Novel simulations of fuel injection systems investigating spray characteristics by use of high-fidelity multiphase methods and supercomputers

Transcription

1 Novel simulations of fuel injection systems investigating spray characteristics by use of high-fidelity multiphase methods and supercomputers Mathis Bode & Heinz Pitsch

2 Contents Optimization of Multiphase Applications Simulations Theory Experiments Multiphase Flows

3 Contents Optimization of Multiphase Applications Data Visualization Simulations Big Data High Performance Computing Computing Numerics Multiphase Flows

4 Motivation Multiphase Flows Geometry of injection systems increasingly complex Emission and pollutant formation of engine strongly depend on injection systems Several impact factors on spray formation: cavitation, geometry, Development of accurate multiphase methods Baumgarten, C., Mixture formation in internal combustion engines, Springer, Aye, M.M. et al., Studying the influence of k-factor of different nozzles on spray and combustion under diesel engine-like conditions in a high pressure spray chamber.

5 Contents Optimization of Multiphase Applications Data Visualization Simulations Big Data High Performance Computing Computing Numerics Multiphase Flows

6 Numerics Spray Simulations Standard solver Interface solver Density ratio Curvature

7 Numerics Eulerian Spray Simulations High-fidelity interface solver 3d unsplit level set/volume of fluid method mass conservation geometric accuracy Hybrid discretization of convective transport term and pressure-projection second-order monotonicity preserving Lagrange-Remap solver Le Chenadec, V. & Pitsch, H., A 3D Unsplit Forward/Backward Volume-of-Fluid Approach and Coupling to the Level Set Method, Journal of Computational Physics 233:10-33, 2013, doi: /j.jcp Le Chenadec, V. & Pitsch, H., A monotonicity preserving conservative sharp interface flow solver for high density ratio two-phase flows, Journal of Computational Physics 249: , 2013, doi: /j.jcp Bode, M., Le Chenadec, V. & Pitsch, H., High Fidelity multiphase simulations studying primary breakup, PRACEdays, Barcelona, Spain, 2014.

8 Numerics Eulerian Spray Simulations Fully developed turbulent inflow Actual nozzle inflow ~20 D Highly accurate and predictive but expensive Le Chenadec, V. & Pitsch, H., A conservative framework for primary atomization computation and application to the study of nozzle and density ratio effects, Atomization and Sprays 23(12): , 2013.

9 Numerics Lagrangian Spray Simulations Commonly used models: Droplet size distribution at nozzle exit (e.g. Rosin-Rammler) Kelvin-Helmholtz and Rayleigh-Taylor breakup models Evaporation model (e.g. Bellan) Tuning required in order to match experiments Droplet size distribution at nozzle exit has to be known Cheap but tuning required Bode, M., Falkenstein, T., Pitsch, H., Kimijima, T., Taniguchi, H. & Arima, T., Numerical study on the impact of cavitation on the spray development processes for GDI Injection, ICLASS, Tainan, Taiwan, 2015.

10 Example 6-hole Gasoline Direct Injector Fuel Fuel injection pressure Ambient gas pressure Fuel temperature Injection time n-heptane 8 MPa MPa 295 K 2 ms

11 Simulation Euler/Lagrange Approach Simulation Input: Inflow LES Mass flow rate Eulerian DNS Velocity Lagrangian Spray Velocity, Droplets Result: Velocity Velocity, Droplets Spray characteristics

12 Contents Optimization of Multiphase Applications Data Visualization Simulations Big Data High Performance Computing Computing Numerics Multiphase Flows

13 High Performance Computing Scaling CIAO is MPI-only parallelized CIAO scales (for simple setups) extremely well on JUQUEEN CIAO is member of High-Q club CIAO performs well

14 High Performance Computing Performance JUQUEEN Power BQC 16C 1.6 GHz cores Rpeak: TFlop/s Rmax: TFlop/s Multiphase module 0.64% of Rpeak Why? Others?

15 High Performance Computing Performance Mora, J., Do theoretical FLOPs matter for real application s performance, HPC Advisory Council, Spain, 2012.

