Optimal prediction of the peak load of the cooling system of the combined cycle of a gas turbine and a combined cycle gas plant using artificial neural networks: a practical example in Iraq

Obtaining highly efficient production of electric energy is achieved by using a combined cycle of combined-cycle and gas turbine installations. The indicators of thermal emissions and specific fuel consumption have reduced values, the efficiency and mobility of the workflow are increased.

Let's consider the operation of the Bazian combined cycle electric power plant in Iraq, which has a capacity of 750 MW. This station is located in the Kurdistan region. It was put into operation in 2016. The main disadvantage of this production is a decrease in the energy efficiency of the installation in the summer. This is directly related to the high outdoor temperatures. These characteristics affect the volume and mass flow of gas. There is an increase in specific fuel consumption and a decrease in pressure in the compressor outlet pipe. Electrical power indicators are decreasing. This problem entails economic costs as a result of shutdowns of the entire system outside the planned schedule.

The problem can be solved by innovative developments in the field of cooling for combined installations. Heat transfer is intensified, control is restored and efficiency is increased. Consider such innovations as thermodynamic modeling and artificial neural network.

Thermodynamic modeling is the process of creating models capable of estimating thermodynamic values based on parametric data. The installation is evaluated, thermochemical processes are analyzed, theoretical and practical information data are systematized, and the results of interaction with high temperature values are predicted. The innovation uses the following types of software on a global level: ThermoLib, Chemical Workbench, OLI.

An artificial neural network is based on the close interaction of neurons with each other. First, the input data is received, followed by a series of computational actions to determine the solution, and finally, the overall final result is shown. The higher the synoptic weight of a neural connection, the greater the nodal impact between each other.

These innovative methods calculate productivity and assess financial feasibility using the MATLAB programming platform.

1. N. Hasan, J. N. Rai, B. B. Arora. Optimization of CCGT power plant and performance analysis using MATLAB/Simulink with actual operational data // Springer Plus. – 2014. – № 3. – P. 275. DOI: 10.1186/2193-1801-3-275.

2. M. E. Abdalla, S. Pannir and A. M. H. Mahjob. Performance and Efficiency of Combined Cycle Power Plants // 19th International Refrigeration and Air Conditioning Conference at Purdue. – 2022. – № 2 (2102). – Pp. 1-10. DOI: 10.30724/1998-9903-2019-21-1-2-43-54

3. Z. Liu, Iftekhar A. Karimi and T. He. A novel inlet air cooling system based on liquefied natural gas cold energy utilization for improving power plant performance // Energy Conversion and Management. – 2019. – № 187. – Pp. 41-52. DOI: 10.1016/j.enconman.2019.03.015.

4. V. Zare. Performance improvement of biomass-fueled closed cycle gas turbine via compressor inlet cooling using absorption refrigeration; thermo economic analysis and multi-objective optimization // Energy Conversion and Management. – 2020. – № 215. – P. 112946. DOI: 10.1016/j.enconman.2020.112946.

5. Ali Darwesh. RIH intermediate section casing in Bazian-1 exploration oil well // Energy and sustainability. – 2014. – № 186. – Pp. 559-669. DOI: 10.2495/ESUS140491.

6. A. Paudel and T. Bandhauer. Techno-economic analysis of waste heat recovery systems for wet-cooled combined cycle power plants // Applied Thermal Engineering. – 2018. – № 143(10). DOI: 0.1016/j.applthermaleng.2018.07.138.

7. R. Konijeti, Ramchandra P. Chopade and Ravindra S. Surase. Thermal performance analysis of gas turbine power plant using soft computing techniques: a review // Engineering Applications of Computational Fluid Mechanics. – 2024. – № 18(1) – P. 2374317. DOI: 10.1080/19942060.2024.2374317.

8. J. T. Lim, A. Habibullah and E. Y. K. Ng. Towards a Digital Twin for Gas Turbines: Thermodynamic Modeling, Critical Parameter Estimation, and Performance Optimization Using PINN and PSO // Energies. – 2025. – № 18 – P. 3721. DOI: 10.3390/en18143721.

9. M. M. Rahman, M. Shakeri, S. K. Tiong, F. Khatun, N. Amin, J. Pasupuleti and M. K. Hasan. Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks // Sustainability. – 2021. – № 13(4) – P. 2393. DOI: 10.3390/su13042393.

