Detail publikace

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)

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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)

Vydáno

1. 4. 2024

Nakladatel

PERGAMON-ELSEVIER SCIENCE LTD

Místo

OXFORD

ISSN

0360-5442

Periodikum

Energy

Ročník

292

Číslo

130354

Stát

Spojené království Velké Británie a Severního Irska

Strany od

130354

Strany do

130354

Strany počet

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"
}