Modeling of a turbine bladerow with stagger angle variation using the multi-fidelity influence superposition method

Manufacturing variabilities can significantly affect a turbine bladerow's performance. With the push for turbine's efficiency and reliability more than ever, understanding of manufacturing variability impacts is sought after. It has been shown that multi-passage simulation domain required to capture interaction among varied blades. This challenge conventional single-passage methods, largely due requirement prohibitive computational resources. The present work introduces an attempt solve this by combining multi-fidelity method influence superposition approach. proposed methodology combines accuracy high-fidelity speed low-fidelity simulation. Therefore, predictions tend be both accurate fast. key enabler only small set configurations needed pre-compute source term. Two representative geometries subsonic low-pressure transonic high-pressure have chosen as test cases. Each case subject two further stagger angle variation patterns, namely alternating sinusoidal pattern. inadequate case, owning its incapability correctly shockwave formation shockwave/wake interaction. On other hand, successful match qualitatively quantitatively compared direct solution. More interestingly, reduced overhead one order magnitude ability accurately efficiently predict effects, provides tool engineers explore optimize their blading designs.