Modeling of hydrogen-induced fracture toughness degradation of pipe steel
| Назва | Modeling of hydrogen-induced fracture toughness degradation of pipe steel |
| Назва англійською | Modeling of hydrogen-induced fracture toughness degradation of pipe steel |
| Автори | Oleh Venhryniuk, Olha Zvirko |
| Принадлежність | Karpenko Physico-Mechanical Institute of the NAS of Ukraine, Lviv, Ukraine |
| Бібліографічний опис | Modeling of hydrogen-induced fracture toughness degradation of pipe steel / Oleh Venhryniuk, Olha Zvirko // Scientific Journal of TNTU. — Tern.: TNTU, 2025. — Vol 120. — No 4. — P. 120–129. |
| Bibliographic description: | Venhryniuk O., Zvirko O. (2025) Modeling of hydrogen-induced fracture toughness degradation of pipe steel. Scientific Journal of TNTU (Tern.), vol 120, no 4, pp. 120–129. |
| DOI: | https://doi.org/10.33108/visnyk_tntu2025.04. 120 |
| УДК | 539.3 |
| Ключові слова |
steel, mechanical characteristics, hydrogen, degradation, modeling, experimental data, fracture toughness, J-integral, diffusion equation, error reduction. |
A correction to the existing hydrogen-induced fracture toughness degradation model was introduced. We formulated a new degradation model that simplifies predictions without compromising accuracy, effectively reducing the mean absolute error from 93 N/mm to 1.4 N/mm on experimental data – a reduction of more than 60 times. The fundamental difference between degraded steel and its as-delivered (reserve) state was demonstrated, and two separate models were proposed for each case. It was shown that the difference between using a constant stress value and the solution to the Lamé equation is only 2%, which justified simplifying the chosen diffusion equation to a classical form. An example application of the new model, together with the solution to the diffusion equation, was presented. The developed models were applied to the parameters of real pipes and validated against experimental data. | |
| ISSN: | 2522-4433 |
| Перелік літератури |
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2. Zvirko O., Tsyrulnyk O., Venhryniuk O., Nykyforchyn H. (2025). Hydrogen Related Issues at Hydrogen Transport via Existing Gas Pipelines. In: Prentkovskis O., Yatskiv (Jackiva) I., Skačkauskas P., Karpenko M., Stosiak M. (eds). Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham, pp. 210–219.
3. Poberezhnyi L. (2025) The deformation behavior of the long-term exploited pipelines in soil electrolyte imitates. Scientific Journal of TNTU, vol. 118, no. 2, pp. 5–19.
4. Zvirko O. I., Tsyrulnyk O. T., Venhrynyuk O. I., Nykyforchyn H. M. (2024) Sensitivity of the J-integral method for estimating the hydrogen embrittlement of ferritic-pearlitic pipe steel. Strength Mater, vol. 56, no. 5, art. no. 104534, pp. 928–935.
5. Сabrini M. et al. (2019) Hydrogen embrittlement evaluation of micro alloyed steels by means of J-integral curve. Materials, vol. 12. Art. No. 1843, pp. 1–17.
6. Kyriakopoulou H. P. et al. (2020) Investigation of hydrogen embrittlement susceptibility and fracture toughness drop after in situ hydrogen cathodic charging for an X65 pipeline steel. Micromachines, vol. 11, art. no. 20, pp. 1–20.
7. Huang C., Gao X. (2020) Phase field modeling of hydrogen embrittlement. Int. J. Hydrogen Energy, vol. 45, no. 38, pp. 20053–20068.
8. Manav M., Molinaro R., Mishra S., De Lorenzis L. (2024) Phase-field modeling of fracture with physics-informed deep learning. Comput. Methods in Appl. Mech. Eng, vol. 429, art. no. 117104, pp. 1–21.
9. Malitckii E., Fangnon E., Vilaça P. (2020) Evaluation of steels susceptibility to hydrogen embrittlement: A thermal desorption spectroscopy-based approach coupled with artificial neural network. Materials, vol. 13, no. 23, art. no. 5500, pp. 1–14.
10. Yasniy O., Tymoshchuk D., Didych I., Iasnii V., Pasternak I. (2025) Modelling the properties of shape memory alloys using machine learning methods. Procedia Struct. Integr, vol. 68, pp. 132–138.
11. Yasniy O., Demchyk V., Lutsyk N. (2022) Modelling of functional properties of shape-memory alloys by machine learning methods. Scientific Journal of TNTU, vol. 108, no. 4, pp. 74–78.
12. He X. et al. (2024) Prediction model for the evolution of hydrogen concentration under leakage in hydrogen refueling station using deep neural networks. Int. J. Hydrogen Energy, vol. 51D, pp. 702–712.
