Journal of Engineering Sciences

Author(s): Skrynkovskyy R. M.¹, Yuzevych L. V.^{2, 3}, Ogirko O. I.⁴, Pawlowski G.^5*

Affilation(s):
¹ Lviv University of Business and Law, 99 Kulparkіvska St., 79021 Lviv, Ukraine;
² Lviv Polytechnic National University, 12 Stepana Bandery St., 79013 Lviv, Ukraine;
³ Karpenko Physico-Mechanical Institute of the NAS of Ukraine, 5 Naukova St., 79060 Lviv, Ukraine;
⁴ Lviv State University of Internal Affairs, 26 Horodotska St., 79007 Lviv, Ukraine;
⁵ Zaklad Handlowo-Uslugowy BHP, 17 Kostrzynska St., 69-113 Gorzyca, Poland

^*Corresponding Author’s Address: [email protected]

Issue: Volume 5; Issue 2 (2018)

Dates:
Paper received: May 2, 2018
The final version of the paper received: October 6, 2018
Paper accepted online: October 10, 2018

Citation:
Skrynkovskyy R. M. Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue / R. M. Skrynkovskyy, L. V. Yuzevych, O. I. Ogirko, G. Pawlowski // Journal of Engineering Sciences. – Sumy : Sumy State University, 2018. – Volume 5, Issue 2. – P. E27-E32.

DOI: 10.21272/jes.2018.5(2).e6

Research Area: MECHANICAL ENGINEERING: Computational Mechanics

Abstract. This paper presents results of the use of Big Data approach and neural network for the pipelines diagnosis problem. In this case the pipeline is in the conditions of crack growth of corrosion fatigue and exposed to hydrogen. It is proposed to use graphene protective coatings. The mathematical model for estimating the changes in the effective surface energy of WPL during plastic deformation, electrochemical overstrain, polarization potential and current density of the metal dissolution reaction at the top of the crack on the pipeline surface during its mechanical loading in an aqueous electrolyte solution is given. The dissolution of the metal is considered on the juvenile surface, taking into account the anode and cathode regions based on the approaches of surface physics and electrochemistry. An element of a mathematical model is a quality functional, taking into account information flows and a sensitivity coefficient. Functional quality is used to specify the feedback between the investment project methodology and risk estimates, as well as to optimize the information flows of enterprises and improve the system of protection of metallic underground pipelines that operate under conditions of corrosion fatigue. The purpose of this project is to improve the relevant regulatory and technical documents as well as software.

Keywords: gas pipeline, monitoring, fatigue crack, corrosion, databases, Big Data, neural network, intelligent software, hardware, databases.

References:

1. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, Vol. 36, No. 4, pp. 1165–1188.
2. Pavlyshenko, B. M. (2016). Linear, machine learning and probabilistic approaches for time series analysis. IEEE International Conference on Data Stream Mining and Processing (DSMP), Aug. 2016. doi: 10.1109/dsmp.2016.7583582.
3. Nanninga, N., Slifka, A., Levy, Y., & White, C. (2010). A review of fatigue crack growth for pipeline steels exposed to hydrogen. Journal of Research of the National Institute of Standards and Technology, Vol. 115, No. 6, pp. 437, doi: 10.6028/jres.115.030.
4. Mohamed, A., Hamdi, M. S., & Tahar, S. (2015). A Machine Learning Approach for Big Data in Oil and Gas Pipelines. 3rd International Conference on Future Internet of Things and Cloud.
5. Nazeer, H., Iqbal, W., Bokhari, F., Bukhari, F., & Baig, S. (2017). Real-time Text Analytics Pipeline Using Open-source Big Data Tools. Distributed, Parallel, and Cluster Computing, pp. 1–6.
6. K. Raman, A. Swaminathan, J. Gehrke, and T. Joachims, “Beyond myopic inference in big data pipelines,” Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining – KDD ’13, 2013. doi: 10.1145/2487575.2487588.
7. Hahanov, V. I., Litvinova, E. I., Chumachenko, S. V., Yemelianov, I., & Amer, T. B. (2016). Processor structures for Big Data analysis. Radio Electronic and Computer Systems, No. 6(80), pp. 163–175.
8. Dzhala, R. М., Verbenets’, B. Y., Mel’nyk, М. І., Mytsyk, А. B., Savula, R. S., & Semenyuk, О. М. (2017). New Methods for the Corrosion Monitoring of Underground Pipelines According to the Measurements of Currents and Potentials. Materials Science, Vol. 52, No. 5, pp. 732–741.
9. Krap, N., & Yuzevych, V. (2013). Neural Networks as a tool for managing the configurations of tourist flow projects. Management of Development of Complex Systems, No. 14, pp. 37–40.
10. Thein, M., Nyunt, T. T., & Aye, K. N. (2017). Security of real-time Big Data analytics pipeline. International Journal of Advances in Electronics and Computer Science, Vol. 4, No. 2, pp. 1–5.
11. Pavan, A. S. S., & Ramanan, S. R. (2016). A study on corrosion resistant graphene films on low alloy steel. Applied Nanoscience, Vol. 6, No. 8, pp. 1175–1181, doi: 1007/s13204-016-0530-2.
12. Koman, B. P., & Yuzevich, V. M. (2015). Energy Parameters of Interfacial Layers in Composite Systems: Graphene – (Si, Cu, Fe, Co, Au, Ag, Al, Ru, Hf, Pb) and Semiconductor (Si, Ge) – (Fe, Co, Cu, Al, Au, Cr, W, Pb). Journal of Nano- and Electronic Physics, Vol. 7, No 4, pp. 04059-1–04059-7.
13. Novoselov, K. S., Morozov, S. V., Mohinddin, T. M. G., Ponomarenko, L. A., Elias, D. C., Yang, R., Barbolina, I. I., Blake, P., Booth, T. J., Jiang, D., Giesbers, J., Hill, E. W., & Geim, A. K. (2007). Electronic properties of grapheme. Physica Status Solidi, Vol. 244, No. 11, pp. 4106–4111, doi: 1002/pssb.200776208.
14. Yuzevych, V. M., Dzhala, R. M., & Koman, B. P. (2018). Analysis of Metal Corrosion under Conditions of Mechanical Impacts and Aggressive Environments. Metallofizika i Noveishie Tekhnologii, Vol. 39, No. 12, pp. 1655–1667, doi: 15407/mfint.39.12.1655.
15. Yuzevych, V., Klyuvak, O., & Skrynkovskyy, R. (2016). Diagnostics of the system of interaction between the government and business in terms of public e-procurement. Economic Annals-ХХI, Vol. 160, No. 7–8, pp. 39–44, doi: 21003/ea.v160-08.
16. Cielen, D., Meysman, A. D. B., & Ali, M. (2016). Introducing Data Science: Big Data, Machine Learning, and More, using Python Tools. Manning Publications, New York, USA.

Full Text