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DLR.de • Chart 1

2 nd Workshop on Benchmark Problems for Airframe Noise Computations (BANC-II) 7-8 June 2012 Colorado Springs, Colorado, USA Category 1: Trailing-Edge Noise M. Herr , German Aerospace Center, DLR C. Bahr, NASA Langley Research Center M. Kamruzzaman, University of Stuttgart (IAG).

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DLR.de • Chart 1

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  1. 2nd Workshop on Benchmark Problems for Airframe Noise Computations (BANC-II)7-8 June 2012 Colorado Springs, Colorado, USACategory 1: Trailing-Edge NoiseM. Herr, German Aerospace Center, DLR C. Bahr, NASA Langley Research CenterM. Kamruzzaman, University of Stuttgart (IAG) www.DLR.de • Chart 1 > M. Herr > BANC-II > 07.06.2012, Colorado Springs, Colorado, USA BANC-II-1: (TBL-)Trailing-Edge Noise

  2. Agenda 7 June 2012 – BANC-II-1: Trailing-Edge Noise • Introduction • Problem statement • Overview on contributions & participants • Overview of used codes • Participant’s presentations on computational approach & on selected results • Cristobal A. Albarracin et al., University of Adelaide, Australia (UoA) • Mohammad Kamruzzaman, University of Stuttgart, Germany (IAG) • Roland Ewert et al., German Aerospace Center (DLR) • Lawrence Cheung & Giridhar Jothiprasad, GE Global Research, NY (GE-GRC) • Damiano Casalino et al., EXA GmbH, Stuttgart, Germany (EXA) • Overall comparisons, summary, conclusions & outlook • Discussion

  3. Conclusions from BANC-I-1 During BANC-I we faced (low number of participants) the need for improvements of the problem statement (definition of tripping, wing span for far field noise data, definition of a single core case for those who can not afford working on the full matrix, …) the need to offer benchmark data together with the updated problem statement. This should allow the participants to elaborate deeper on their data and to give their view on linking flow features with noise. For generating a benchmark data base it was agreed that we do not focus on a single facility/measurement technique but take all available data from different facilities/measurement techniques. Obviously, there will be a few dB deviation among different datasets which needs to be handled as a tolerance range. Thus, gathering trailing edge noise data will be a big multidimensional puzzle. Very probably, the first set of data will consider a NACA0012 configuration. The updated problem statement should define input data which will be particularly linked to this configuration, i.e. inflow turbulence, tripping details BANC-II-1 Problem Statement Introduction

  4. BANC-II-1 Problem Statement Introduction Preparation of BANC-II-1 • Unfortunately: Definition of the final problem statement for BANC-II was late due to the necessary collection and review of usable test data, clearance of GE proprietary DU-96 data (many thanks to GE!), data scaling, were necessary…  BANC-II-1 is understood as ‘warm-up’ (majority of participants apply faster prediction methods based on SNT) and will hopefully activate multiplied follow-on activity by anyone interested to join the community. • The finally provided comparison data is not “perfect” due to the non-existence of a fully consistent data set covering the full measurement chain from near field source quantities to farfield noise.

  5. BANC-II-1 Problem Statement Simulation Matrix BANC-II-1 Test Cases Full problem statement with more specified definitions of • Profile coordinates (sharp TE!) • Tripping devices (TBL-TE noise!) • TBL transition locations • Ambient conditions, etc. • Data formatting instructions including templates is available at the BANC-II homepage: https://info.aiaa.org/tac/ASG/FDTC/ DGBECAN_files_/BANCII_category1 CASE#1: single core test case for those who can not afford the full matrix • Provide cp(x1), cf(x1), near-wake mean flow/ turbulence profiles, Gpp(f), Lp(fc) and FF noise directivities for CASES#1-5

  6. BANC-II-1 Problem Statement Coordinate System and Parameter Definition Simulation Matrix BANC-II-1 Test Cases WPF sensor position @ 99 % lc PSDs (measurement data normalized to Df = 1 Hz) Orientation of flow profiles Position @ 100.38 % lc SS PS q = 90° chord-normal view direction for noise prediction b = 1 m r = 1 m in 1/3-octave bands

  7. BANC-II-1 Problem Statement Available comparison data sets for CASES#1-5: Simulation Matrix BANC-II-1 Test Cases

  8. BANC-II-1 Problem Statement IAG-LWT 2-point correlation measurements Overview of Comparison Data Near-Wake Data CASES#1-4 IAG-LWT (Herrig et al.)

