Fast Prediction of the Temperature Field Surrounding a Hot Oil Pipe Using the POD-BP Model

نویسندگان

چکیده

The heat transfer assessment of a buried hot oil pipe is essential for the economical and safe transportation pipeline, where basis to determine temperature field surrounding quickly. This work proposes novel method efficiently predict pipe, which combines proper orthogonal decomposition (POD) backpropagation (BP) neural network, named POD-BP model. Specifically, BP network used establish mapping relationship between spectrum coefficients preset parameters sample. Compared with classical POD reduced-order model, model avoids solving system governing equations as variables, thus improving prediction speed. Another advantage that it easy implement does not require tremendous mathematical derivation equations. then sample matrix obtained from numerical results using finite volume (FVM). In validation cases, both steady unsteady states are investigated, multiple boundary conditions, thermal properties, even geometry (different depths diameters) tested. mean errors cases 0.845~3.052% 0.133~1.439%, respectively. Appealingly, almost no time, around 0.008 s, consumed in predicting situations proposed while FVM requires computational time 70 s.

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ژورنال

عنوان ژورنال: Processes

سال: 2023

ISSN: ['2227-9717']

DOI: https://doi.org/10.3390/pr11092666