Physics-based probabilistic assessment of creep-fatigue failure for pressurized components

Abstract

To achieve a high-reliability design of high-temperature structures with a feasible balance between accuracy and efficiency, the physics-based probabilistic assessment for creep-fatigue failure is proposed under the probabilistic Linear Matching Method (pLMM) framework. At the physical level, the structural failure mechanism is reflected in the prepared training database, which is generated by the direct method procedures. And to efficiently express the relationship between design parameters and structural responses implicitly, the direct method-driven artificial neural network is built with the superior fitting quality of damage and lifetime. With the benchmarks provided, the applicability of the proposed probabilistic analysis approach for risk management of critical infrastructures is demonstrated, where the reliability-based creep-fatigue evaluation diagram is established according to different requirements. Furthermore, a novel data classification scheme is proposed to deal with the randomness in creep damage-dominated probabilistic assessment.