Tobias Willeke

Mr. Tobias Willeke


Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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24 July 2018 in Journal of Turbomachinery
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Journal Article
Closed access
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XLXiaohua Liu
Xiaohua Liu
TWTobias Willeke
Tobias Willeke
FHFlorian Herbst
Florian Herbst
Yang JunYang Jun
Yang Jun
JSJoerg Seume
Joerg Seume

10 June 2019 in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B. Mechanical Engineering
Verified
Journal Article
Closed access
PDF file
SMShorash Miro
Shorash Miro
TWTobias Willeke
Tobias Willeke
MBMatteo Broggi
Matteo Broggi
JSJoerg R. Seume
Joerg R. Seume
MBMichael Beer
Michael Beer
This paper presents a procedure for the reliability analysis of a multistage axial compressor regarding blade-specific roughness effects, based on the survival signature approach. As a result, a time-dependent evolution of the system reliability is obtained along with a prioritization technique for monitoring and regeneration of the rough blade rows by capturing the most critical system components. For this purpose, a one-dimensional flow model is developed and utilized to evaluate the aerodynamic influences of the blade-specific roughness on the system performance parameters, namely the overall pressure ratio and the isentropic efficiency. In order to achieve transparency and high numerical efficiency for time-dependent analyses in practice, the physics-based compressor model is translated into an illustrative, function-based system model. This system model is established by conducting a Monte Carlo simulation along with a variance-based global sensitivity analysis, with the input variables being the row-specific blade roughness. Based on the system model, the roughness impact in different blade-rows is ranked by the relative importance (RI) index, and the corresponding time-dependent reliability of the compressor system in terms of pressure ratio and efficiency is estimated through its survival function. Furthermore, uncertainties in the roughness-induced failure rates of the components are modeled using imprecise probabilities. Consequently, bounds on the reliability function and the importance indices for the blade-surface roughness in each blade row are captured, which enhances the decision-making process for maintenance activities under uncertainty.