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Research Papers

Compressible Direct Numerical Simulation of Low-Pressure Turbines—Part I: Methodology

[+] Author and Article Information
Richard D. Sandberg

Professor
Mem. ASME
Aerodynamics and Flight Mechanics
Research Group,
Faculty of Engineering and the Environment,
University of Southampton,
Southampton SO17 1BJ, UK
e-mail: R.D.Sandberg@soton.ac.uk

Vittorio Michelassi

Professor
Mem. ASME
Aero-Thermal Systems,
GE Global Research,
Munich D-85748, Germany
e-mail: vittorio.michelassi@ge.com

Richard Pichler, Liwei Chen, Roderick Johnstone

Aerodynamics and Flight Mechanics
Research Group,
Faculty of Engineering and the Environment,
University of Southampton,
Southampton SO17 1BJ, UK

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received September 10, 2014; final manuscript received September 16, 2014; published online November 26, 2014. Editor: Ronald Bunker.

J. Turbomach 137(5), 051011 (May 01, 2015) (10 pages) Paper No: TURBO-14-1233; doi: 10.1115/1.4028731 History: Received September 10, 2014; Revised September 16, 2014; Online November 26, 2014

Modern low pressure turbines (LPT) feature high pressure ratios and moderate Mach and Reynolds numbers, increasing the possibility of laminar boundary-layer separation on the blades. Upstream disturbances including background turbulence and incoming wakes have a profound effect on the behavior of separation bubbles and the type/location of laminar-turbulent transition and therefore need to be considered in LPT design. Unsteady Reynolds-averaged Navier–Stokes (URANS) are often found inadequate to resolve the complex wake dynamics and impact of these environmental parameters on the boundary layers and may not drive the design to the best aerodynamic efficiency. LES can partly improve the accuracy, but has difficulties in predicting boundary layer transition and capturing the delay of laminar separation with varying inlet turbulence levels. Direct numerical simulation (DNS) is able to overcome these limitations but has to date been considered too computationally expensive. Here, a novel compressible DNS code is presented and validated, promising to make DNS practical for LPT studies. Also, the sensitivity of wake loss coefficient with respect to freestream turbulence levels below 1% is discussed.

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Figures

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Fig. 1

Modified wave number of the parallel compact scheme used in HiPSTAR [26], compared with traditional nonoptimized standard and compact schemes

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Fig. 2

Combined O-type/H-type grid topology typically used for linear cascade simulations

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Fig. 3

Structures generated using the proposed method visualized using the Q-criterion at a level of 50

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Fig. 4

Time spectra at various streamwise positions where the legend label is the distance from the inlet

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Fig. 5

Strong scaling for single-block test case with 1 × 109 grid points (top) and weak scaling for cases with 643 grid points per core (bottom); data obtained on CRAY XE6

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Fig. 7

Amplification rate of the inner maximum of the streamwise velocity component; (—) reference DNS, (○) HiPSTAR without skew-symmetric splitting, (+) HiPSTAR with skew-symmetric splitting

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Fig. 8

Turbulent shear stress 〈u'zu'r〉 (left) and rms of the radial velocity fluctuations 〈u'ru'r〉 (right) as a function of y+= (1 − r)+; (—) reference DNS [41], () HiPSTAR periodic setup, (○) HiPSTAR spatial setup with turbulent inflow condition

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Fig. 9

Distance of first off-the-wall grid point in wall units for DNS at Re2is = 100,000

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Fig. 10

Pressure coefficient on blade compared with experiments [44] for case with Re2is = 60,000

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Fig. 6

Strong scaling for a nine-block LPT case with 300 × 106 grid points using parallel compact finite differences with curvilinear coordinates, using 2048 MPI processes and varying the number of OMP threads

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Fig. 13

Wake loss compared with experiments [44] and grid convergence study for Re2is = 60,000

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Fig. 14

Wall shear stress; DNS of T-106A cascade with incoming wakes at a reduced frequency FRED = 0.61, generated by simulating the bars by two different methods, compared to incompressible DNS data [20]. P = pressure side, S = suction side of the blade.

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Fig. 11

Spectra of turbulent kinetic energy in the inlet region for clean and turbulent cases at Re = 60,000

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Fig. 12

Divergence of the velocity field for clean inflow cases at Re2is = 60,000 with (top) and without (bottom) sponge at inlet

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