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Applicability of correlational data-mining to small-scale turbojet performance map generation

  • Francisco Villarreal-Valderrama , Pedro Juárez-Pérez , Ulises García-Pérez and Luis Amezquita-Brooks EMAIL logo
Published/Copyright: November 23, 2021
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Abstract

Turbojet applications benefit from accurate performance models. The aim of this study is to explore the applicability of data-mining algorithms to determine relationships between the generated thrust, the environmental conditions (free stream air-speed, inlet temperature and pressure) and the operating conditions (input fuel flow and shaft angular speed). For this purpose, experimental tests were carried out within wind-tunnel facilities using an experimental single-spool turbojet test bench. It is well-known that a large set of data-mining approaches relies on establishing linear correlations among input and output variables. The scope of this article is to assess the applicability of correlational data-mining approaches by i) an exploratory data analysis to find underlying data patterns and ii) principal component regressions to obtain a suitable predictive model for the generated thrust. Validation experiments demonstrated that the data-based model allows capturing the effects of the environmental and operating conditions with good accuracy (Root Mean Squared Error RMSE = 3.5100%), while maintaining a low complexity in the resulting structure. These results show that it is possible to generate turbojet experimental performance maps through data-mining algorithms with a correlational approach.


Corresponding author: Luis Amezquita-Brooks, Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autonoma de Nuevo Leon, Av. Universidad S/N, San Nicolas de los Garza, Mexico, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

1. Mason, J, Walter, S, Chow, P. The ice particle threat to engines in flight. In: 44th AIAA aerospace sciences meeting and exhibit. Reno, Nevada: AIAA; 2006:206 p.10.2514/6.2006-206Search in Google Scholar

2. Dinc, A, Şöhret, Y, Ekici, S. Exergy analysis of a three-spool turboprop engine during the flight of a cargo aircraft. Aircraft Eng Aero Technol 2020;92:1495–503. https://doi.org/10.1108/AEAT-05-2020-0087.Search in Google Scholar

3. Tsoutsanis, E, Meskin, N, Benammar, M, Khorasani, K. An efficient component map generation method for prediction of gas turbine performance. In: Turbo expo: power for land, sea, and air. Düsseldorf, Germany: American Society of Mechanical Engineers; 2014, 45752:V006T06A006.10.1115/GT2014-25753Search in Google Scholar

4. Coban, K, Ekici, S, Ozgur Colpan, C, Karakoç, TH. Performance of a microjet using component map scaling. Aircraft Eng Aero Technol 2021. https://doi.org/10.1108/AEAT-02-2021-0056 [ahead of print].Search in Google Scholar

5. Tavakolpour-Saleh, AR, Nasib, SAR, Sepasyan, A, Hashemi, SM. Parametric and nonparametric system identification of an experimental turbojet engine. Aero Sci Technol 2015;43:21–9. https://doi.org/10.1016/j.ast.2015.02.013.Search in Google Scholar

6. Zhao, Y-P, Tan, J-F, Wang, J-J, Yang, Z. C-loss based extreme learning machine for estimating power of small-scale turbojet engine. Aero Sci Technol 2019;89:407–19. https://doi.org/10.1016/j.ast.2019.04.023.Search in Google Scholar

7. Elzahaby, AM, Khalil, MK, Khalil, HE. Theoretical and experimental analysis of a micro turbojet engine’s performance. Int J Sci Eng Res 2016;7:404–10.Search in Google Scholar

8. Koruyucu, E, Ekici, S, Karakoc, TH. Performing thermodynamic analysis by simulating the general characteristics of the two-spool turbojet engine suitable for drone and UAV propulsion. J Therm Anal Calorim 2021;145:1–13. https://doi.org/10.1007/s10973-020-10449-9.Search in Google Scholar

9. Briones, A, Sykes, J, Rankin, BA, Caswell, AW. Steady-state cfd simulations of a small-scale turbojet engine from idle to cruise conditions. In: AIAA scitech 2020 forum. Orlando, Florida: AIAA; 2020:2084 p.10.2514/6.2020-2084Search in Google Scholar

