Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport

A new deep-learning-based surrogate model is developed and applied for predicting dynamic temperature, pressure, gas rate, oil rate, and water rate with different boundary conditions in pipeline flow. The surrogate model is based on the multilayer perceptron (MLP), batch normalization and Parametric...

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Bibliographic Details
Main Authors: Feng Qin, Zhenghe Yan, Peng Yang, Shenglai Tang, Hu Huang
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Energy Research
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2022.979168/full