The data driven surrogate model based dynamic design of aero-engine fan systems

Yunpeng Zhu, Jie Yuan, Zi-Qiang Lang, Christoph W. Schwingshackl, Loïc Salles, Visakan Kadirkamanathan

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
17 Downloads (Pure)


High cycle fatigue failures of fan blade systems due to vibrational loads are of great concern in the design of aero engines, where energy dissipation by the relative frictional motion in the dovetail joints provides the main damping to mitigate the vibrations. The performance of such a frictional damping can be enhanced by suitable coatings. However, the analysis and design of coated joint roots of gas turbine fan blades are computationally expensive due to strong contact friction nonlinearities and also complex physics involved in the dovetail. In this study, a data driven surrogate model, known as the Nonlinear in Parameter AutoRegressive with eXegenous input (NP-ARX) model, is introduced to circumvent the difficulties in the analysis and design of fan systems. The NP-ARX model is a linear input-output model, where the model coefficients are nonlinear functions of the design parameters of interest, such that the Frequency Response Function (FRF) can be directly obtained and used in the system analysis and design. A simplified fan bladed disc system is considered as the test case. The results show that by using the data driven surrogate model, an efficient and accurate design of aero-engine fan systems can be achieved. The approach is expected to be extended to solve the analysis and design problems of many other complex systems.
Original languageEnglish
Number of pages10
JournalJournal of Engineering for Gas Turbines and Power
Early online date6 Jan 2021
Publication statusE-pub ahead of print - 6 Jan 2021


  • fan blade system
  • contact friction
  • dry film lubricant coating
  • data driven
  • surrogate model
  • design


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