | 2023 | 中国农村水利水电2023，，4： | 1 |
| 2 | Prediction of hydro-suction dredging depth using data-driven methods显示文摘In this study,data-driven methods(DDMs)including different kinds of group method of data handling(GMDH)hybrid models with particle swarm optimization(PSO)and Henry gas solubility optimization(HGSO)methods,and simple equations methods were applied to simulate the maximum hydro-suction dredging depth(h_(s)).Sixty-seven experiments were conducted under different hydraulic conditions to measure the h_(s).Also,33 data samples from three previous studies were used.The model input variables consisted of pipeline diameter(d),the distance between the pipe inlet and sediment level(Z),the velocity of flow passing through the pipeline(u_(0)),the water head(H),and the medium size of particles(D_(50)).Data-driven simulation results indicated that the HGSO algorithm accurately trains the GMDH methods better than the PSO algorithm,whereas the PSO algorithm trained simple simulation equations more precisely.Among all used DDMs,the integrative GMDH-HGSO algorithm provided the highest accuracy(RMSE=7.086 mm).The results also showed that the integrative GMDHs enhance the accuracy of polynomial GMDHs by∼14.65%(based on the RMSE). | Amin MAHDAVI-MEYMAND Mohammad ZOUNEMAT-KERMANI Kourosh QADERI | 2021 | Frontiers of Structural and Civil Engineering2021，15，3： | 0 |
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