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    Efficient and Robust Temperature Field Simulation of Long-Distance Crude Oil Pipeline Based on Bayesian Neural Network and PDE

    Weixin Jiang1,*, Qing Yuan2, Zongze Li3, Junhua Gong3, Bo Yu4

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.4, pp. 1-2, 2023, DOI:10.32604/icces.2023.08861

    Abstract The hydraulic and thermal simulation of crude oil pipeline transportation is greatly significant for the safe transportation and accurate regulation of pipelines. With reasonable basic parameters, the solution of the traditional partial differential equation (PDE) for the axial soil temperature field on the pipeline can obtain accurate simulation results, yet it brings about a low calculation efficiency problem. In order to overcome the low-efficiency problem, an efficient and robust hybrid solution model for soil temperature field coupling with Bayesian neural network and PDE is proposed, which considers the dynamic changes of boundary conditions. Four models, including the proposed hybrid model,… More >

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