Skip to main content
SpringerLink
Log in

Dynamic evolution computing of leakage and diffusion from pipeline gas and risk analysis

  • Original Paper
  • Published:
International Journal of Environmental Science and Technology Aims and scope Submit manuscript

Abstract

Urban gas pipelines are typically distributed in crowded areas among several people and buildings. The leakage of gas from damaged pipelines may result in significant property loss or casualties. Therefore, it is necessary to study the dynamic evolution characteristics of gas leakage and diffusion to quantify the harmful consequences and influence ranges of urban gas leakage and diffusion. To this end, through the analysis of the dynamic leakage process and pressure state of gas pipelines, a dynamic model of gas leakage is established by considering the sudden closure of the pipeline valve. According to the dynamic characteristics of the leakage rate, the Gaussian puff integral model is set up to simulate the dynamic diffusion of gas leakage. Combined with an actual case, the leakage and diffusion laws of urban gas pipelines caused by third-party damage to pipelines and the hazardous zone risk are quantitatively analyzed and characterized. The results indicate that the leakage rate decreases with continuous dynamic leakage time and the leakage and diffusion of pipeline gas result in certain hazardous zones. The concentration of the diffusion gas generally reaches a maximum value at a certain point and lasts for a period that corresponds to the time of steady sonic leakage. Timely detection of gas leakage and timely closing of the valve play an essential role in effectively slowing down and preventing the occurrence of serious accidents.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Abbreviations

P α :

Atmospheric pressure, Pa

P c :

Critical pressure of the pipeline, Pa

K :

Isentropic index of gas

CPR:

Critical pressure ratio value

t s :

Beginning time of unsteady sonic flow of gas leakage, s

t c :

Beginning time of unsteady subsonic flow of gas leakage, s

t p :

End time of gas leakage, s

Q :

Leakage rate of gas, kg/s

M :

Leakage mass, kg

V :

Volume of the pipeline gas, m3

Ρ :

Density of gas in pipeline, kg/m3

C 0 :

Coefficient of the hole flow

A h :

Area of the hole, m2

M :

Molar mass of gas, kg/mol

Z :

Compression factor of the gas

R :

Constant of ideal gas, J/(mol·K)

T :

Gas temperature of the pipeline, K

P :

Pressure of the pipeline, Pa.

Q 0 :

Initial leakage rate at the fixed leakage time ts, kg/s

m 0 :

Initial mass at the fixed leakage time ts, kg

T 0 :

Initial temperature at the fixed leakage time ts, K

ρ 0 :

Initial density at a fixed leakage time ts, kg/m3

P 0 :

Initial pressure at the fixed leakage time ts, Pa

Q 0, sub :

Initial leakage rate for subsonic flow at a fixed leakage time tc, kg/s

m 0, sub :

Initial mass for subsonic flow at a fixed leakage time tc, kg

T 0, sub :

Initial temperature for subsonic flow at fixed leakage time tc, K

P 0, sub :

Initial pressure for subsonic flow at the fixed leakage time tc, Pa

Ci :

Gas concentration from the i-th puff mass at point (x, y, z), kg/m3

C :

Gas concentration at point (x, y, z), kg/m3

m i :

i-th puff mass during the leakage accident, kg

t' :

Any time corresponding to appearance of puff mass, s

σ x :

Diffusion coefficients in the x directions, m

σ y :

Diffusion coefficients from the y directions, m

σ z :

Diffusion coefficients from z direction, m

u :

Wind speed, m/s

H :

Effective source height, m

D :

Equivalent diameter of the failure hole

D :

Internal diameter of the pipeline, m

References

  • Chen P, Mei H, Zhao H (2014) Modeling and analysis for the big-hole leakage rate of gas pipeline[J]. J Graphics 35(3):486–489

    CAS  Google Scholar 

  • Cheng H, Liu D (2011) Numerical simulation of city gas pipeline leakage and the release and dispersion Model[J]. J Graphics 31(2):27–31

    Google Scholar 

  • Department Of Petroleum And Natural Gas N. E. A, Institute Of Resources And Environmental Policy D. R. C, Strategic Research Center Of Oil And Gas Resources M. O. N. R. Natural Gas Development Report (2021)[M]. Beijing: Petroleum Industry Press, 2021: 1–23.

  • Dong Y, Gao H (2003) Mathematical modeling of gas release through holes in pipelines[J]. Chem Eng J 92(1–3):237–241

    CAS  Google Scholar 

  • Gu Q (2002) Calculation Method of Atmospheric Environment Model[M]. China Meteorological Press, Beijing, pp 15–23

    Google Scholar 

  • Iso 12213–3:2006. Natural gas -Calculation of compression factor Part-3: Calculation using physical properties[S]. 2006.

