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Research Papers

The Influence of Compressor Blade Row Interaction Modeling on Performance Estimates From Time-Accurate, Multistage, Navier–Stokes Simulations

[+] Author and Article Information
Dale Van Zante, Randall Chriss

 NASA Glenn Research Center, Cleveland, OH 44135

Jenping Chen

 Ohio State University, Columbus, OH 43210

Michael Hathaway

 ARL Vehicle Technology Directorate, Cleveland, OH 44135

J. Turbomach 130(1), 011009 (Jan 14, 2008) (10 pages) doi:10.1115/1.2775486 History: Received July 14, 2006; Revised August 21, 2006; Published January 14, 2008

The time-accurate, multistage, Navier–Stokes, turbomachinery solver TURBO was used to calculate the aeroperformance of a 2 12 stage, highly loaded, high-speed, axial compressor. The goals of the research project were to demonstrate completion times for multistage, time-accurate simulations that are consistent with inclusion in the design process and to assess the influence of differing approaches to modeling the effects of blade row interactions on aeroperformance estimates. Three different simulation setups were used to model blade row interactions: (1) single-passage per blade row with phase lag boundaries, (2) multiple passages per blade row with phase lag boundaries, and (3) a periodic sector (12 annulus sector). The simulations used identical inlet and exit boundary conditions and identical meshes. To add more blade passages to the domain, the single-passage meshes were copied and rotated. This removed any issues of differing mesh topology or mesh density from the following results. The 12 annulus simulation utilizing periodic boundary conditions required an order of magnitude fewer iterations to converge when all three simulations were converged to the same level as assessed by monitoring changes in overall adiabatic efficiency. When using phase lag boundary conditions, the necessity to converge the time history information requires more iterations to obtain the same convergence level. In addition to convergence differences, the three simulations gave different overall performance estimates where the 12 annulus case was 1.0 point lower in adiabatic efficiency than the single-passage phase lag case. The interaction between blade rows in the same frame of reference sets up spatial variations of properties in the circumferential direction, which are stationary in that reference frame. The phase lag boundary condition formulation will not capture this effect because the blade rows are not moving relative to each other. Thus, for simulations of more than two blade rows and strong interactions, a periodic simulation is necessary to estimate the correct aeroperformance.

Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

The UEET 2.5 stage compressor (from Larosiliere (19))

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Figure 2

Case 1: Single-passage phase lag

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Figure 3

Case 2: Multipassage phase lag

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Figure 4

Computer resources used

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Figure 5

Adiabatic efficiency convergence history (0.01=1 point)

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Figure 6

Time to complete solution for 2.5 stage compressor

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Figure 7

Sensitivity of efficiency to operating point (PR characteristic from APNASA ).

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Figure 8

Axisymmetric static pressure on the POCC casing

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Figure 9

Adiabatic efficiency at the inlet plane of the stator 1 mesh, 1∕2 annulus solution. Contour increments are 1 point in efficiency.

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Figure 10

(a) Circumferential variation of adiabatic efficiency at 50% span, stator 1 inlet plane for the 1∕2 annulus solution (average value has been removed). (b) Modes present in the 1∕2 annulus solution.

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Figure 11

(a) Adiabatic efficiency at 50% span, stator 1 inlet for the multipassage case. (b) Modes present in the multipassage case.

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Figure 12

(a) Adiabatic efficiency at 50% span, stator 1 inlet for the single-passage case. (b) Modes present in the single passage case.

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Figure 13

The phase lag boundary condition behavior for more than two blade rows. To create the time history at “B,” the boundary condition will apply a phase shift but not the necessary change in the mean.

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Figure 14

Difference in adiabatic efficiency from a calculation using time average total temperature and total pressure and a calculation using time average state variables

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Figure 15

Difference in adiabatic efficiency from a calculation using mass weighted, time average total temperature, and total pressure from mass-weighted entropy and a calculation using time average state variables

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