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J. Turbomach. 2018;140(4):041001-041001-12. doi:10.1115/1.4038736.

In this work, the flows inside a high-pressure turbine (HPT) vane and stage are studied with a delayed detached eddy simulation (DDES) code. The fundamental nozzle/blade interaction is investigated with special attention paid to the development and transportation of the vane wake vortices. There are two motivations for this work. First, the extreme HPT operation conditions, including both transonic Mach numbers and high Reynolds numbers, impose a great challenge to modern computational fluid dynamics (CFD), especially for scale-resolved simulation methods. An accurate and efficient high-fidelity CFD solver is very important for a thorough understanding of the flow physics and the design of more efficient HPT. Second, the periodic wake vortex shedding is an important origin of turbine losses and unsteadiness. The wake and vortices not only cause losses themselves, but also interact with the shock wave (under transonic working condition), pressure waves, and have a strong impact on the downstream blade surface (affecting boundary layer transition and heat transfer). Based on one of our previous DDES simulations of a HPT vane, this work further investigates the development and length characteristics of the wake vortices, provides explanations for the length characteristics, and reveals the transportation of the wake vortices in the downstream rotor passages along with its impact on the downstream aero-thermal performance.

Commentary by Dr. Valentin Fuster
J. Turbomach. 2018;140(4):041002-041002-8. doi:10.1115/1.4038870.

The mean flow field in a smooth rotating channel was measured by particle image velocimetry (PIV) under the effect of buoyancy force. In the experiments, the Reynolds number, based on the channel hydraulic diameter (D) and the bulk mean velocity (Um), is 10,000, and the rotation numbers are 0, 0.13, 0.26, 0.39, and 0.52, respectively. The four channel walls are heated with indium tin oxide (ITO) heater glass, making the density ratio (d.r.) about 0.1 and the maximum value of buoyancy number up to 0.27. The mean flow field was simulated on a three-dimensional (3D) reconstruction at the position of 3.5 < X/D < 6.5, where X is along the mean flow direction. The effect of Coriolis force and buoyancy force on the mean flow was taken into consideration in the current work. The results show that the Coriolis force pushes the mean flow to the trailing side, making the asymmetry of the mean flow with that in the static conditions. On the leading surface, due to the effect of buoyancy force, the mean flow field changes considerably. Comparing with the case without buoyancy force, separated flow was captured by PIV on the leading side in the case with buoyancy force. More details of the flow field will be presented in this work.

Commentary by Dr. Valentin Fuster
J. Turbomach. 2018;140(4):041003-041003-11. doi:10.1115/1.4038839.
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Turbomachinery active subspace performance maps are two-dimensional (2D) contour plots that illustrate the variation of key flow performance metrics with different blade designs. While such maps are easy to construct for design parameterizations with two variables, in this paper, maps will be generated for a fan blade with twenty-five design variables. Turbomachinery active subspace performance maps combine active subspaces—a new set of ideas for dimension reduction—with fundamental turbomachinery aerodynamics and design spaces. In this paper, contours of (i) cruise efficiency, (ii) cruise pressure ratio (PR), (iii) maximum climb flow capacity, and (iv) sensitivity to manufacturing variations are plotted as objectives for the fan. These maps are then used to infer pedigree design rules: how best to increase fan efficiency; how best to desensitize blade aerodynamics to the impact of manufacturing variations? In the present study, the former required both a reduction in PR and flow capacity—leading to a reduction of the strength of the leading edge bow wave—while the latter required strictly a reduction in flow capacity. While such pedigree rules can be obtained from first principles, in this paper, these rules are derived from the active subspaces. This facilitates a more detailed quantification of the aerodynamic trade-offs. Thus, instead of simply stating that a particular design is more sensitive to manufacturing variations; or that it lies on a hypothetical “efficiency cliff,” this paper seeks to visualize, quantify, and make precise such notions of turbomachinery design.

Commentary by Dr. Valentin Fuster

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