  9. BANC-II-1 Problem Statement Overview of Comparison Data Acoustical Data Sets CASES#1 and #2 (IAG, DLR, UFL, BPM) • Scaling to problem statement conditions required for both Gpp(f) and Lp(1/3)(fc)! +/3 dB scatter among all available data sets

  10. Acoustical Data Sets CASES#3 and #5 (CASE#4 not shown) Scaling to problem statement conditions required! BANC-II-1 Problem Statement Overview of Comparison Data

  11. BANC-II-1 Contributions & Participants Overview Overview on Contributions

  12. Overview of Methods Contribution Albarracin et al.: UoA’s RSNM code RSNM: RANS-based Statistical Noise Model • Fast TE noise prediction method, based on a statistical model of the turbulent velocity cross-spectrum. Acoustic spectrum in the far field RANS CFD • OpenFOAM package • k-omegaSST model RSNM Turbulent velocity cross-spectrum model + Half-PlaneGreen´s function Example results: 30.48 cm chord NACA 0012 airfoil at AoA=0 and flow velocities of 31.7 m/s, 39.6 m/s, 55.5 m/s and 71.3 m/s CFD Mesh cf. AIAA-2012-2181

  13. Overview of Methods RANS Simulation Source Modeling BL & Correlations Noise Spectra WPF Wind Tunnel Exp. & Validation Governing Eqns. Contribution Kamruzzaman et al.: IAG‘s simplified theoretical prediction code Rnoise Rnoise: RANS Based Trailing-edge Noise Prediction Model • Simplified theoretical airfoil trailing-edge far-field noise prediction model based on steady RANS: highly accurate and very fast

  14. Overview of Methods k Spectral analysis Sound Field source 4D-Stochastic Sound Sources FRPM vortex sound sources Contribution Ewert et al.: DLR‘s CAA-Code PIANO with stochastic source model FRPM PIANO: Perturbation Investigation of Aeroacoustic Noise • “Low-cost“ steady RANS-based CAA with stochastic source models: 2-4 orders faster than LES mean flow; here: DLR codeTAU with RSM CFD RANS CAA APE turbulence

  15. Overview of Methods Contribution GE GRC: LES with Amiet’s Theory (CharLES code, Cascade Technologies) CharLES: LES-based trailing edge noise prediction • High-fidelity incompressible LES calculation combined with Amiet’s theory for far-field noise Unstructured mesh LES simulation Amiet’sTheory Far-fieldSound High-fidelity grid near TE and airfoil surface Capture boundary layer, wall-pressure spectra, and correlation data near TE Project TE information to far-field observer locations cf. AIAA-2012-2055

  16. Overview of Methods Contribution Damiano Casalino et al.: EXA’s PowerFlow / PowerAcoustics code PowerFLOW / PowerACOUSTICS • Unsteady-flow simulations performed with Lattice Boltzmann based solver PowerFLOW 4.3 • D3Q19 LBM • Cubical Lattices (Voxels) • Surface elements (Surfels) • Explicit solver • Fully transient • Turbulence model • Modified RNG k-ε model • Swirl model • Anisotropic “large” eddies resolved • Statistically universal eddies modeled • Extended wall model • Taking pressure gradient effect into account • Acoustic fluctuations directly simulated with low-dispersion and low dissipation • Far-field noise computed using a FW-H acoustic analogy (PowerACOUSTICS 2.0) • Solid/permeable formulation • Forward-time formulation based on the retarded-time formulation 1A by Farassat • Mean flow convective effects (wind-tunnel modality) taken into account • Spectral analyses carried out using PowerACOUSTICS 2.0 1 2 3 cf. AIAA-2012-2235

  17. Thank you for your attention!

  18. Agenda 7 June 2012 – BANC-II-1: Trailing-Edge Noise • Introduction • Problem statement • Overview on contributions & participants • Overview of used codes • Participant’s presentations on computational approach & on selected results • Cristobal A. Albarracin et al., University of Adelaide, Australia (UoA) • Mohammad Kamruzzaman, University of Stuttgart, Germany (IAG) • Roland Ewert et al., German Aerospace Center (DLR) • Lawrence Cheung & Giridhar Jothiprasad, GE Global Research, NY (GE-GRC) • Damiano Casalino et al., EXA GmbH, Stuttgart, Germany (EXA) • Overall comparisons, summary, conclusions & outlook • Discussion

