Int. J. Turbomach. Propuls. Power, Volume 7, Issue 3 (September 2022) – 7 articles

The present paper investigates the aerodynamic performance of a cantilevered tandem stator based on experiments conducted within a high speed annular test rig at the Technische Universitaet Berlin. A tandem blade in this context describes a double rowed stator configuration where the turning of the incoming flow is split up between two blades arranged in succession. For evaluation purposes, a conventional single bladed stator is used as reference. To provide machine relevant boundary conditions of cantilevered stator assemblies, the moving hub wall is recreated by a rotating disk. Overall, the tandem stator is able to achieve higher flow turning while keeping the total pressure losses below those of a single stator. It is found that the tandem stator in general behaves similar to the conventional stator. When installed in cantilevered fashion, both stator types benefit considerably in terms of loss reduction. Without the hub clearance and therefore absence of the clearance flow, each of the configurations suffered from severe corner separation. The tandem stator responds more sensitively to change in clearance height. Full article

In centrifugal compressors, Mild Surge (MS) leads to unstable operation. Previous experimental work on a centrifugal compressor designed and built by Safran Helicopter Engines (SafranHE) showed that MS corresponds to the pulsation of an alternate stall pattern at the Helmholtz frequency of the test rig on two channels in the radial diffuser. The present contribution experimentally investigates the impact of the Inlet Guide Vane (IGV) stagger angle on this alternate flow and numerically studies the topology of this pulsating alternate flow. The experimental investigation is performed with unsteady pressure sensors, and shows that the IGV stagger angle only impacts the pulsation frequency of the alternate flow pattern. This change is explained by the dependence of the Helmholtz frequency on the compressor inlet section. The topological analysis of the average flow field, computed from wall-resolved Unsteady Reynolds-Averaged Navier–Stokes (URANS) simulations, demonstrates that the saddle point (major critical point) in the corner hub/suction side of the stalled blade migrates upstream while staying in the corner if the mass flow rate decreases. One main blade over two is stalled on both sides because the flow originating from this corner separation circumvents the trailing edge and migrates upstream along the pressure side. In the simulation, the pulsation of the alternate stall is coupled with the reflection of acoustic waves on the inlet and outlet planes, regarded as an environmental effect. Full article

Design of cooling systems for rotor platforms is critical due to the complex flow field and heat transfer phenomena related to the secondary flow structures originating at the blade leading edge. Horseshoe vortex and passage vortex are the fluid-dynamic features that largely influence the aerodynamic behaviour and the thermal protection level of the platform. The driving parameter is the coolant to mainstream momentum flux ratio, but several issues have to be considered in the design process of cooling technologies. As well acknowledged, an in-depth understanding of losses and heat transfer phenomena are deemed necessary to design effective cooling systems. In the present review, measurements and predictions on the behaviour of the HPT rotor cooled platform, obtained during the last two decades by several research groups, are gathered, described and analysed in terms of aerodynamic losses and heat transfer performance, and are compared with one another with respect to the effectiveness level that is ensured. Full article

Boundary-layer flow separation is a common flow feature in many engineering applications. The consequences of flow separation in turbomachinery can be disastrous in terms of performance, stability and noise. In this context, flow separation is particularly difficult to understand because of its three-dimensional and confined aspects. Analyzing the skin friction lines is one key point to understanding and controlling this phenomenon. In the case of separation, the flow at the wall agglutinates around a manifold while the fluid from the boundary layer is ejected toward the flow away from the wall. The analysis of a three-dimensional separation zone based on topology is well addressed for a simple geometry. This paper aims at providing simple rules and methods, with a clear vocabulary based on mathematical background, to conduct a similar analysis with complex turbomachinery geometry (to understand a surface with a high genus). Such an analysis relies on physical principles that help in understanding the mechanisms of flow separation on complex geometries. This paper includes numerous typical turbomachinery surfaces: the stator row, vaneless diffuser, vaned diffuser, axial rotor and shrouded and unshrouded centrifugal impeller. Thanks to surface homeomorphisms, the generic examples presented can easily be converted into realistic shapes. Furthermore, classical turbomachinery problems are also addressed, such as periodicity or rotor clearance. In the last section, the proposed methodology is conducted on a radial diffuser of an industrial compressor. The flow at the wall is extracted from LES computations. This study presents the different closed separation zones in a high-efficiency operating condition. Full article

Optimization methods have been widely applied to the aerodynamic design of gas turbine blades. While applying optimization to high-fidelity computational fluid dynamics (CFD) simulations has proven capable of improving engineering design performance, a challenge has been overcoming the prolonged run-time due to the computationally expensive CFD runs. Reduced-order models and, more recently, machine learning methods have been increasingly used in gas turbine studies to predict performance metrics and operational characteristics, model turbulence, and optimize designs. The application of machine learning methods allows for utilizing existing knowledge and datasets from different sources, such as previous experiments, CFD, low-fidelity simulations, 1D or system-level studies. The present study investigates inserting a machine learning model that utilizes such data into a high-fidelity CFD driven optimization process, and hence effectively reduces the number of required evaluations of the CFD model. Artificial Neural Network (ANN) models were trained on data from over three thousand two-dimensional (2D) CFD analyses of turbine blade cross-sections. The trained ANN models were then used as surrogates in a nested optimization process alongside a full three-dimensional Navier–Stokes CFD simulation. The much lower evaluation cost of the ANN model allows for tens of thousands of design evaluations to guide the search of the best blade profiles to be used in the more expensive, high-fidelity CFD runs, improving the progress of the optimization while reducing the required computation time. It is estimated that the current workflow achieves a five-fold reduction in computational time in comparison to an optimization process that is based on three-dimensional (3D) CFD simulations alone. The methodology is demonstrated on the NASA/General Electric Energy Efficient Engine (E3) high pressure turbine blade and found Pareto front designs with improved blade efficiency and power over the baseline. Quantitative analysis of the optimization data reveals that some design parameters in the present study are more influential than others, such as the lean angle and tip scaling factor. Examining the optimized designs also provides insight into the physics, showing that the optimized designs have a lower amount of pressure drop near the trailing edge, but have an earlier onset of pressure drop on the suction side surface when compared to the baseline design, contributing to the observed improvements in efficiency and power. Full article

Conventional signal processing methods such as Principle Component Analysis (PCA) focus on the decomposition of signals in the 2D time–frequency domain. Parallel factor analysis (PARAFAC) is a novel method used to decompose multi-dimensional arrays, which focuses on analyzing the relevant feature information by deleting the duplicated information among the multiple measurement points. In the paper, a novel hybrid intelligent algorithm for the fault diagnosis of a mechanical system was proposed to analyze the multiple vibration signals of the centrifugal pump system and multi-dimensional complex signals created by pressure and flow information. The continuous wavelet transform was applied to analyze the high-dimensional multi-channel signals to construct the 3D tensor, which makes use of the advantages of the parallel factor decomposition to extract feature information of the complex system. The method was validated by diagnosing the nonstationary failure modes under the faulty conditions with impeller blade damage, impeller perforation damage and impeller edge damage. The correspondence between different fault characteristics of a centrifugal pump in a time and frequency information matrix was established. The characteristic frequency ranges of the fault modes are effectively presented. The optimization method for a PARAFAC-BP neural network is proposed using a genetic algorithm (GA) to significantly improve the accuracy of the centrifugal pump fault diagnosis. Full article