Startseite Analysis of pressure drop, energy requirements, and entropy generation in natural gas pipelines at dense and pseudo-dense phases: a CFD study
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Analysis of pressure drop, energy requirements, and entropy generation in natural gas pipelines at dense and pseudo-dense phases: a CFD study

  • Moslem Abrofarakh , Mortaza Zivdar EMAIL logo und Davod Mohebbi-Kalhori
Veröffentlicht/Copyright: 31. März 2025
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Chemical Product and Process Modeling
Aus der Zeitschrift Chemical Product and Process Modeling Band 20 Heft 4

Abstract

Reducing pressure drop and energy requirements in the natural gas transmission is crucial for enhancing the performance of pipelines and reducing the greenhouse emission effect. In this study, a steady-state CFD modeling was conducted to examine the pressure drop, energy-specific toll (EST), and entropy generation rate (ER) of natural gas pipelines in the dense phase (DP), pseudo-dense phase (PDP), and vapor phase (VP). Three natural gas cases with varying compositions were utilized. The findings revealed that at all three cases, the pressure drop, EST, and ER in the DP were lower compared to those in the PDP and VP phases. For instance, in case 1, the EST in DP was 15 % and 67 % lower than in the PDP and VP, respectively. Similarly, the pressure drop in DP for case 1 was 7.5 % and 42.8 % lower than in the PDP and VP, respectively. Additionally, the ER in DP for case 1 was 12 % and 60 % lower than in the PDP and VP, respectively. The effect of mass flow rate on the pipeline performance indicated that as the mass flow rate increased from 30 to 50 kg/s, the pressure drop, EST, and ER for all cases and phases increased almost 2.7 times. Additionally, when the pipeline diameter increased from 0.3 to 0.6 m, the pressure drop, EST, and ER decreased almost 38, 38, and 16 times, respectively. The results of surface roughness revealed that for all cases and phases, the pressure drop, EST, and ER increased by almost 2.39, 2.39, and 2.2 times, respectively, as the surface roughness increased from 5 μm to 260 μm. Finally, this study developed mathematical models to investigate the pressure drop for pipelines in DP and PDP. The diameter of the pipeline had a greater effect on presser drop compared to the inlet mass flow rate and surface roughness.

Keywords: natural gas; pipeline; dense phase; pseudo-dense phase; CFD
Corresponding author: Mortaza Zivdar, Department of Chemical Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: MA: Writing – original draft, Investigation, Software, Validation; MZ: Methodology, Supervision, Writing – original draft, Writing – review & editing. DM: Writing – original draft, Writing – review & editing.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Data will be made available on request.

Nomenclature

C1ε

Constant

D

pipeline diameter (m)

f

friction factor

k

Turbulence kinetic energy (m2. s−2)

ks

Surface roughness (m)

LHV

lower heating values (kJ/kg)

p

Pressure (Pa)

Re

Reynolds number

S

Total EGR (W. K−1)

s

Local EGR (W. m−3. K−1)

T

Temperature (K)

u

Velocity vector (m.s−1)

Greek letters

ρ

Density (kg. m−3)

ε

Rate of dissipation (m. s−2)

μ

Viscosity (Pa. s)

μT

Turbulent viscosity (Pa. s)

Abbreviation

CFD

Computational Fluid Dynamics

DP

Dense Phase

EST

energy-specific toll

PDP

Pseudo-Dense Phases

VP

Vapor Phase

References

1. Zhou, D, Jia, X, Ma, S, Shao, T, Huang, D, Hao, J, et al.. Dynamic simulation of natural gas pipeline network based on interpretable machine learning model. Energy 2022;253:124068. https://doi.org/10.1016/j.energy.2022.124068.Suche in Google Scholar

2. Du, J, Zheng, J, Liang, Y, Wang, B, Klemeš, JJ, Lu, X, et al.. A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction. Energy 2023;263:125976. https://doi.org/10.1016/j.energy.2022.125976.Suche in Google Scholar

3. Mohammad, NEG, Rawash, YY, Aly, SM, Awad, MES, Mohamed, MHH. Enhancing gas pipeline network efficiency through VIKOR method. Decis Mak: Appl Manag Eng 2023;6:853–79. https://doi.org/10.31181/dmame622023868.Suche in Google Scholar

4. Economides, MJ, Wood, DA. The state of natural gas. J Nat Gas Sci Eng 2009;1:1–13. https://doi.org/10.1016/j.jngse.2009.03.005.Suche in Google Scholar