10. Klein, D, Abeykoon, C. Modelling of a turbojet gas turbine engine. In: 2015 internet technologies and applications (ITA). Wrexham, UK: IEEE; 2015:200–6 pp.10.1109/ITechA.2015.7317395Search in Google Scholar

11. Mendrea, B, Sterniak, J, Bohac, SV. Effect of ambient temperature and humidity on combustion and emissions of a spark-assisted compression ignition engine. Ann Arbor 2017;1001:48109.Search in Google Scholar

12. Villarreal-Valderrama, F, Liceaga-Castro, E, Zambrano-Robledo, P, Amezquita-Brooks, L. Experimental evaluation of different microturbojet EGT modeling approaches. J Aero Eng 2020;34:04020087. https://doi.org/10.1061/(ASCE)AS.1943-5525.0001205.Search in Google Scholar

13. Yildirim Dalkiran, F, Toraman, M. Predicting thrust of aircraft using artificial neural networks. Aircraft Eng Aero Technol 2020;93:35–41. https://doi.org/10.1108/AEAT-05-2020-0089.Search in Google Scholar

14. Kaya, F, Şahin, G, Alma, MH. Investigation effects of environmental and operating factors on PV panel efficiency using by multivariate linear regression. Int J Energy Res 2020;45:554–67. https://doi.org/10.1002/er.5717.Search in Google Scholar

15. Ali Elfaki, E, Ahmed, AH. Prediction of electrical output power of combined cycle power plant using regression ANN model. J Power Energy Eng 2018;6:17. https://doi.org/10.4236/jpee.2018.612002.Search in Google Scholar

16. Wadhvani, R, Shukla, S. Analysis of parametric and non-parametric regression techniques to model the wind turbine power curve. Wind Eng 2019;43:225–32. https://doi.org/10.1177/0309524x18780398.Search in Google Scholar

17. Dongre, B, Pateriya, RK. Statistical power curve modeling to estimate wind turbine power output. Wind Eng 2019;45:325–36. https://doi.org/10.1177/0309524X19891671.Search in Google Scholar

18. Kenbeek, T, Kapodistria, S, Di Bucchianico, A. Data-driven online monitoring of wind turbines. In: Proceedings of the 12th EAI international conference on performance evaluation methodologies and tools. New York, NY: Cornell University Library; 2019:143–50 pp.10.1145/3306309.3306330Search in Google Scholar

19. Reder, M, Melero, JJ. Modelling the effects of environmental conditions on wind turbine failures. Wind Energy 2018;21:876–91. https://doi.org/10.1002/we.2201.Search in Google Scholar

20. Piegorsch, WW. Statistical data analytics: foundations for data mining, informatics, and knowledge discovery. Hoboken, New Jersey: John Wiley & Sons; 2015.Search in Google Scholar

21. Ratner, B. Statistical and machine-learning data mining: techniques for better predictive modeling and analysis of big data. Oxfordshire, United Kingdom: CRC Press; 2017.Search in Google Scholar

22. Kulikov, GG, Thompson, HA. Dynamic modelling of gas turbines: identification, simulation, condition monitoring and optimal control. New York, NY: Springer Science & Business Media; 2004.Search in Google Scholar

23. Villarreal-Valderrama, F, Santana Delgado, C, Zambrano-Robledo, PDC, Amezquita-Brooks, L. Turbojet direct-thrust control scheme for full-envelope fuel consumption minimization. Aircraft Eng Aero Technol 2020;93:437–47. https://doi.org/10.1108/AEAT-08-2020-0190.Search in Google Scholar

24. Soares, C. Gas turbines: a handbook of air, land and sea applications. Amsterdam, Netherlands: Elsevier; 2011.Search in Google Scholar

25. Jaw, LC, Mattingly, JD. Aircraft engine controls. Reston, VA: AIAA; 2009:37–65 pp.10.2514/4.867057Search in Google Scholar

