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中文摘要：
在由使用计算液体动力学(CFD ) 在事故状况下面收到水预言放射性的沾染物散开需要长时间模型。以便弄短计算时间，一个混合模型基于 CFD 和时间系列，神经网络(TSNN ) 在这份报纸被建议。在在一个要求的事故以后的一座内陆水库的放射性的污染的集中变化作为一个盒子被学习。结果证明这个混合模型能预言沾染物散开趋势并且弄短至少 50% 重复时间。集成于神经网络模型的 Priori 知识能把网络输出的均方差归结为 9.66 桸扩瑩摥栠杩?敲灳湯楳楶祴漠??????湵敤?汩畬業慮楴湯漠????渿?湡?楬桧?湩整獮瑩?????坭振???汣獡?愢瀭畬?汰獵??猯灵?眠楨敬琠敨映敬楸汢?敤楶散搠獩汰祡?楨桧牥搠瑥'虡N楶祴漠????嶑??吗??鎪?蒠????????颬?辬????鎬?野?栜?栜？？
英文摘要：
It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model based on CFD and time series neural network （TSNN） is proposed in this paper. The concentration change of radioactive contamination in an inland reservoir after a postulated accident is studied as a case. The result shows that this hybrid model can predict the contaminant diffusion trend and shorten at least 50% of iteration time. Priori knowledge integrated into the neural network model is able to reduce the mean square error of network output to 9.66×10 8 , which makes neural network output more close to the simulated contaminant concentration.
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Extension of Parametric Rule with the Hedging Rule for Managing Multireservoir System during Drought
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