The interaction of free-stream turbulence with a compressor cascade

2000 ◽  
Author(s):  
Chittiappa Muthanna ◽  
Diego de la Riva ◽  
William Devenport ◽  
Stewart Glegg
Author(s):  
Vincent Marciniak ◽  
Marco Longhitano ◽  
Edmund Kügeler

The aim of this paper is to investigate whether correlation-based transition models can be used for the design of CDA profiles. To this end, a CDA compressor cascade has been widely experimentally investigated at DLR Cologne. Off-design measurements have been carried out and the influence of the variation of four flow parameters has been investigated: The inlet Mach number, the incidence, the chord-based Reynolds number and the free-stream turbulence intensity. The inlet Mach number has been varied from 0.5 up to 0.8. The incidence was varied over the whole working range and beyond. Realistic values of the Reynolds number and of the free-stream turbulence intensity have been attained. Hence, the test case apt to assess the capacity of the DLR’s in-house turbomachinery specific CFD code TRACE to design modern compressor blades. In this paper, computations simulating the influence of those four parameters on the performance of the CDA profile are presented and compared to the measurements. Two transition models are used for this study: an in-house model denoted MultiMode model and the γ-ReΘ model. In addition, two turbulence models (Wilcox k-ω and Menter k-ω SST) and their turbomachinery extensions have also been used for this study. The results between the different numerical simulations and the measurements are discussed in term of loss coefficients and Mach number distributions. The computed losses are close to the experimental values and the physics of the flow is also well reproduced. Bypass transition as well as laminar separation bubbles have been simulated in accordance with the experimental observations. Hence, the TRACE code is able to predict the onset of transition over a wide range of flow conditions.


1992 ◽  
Vol 114 (3) ◽  
pp. 607-616 ◽  
Author(s):  
N. Suryavamshi ◽  
B. Lakshminarayana

The results of a numerical investigation to predict the flow in the wake regions of compressor cascades, and wakes and mixing in rotors are presented in this paper. Part I deals with flow in compressor cascades including the effects of change in loading (incidence) and the inlet free-stream turbulence intensity. Part II of the paper deals with the predictions of the rotor flow field, including wakes and spanwise mixing. The wake behavior has been studied numerically using a three-dimensional incompressible Navier–Stokes solver with a high Reynolds number form of the k–ε turbulence model. The equations are solved using a time-dependent implicit technique. The agreement between the measured data and predictions is good, including the wake profile, decay, and losses. The ability of the pseudocompressibility scheme to predict the entire flow field including the wake profile and its decay characteristics, effect of loading, and the viscous losses of a compressor cascade is demonstrated. The numerical analysis shows a slight increase in the total pressure loss coefficient through the cascade with increasing turbulence levels. The results also show a slight increase in the rate of decay of the wake at higher turbulence levels but the change in the spreading of the wake was found to be very small with increased turbulence levels.


2018 ◽  
Vol 140 (7) ◽  
Author(s):  
John Leggett ◽  
Stephan Priebe ◽  
Aamir Shabbir ◽  
Vittorio Michelassi ◽  
Richard Sandberg ◽  
...  

Axial compressors may be operated under off-design incidences due to variable operating conditions. Therefore, a successful design requires accurate performance and stability limits predictions under a wide operating range. Designers generally rely both on correlations and on Reynolds-averaged Navier–Stokes (RANS), the accuracy of the latter often being questioned. The present study investigates profile losses in an axial compressor linear cascade using both RANS and wall-resolved large eddy simulation (LES), and compares with measurements. The analysis concentrates on “loss buckets,” local separation bubbles and boundary layer transition with high levels of free stream turbulence, as encountered in real compressor environment without and with periodic incoming wakes. The work extends the previous research with the intention of furthering our understanding of prediction tools and improving our quantification of the physical processes involved in loss generation. The results show that while RANS predicts overall profile losses with good accuracy, the relative importance of the different loss mechanisms does not match with LES, especially at off-design conditions. This implies that a RANS-based optimization of a compressor profile under a wide incidence range may require a thorough LES verification at off-design incidence.


Author(s):  
John Leggett ◽  
Stephan Priebe ◽  
Aamir Shabbir ◽  
Richard Sandberg ◽  
Edward Richardson ◽  
...  

It is well known that an axial compressor cascade will exhibit variation in loss coefficient, described as a loss bucket, when run over a sweep of incidences, and that higher levels of free stream turbulence are likely to suppress separation bubbles and cause earlier transition (see e.g. [23]). However, it remains difficult to achieve accurate quantitative prediction of these changes using numerical simulation, particularly at off-design conditions, without the added computational expense of using eddy-resolving techniques. The aim of the present study is to investigate profile losses in an axial compressor under such conditions using wall-resolved Large Eddy Simulation (LES) and RANS. The work extends on previous work by Leggett et al.[11] with the intention of furthering our understanding of loss prediction tools and improving our quantification of the physical processes involved in loss generation. The results show that while RANS predicts losses with good accuracy the breakdown of these losses are attributed to different processes, meaning that optimisation of a compressor cascade profile, based solely on RANS, may be hard to achieve.


2016 ◽  
Vol 47 (1) ◽  
pp. 15-28 ◽  
Author(s):  
Mikhail Aleksandrovich Pugach ◽  
Alexander Aleksandrovich Ryzhov ◽  
Alexander Vitalievich Fedorov

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