  19. Overall Comparisons Introduction Scope • Code-to-code comparisons for the following parameters: • 4 slides: cp, cf for CASES#1, #2, #3, #5 • 5 slides (1 per case): Near-wake profiles of mean velocity and turb. characteristics • 1 survey slide on integral TBL properties • 2 slides: Surf. pressure (WPF) PSD for CASES#1, #2, #3, #5 • 2 slides: FF TBL-TE noise spectra for CASES#1, #2, #3, #5 • 1 slide: Selected FF noise directivities • Changed representation format to extract principle relative effects on noise and on WPF spectra (are those well-predicted?) • Effect of test velocity  CASES#1, #4 • Effect of a-o-a  CASES#1, #2, #3 • Effect of profile shape  CASES #2, #5

  20. Overall Comparisons Aerodynamical data Format: comparison data in black! Cp-Distributions CASES#1 & #2 UoA: OpenFOAM - SST IAG: FLOWER (DLR) - SST DLR: TAU (DLR) - RSM

  21. Cp-Distributions CASES#3 & #5 Overall Comparisons Aerodynamical data Format: comparison data in black! UoA: OpenFOAM - SST IAG: FLOWER (DLR) - SST DLR: TAU (DLR) - RSM

  22. Overall Comparisons Cf-Distributions CASES#1 & #2 Aerodynamical data UoA: OpenFOAM - SST IAG: FLOWER (DLR) - SST DLR: TAU (DLR) - RSM UoA: fully turbulent, no transition!

  23. Overall Comparisons Aerodynamical data Cf-Distributions CASES#3 & #5 UoA: OpenFOAM - SST IAG: FLOWER (DLR) - SST DLR: TAU (DLR) - RSM UoA: fully turbulent, no transition!

  24. Overall Comparisons Aerodynamical data Near-Wake Flow Characteristics

  25. Near-Wake Flow Characteristics CASE#1 SS Overall Comparisons Aerodynamical data UoA IAG DLR

  26. Near-Wake Flow Characteristics CASE#2 SS Overall Comparisons Aerodynamical data UoA IAG DLR

  27. Near-Wake Flow Characteristics CASE#3 SS Overall Comparisons Aerodynamical data UoA IAG DLR

  28. Near-Wake Flow Characteristics CASE#4 SS Overall Comparisons Aerodynamical data UoA IAG DLR

  29. Near-Wake Flow Characteristics CASE#5 SS Overall Comparisons Aerodynamical data UoA IAG DLR

  30. Overall Comparisons Aerodynamical data IAG DLR UoA Integral “TBL” Properties CASES#1-5 as measured (IAG):

  31. Overall Comparisons Surface Pressure Data Position @ 99 % lc PSDs (measurement data normalized to Df = 1 Hz) SS PS

  32. Overall Comparisons Surface Pressure Data Unsteady Surface Pressure PSD Gpp(f) CASES#1 & #2 IAG: Rnoise DLR: PIANO-FRPM Gpp, dB (Df = 1 Hz) Gpp, dB (Df = 1 Hz) f, kHz f, kHz UoA: no surface pressure data provided

  33. Unsteady Surface Pressure PSD Gpp(f) CASES#3 & #5 Overall Comparisons Surface Pressure Data IAG: Rnoise DLR: PIANO-FRPM GE-GRC: CHARLES Gpp, dB (Df = 1 Hz) Gpp, dB (Df = 1 Hz) Data has been scaled from different case! no measured comparison data available!

  34. Overall Comparisons TBL-TE FF Noise Data b = 1 m r = 1 m 1/3-octave band spectra q = 90° chord-normal view direction for noise prediction

  35. 1/3-Octave Band FF Noise Spectra Lp(1/3)(fc) CASES#1 & #2 Overall Comparisons Farfield Noise Data UoA: RSNM IAG: Rnoise DLR: PIANO-FRPM

  36. 1/3-Octave Band FF Noise Spectra Lp(1/3)(fc) CASES#3 & #5 Overall Comparisons Farfield Noise Data UoA: RSNM IAG: Rnoise DLR: PIANO-FRPM GE-GRC: CHARLES Data has been scaled from different case!

