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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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In high head dams, the kinetic energy at the spillway toe is very high and the tail-water depth available for energy dissipation is relatively small. Cascade stilling basins are energy dissipation systems for high head dams, the design of which is based on a trial-and-error procedure. Although such an approach yields feasible designs in which hydraulic and topographic considerations are met, there may exist many cost-effective designs. Therefore, optimization tools can help find the least construction cost while keeping hydraulic and topographic considerations satisfied. Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) were used to determine the optimal design of cascade stilling basins in terms of the height of falls and length of stilling basins. The approach was evaluated by application to the design of an energy dissipation system for the Tehri Dam on the Bhagirathi River. Comparison of the proposed methods with dynamic programming and an alternative approach not utilizing an optimization tool revealed that GA and PSO lead to significant savings in the construction cost with less computational 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。梯级消融池是高水头大坝的消能系统，其设计基于反复试验程序。尽管这种方法产生了可以满足水力和地形考虑的可行设计，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少的建筑成本，同时又能满足水力和地形方面的考虑。运用粒子群优化算法（PSO）和遗传算法（GA），从瀑布高度和消融池长度的角度确定梯级消融池的优化设计。通过将该方法应用于Bhagirathi河上的Tehri大坝的消能系统的设计，对该方法进行了评估。将拟议的方法与动态规划和不使用优化工具的替代方法进行比较后发现，GA和PSO可以显着节省施工成本，而计算量却更少。

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