Performance Sensitivity of a Grooved Tip Seal to Manufacturing and Operational Geometry Deviations

The next generation of narrowbody commercial aircraft requires advancements in small-core gas turbines to reduce fuel consumption. However, small-core turbines include a number of physical features which are challenging to scale, including larger relative clearances between the rotating and stationary parts than the current power plants for this class of aircraft. These larger relative clearances have deleterious effects on high-pressure turbine efficiency and durability when compared to other state-of-the-art engines, which reduce the engine thermal efficiency and engine time on wing due to greater turbine thermal loads. The application of axisymmetric grooves in the stationary casing (or tip seal) over the turbine blades is a promising method for mitigating both of these effects, and past work has identified optimal designs for further investigation. In this computational study, one such optimal design is explored in greater detail, and practical considerations of grooved turbine casings are evaluated, including: the effect of groove alignment, manufacturing limitations, and wear of the tip seal due to thermal stresses. The groove geometries were applied to a simulation of the National Experimental Turbine (NExT), a high-pressure turbine design incorporating industry-standard geometric features and operated at engine-relevant conditions and small-core relevant tip clearances. Through this study, deviations in axial alignment of the groove and manufacturing artifacts had little impact on turbine performance; however, simulated wear reduced the efficiency of the turbine rotor below that of the unmodified tip seal—thereby negating the initial benefits provided by the grooves. Despite these performance behaviors, the heat load into the blade tip was still lower with the worn grooves than the ungrooved baseline configuration, indicating that the grooved tip seal would likely maintain its durability benefit throughout its useful life. These results lay a foundation for understanding how optimized designs perform when subjected to realistic deployment influences, such as manufacturing constraints, operational variations, and degradation over their lifecycle.