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首页> 外文期刊>Journal of Hydroinformatics >Optimization of cascade stilling basins using GA and PSO approaches
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Optimization of cascade stilling basins using GA and PSO approaches

机译:利用GA和PSO方法优化梯级消减盆

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摘要

In high head dams, the kinetic energy at the spillway toe is very high and the tail-water depthnavailable for energy dissipation is relatively small. Cascade stilling basins are energy dissipationnsystems for high head dams, the design of which is based on a trial-and-error procedure.nAlthough such an approach yields feasible designs in which hydraulic and topographicnconsiderations are met, there may exist many cost-effective designs. Therefore, optimizationntools can help find the least construction cost while keeping hydraulic and topographicnconsiderations satisfied. Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) werenused to determine the optimal design of cascade stilling basins in terms of the height of falls andnlength of stilling basins. The approach was evaluated by application to the design of an energyndissipation system for the Tehri Dam on the Bhagirathi River. Comparison of the proposednmethods with dynamic programming and an alternative approach not utilizing an optimizationntool revealed that GA and PSO lead to significant savings in the construction cost with lessncomputational effort.
机译:在高水头大坝中,溢洪道脚趾处的动能非常高,可用于消能的尾水深度相对较小。级联静水池是用于高水头大坝的能量消散系统,其设计基于反复试验程序。n尽管这种方法产生了可以满足水力和地形考虑因素的可行设计,但可能存在许多具有成本效益的设计。因此,优化工具可以帮助您找到最低的建造成本,同时又能满足水力和地形方面的考虑。利用粒子群优化(PSO)和遗传算法(GA)从落差高度和落差长度的角度确定级联落差盆的优化设计。通过将该方法应用于Bhagirathi河上的Tehri大坝的能量吸收系统的设计,对该方法进行了评估。将建议的方法与动态编程和不使用优化工具的替代方法进行比较,结果表明,GA和PSO可以用较少的计算量来显着节省建筑成本。

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