Publication detail

Hybrid data-mechanism-driven model of the unsteady soil temperature field for long-buried crude oil pipelines with non-isothermal batch transportation

Jiang, WX (Jiang, Weixin) Wang, JF (Wang, Junfang) Varbanov, PS (Varbanov, Petar Sabev) Yuan, Q (Yuan, Qing) Chen, YJ (Chen, Yujie) Wang, BH (Wang, Bohong) Yu, B (Yu, Bo)

Original Title

Hybrid data-mechanism-driven model of the unsteady soil temperature field for long-buried crude oil pipelines with non-isothermal batch transportation

Type

journal article in Web of Science

Language

English

Original Abstract

The thermal simulation of oil pipeline transportation is crucial for ensuring safe transportation of pipelines and optimizing energy consumption. The prediction of the soil temperature field is the key to the thermal calculation for the non-isothermal batch transportation of the buried pipeline, while the standard numerical simulation of the soil temperature field is time-consuming. Coupling with a data-driven Bayesian neural network and mechanism-informed partial differential equation, an efficient and robust prediction model of soil temperature field is proposed to dynamically adapt the spatio-temporal changes of boundary conditions. Based on the soil temperature field predicted by the proposed model, the oil temperature at the outlet of the pipeline is further obtained, which is compared with that from the field data and the standard numerical simulation. It is found that the former is in good agreement with the latter two, verifying the proposed model. However, the calculation of the proposed model only takes 10.59 s, which is 29.53 times faster than the standard numerical simulation. Moreover, the predicted error of the proposed model only changes by 0.12 % (from 3.05 % to 3.17 %) when the training data decreases from 100 % to 2.2 %, which is lower than that of two data-driven surrogate models.

Keywords

Crude oil pipeline; Soil temperature field; Hybrid data-mechanism-driven model; Data insensitivity; Fast prediction; Numerical simulation

Authors

Jiang, WX (Jiang, Weixin); Wang, JF (Wang, Junfang) ; Varbanov, PS (Varbanov, Petar Sabev); Yuan, Q (Yuan, Qing); Chen, YJ (Chen, Yujie) ; Wang, BH (Wang, Bohong) ; Yu, B (Yu, Bo)

Released

1. 4. 2024

Publisher

PERGAMON-ELSEVIER SCIENCE LTD

Location

OXFORD

ISBN

0360-5442

Periodical

Energy

Year of study

292

Number

130354

State

United Kingdom of Great Britain and Northern Ireland

Pages from

130354

Pages to

130354

Pages count

14

URL

https://www.sciencedirect.com/science/article/pii/S0360544224001257#:~:text=Coupling%20with%20a%20data-driven%20Bayesian%20neural%20network%20and,of%20soil%20temperature%20field%20is%20proposed%20to%20dynamical

BibTex

@article{BUT196910,
  author="Jiang, WX (Jiang, Weixin) and Wang, JF (Wang, Junfang) and Varbanov, PS (Varbanov, Petar Sabev) and Yuan, Q (Yuan, Qing) and Chen, YJ (Chen, Yujie) and Wang, BH (Wang, Bohong) and Yu, B (Yu, Bo)",
  title="Hybrid data-mechanism-driven model of the unsteady soil temperature field for long-buried crude oil pipelines with non-isothermal batch transportation",
  journal="Energy",
  year="2024",
  volume="292",
  number="130354",
  pages="14",
  doi="10.1016/j.energy.2024.130354",
  issn="0360-5442",
  url="https://www.sciencedirect.com/science/article/pii/S0360544224001257#:~:text=Coupling%20with%20a%20data-driven%20Bayesian%20neural%20network%20and,of%20soil%20temperature%20field%20is%20proposed%20to%20dynamical"
}