Abstract

Thermomechanical fatigue (TMF) is a critical damage process incurred by turbine components. The most common methods for simulating the operating conditions of turbine components are either in-phase (IP) or out-of-phase (OP) TMF tests. However, due to the constantly changing temperature and applied load profile, it is challenging to decouple dominant damage mechanisms occurring in the material when relying solely on TMF test conditions. Further, the time to perform low(er) inelastic strain range TMF tests to failure can be cumbersome. This work proposes implementation of bithermal fatigue (BiF) tests to address these issues. Out-of-phase BiF tests are strain-controlled fatigue tests whereby a single cycle consists of an isothermal compressive half-cycle at the maximum temperature, followed by stress-free temperature change to the minimum temperature; the tensile half-cycle then occurs isothermally at the minimum temperature, followed by stress-free temperature change to the maximum temperature. Both conventional OP TMF and OP BiF tests were performed on nominally 〈001〉 oriented single crystal superalloy specimens. When plotting test results as inelastic strain range versus cycles to crack initiation, the OP BiF results exhibit a clear demarcation from the TMF data at a particular value of inelastic strain range; above which the results are primarily fatigue dominated and follow the trend of the OP TMF tests while below the results are environmentally dominated, creating a separate trend. Thermally-activated base material degradation supports the theory of damage driver segregation. A relationship is proposed relating the inelastic strain of BiF to that of TMF, for identical lives, within the environmentally dominated fatigue region. Finally, a life prediction model is proposed that includes fatigue and environmentally assisted damage mechanisms, which enables the life estimation of either test type. These relationships enable the use of BiF tests in place of, or in conjunction with, TMF tests, thereby providing insight into the dominant damage mechanisms present during testing and simplifying life prediction for more complex TMF cycles.

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