10. S. N. Lenev, Yu. A. Radin and I. S. Belyankin. Increasing Turbine Efficiency at PJSC Mosénergo Combined Cycle Gas Turbine Plants // Power Technology and Engineering. – 2023. – № 56 – Pp. 872-877. DOI: 10.1007/s10749-023-01603-y.

11. A. Mokhtari, M. A. Latify and Y. Zakeri. Temperature-dependent performance of CCGTs: Unit commitment analysis // Energy. – 2025. – № 3(318) – P. 134680. DOI: 10.1016/j.energy.2025.134680.

12. Gubarev A. Yu. Neural network for predicting operating modes and calculating energy characteristics of steam turbine plants. // Reliability and Safety of Power Engineering. – 2024. – № 17(1). – Pp. 12-18. DOI: 10.24223/1999-5555-2024-17-1-12-18.

13. Borisov D. S. and Andreev K. D. Efficiency of various types of cooling blades of high-temperature gas turbines with various types of cooler // International Journal of Humanities and Natural Sciences. – 2021. – № 4(1) – P. 55. DOI: 10.24412/2500-1000-2021-4-1-23-31.

14. Taranov D. D. Prospects for the Application of the Russian GTE-170 Gas Turbine Engine // Bulletin of Science. – 2025. – No. 6(87). – Pp. 1785-1789.

15. A. V. Rodionov, I. A. Krivosheev. Analysis of the Thermodynamic Parameters of Foreign Small-Scale Gas Turbine Plants // Young Scientist. – 2009. – No. 9. – Pp. 21-24.

16. Kuznetsov A. V. and Makaryanets G. M. Development of a Neural Network Model of a Small-Size Gas-Turbine Engine // Bulletin of the Samara State Aerospace University named after Academician S. P. Korolev (National Research University). – 2016. – № 15 (2). – Pp. 131-144. DOI: 10.18287/2412-7329-2016-15-2-131-144.

17. Abdulnagimov A. I., Antonov V. V., Chepaykin A. S., Palchevsky E. V. and Nasyrov N. T. Neural network simulator of gas turbine engines for prototyping and debugging control systems for unsteady modes // Mechatronics, automation, management. – 2025. – No. 26 (1). – Pp. 22-27. DOI: 10.17587/mau.26.22-27.

18. Imaev D. Kh., Sinitsa A. M., Shestopalov M. Yu., and Kvashnin S. V. Development of Neural Network Models for Diagnosing Turbine Unit Control Systems // Izvestiya SPbGETU «LETI». – 2018. – No. 1. – Pp. 44-48. DOI: 10.17587/mau.26.22-27.

19. Galashov N. N., Tubolev A. A., Bespalov V. V., Minor A. A. Calculation of the Parameters of a Gas-Steam Plant Scheme with Deep Utilization and Heat Release // Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering. – 2022. – No. 333. – Pp. 43-45. DOI: 10.18799/24131830/2022/5/3693.

20. Gorelov Yu. G., Ananyev V. V., Zolotukhina D. A. 3D studies of heat transfer and hydrodynamics of the duct shelves of high-pressure turbine nozzle blocks with various cooling schemes // Vestnik Samarskogo universiteta. Aerospace engineering, technologies and mechanical engineering. – 2022. – No. 21(2) – Pp. 16-27. DOI: 10.18287/2541-7533-2022-21-2-16-27.

21. Galashov N. N., Boldushevsky E. S. Development and Calculation of the Scheme of a Combined Gas-Steam Plant with Deep Utilization of Heat and Moisture // Izvestiya Tomskogo Politekhnicheskogo Universiteta. Georesursy Engineering. – 2025. – No. 336(3). – Pp. 100-111. DOI: 10.18799/24131830/2025/3/4925.

22. Galashov N. N., Tubolev A. A., Minor A. A., Boldushevsky E. S. Influence of the steam injection temperature in the combustion chamber of a gas-steam plant on its energy characteristics // Izvestiya of Tomsk Polytechnic University. Engineering of Georesources. – 2023. – No. 334 (5). – Pp. 27-36. DOI: 10.18799/24131830/2023/5/4027.

23. Boven G., Izbyakov N. S., Barskov V. V., Rassokhin V. A. Analytical study of thermal schemes of recovery vapors of gas-turbine units used to achieve carbon neutrality // Izvestiya vysshikh uchebnykh zavedeniy. Mechanical Engineering. – 2024. – No. 773 (8). – Pp. 83-93. DOI: 10.18698/0536-1044-2024-8.

