PSI - Issue 41

Venanzio Giannella et al. / Procedia Structural Integrity 41 (2022) 298–304 V. Giannella / Structural Integrity Procedia 00 (2022) 000–000 component was obtained, see Figure 5. The overall fatigue life distribution resulted to be well replicated by a lognormal distribution with an average value of 10 ����� � ���00 fatigue cycles. However, it is worth noting that the life distribution demonstrated a large range of variability, with the fatigue life ranging between 9800 and 61800 cycles, if assuming a � � �� of variation (95.5% of probability). This large range of variability of the component life came from unavoidable sources of uncertainty, such as the inherent scattering of material data, as well as from more controllable parameters, such as the geometrical tolerances, and eventually from the loading conditions, i.e. parameters hard to be continuously monitored during the in-service life of the engine. With the aim of ranking all the distinct contributes, the Monte Carlo simulation procedure was run multiple times with/without some of the uncertainty sources, see the results in Table 1. It was found that the fillet radius variability played an insignificant role since producing a negligible impact on the fatigue life standard deviation. This is due to the particular trends observed in the K I curves for various fillet radii, see Figure 3. On the contrary, the loading conditions variability demonstrated to be a very important contribute for obtaining an accurate life prediction; however, it has to be mentioned that this is a parameter continuously varying along with the component life, whereas it was considered here as having a given value for each simulation, i.e. engine speed was constant through the crack propagation. A more thorough approach should account for a load variability during the life of the engine too. Finally, material scattering contribute resulted to be the most important parameter, then it represents the primary contribute that needs to be considered in every probabilistic life assessment. Additionally, this contribute cannot be reduced in size but needs to be always quantified in the most accurate way so as to allow for thorough evaluations of the component lives. 303 6

Figure 5. Probabilistic fatigue life assessment.

Table 1. Monte Carlo simulation results for different combinations of uncertainty sources.

Material scattering contribute

Loading conditions contribute

Geometrical tolerances contribute

Fatigue life – standard deviation

Fatigue life – mean value

Yes Yes

Yes

Yes

4.391 4.404 4.427 4.416

0.200 0.146 0.118 0.006

No

No No

No No

Yes

No

Yes

5. Conclusions The aim of this investigation was to present the application of a straightforward procedure for the damage tolerance assessment of the fist-stage of an aircraft compressor under input data uncertainty. Such a procedure was based on the numerical simulation, to assess the fracture behaviour of the component during the advancing of a crack, whose outputs were inputted to a Monte Carlo simulation procedure, to eventually predict the component life in a statistical way. A probabilistic fatigue life assessment was obtained and statistical evaluations were made. The contributes of all the considered uncertainty sources were compared and classified. It was found that the geometrical