Cascade sampling-driven block mapping for coupled reliability evaluation of turbine cooling systems

In modern aeroengine systems such as cooling turbines, multi-physics loads (aerodynamic, thermal, and structural) often induce coupled failures, posing significant challenges for accurate reliability evaluation. To tackle this challenge, a novel sampling-mapping linkage method, termed cascade sampling-driven block mapping (CS-BM) approach, is proposed for the first time. In this study, a cascade sampling strategy is presented to hierarchically decouple the complex evaluation into tiered sub-evaluations, a block mapping model with stepwise updating is established to capture stage-wise relationships, and the sampling-mapping linkage model is embedded into a coupled reliability framework. Comparative studies show that CS-BM methods achieve up to 99.7 % accuracy with over 200 × computational speedup compared to direct Monte Carlo simulations. The main advantage of the study lies in its hierarchical decoupling and stage-wise modeling, which can effectively reduce computing complexity while maintaining high accuracy in coupled reliability evaluation under multi-physics interactions. This study advances high-fidelity reliability evaluation paradigms for interdisciplinary engineering systems, offering critical insights for the design and prognosis of next-generation turbomachinery.