24. Feoktistov D. V., Kuznetsov G. V., Abedtazehabadi A., Orlova E. G., Bondarchuk S. P., Dorozhkin A. V. Improving the energy efficiency of cooling systems for energy-intensive equipment by shifting the second-order heat transfer crisis to higher temperatures // Izvestiya of Tomsk Polytechnic University. Georesources Engineering. – 2023. – No. 334 (4). – Pp. 72-88. DOI: https://doi.org/10.18799/24131830/2023/4/4097.

25. Polovnikov V. Yu., Shelemekhova S. D. Reduction of Heat Inflows to Geothermal Heat Exchangers under Conditions of Long-Term Operation // Izvestiya Tomskogo Politekhnicheskogo Universiteta. Georesursy Engineering. – 2023. – No. 334 (9). – Pp. 72-88. DOI: https://doi.org/10.18799/24131830/2023/9/4197.

26. Mandrikova O. V. Intelligent Methods for Natural Data Analysis: Application to Space Weather // Computer Optics. – 2024. – No. 48 (1). – Pp. 139-148. DOI: 10.18287/2412-6179-CO-1367.

27. Mendeleev D. I., Maryin G. E., Galitsky Yu. Ya., Akhmetshin A. R. Research of the Influence of Operating Conditions on the Efficiency of Using an Absorption Refrigeration Machine in the Cycle of Gas-Turbine and Combined-Cycle Plants // Bulletin of Irkutsk State Technical University. – 2020. – No. 24 (4). – Pp. 821-831. DOI: https://doi.org/10.21285/1814-3520-2020-4-821-831.

28. Shvalev S. O., Sendyurev S. I. Research of the Influence of the Geometry of the Cooling Air Supply System on the Cooling Efficiency of the Gas Turbine Blade // Bulletin of Irkutsk State Technical University. – 2023. – No. 75. – Pp. 5-15. DOI: 10.15593/2224-9982/2023.75.01.

29. Borush O. V., Grigorieva O. K., Frantseva A. A. Thermal and aerodynamic characteristics of an air-condensation unit as part of a steam turbine power unit // Izvestiya vysshikh uchebnykh zavedeniy. Problemy energetiki. – 2024. – No. 26 (1). – Pp. 93-106. DOI: 10.30724/1998-9903-2024-26-1-93-106.

30. Nigam S., Dr. Tiwari S. K. Bayesian Regularization (BR) Optimization Based DDoS Detection for SDN and Next Generation Communication Network / Tuijin Jishu // Journal of Propulsion Technology. – 2023. – № 4 (44). – Pp. 4104-4116. DOI: https://doi.org/10.52783/tjjpt.v44.i4.1623.

31. Samigulin T. I., Stepanenko I. P. Development of a digital model of the electrolysis process based on the optimization algorithm of an artificial neural network using the Levenberg-Marquardt method // Universum: Technical Sciences. – 2025. – № 5 (134) – Pp. 14-21. DOI: 10.32743/UniTech.2025.134.5.20129.

32. Smorodinov A.D., Gavrilenko T. V., Galkin V. A. Theoretical foundations of artificial neural networks for solving the problem of approximation and interpolation // Successes of Cybernetics. – 2023. – №. 4 (4). – Pp. 41-53. DOI: https://doi.org/10.51790/2712-9942-2023-4-4-04.

33. Яхья М. А. К., Головкина А. Г. Математическое моделирование и оптимальное управление энергопотреблением микросети комбинированной выработки тепла и электроэнергии // Вестник Санкт-Петербургского университета. Прикладная математика. Информатика. Процессы управления. – 2024. – № 4 (20). – С. 446-466. DOI: https://doi.org/10.21638/spbu10.2024.402.

34. Akatev V. A., Volkova L. V., Sedlyarov O. I., Tyurin M. P. Improving the Efficiency of the Electric Power Industry by Implementing Power Supply Agreements Based on Step-by-Step Heating in Condensate Recovery Boilers of Steam Turbine Plants // International Research Journal. – 2024. – No. 7 (145). – Pp. 1-9. DOI: https://doi.org/10.60797/IRJ.2024.145.27.