13. Zhang X. et al. (2025) Hydrogen jet and diffusion modeling by physics-informed graph neural network. Renew. Sustain. Energy Rev, vol. 207, art. no. 114898.
14. Larche F. C., Cahn J. W. (1982) The effect of self-stress on diffusion in solids. Acta Metall, vol. 30. pp. 1835–1845.
15. Beer F. P. et al. (2012). Mechanics of Materials, 6th ed. McGraw-Hill, New York.
16. Raichenko A. I. (1981). Mathematical Theory of Diffusion in Applications. Naukova Dumka. Kyiv, 396 p.
17. Wang R. (2009) Effects of hydrogen on the fracture toughness of a X70 pipeline steel. Cor. Sci, vol. 51, pp. 2803–2810.
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| References: |
1. Campari A., Ustolin F., Alvaro A., Paltrinieri N. (2023) A review on hydrogen embrittlement and risk-based inspection of hydrogen technologies. Int. J. Hydrogen Energy, vol. 48, no. 90, pp. 35316–35346.
2. Zvirko O., Tsyrulnyk O., Venhryniuk O., Nykyforchyn H. (2025). Hydrogen Related Issues at Hydrogen Transport via Existing Gas Pipelines. In: Prentkovskis O., Yatskiv (Jackiva) I., Skačkauskas P., Karpenko M., Stosiak M. (eds). Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham, pp. 210–219.
3. Poberezhnyi L. (2025) The deformation behavior of the long-term exploited pipelines in soil electrolyte imitates. Scientific Journal of TNTU, vol. 118, no. 2, pp. 5–19.
4. Zvirko O. I., Tsyrulnyk O. T., Venhrynyuk O. I., Nykyforchyn H. M. (2024) Sensitivity of the J-integral method for estimating the hydrogen embrittlement of ferritic-pearlitic pipe steel. Strength Mater, vol. 56, no. 5, art. no. 104534, pp. 928–935.
5. Сabrini M. et al. (2019) Hydrogen embrittlement evaluation of micro alloyed steels by means of J-integral curve. Materials, vol. 12. Art. No. 1843, pp. 1–17.
6. Kyriakopoulou H. P. et al. (2020) Investigation of hydrogen embrittlement susceptibility and fracture toughness drop after in situ hydrogen cathodic charging for an X65 pipeline steel. Micromachines, vol. 11, art. no. 20, pp. 1–20.
7. Huang C., Gao X. (2020) Phase field modeling of hydrogen embrittlement. Int. J. Hydrogen Energy, vol. 45, no. 38, pp. 20053–20068.
8. Manav M., Molinaro R., Mishra S., De Lorenzis L. (2024) Phase-field modeling of fracture with physics-informed deep learning. Comput. Methods in Appl. Mech. Eng, vol. 429, art. no. 117104, pp. 1–21.
9. Malitckii E., Fangnon E., Vilaça P. (2020) Evaluation of steels susceptibility to hydrogen embrittlement: A thermal desorption spectroscopy-based approach coupled with artificial neural network. Materials, vol. 13, no. 23, art. no. 5500, pp. 1–14.
10. Yasniy O., Tymoshchuk D., Didych I., Iasnii V., Pasternak I. (2025) Modelling the properties of shape memory alloys using machine learning methods. Procedia Struct. Integr, vol. 68, pp. 132–138.
11. Yasniy O., Demchyk V., Lutsyk N. (2022) Modelling of functional properties of shape-memory alloys by machine learning methods. Scientific Journal of TNTU, vol. 108, no. 4, pp. 74–78.
12. He X. et al. (2024) Prediction model for the evolution of hydrogen concentration under leakage in hydrogen refueling station using deep neural networks. Int. J. Hydrogen Energy, vol. 51D, pp. 702–712.
13. Zhang X. et al. (2025) Hydrogen jet and diffusion modeling by physics-informed graph neural network. Renew. Sustain. Energy Rev, vol. 207, art. no. 114898.
14. Larche F. C., Cahn J. W. (1982) The effect of self-stress on diffusion in solids. Acta Metall, vol. 30. pp. 1835–1845.
15. Beer F. P. et al. (2012). Mechanics of Materials, 6th ed. McGraw-Hill, New York.
16. Raichenko A. I. (1981). Mathematical Theory of Diffusion in Applications. Naukova Dumka. Kyiv, 396 p.
17. Wang R. (2009) Effects of hydrogen on the fracture toughness of a X70 pipeline steel. Cor. Sci, vol. 51, pp. 2803–2810.
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