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依据混凝土水电站气候温度条件,采用传统经验法设计了大坝混凝土骨料预冷系统。基于共轭传热理论,分析了影响骨料冷却效率的关键参数。以冷却系统的能源消耗最低为目标,应用遗传算法对不同冷风速度、料层厚度和冷却时间进行了综合优化。利用Ansys Workbench软件建立了骨料颗粒冷却的流固共轭传热模型,采用加权法求取骨料平均温度作为遗传优化的判别条件。仿真研究了优化前后骨料仓内的颗粒温度分布,结果表明在保证总体冷却效果的同时,优化方案的经济性可提高36%。所提优化方法为混凝土预冷系统的高效节能奠定了理论基础,可在工程建设中推广应用。
According to the climatic temperature conditions of concrete hydropower station, the traditional experience method was used to design the dam concrete aggregate precooling system. Based on the theory of conjugate heat transfer, the key parameters affecting the cooling efficiency of aggregate are analyzed. Taking the lowest energy consumption of the cooling system as the goal, genetic algorithms are used to optimize the different cooling air velocities, material thickness and cooling time. The fluid-solid conjugate heat transfer model of aggregate particle cooling was established by Ansys Workbench software. The weighted average method was used to determine the average temperature of aggregate as a criterion for genetic optimization. The particle temperature distribution in the bulk silo before and after optimization was simulated and the results showed that the economy of the optimized scheme could be increased by 36% while ensuring the overall cooling effect. The proposed optimization method has laid a theoretical foundation for the high efficiency and energy saving of concrete precooling system and can be popularized and applied in engineering construction.