35. Yankovsky S. A., Lavrinenko S. V., Tsibulsky S. A., Yankovskaya N. S., Gamov D. L. Thermal Schemes of Geothermal Energy // Izvestiya of Tomsk Polytechnic University. Engineering of Georesources. – 2023. – No. 7 (334). – Pp. 122-136. DOI: https://doi.org/10.18799/24131830/2023/7/4130.

36. Sechenov P. A., Rybenko I. A. Numerical method and mathematical model for finding the equilibrium composition of a thermodynamic system using the T-Energy software package // Bulletin of Dagestan State Technical University. Technical Sciences. – 2022. – No. 4 (49). – Pp. 104-112. DOI: 10.21822/2073-6185-2022-49-4-104-112.

37. Barbin N. M., Devyatkin N. O., Terentyev D. I., and Kobelev A. M. Thermodynamic Modeling of Thermal Processes Involving Actinides (U, Am, Pu) During Heating of Radioactive Graphite in a Mixture of Water Vapor and Oxygen // Radiochemistry. – 2023. – No. 1 (65). – Pp. 36-40. DOI: 10.31857/S0033831123010057.

38. Petrunin Yu. Yu. Artificial Neural Networks in Economics: A Mathematical Tool, a Model, or a Methodology? // Vestnik Moskovskogo Universiteta. Series 6. Economics. – 2024. – No. 4. – Pp. 92-113. – DOI: 10.55959/MSU0130-0105-6-59-4-5.

39. Sokolov D. Y. Application of an artificial neural network to solve problems of predicting the movement of ground objects // Proceedings of MAI. – 2022. – № 123. – Pp. 1-32. DOI: 10.34759/trd-2022-123-17.

40. Kulikov A. L., Loskutov A. A., Sovina A. N. Usage machine learning and artificial neural networks for recognizing loop faults in power transformers // Electricity. – 2022. – No. 10. – Pp. 34-44. – DOI: https://doi.org/10.24160/0013-5380-2022-10-34-44.

41. Oskin A. F., Oskin D. A. Algorithm and program of numerical optimization implementing the particle swarm method // Bulletin of Polotsk State University. Series C. Fundamental Sciences. – 2022. – No. 4. – Pp. 26-31. – DOI: https://doi.org/10.52928/2070-1624-2022-38-4-26-31.

42. Moskalenko A. B. Research of the economic efficiency of the CHP power unit when using bypass steam distribution on a steam turbine // Bulletin of Polotsk State University. Series C. Fundamental Sciences. – 2022. – No. 10(124). – Pp. 1-5. – DOI: https://doi.org/10.23670/IRJ.2022.124.10.

43. Shamraeva V. V. Mathematical methods for predicting changes in stock prices and their implementation using machine learning methods // Fundamental Research. – 2024. – No. 11. – Pp. 88-96. – DOI: https://doi.org/10.17513/fr.43718.

44. Vyalkova S. A., Kornyukova O. A., Nadtoka I. I. Mathematical modeling and forecasting of electricity consumption values based on the decision tree algorithm // Izvestiya Vysshikh Uchebnykh Zavedeniy. Elektromekhanika. – 2025. – No. 68(3). – Pp. 130-136. – DOI: https://doi.org/10.17213/0136-3360-2025-3-130-136.

45. Liventsov V. S., Kostinsky S. S., Troitsky A. I., Savelov N. S. Research of the Influence of the Load Magnitude and Power Factor on the Absolute Error of Calculating the Total Active Power Losses in the Power Supply System // Izvestiya of Higher Educational Institutions. Electromechanics. – 2023. – No. 4(66). – Pp. 144-155. – DOI: doi.org/10.17213/0136-3360-2023-4-144-155.

46. Tsochev, G. R., Yoshinov, R. D., Zhukova, N. A. Problems of Industrial Internet of Things Security and Comparison with SCADA Systems // Proceedings of SPIIRAN. – 2020. – No. 19(2). – Pp. 358-382. – DOI: https://doi.org/10.15622/sp.2020.19.2.5.

47. Kazakova M. V., Fokin N. D. Testing the predictive properties of various approaches to interval forecasting (using the example of inflation in Russia) // Statistics Issues. – 2024. – No. 5 (31). – Pp. 23-40. – DOI: https://doi.org/10.34023/2313-6383-2024-31-5-23-40.