  37. Selected 1/3-Octave Band FF Noise Directivities: CASE#1 Overall Comparisons Farfield Noise Data IAG DLR

  38. Overall Comparisons Pressure Data Lp(1/3)(fc) and Gpp(f) data revisited to identify common trends; are relative effects captured by the predictions?

  39. Overall Comparisons Effect of Flow Velocity on Lp(1/3)(fc) and Gpp(f): CASE#1 vs. #4 U∞ = 56 m/s U∞ =38 m/s Pressure Data Format: measured comparison data in black!

  40. Overall Comparisons Effect of a-o-a on Lp(1/3)(fc): CASES#1 to #3 IAG LWT data DLR AWB data Pressure Data 0° Measurement data a-o-a 4° 6°

  41. Overall Comparisons Effect of a-o-a on Lp(1/3)(fc): CASES#1 to #3 Pressure Data Symbols: Measurement data Lines: Simulation results 0° a-o-a 4° 6° IAG LWT data DLR AWB data

  42. Overall Comparisons Effect of a-o-a on Gpp(f): CASES#1 to #3 IAG simulation DLR simulation Pressure Data Measurement data SS PS

  43. Overall Comparisons Effect of Profile on Lp(1/3)(fc) and Gpp(f): CASES#2 vs. #5 Farfield Noise Data SS Measurement data PS

  44. Overall Comparisons Effect of Profile on Lp(1/3)(fc) and Gpp(f): CASES#2 vs. #5 Farfield Noise Data Symbols: Measurement data Lines: Simulation results SS PS

  45. Summary • Still comparatively low number of participants (however, increased w.r.t BANC-I!) • Mainly results of faster approaches using SNT have been shown (UoA, IAG, DLR); two “last minute” LES contributors joined us; however, overall comparisons were limited (GE-GRC: existent results for a different test case have been roughly scaled to correspond to CASE#5 in the statement; EXA: data provided for single core test CASE#1?). • We have seen very interesting results (with some room for improvement) with many similarities but also significant differences within the delivered data: • In most of the cases TBL-TE FF noise predictions were within the provided data scatter band (reproducing systematic error between test facilities) • General trends (shape effect, velocity scaling) are mostly covered • But: spectral shapes/ main spectral characteristics are not always perfectly predicted (here: expected measurement data scatter is much smaller; IAG and DLR data collapse within +/- 1.5 dB!)

  46. Outlook 1/2 • Extension of the existing data base by additional DU-96 data sets by Virginia Tech (cp-distributions and acoustical data): • Data measured under NREL funding (described in the report Devenport W., Burdisso R.A., Camargo H., Crede E., Remillieux M., Rasnick M., van Seeters P., Aeroacoustic Testing of Wind Turbine Airfoils, Subcontract Report NREL/SR-500-43471, 2010 ). •  63-microphone phased array data with conventional beamforming processing (test performed in 2007). • New DU-96 data (currently being processed) at 4 speeds and 5 a-o-a; 0°, 4°, 8°, 12°, 16° • 128 microphone phased array with advanced beamformer. • Others? • Data owners of additional suitable data sets are highly encouraged to contribute to the BANC-II, III… data base; please contact michaela.herr@dlr.de

  47. Outlook 2/2 • BANC-III (if desired) will keep the existing CASES#1-5, the by now established BANC-II data base is open for use to anyone interested and will be maintained according to your feed-back • Need for additional test cases, add-ons (wind tunnel environment, additional mechanisms, etc.)? • BANC-II documentation (presentations, reports, workshop minutes) will be uploaded at the BANC-II website after the workshop: https://info.aiaa.org/tac/ASG/FDTC/DGBECAN_files_/BANCII_category1

  48. Thank you for your attention!

  49. Agenda 7 June 2012 – BANC-II-1: Trailing-Edge Noise • Introduction • Problem statement • Overview on contributions & participants • Overview of used codes • Participant’s presentations on computational approach & on selected results • Cristobal A. Albarracin et al., University of Adelaide, Australia (UoA) • Mohammad Kamruzzaman, University of Stuttgart, Germany (IAG) • Roland Ewert et al., German Aerospace Center (DLR) • Lawrence Cheung & Giridhar Jothiprasad, GE Global Research, NY (GE-GRC) • Damiano Casalino et al., EXA GmbH, Stuttgart, Germany (EXA) • Overall comparisons, summary, conclusions & outlook • Discussion

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