5. Kan, S, Chen, B, Wu, X, Chen, Z, Chen, G. Natural gas overview for world economy: from primary supply to final demand via global supply chains. Energy Pol 2019;124:215–25. https://doi.org/10.1016/j.enpol.2018.10.002.Suche in Google Scholar

6. Holz, F, Richter, PM, Egging, R. A global perspective on the future of natural gas: resources, trade, and climate constraints. USA: The University of Chicago Press; 2015.10.1093/reep/reu016Suche in Google Scholar

7. Safari, A, Das, N, Langhelle, O, Roy, J, Assadi, M. Natural gas: a transition fuel for sustainable energy system transformation? Energy Sci Eng 2019;7:1075–94. https://doi.org/10.1002/ese3.380.Suche in Google Scholar

8. Gordon, D, Gunsch, K, Pawluk, C. A natural monopoly in natural gas transmission. Energy Econ 2003;25:473–85. https://doi.org/10.1016/s0140-9883(03)00057-4.Suche in Google Scholar

9. Woldeyohannes, AD, Abd Majid, MA. Simulation model for natural gas transmission pipeline network system. Simulat Model Pract Theor 2011;19:196–212. https://doi.org/10.1016/j.simpat.2010.06.006.Suche in Google Scholar

10. Ríos-Mercado, RZ, Kim, S, Boyd, EA. Efficient operation of natural gas transmission systems: a network-based heuristic for cyclic structures. Comput Oper Res 2006;33:2323–51. https://doi.org/10.1016/j.cor.2005.02.003.Suche in Google Scholar

11. Ríos-Mercado, RZ, Borraz-Sánchez, C. Optimization problems in natural gas transportation systems: a state-of-the-art review. Appl Energy 2015;147:536–55. https://doi.org/10.1016/j.apenergy.2015.03.017.Suche in Google Scholar

12. Wu, X, Li, C, He, Y, Jia, W. Operation optimization of natural gas transmission pipelines based on stochastic optimization algorithms: a review. Math Probl Eng 2018;2018. https://doi.org/10.1155/2018/1267045.Suche in Google Scholar

13. Zhang, Y, Tan, H, Li, Y, Zheng, J, Wang, C. Feasibility analysis and application design of a novel long-distance natural gas and electricity combined transmission system. Energy 2014;77:710–19. https://doi.org/10.1016/j.energy.2014.09.059.Suche in Google Scholar

14. Molnar, G. Economics of gas transportation by pipeline and LNG, the Palgrave handbook of international energy economics. Cham: Springer International Publishing; 2022:23–57 pp.10.1007/978-3-030-86884-0_2Suche in Google Scholar

15. Pirumyan, N, Stakyan, M, Yazyev, B. Reliability enhancement of the operation of main pipelines in order to ensure the sustainable development of the gas transmission system. In: International school on neural networks, Initiated by IIASS and EMFCSC. Russia: Springer; 2022:1283–91 pp.10.1007/978-3-031-11058-0_130Suche in Google Scholar

16. Rothfarb, B, Frank, H, Rosenbaum, D, Steiglitz, K, Kleitman, DJ. Optimal design of offshore natural-gas pipeline systems. Oper Res 1970;18:992–1020. https://doi.org/10.1287/opre.18.6.992.Suche in Google Scholar

17. Pellegrini, LA, De Guido, G, Lange, S. Handbook of natural gas transmission and processing-principles and practices, handbook of natural gas transmission and processing: principles and practices. USA: Elsevier-Gulf Professional Publishing; 2019:669–739 pp.Suche in Google Scholar

18. Lanzano, G, Salzano, E, de Magistris, FS, Fabbrocino, G. Seismic vulnerability of natural gas pipelines. Reliab Eng Syst Saf 2013;117:73–80. https://doi.org/10.1016/j.ress.2013.03.019.Suche in Google Scholar

19. Wei, Q, Zhou, P, Shi, X. The congestion cost of pipeline networks under third-party access in China’s natural gas market. Energy 2023;284:128521. https://doi.org/10.1016/j.energy.2023.128521.Suche in Google Scholar

20. Moore, R, Bishnoi, P, Donnelly, J. Rigorous design of high pressure natural gas pipelines using BWR equation of state. Can J Chem Eng 1980;58:103–12. https://doi.org/10.1002/cjce.5450580115.Suche in Google Scholar

