用卷积神经网络模型预测废玻璃熔炼机内部温度

Alexander W. Abboud, D. Guillen, R. Pokorný
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引用次数: 0

摘要

使用焦耳加热的陶瓷衬里熔体,在废物处理和固定板(WTP)上对遗留的放射性坦克废物进行玻璃化。建立了DM1200中试规模熔炼机的高保真计算流体动力学(CFD)模型,以提供对WTP熔炼机内部传热和流体动力学的理解。对非放射性中试规模系统的监测主要是通过目视观察进行的。然而,在全尺寸的放射性熔体中不可能进行目视观察,过程控制将基于测量的充气温度。利用CFD模型,可以评估冷帽覆盖率对充气室温度的影响。DM1200内的整体温度主要由熔池上冷帽覆盖的数量和分布决定,因为在这些温度下热辐射是主要的传热方式。在玻璃状态实验室的中试规模测试中获得的DM1200的静压温度用于模型验证。使用标准的LeNet-1卷积神经网络(CNN)根据已知冷帽拓扑计算的室内温度分布来预测模型的空间冷帽覆盖率。采用16 cm × 16 cm滤波器,准确度达到89%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convolutional Neural Network Model for the Prediction of Plenum Temperature in a Waste Glass Melter
Legacy radioactive tank waste is slated to undergo vitrification at the Waste Treatment and Immobilization Plate (WTP) using Joule-heated, ceramic-lined melters. A high-fidelity, computational fluid dynamics (CFD) model of the pilot-scale DM1200 melter has been developed to provide an understanding of the heat transfer and fluid dynamics within the WTP melters. Monitoring of the non-radioactive pilot-scale system has been primarily done through visual observations. However, visual observations will not be possible in the full-scale radioactive melter and process control will be based upon the measured plenum temperatures. Using the CFD model, the effect of the cold cap coverage on the plenum temperature can be assessed. The plenum temperature within the DM1200 is primarily driven by the amount and distribution of the cold cap coverage on the melt pool, since thermal radiation is the dominant mode of heat transfer at these temperatures. Plenum temperatures in the DM1200 obtained during pilot-scale testing by the Vitreous State Laboratory were used for model validation. A standard LeNet-1 convolutional neural network (CNN) is used to predict the spatial cold cap coverage of the model from the computed plenum temperature distributions derived from known cold cap topologies. With a 16 cm × 16 cm filter applied, an accuracy of 89% was achieved.
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