  • Jo Y (2003) A simple model for the release rate of hazardous gas from a hole on high-pressure pipelines. J Hazard Mater 97(1–3):31–46. https://doi.org/10.1016/S0304-3894(02)00261-3

    Article  CAS  Google Scholar 

  • Jo YD, Ahn BJ (2005) A method of quantitative risk assessment for transmission pipeline carrying natural gas[J]. J Hazard Mater 123(1–2):1–12

    Article  CAS  Google Scholar 

  • Mccallen RC (1988) An investigation of the finite element incompressible flow code FEM3: the model, some of its options, a guide in how to obtain and run the code, and its performance on some chosen problems[J]. NASA STI/Recon Technical Report N 89:15358

    Google Scholar 

  • Montiel H, Vilchez JA, Casal J, Arnaldos J (1998) Mathematical modelling of accidental gas releases. J Hazard Mater [J] 59(2–3):211–233

    Article  CAS  Google Scholar 

  • Park KA, Choi YM, Choi HM, Cha TS, Yoon BH (2001) The evaluation of critical pressure ratios of sonic nozzles at low Reynolds numbers[J]. Flow Meas Instrum 12(1):37–41

    Article  Google Scholar 

  • Qin C, Zhang W, Wang Z (2021) Research on the diffusion of harmful gases from ships based on gaussian plume model[c]. iop Conf Series: Earth Environ Sci IOP Publishing 781(3):32034

    Google Scholar 

  • Robins Alan (2003) Wind tunnel dispersion modelling some recent and not so recent achievements. J Wind Eng Industrial Aerodyn[J] 91(12–15):1777–1790. https://doi.org/10.1016/j.jweia.2003.09.025

    Article  Google Scholar 

  • Sklavounos S, Rigas F (2006) Estimation of Safety Distances in the Vicinity of Fuel Gas Pipelines[J]. J Loss Prev Process Ind 19(1):24–31

    Article  Google Scholar 

  • Stockie JM (2012) The mathematics of atmospheric dispersion modeling[J]. SIAM Rev 53(2):349–372

    Article  Google Scholar 

  • Tan C, Liu Y, Wang T (2017) CFD analysis of gas diffusion and ventilation protection in municipal pipe tunnel[C]. In: Proceedings of the 2017 International Conference on Data Mining, Communications and Information Technology. pp 1–6

  • Tian G (1999) Study on simulating leak of gas from pipe[J]. J Shandong Inst Architect Eng 14(4):56–60

    CAS  Google Scholar 

  • Wang D, Zhang P (2012) Non-steady-state Gas Leakage Model for Pressure Vessel Failure[J]. China Safety Sci J 22(7):154–158

    Google Scholar 

  • Woodward JL, Mudan KS (1991) Liquid and gas discharge rates through holes in process vessels. J Loss Prev Process Industries[J] 4(3):161–165

    Article  Google Scholar 

  • Xu R, Yue Y, Hu X (2020) Statistical Analysis and Countermeasure of Urban Gas Explosion Accidents[J]. Gas Heat 40(7):33–36

    Google Scholar 

  • Yang Z, Li X, Lai J (2007) Analysis on the diffusion hazards of dynamic leakage of gas pipeline[J]. Reliab Eng Syst Saf 92:47–53

    Article  Google Scholar 

  • Zhang F (2007) Analysis on the diffusion hazards of unsteady leakage of gas pipeline[J]. J Chongqing Jianzhu Univ 29(7):103–109

    Google Scholar 

Download references

Acknowledgments

This study was supported by the Qinzhou Scientific Research and Technology Development Project of Guangxi, China (No.20198513) and Project of Scientific Research Basic Ability Enhancement of Young and Middle-aged Teachers in Colleges and Universities of Guangxi, China (No. 2019KY0472).

Author information

Authors and Affiliations

  1. Guangxi Key Laboratory of Green Chemical Materials and Safety Technology, Guangxi Engineering Research Center for New Chemical Materials and Safety Technology, Beibu Gulf University, Qinzhou, 535011, Guangxi, China

    X. Zhou, X. Chen & L. Fang

  2. Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology (Beibu Gulf University), Education Department of Guangxi Zhuang Autonomous Region, Qinzhou, 535011, Guangxi, China

    X. Zhou & D. Yang

Authors
  1. X. Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. X. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. L. Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to X. Zhou.

Ethics declarations

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Editorial responsibility: Chenxi Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, X., Yang, D., Chen, X. et al. Dynamic evolution computing of leakage and diffusion from pipeline gas and risk analysis. Int. J. Environ. Sci. Technol. 20, 6091–6102 (2023). https://doi.org/10.1007/s13762-022-04366-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13762-022-04366-7

Keywords

Search

Navigation