48. Korobeynikov, A. G. Application of MATLAB in the Design of Digital Filters for the Extraction of Pc5-Type Geomagnetic Ripples // Software Products and Systems. – 2023. – No. 2(36). – Pp. 257-262. – DOI: 10.15827/0236-235X.142.257-262.

49. Chahartaghi M., Golmohammadi H., Shojaei A. F. Performance analysis and optimization of new double effect lithium bromide–water absorption chiller with series and parallel flows // International Journal of Refrigeration. – 2019. – № 97. – Pр. 73-87. – DOI: 10.1016/j.ijrefrig.2018.08.011.

50. Malinina O. S., Baranenko A. V. Comparison of the Efficiency of Thermodynamic Cycles of Absorption Bromide-Lithium Refrigeration Machines // Omsk Scientific Bulletin. Ser. Aviation, Rocket, and Power Engineering. – 2020. – No. 2(4). – Pp. 27-36. – DOI: 10.25206/2588-0373-2020-4-2-27-36.

51. Smirnov M. N., Smirnova M. A. Mathematical and Computer Modeling of Automatic Control in the Presence of Disturbances // Bulletin of St. Petersburg University. Applied Mathematics. Computer Science. Control Processes. – 2024. – No. 1(20). – Pp. 109-116. – DOI: https://doi.org/10.21638/11701/spbu10.2024.109.

52. Belyaev N. A., Korovkin N. V. Optimization of a Prospective Power System Structure Based on the Particle Swarm Algorithm // Elektrichestvo. – 2026. – No. 2 – Pp. 193-200. – DOI: 10.24160/0013-5380-2026-2-40-50.

53. Malinina O. S., Baranenko A. V., Lyadova E. E. Analysis of the Efficiency of the Thermodynamic Cycle of an Absorption Bromide-Lithium Refrigeration Machine with Two-Stage Absorption and Three-Stage Generation with a Bound Mass Flow // Bulletin of the International Academy of Refrigeration. – 2020. – No. 4 – Pp. 12-19. – DOI: 10.17586/1606‑4313‑2020‑19‑4-12-19.

54. Chahartaghi M., Golmohammadi H., Shojaei A. F. Performance analysis and optimization of new double effect lithium bromide–water absorption chiller with series and parallel flows // International Journal of Refrigeration. – 2019. – № 97 – С. 73-87. DOI: 10.1016/j.ijrefrig.2018.08.011.

55. Lyadova E. E., Komarov K. A. Efficiency of an Absorption Bromide-Lithium Refrigeration Machine with Multistage Absorption and Generation Processes with a Bound Mass Flow // Omsk Scientific Bulletin. Ser. Aviation, Rocket, and Power Engineering. – 2021. – No. 2(5) – Pp. 9-17. – DOI: 10.25206/2588-0373-2021-5-2-9-17.

56. Nosyreva D. A. Assessment of the Influence of Outdoor Air Temperature on the Efficiency of GTE Operation // Economics and Business: Theory and Practice. – 2020. – No. 1-2(59). – Pp. 65-70. – DOI: 10.24411/2411-0450-2020-10049.

57. Katkov V. V. Increasing the Electric Power Output of Thermal Power Plants in the Summer Period // Universum: Technical Sciences. – 2025. – No. 5(134). – Pp. 36-38. – DOI: 10.32743/UniTech.2025.134.5.20156.

58. Malyshev A. A., Malinina O. S., Ignatiev V. E. Exergy analysis of a vapor-compression refrigeration unit under changing operating conditions // Bulletin of the International Academy of Refrigeration. – 2024. – No. 4 – Pp. 25-31. – DOI: 10.17586/1606‑4313‑2024‑23‑4-25-31.

59. Malinina O. S., Baranenko A. V., Al-Furaidji M. A. O., Lyadova E. E., Komarov K. A. Efficiency of an Absorption Bromide-Lithium Refrigeration Machine with Multistage Absorption and Generation Processes with a Bound Mass Flow // Omsk Scientific Bulletin. Series «Aviation, Rocket and Power Engineering». – 2021. – No. 5(2). – Pp. 9-17. – DOI: 10.25206/2588-0373-2021-5-2-9-17.

60. Kortikov N. N. Optimization of the Cooling System of the Nozzle Blade of an Energy Gas Turbine // Materialovedenie. Energetika. – 2021. – No. 4(27). – Pp. 85-95. – DOI: 10.18721/JEST.27406.