21. Gregory, G, Aziz, K, Moore, R. Computer design of dense-phase pipelines. J Petrol Technol 1979;31:40–50. https://doi.org/10.2118/6876-pa.Suche in Google Scholar

22. Shariati, A, Moshfeghian, M, Maddox, R. Effect of C6+ characterization on two-phase flow pipelines. Int J Model Simulat 1999;19:352–6. https://doi.org/10.1080/02286203.1999.11760434.Suche in Google Scholar

23. Gato, L, Henriques, J. Dynamic behaviour of high-pressure natural-gas flow in pipelines. Int J Heat Fluid Flow 2005;26:817–25. https://doi.org/10.1016/j.ijheatfluidflow.2005.03.011.Suche in Google Scholar

24. Peretti, A, Toth, P. Optimization of a pipe-line for the natural gas transportation. Eur J Oper Res 1982;11:247–54. https://doi.org/10.1016/0377-2217(82)90248-x.Suche in Google Scholar

25. Chaczykowski, M, Osiadacz, AJ. Dynamic simulation of pipelines containing dense phase/supercritical CO2-rich mixtures for carbon capture and storage. Int J Greenh Gas Control 2012;9:446–56. https://doi.org/10.1016/j.ijggc.2012.05.007.Suche in Google Scholar

26. Zhang, Z, Wang, G, Massarotto, P, Rudolph, V. Optimization of pipeline transport for CO2 sequestration. Energy Convers Manag 2006;47:702–15. https://doi.org/10.1016/j.enconman.2005.06.001.Suche in Google Scholar

27. Wang, Z, Zheng, Y. Critical flow velocity phenomenon in erosion-corrosion of pipelines: determination methods, mechanisms and applications. J Pipeline Sci Eng 2021;1:63–73. https://doi.org/10.1016/j.jpse.2021.01.005.Suche in Google Scholar

28. Mokhatab, S. Explicit method predicts temperature and pressure profiles of gas-condensate pipelines. Energy Sources, Part A 2007;29:781–9. https://doi.org/10.1080/009083190957757.Suche in Google Scholar

29. Witkowski, A, Rusin, A, Majkut, M, Stolecka, K. Analysis of compression and transport of the methane/hydrogen mixture in existing natural gas pipelines. Int J Pres Ves Pip 2018;166:24–34. https://doi.org/10.1016/j.ijpvp.2018.08.002.Suche in Google Scholar

30. Ebrahimi-Moghadam, A, Farzaneh-Gord, M, Arabkoohsar, A, Moghadam, AJ. CFD analysis of natural gas emission from damaged pipelines: correlation development for leakage estimation. J Clean Prod 2018;199:257–71. https://doi.org/10.1016/j.jclepro.2018.07.127.Suche in Google Scholar

31. Desamala, AB, Dasamahapatra, AK, Mandal, TK. Oil-water two-phase flow characteristics in horizontal pipeline–a comprehensive CFD study, International journal of Chemical, Molecular, Nuclear, Materials and Metallurgical Engineering. World Acad Sci, Eng Technol 2014;8:360–4.Suche in Google Scholar

32. Fan, M, Kim, WJ, Heldman, DR. Comparison of flow characteristics in a bench-scale system with commercial-scale pipelines: use of computational fluid dynamics (CFD). J Food Sci 2021;86:3097–108. https://doi.org/10.1111/1750-3841.15814.Suche in Google Scholar PubMed

33. Cadorin, M, Morini, M, Pinelli, M. Numerical analyses of high Reynolds number flow of high pressure fuel gas through rough pipes. Int J Hydrogen Energy 2010;35:7568–79. https://doi.org/10.1016/j.ijhydene.2010.04.017.Suche in Google Scholar

34. Umuteme, OM. Computational fluid dynamics (CFD) transient pressure and temperature simulation of a natural gas–hydrogen gas blend transportation pipeline. Int J Innovat Res Dev 2020;9. https://doi.org/10.24940/ijird/2020/v9/i6/jun20056.Suche in Google Scholar

35. Tan, K, Mahajan, D, Venkatesh, T. Computational fluid dynamic modeling of methane-hydrogen mixture transportation in pipelines: understanding the effects of pipe roughness, pipe diameter and pipe bends. Int J Hydrogen Energy 2024;49:1028–42. https://doi.org/10.1016/j.ijhydene.2023.06.195.Suche in Google Scholar

36. Moshfeghian. Transportation of natural gas in dense phase. USA: PetroSkills, Jmcampbell; 2012.Suche in Google Scholar

