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首页> 外文期刊>Current Science: A Fortnightly Journal of Research >Development of artificial neural network-based model for prediction of temperature field in host rock of a geological disposal facility for radioactive waste
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Development of artificial neural network-based model for prediction of temperature field in host rock of a geological disposal facility for radioactive waste

机译：放射性废物地质处理设施主体岩石温度场预测基于人工神经网络的研制

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Calculation of temperature field in a deep geological repository (DGR) after emplacement of a large number of heat emitting vitrified radioactive canisters is important and requires large computational time and hence in this study an effort has been made towards development of artificial neural network (ANN) based model that can predict the temperature quickly. The datasets required to train the ANN model were generated using an in-house developed GUI tool for simulating heat diffusion process. Various numerical studies were conducted with different configurations of the ANN model and different datasets of size 50, 100, 150, 200, to optimize the number of input data required to train the model. The results in the form of temperature values predicted by the trained ANN model have been compared with those for the same problem calculated using analytical and finite difference based methods. The trained ANN model can predict temperature values with less than 0.001% error.
机译：在大量热发射玻璃放射罐的施加后，在深层地质储存库（DGR）中的温度场的计算重要性，并且需要大的计算时间，因此在这项研究中，已经努力发展人工神经网络（ANN）的发展 基于模型可以快速预测温度。 使用内部开发的GUI工具产生培训ANN模型所需的数据集，用于模拟热扩散过程。 使用ANN模型的不同配置和大小50,100,150,200的不同配置进行了各种数值研究，以优化培训模型所需的输入数据的数量。 将培训的ANN模型预测的温度值的结果与使用分析和有限差分的方法计算的相同问题的结果进行了比较。 培训的ANN模型可以预测误差小于0.001％的温度值。

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