61. Abakumov A. M., Zubkov Yu. V., Abakumov O. A. Control of Electric Drives of Gas Cooling Plants Taking into Account the Individual Characteristics of Air-Cooling Devices // Izvestiya of Tula State University. Technical Sciences. – 2024. – No. 8 – Pp. 567-571. – DOI: 10.24412/2071-6168-2024-8-567-568.

62. Zhang Y., Fang S. and Li J. Discussion on cold plate liquid cooling technology and its application in data center temperature control // Front. Renewable Energy. – 2022. – No. 10 – Pp. 567-568. – DOI: 10.3389/fenrg.2022.954718.

63. Fot A. N., Melnikov D. K. Features of the calculation of refrigeration machines with combined cooling of condensers, taking into account climatic and economic factors // Omsk Scientific Bulletin. Series «Aviation, Rocket and Power Engineering». – 2021. – No. 4(5). – Pp. 17-23. – DOI: 10.25206/2588-0373-2021-5-4-17-23.

64. Popov G. M., Goryachkin E. S., Melnikov S. A., Shcherban A. I., Gataullina E. D. Gas-dynamic Optimization of the Working Process of a Single-Stage Cooled Axial Turbine with an Internal Deflector // Vestnik of Samara University. Aerospace Engineering, Technologies, and Mechanical Engineering. – 2024. – No. 3(23). – Pp. 144-153. – DOI: 10.18287/2541-7533-2024-23-3-144-153.

65. Saenko I. B., Kotenko I. V., Al-Barri M. Kh. Application of Artificial Neural Networks for Detecting Anomalous Behavior of Users in Data Processing Centers // Cybersecurity Issues. – 2022. – No. 2(48). – Pp. 87-97. – DOI: 10.21681/2311-3456-2022-2-87-97.

66. Ustinova L. N., Sirazetdinov R. M., Sirazetdinova E. R., Ustinov D. A. The Russian Energy Sector: The Economic Aspect // Economics, entrepreneurship and law. – 2023. – № 13 (2). – Pp. 401-412. – DOI:10.18334/epp.13.2.117105.

67. Avdokunin N. V., Yakovlev I. V. Utilization of waste heat from data processing centers in conditions of direct refrigeration // Bulletin of the MEI. – 2025. – No. 4. – Pp. 86-93. – DOI: 10.24160/1993-6982-2025-4-86-93.

68. Karelin D. L., Boldyrev A. V., Boldyrev S. V., Kolbin T. Yu. Analysis of Evaporative and Liquid Cooling Methods for Fuel Cell Blocks Using Relative Energy Indicators // Mechanical Engineering. – 2023. – № 3 (10). – Pp. 25-28. – DOI: 10.24892/RIJIE/20230305.

69. Semenov I. A., Petrov A.V. Forecasting properties and modeling the phase state of multicomponent systems in Python // Modern technologies and scientific and technical progress – 2023. – No. 1. – S. 131-132. – DOI: https://doi.org/10.36629/2686-9896-2023-1-131-132.

70. Abramov V. I., Gordeev V. V., Stolyarov A. D. Digital Twins: Characteristics, Typology, and Development Practices // Issues of Innovative Economics. – 2024. – No. 14(3). – Pp. 691-716. – DOI: 10.18334/vinec.14.3.121484.

71. Ignatova T. V., Zhitomirsky B. L. On the Influence of Ambient Temperature on the Efficiency of Gas-Pumping Units // Proceedings of the Gubkin Russian State University of Oil and Gas. – 2022. – No. 1(306). – Pp. 83-91. – DOI: 10.33285/2073-9028-2022-1(306)-83-91.

72. Kitaev S. V., Smorodova O. V., and Suleymanov A. M. Improving the Energy Efficiency of Gas-Pumping Units with Stationary Gas-Turbine Drives // Energy Safety and Energy Conservation. – 2020. – No. 3. – Pp. 16-19. – DOI: 10.18635/2071-2219-2020-3-16-19.

73. Pivovarova M. V., Gladkiy I. L. Resource of parts of gas-turbine engines under conditions of exposure to corrosive media // Vestnik Samarskogo universiteta. Aerospace engineering, technologies and mechanical engineering. – 2019. – Vol. 18. – No. 3. – Pp. 109-117. – DOI: 10.18287/2541-7533-2019-18-3-109-117.