Abstract

Abstract High-pressure (HP) nozzle guide vane (NGV) capacity is one of the most important parameters for setting overall turbine power output, for stage matching, and for understanding turbine performance. Accurate capacity prediction early in the engine design process reduces the cost and risk of late-stage changes in the overall turbine design. High accuracy predictions rely on calibrated computational fluid dynamics (CFD) methods. Because of the numerous sensitivities in the methods, they are generally restricted to certain classes of design for which there is experimental validation. This paper considers the effect of changes in trailing edge (TE) geometry—particularly suction side (SS) TE overhang length—on the flow capacity of a modern HP NGV. Ultra-low uncertainty experimental measurements and complementary numerical predictions of capacity are presented for four TE geometries, in a parametric study bridging the gap between a classical centered-ejection design and a SS overhang TE design. This study discusses the absolute and relative (differences between designs) accuracies of the numerical method and develops understanding of the sensitivity of capacity to TE geometry. Fundamental mechanisms responsible for the observed capacity changes are elaborated. The impacts on capacity of both changes in the coolant flows and the boundary layers in the controlling region of the vane passage are considered. These effects are found to be very small. A simple total pressure loss model was also developed but was found to be a poor predictor of the observed capacity changes across the entire range of overhang lengths. To understand the observed changes, two more sophisticated models of the vane passage are proposed. In the first model—for relatively long TE overhangs—the mainstream and TE flows (and a wake) pass through a common minimum area. In the second model—for relatively short TE overhangs—the two flows are considered to pass through independent, non-interacting minimum areas. This framework reconciles the experimental and numerical data with the models but illustrates the complexity of the problem. In particular, it demonstrates the arbitrariness of considering a single minimum area. Contrary to some received industrial wisdom, it is argued that deemphasising this construct, and explaining capacity changes by examining aerodynamic changes in the entire controlling region of the passage, is more helpful in attempting to understand design sensitivities.

Highlights

  • Capacity provides a quasi-non-dimensional measure of the mass flowrate passing through a turbomachine row or stage

  • For the purpose of plotting, U is defined as the velocity at a normalized wall-normal distance of y/wPS = 0.03. Both surface profiles are characteristic of fully turbulent flow

  • This is expected at these locations for a fully cooled HP nozzle guide vane (NGV)

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Summary

Introduction

Capacity provides a quasi-non-dimensional measure of the mass flowrate passing through a turbomachine row or stage. Högner et al [10] numerically quantified the impacts on NGV capacity of a range of typical geometric variations which may arise due to manufacturing scatter These included thickness and position of the TE slot, SS and pressure side (PS) TE overhang lengths, and pressure and suction surface offsets. Each term is a surface of capacity—i.e., a 2D function of the two controlling pressure ratios given in parentheses This definition allows—in an appropriate experiment—the sensitivity of capacity to changes in individual constituent cooling flows (e.g., TE coolant ejection) to be isolated and examined. In order to confidently isolate and assess the impact of varying, for example, TE overhang length, it is critical to ensure geometry remains otherwise identical This objective was achieved by generating all four tested TE geometries on the same set of vane parts, without disassembling the cascade throughout the duration of testing. The process for measuring the coolant capacity characteristic is described in detail in Ref. [3] (see Ref. [5])

Numerical Method
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Summary and Conclusions
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