STAR SOUTHEAST ASIAN CONFERENCE 2015 June 8-9, 2015 Results within a Reasonable Time Frame. Dr Siauw Wei Long. Co-presenter R.Selvakumar.

Transcription

1 STAR SOUTHEAST ASIAN CONFERENCE 2015 June 8-9, 2015 Results within a Reasonable Time Frame Dr Siauw Wei Long Co-presenter R.Selvakumar Slide 1

2 Aerodynamics Design Personal Introduction Jet Flow Control Int journal of Heat & Fluid Flow Vol29, No4, 2008 Noise Emission Prediction Using LES/DES 2013 Propeller Noise Prediction ASME Turbo Expo 2014 Wind tunnel Soufflerie-Béton 2.4m by 2.6m (AIAA Aerospace Meeting ) Separation Detection & Control Int Journal of Heat & Fluid Flow IUTAM Symposium on Flow Control & MEMS nd European Forum on Flow Control 2006 SWIFT Singapore National Wind Tunnel 1.2m by 1.2m (AIAA ) Decay of isotropic turbulence 2006 Adaptive mesh strategy: int journal of Heat & Fluid Flow Vol9, No8, 1999 Int journal of Computer & Mathematics 41, 2001 Int journal of Computational Eng Science Vol2, 2001 Slide 2

3 OVERVIEW What s Results within a Reasonable Time Frame? The role of CFD in aerodynamic prediction/design What sort of facility to support aero design such that we have results within reasonable time frame? Slide 3

4 Results within Reasonable Time Frame What RESULTS? Aerodynamic design to achieve a set of specific requirements: how efficient can it fly? how far can it fly & at what speed? how long does it take to turn? Slide 4

5 Results within Reasonable Time Frame What is REASONABLE? WHEN? Depends on the extent of 1. Experience + historical data 2. Empirical prediction 3. Computational Fluid Dynamics (CFD) simulation 4. Wind tunnel testing Flight testing Slide 5

6 Results within Reasonable Time Frame What is REASONABLE? WHEN? Depends on the extent of 1. Experience + historical data Flight testing LOWEST COST! BEST WAY? Slide 6

7 Results within Reasonable Time Frame What is REASONABLE? WHEN? Depends on the extent of 1. Experience + historical data 2. Empirical prediction 3. Computational Fluid Dynamics (CFD) simulation Flight testing Possible? Slide 7

8 Results within Reasonable Time Frame What is REASONABLE? WHEN? Depends on the extent of 1. Experience + historical data 2. Empirical prediction 3. Computational Fluid Dynamics (CFD) simulation 4. Wind tunnel testing Adapted for illustration from Japan Aerospace Exploration Agency (JAXA) Adapted for illustration from Japan Aerospace Exploration Agency (JAXA) Slide 8

9 Reasonable within Reasonable Time Frame What is REASONABLE? WHEN? Depends on the extent of 1. Experience + historical data 2. Empirical prediction 3. Computational Fluid Dynamics (CFD) simulation 4. Wind tunnel testing Complexity of the problem Efficiency of Computing Facility Slide 9

10 Reasonable within Reasonable Time Frame What is REASONABLE? Depends on the extent of 1. Experience + historical data 2. Empirical prediction 3. Computational Fluid Dynamics (CFD) simulation 4. Wind tunnel testing WHEN? Complexity of the problem RANS URANS DES/LES Slide 10

11 Reasonable within Reasonable Time Frame What is REASONABLE? Turbulent models: WHEN? RANS-URANS: predict mean flow structures, efficient inside BL but excessive diffusion in the separated region. LES: accurate in separation region and unaffordable for resolving BL DES: RANS inside BL, LES in separated regions. DNS: most accurately solve NS equations, but too expensive for turbulent flows Resource intensity Complexity of the problem RANS URANS DES/LES Slide 11

12 Reasonable within Reasonable Time Frame What is REASONABLE? Euler code will give relatively good prediction, minor correction to data would represent the real situation. In cases where mild separation occurs, RANS turbulent class of simulation would be applicable. In cases where large unsteady separation occurs, RANS might give some indication. URANS, DES/LES class of simulation would be applicable to probe unsteady effects. Slide 12