37. Vargas-Vera, B-H, Rada-Santiago, A-M, Cabarcas-Simancas, M-E. Gas transport at dense phase conditions for the development of deepwater fields in the Colombian Caribbean sea, CT&F-Ciencia. Tecnol y Futuro 2020;10:17–32.10.29047/01225383.131Suche in Google Scholar

38. Zivdar, M, Abrofarakh, M. Natural gas transmission in dense phase mode. J Gas Technol JGT 2021;6.Suche in Google Scholar

39. Almara, LM, Wang, G-X, Prasad, V. Conditions and thermophysical properties for transport of hydrocarbons and natural gas at high pressures: dense phase and anomalous supercritical state. Gas Sci Eng 2023;117:205072. https://doi.org/10.1016/j.jgsce.2023.205072.Suche in Google Scholar

40. López-Núñez, OA, Alfaro-Ayala, JA, Ramírez-Minguela, J, Cano-Banda, F, Ruiz-Camacho, B, Belman-Flores, JM. Numerical analysis of the thermo-hydraulic performance and entropy generation rate of a water-in-glass evacuated tube solar collector using TiO2 water-based nanofluid and only water as working fluids. Renew Energy 2022;197:953–65. https://doi.org/10.1016/j.renene.2022.07.156.Suche in Google Scholar

41. Deymi-Dashtebayaz, M, Ebrahimi-Moghadam, A, Pishbin, SI, Pourramezan, M. Investigating the effect of hydrogen injection on natural gas thermo-physical properties with various compositions. Energy 2019;167:235–45. https://doi.org/10.1016/j.energy.2018.10.186.Suche in Google Scholar

42. Rubaiee, S. High sour natural gas dehydration treatment through low temperature technique: process simulation, modeling and optimization. Chemosphere 2023;320:138076. https://doi.org/10.1016/j.chemosphere.2023.138076.Suche in Google Scholar PubMed

43. Bagheri, M, Sari, A. Study of natural gas emission from a hole on underground pipelines using optimal design-based CFD simulations: developing comprehensive soil classified leakage models. J Nat Gas Sci Eng 2022;102:104583. https://doi.org/10.1016/j.jngse.2022.104583.Suche in Google Scholar

44. Botros, KK. Performance of five equations of state for the prediction of VLE and densities of natural gas mixtures in the dense phase region. Chem Eng Commun 2002;189:151–72. https://doi.org/10.1080/00986440211837.Suche in Google Scholar

45. Liu, B, Liu, X, Lu, C, Godbole, A, Michal, G, Teng, L. Decompression of hydrogen—natural gas mixtures in high-pressure pipelines: CFD modelling using different equations of state. Int J Hydrogen Energy 2019;44:7428–37. https://doi.org/10.1016/j.ijhydene.2019.01.221.Suche in Google Scholar

46. Peng, D-Y, Robinson, DB. A new two-constant equation of state. Ind Eng Chem Fundam 1976;15:59–64. https://doi.org/10.1021/i160057a011.Suche in Google Scholar
Received: 2024-10-15
Accepted: 2025-03-14
Published Online: 2025-03-31

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Numerical investigation of superheating secondary flow on performance of steam ejector by considering non-equilibrium condensation in renewable refrigeration cycle
  4. Analysis of pressure drop, energy requirements, and entropy generation in natural gas pipelines at dense and pseudo-dense phases: a CFD study
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  6. Energy recovery from mechanical energy of high-pressure natural gas pipeline: a case study simulation
  7. A numerical simulation of nucleate boiling of water on inclined and rough surfaces
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https://doi.org/10.1515/cppm-2024-0101
Schlagwörter für diesen Artikel
natural gas; pipeline; dense phase; pseudo-dense phase; CFD

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Numerical investigation of superheating secondary flow on performance of steam ejector by considering non-equilibrium condensation in renewable refrigeration cycle
  4. Analysis of pressure drop, energy requirements, and entropy generation in natural gas pipelines at dense and pseudo-dense phases: a CFD study
  5. Random Forest model for precise cooling load estimation in optimized and non-optimized form
  6. Energy recovery from mechanical energy of high-pressure natural gas pipeline: a case study simulation
  7. A numerical simulation of nucleate boiling of water on inclined and rough surfaces
  8. Optimization and modelling of process parameters for single pass plasma arc welded steel using response surface methodology
  9. Forecasting gasification process results via radial basis function optimization schemes
  10. Machine learning approaches for predicting syngas production in biomass gasification
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