溪洛渡拱坝内部温度预测的串并混联融合模型  

Parallel-over-series fusion model for predicting internal temperature of Xiluodu arch dam

作　　者：刘畅 李庆斌[1] 胡昱[1] 马睿[1] LIU Chang;LI Qingbin;HU Yu;MA Rui(State Key Laboratory of Hydroscience and Engineering,Tsinghua University,Beijing 100084,China)

机构地区：[1]清华大学水沙科学与水利水电工程国家重点实验室,北京100084

出　　处：《水力发电学报》2022年第10期53-63,共11页Journal of Hydroelectric Engineering

基　　金：国家自然科学基金(51839007,52130901)。

摘　　要：准确、可靠、高效地预测反映大坝工作性态的关键变量对大坝安全建设与运行具有重要作用。本文针对大坝全过程温度场分析预测问题,构建了机理与数据驱动的串、并混联式融合模型,以溪洛渡大坝工程为例对大坝内部温度进行了预测建模。在串联融合中,采用营地链方法及主成分分析的辅助技术对大坝材料的热传导系数进行反演分析,以获得经参数优化后的机理模型响应输出。在并联融合中,利用解释因子法以及线性-高斯模型,建立了从优化的机理模型到实际监测数据的预测模型。试验表明,解释因子串并混联融合模型受益于机理模型的解释能力及线性-高斯模型的预测优势,可对测试数据的波动做出较准确的预测,并具中长期预测优势。串联模型相对于原始机理模型的整体预测误差缩减了13%,串并混联模型相对于串联模型的预测误差缩减了81%。Accurate,reliable and efficient prediction of key variables reflecting the working behaviour of dams plays a vital role in safe construction and operation of dams.To predict and analyse the temperature field of a dam,this paper develops a parallel-over-series fusion model driven by both a mechanism model and a data model,and examines its feasibility by using the internal temperature of the Xiluodu arch dam.In series fusion,the camp chain method and the auxiliary technology of principal component analysis are adopted for back-calculating the thermal conductivities of dam concrete,by which optimised outputs of the mechanism model are obtained.In parallel fusion,the explanatory factor method and the linearGaussian model are used to construct a prediction model that enables the optimised mechanism model to be mapped to the dam temperature data.Experimental verification shows this fusion model,benefits from the explanatory power of the mechanism-driven model and the predictive advantage of the linearGaussian model.It can predict data fluctuations and has advantages in medium-and long-term forecasting.It reduces the overall prediction error by 81%compared with the series fusion model,while the latter reduces the error by 13%relative to the mechanism model.

关 键 词：混凝土高坝 融合模型 温度预测 解释因子 线性-高斯模型 反演分析 

分 类 号：TV642.4[水利工程—水利水电工程]