Detail publikace

A rapid method for composition tracking in hydrogen-blended pipeline using Fourier neural operator

Gong, JH (Gong, Junhua) Shi, GY (Shi, Guoyun) Fan, ZY (Fan, Ziying) Yu, B (Yu, Bo) Chen, YJ (Chen, Yujje) Chen, B (Chen, Bin) Li, JF (Li, Jingfa) Wang, BH (Wang, Bohong) Li, ZZ (Li, Zongze) Jiang, WX (Jiang, Weixin) Varbanov, PS (Varbanov, Petar Sabev)

Originální název

A rapid method for composition tracking in hydrogen-blended pipeline using Fourier neural operator

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Blending hydrogen into natural gas for transportation is a crucial approach for achieving the widespread utilization of hydrogen. Tracking the concentration of the hydrogen within the pipeline is important for monitoring gas quality and managing pipeline operations. This study develops a rapid computational model to predict the hydrogen and natural gas concentrations within the pipeline during transportation based on the Fourier Neural Operator (FNO), an operator neural network capable of learning the differential operator in the partial differential equation. In the proposed model, the numerical method is employed to generate datasets, with the spline interpolation used to enhance data smoothness. The initial and boundary conditions are taken as the inputs to accommodate varying transportation scenarios. Comparison results indicate that the proposed model can notably reduce the time needed to predict the hydrogen and natural gas concentrations while maintaining prediction accuracy. The accuracy of the proposed model is validated by comparing its calculated results with the analytical solution and the concentrations of hydrogen and natural gas within the pipeline under two transportation scenarios, with relative errors of 0.49%, 0.31%, and 0.45%, respectively. Notably, the trained model demonstrates strong grid invariance, a type of model generalization. Trained on data generated from a coarse grid of 101 x 41 spatial-temporal resolution, the proposed model can accurately predict results on a fine grid of 401 x 81 spatial-temporal resolution with a relative error of only 0.38%. Regarding the prediction efficiency, the proposed model achieves an average 17.7-fold speedup compared to the numerical method. The positive results indicate that the proposed model can serve as a rapid and accurate solver for the composition transport equation.

Klíčová slova

NATURAL-GAS; PIPELINE;SIMULATION; NETWORKS

Autoři

Gong, JH (Gong, Junhua); Shi, GY (Shi, Guoyun) ; Fan, ZY (Fan, Ziying); Yu, B (Yu, Bo); Chen, YJ (Chen, Yujje); Chen, B (Chen, Bin); Li, JF (Li, Jingfa); Wang, BH (Wang, Bohong) ; Li, ZZ (Li, Zongze) ; Jiang, WX (Jiang, Weixin) ; Varbanov, PS (Varbanov, Petar Sabev)

Vydáno

18. 11. 2024

Nakladatel

AIP Publishing

Místo

MELVILLE

ISSN

1070-6631

Periodikum

PHYSICS OF FLUIDS

Ročník

36

Číslo

11

Stát

Spojené státy americké

Strany od

116126

Strany do

116126

Strany počet

15

URL

https://pubs.aip.org/aip/pof/article/36/11/116126/3320026/A-rapid-method-for-composition-tracking-in

BibTex

@article{BUT196914,
  author="Gong, JH (Gong, Junhua) and Shi, GY (Shi, Guoyun) and Fan, ZY (Fan, Ziying) and Yu, B (Yu, Bo) and Chen, YJ (Chen, Yujje) and Chen, B (Chen, Bin) and Li, JF (Li, Jingfa) and Wang, BH (Wang, Bohong) and Li, ZZ (Li, Zongze) and Jiang, WX (Jiang, Weixin) and Varbanov, PS (Varbanov, Petar Sabev)",
  title="A rapid method for composition tracking in hydrogen-blended pipeline using Fourier neural operator",
  journal="PHYSICS OF FLUIDS",
  year="2024",
  volume="36",
  number="11",
  pages="15",
  doi="10.1063/5.0235781",
  issn="1070-6631",
  url="https://pubs.aip.org/aip/pof/article/36/11/116126/3320026/A-rapid-method-for-composition-tracking-in"
}