16 High Performance Computing Performance JUQUEEN Compute Node Vectorized: 8 FLOP/cycle MAD0 MAD1 MAD2 MAD3 64 B L1 cache line 16 kb L1 cache 32 MB L2 cache 8 GB memory 8 GB memory

17 High Performance Computing Performance Multiphase module 0.63% of Rpeak FP_OPS/L1_LDM: MM (band): 1.7 CIAO (structured): 18.5 MM (structured): 17.2 Extended band structure gave performance boost by factor 10

18 Contents Optimization of Multiphase Applications Data Visualization Simulations Big Data High Performance Computing Computing Numerics Multiphase Flows

19 Big Data In-situ Visualization Temporal jet simulation: Grid: 8 Billion cells File size: 4.5 TB/time step Dumping the full domain becomes bottleneck (even with e.g. SIONlib) IDEA: select parts on runtime Göbbert, J.H., Bode, M. & Wylie, J.N., Extreme-Scale In-Situ Visualization of Turbulent Flows on IBM Blue Gene/Q JUQUEEN, E-MuCoCos at ISC, Frankfurt, Germany, 2016 (submitted). Bode, M., Göbbert, J.H. & Pitsch, H., High-Fidelity Multiphase Simulations and In-Situ Visualization Using CIAO, NIC Symposium, Jülich, Germany, Bode, M., Deshmukh, A., Göbbert, J.H. & Pitsch, H., CIAO: Multiphysics, multiscale Navier-Stokes solver for turbulent reacting flows in complex geometries, in Brömmel, D., Frings, W. & Wylie, B.J.N., eds. JUQUEEN Extreme Scaling Workshop 2016, Technical Report, FZJ-JSC-IB , 2016.

20 Big Data JUSITU Coupling layer (CIAO VisIt/Libsim) Interactive- and batch-mode MPI rank 0 collector Modifications added to VisIt Executable increases from 84 MB to 167 MB (but only loaded once per node) VisIt gets copies (not pointers)

21 Big Data JUSITU Data access in seconds (not minutes) Speed up of I/O much simpler

22 Contents Optimization of Multiphase Applications Data Visualization Simulations Big Data High Performance Computing Computing Numerics Multiphase Flows

23 Description 3-hole Gasoline Direct Injector Fuel Fuel injection pressure Ambient gas pressure Fuel temperature Injection time n-heptane 15 MPa MPa 295 K 2 ms

24 Description 3-hole Gasoline Direct Injector Fuel Fuel injection pressure Ambient gas pressure Fuel temperature Injection time n-heptane 15 MPa MPa 295 K 2 ms Reynolds number (liquid) 26,600 Weber number (liquid) 68,500 Density ratio (liquid/gas) Viscosity ratio (liquid/gas) 22.0 Nozzle bulk exit velocity 126 m/s Simulation sizes: LES: 300 million cells DNS: 900 million cells LPS: 7 million cells Bode, M., Falkenstein, T., Pitsch, H., Kimijima, T., Taniguchi, H. & Arima, T., Numerical study on the impact of cavitation on the spray development processes for GDI Injection, ICLASS, Tainan, Taiwan, 2015.

25 Results Mass Flow Rate Cavitation region on sac side Velocity field at nozzle exit not disturbed

26 Results Cone Angle Cone angle on outer side is larger due to redirection Cone angle of experiment is underpredicted by simulations Ave. cone angles: Sim. wo/ cav.: 11.2 Sim. w/ cav.: 19.0 Experiment: 21.4

27 Results Velocity Velocity profile is symmetric without cavitation Good agreement for simulation with cavitation

28 Description 3-hole Gasoline Direct Injector Fuel Fuel injection pressure Ambient gas pressure Fuel temperature Injection time n-heptane 15 MPa MPa 295 K 2 ms

29 Results Moving needle & initial bubble Exp Opening later & smaller Speed of Sound First cavitation at same time

30 46 mm Description Biofuel Case Study 3.9 mm (3 μm / Pixel) Ligament (< 1 µm / Pixel) Jet core Big Droplets Film (51 μm / Pixel) Small Droplets 100 µm

31 Description Biofuel Case Study Ethanol 1-Butanol Tetrahydrofurfuryl Alcohol (THFA) Properties Ethanol 1-Butanol THFA Density (kg/m 3 ) Viscosity (kg/m*s) Surface tension (N/m) Bode, M., Deshmukh, A., Kirsch, V., Reddemann, M., Kneer, R. & Pitsch, H., Direct numerical simulations of novel biofuels for predicting spray characteristics, ICLASS, Tainan, Taiwan, 2015.

32 1 mm Results Interface Ethanol 1-Butanol THFA Decreasing Reynolds number Increasing length scales of liquid structures Atmospheric conditions 1.8 mm downstream the nozzle exit

33 Conclusions Optimization of Multiphase Applications Data Visualization Simulations Big Data High Performance Computing Computing Numerics Multiphase Flows

34 Acknowledgements J.H. Göbbert (FZ Jülich) Cluster of Excellence Taylor-Made Fuels from Biomass Honda R&D Partnership for Advanced Computing (PRACE) under grant pr1e7400 Jülich Aachen Research Alliance (JARA) under grant jara0066 & jhpc18 Ford Research Center Aachen

35 Thank you for your attention! Mathis Bode & Heinz Pitsch