13 Results within Reasonable Time Frame What is REASONABLE? WHEN? Cost$ Number of simulations Slide 13

14 A Feel of the time-frame Difference Between CFD & WTT CFD (RANS, 124 cores, SGI UV2000) 1 day to complete a lift curve characteristics WTT A high resolution characteristic curve in less than 15 sec Model design & making will take ~3 mth or more Slide 14

15 Role of CFD in Aerodynamic Design Speed up design iteration 1. Simulation-based design instead of testing More cost effective and more rapid than testing CFD provides database for diagnosing flow field 2. Simulation of physical fluid phenomena that are difficult for experiments Full scale simulations in realistic flow conditions Exploration of flow physics that s difficult to inquire via experimentation Slide 15

16 Role of CFD in Aerodynamic Design Provide accurate data in the linear regions (RANS production runs) Limited DES/LES simulations for stall in the non-linear regions Slide 16

17 Role of CFD in Aerodynamic Design Numerical test of flow separation control concept URANS prediction of separation control using steady jet with control without control Normal jet Slide 17

18 Role of CFD in Aerodynamic Design Aerodynamic noise emission/suppression -> difficult to study via experiment DES/LES simulation -> a means to inquire, explore & design Slide 18

19 Role of CFD in Aerodynamic Design Aerodynamic noise emission/suppression -> difficult to study via experiment DES/LES simulation -> a means to inquire, explore & design Slide 19

20 Role of CFD in Aerodynamic Design Aerodynamic noise emission/suppression -> difficult to study via experiment DES/LES simulation -> a means to inquire, explore & design Slide 20

21 The turn-over-time of a CFD solution is innately related to the infra-structure support of the COMPUTING FACILITY Slide 21

22 How recent advances in CFD have changed work behavior? Pre-processing mesh topology matching + meshing -> bottle neck Adapted from Cdadapco for illustration 6 to 10 yrs ago -> required minimum of X weeks to a make CFD model 2009 tilltoday -> required 20min to 30min for 4-6 million cells w STARCCM on SGI Cluster Slide 22

23 How has it shaped facility requirement? Demand began to Grow SGI VGXT workstations Early 1990s UNIX SMP 12 Processor 4GB RAM Mid 1990s to 1998 SGI Cluster 352Core 192GB/Node yr to 2010 SGI cluster supported multiple jobs each with typical size of 4 to 6 million cells (RANS class simulation) Increase demand for licenses Requires more efficient job scheduler Slide 23

24 How has it shaped facility requirement? Physics Perspective To better resolved the fluid mechanics CFD model grew larger 10 to 15 millions -> demand for SMP was revived to handle large single job SGI Cluster 192GB/Node UV2000 SMP 3TB RAM Yr 2010 Adapted from Cdadapco for illustration Slide 24

25 How we use Cluster & SMP system? CLUSTER: smaller jobs 4 to 8 million cells SMP: large jobs >150million cells node 1 x processors memory OS OS node 2 x processors memory OS shared file sys users nx processors memory file sys users node n x processors memory OS High speed network/switch latency of the order of msec during data syn network addresses, access to nodes, user network acct, cable and switch problems more admin effort latency of the order of nansec Slide 25

26 How has it shaped facility requirement? Higher fidelity model & large database perspective Construction of finer features on models became easier with efficient control on mesh quality -> cell requirement increase Building high fidelity (RANS class) large aero database became realizable DRIVE demand for higher capacity SMP system UV2000 SMP 3TB RAM Yr 2010 UV2000 SMP 6TB RAM Yr 2012 Slide 26

27 How has it shaped facility requirement? High performance computing perspective Demand for DES/LES simulation (sudden increase in mesh size requirements ~150 to 200 million cells ) DRIVE to larger SMP type system UV2000 SMP 11TB RAM Yr 2014 Slide 27

28 The Near Future Facility Enhanced capacity SMP system for large single job and production runs Improved parallel file systems & job scheduler of the Cluster system The Dream Future -> Cluster of SMP systems x SMP With GPU s x SMP With GPU shared file sys users nx SMP With GPU s High speed network/switch Slide 28

29 Conclusion To obtain results within reasonable time frame depends on Experience & knowledge in aerodynamics design Knowing the limits of the applicability of CFD models Utilizing the right computing resource for the right job size Slide 29

30 RESOURCE IS IMPORTANT RESOURCEFULNESS IS A PREREQUISITE Siauw Wei Long Slide 30