26. El-Sayed, AF. Aircraft propulsion and gas turbine engines. Reston, VA: CRC Press; 2008.10.1201/9781420008777Search in Google Scholar

27. Federal Aviation Administration. FAA regulations for unmanned aerial vehicle (UAV) drones UAV. http://www.faa.gov/regulations_policies [Accessed 26 Jan 2021].Search in Google Scholar

28. Villarreal-Valderrama, F, Amezquita-Brooks, L, Martinez, D, Liceaga-Castro, E. Banco de pruebas no invasivas para caracterización de arrastre aerodinámico: aplicación en turborreactor sr-30. In: 2nd international conference on aeronautics. National Aeronautical Thematic Network; 2018.Search in Google Scholar

29. Sundararaj, RH, Sekar, TC, Arora, R, Kushari, A. Effect of nozzle exit area on the performance of a turbojet engine. Aero Sci Technol 2021;116:106844. https://doi.org/10.1016/j.ast.2021.106844.Search in Google Scholar

30. Ge, Z, Song, Z, Ding, SX, Huang, B. Data mining and analytics in the process industry: the role of machine learning. IEEE Access 2017;5:20590–616. https://doi.org/10.1109/access.2017.2756872.Search in Google Scholar

31. Molina-Solana, M, Ros, M, Ruiz, MD, Gómez-Romero, J, Martín-Bautista, MJ. Data science for building energy management: a review. Renew Sustain Energy Rev 2017;70:598–609. https://doi.org/10.1016/j.rser.2016.11.132.Search in Google Scholar

32. Rogalewicz, M, Sika, R. Methodologies of knowledge discovery from data and data mining methods in mechanical engineering. Manag Prod Eng Rev 2016;7:97–108. https://doi.org/10.1515/mper-2016-0040.Search in Google Scholar

33. Malik, MR, Isaac, BJ, Coussement, A, Smith, PJ, Parente, A. Principal component analysis coupled with nonlinear regression for chemistry reduction. Combust Flame 2018;187:30–41.10.1016/j.combustflame.2017.08.012Search in Google Scholar

34. Gavrilovski, A, Jimenez, H, Mavris, DN, Rao, AH, Shin, S, Hwang, I, et al.. Challenges and opportunities in flight data mining: a review of the state of the art. In: AIAA infotech@ aerospace. San Diego, California: AIAA; 2016:0923 p.10.2514/6.2016-0923Search in Google Scholar

35. Abdelghafar, S, Darwish, A, Ella Hassaniena, A. Intelligent health monitoring systems for space missions based on data mining techniques. In: Machine learning and data mining in aerospace technology. New York, NY: Springer International Publishing; 2020:65–78 pp.10.1007/978-3-030-20212-5_4Search in Google Scholar

36. Baptista, M, Henriques, EMP, de Medeiros, IP, Malere, JP, Nascimento, CLJr, Prendinger, H. Remaining useful life estimation in aeronautics: combining data-driven and Kalman filtering. Reliab Eng Syst Saf 2019;184:228–39. https://doi.org/10.1016/j.ress.2018.01.017.Search in Google Scholar

37. White, FM. Fluid mechanics. New York: McGraw-Hill; 2011.Search in Google Scholar

38. Gao, J-H, Huang, Y-Y. Modeling and simulation of an aero turbojet engine with gasturb. In: 2011 international conference on intelligence science and information engineering. Wuhan, China: IEEE; 2011:295–8 pp.10.1109/ISIE.2011.149Search in Google Scholar

39. Starovoitova, IA, Khozin, VG, Abdrakhmanova, LA, Rodionova, OY, Pomerantsev, AL. Application of nonlinear PCR for optimization of hybrid binder used in construction materials. Chemometr Intell Lab Syst 2009;97:46–51. https://doi.org/10.1016/j.chemolab.2008.07.008.Search in Google Scholar

Received: 2021-10-31
Accepted: 2021-11-03
Published Online: 2021-11-23

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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