ICE Roadmap Japanese STAR Conference. Richard Johns

Transcription

1 ICE Roadmap Japanese STAR Conference Richard Johns

2 Introduction Top-Level Roadmap STAR-CCM+ and Internal Combustion Engines Modeling Improvements and Research Support Sprays LES Chemistry Meshing Summary

3 Top-Level Roadmap for In-Cylinder CFD STAR-CD Will be available and maintained for as long as is required ICE Models Knowledge Experience New Meshing Technologies STAR-CCM+ Will become the successor in-cylinder code Model and Best Practice Refinements

4 Advantage of a Integrated STAR-CCM+ Solution Fuel Injection and Fuel System Optimization Supercharging In-Cylinder Intake/Exhaust Systems 1D-3D coupling Aftertreatment Engine CHT & Structural Analysis Piston undercrown cooling Crankcase/oil splash/ bearings/breather system

5 The Development and Adoption of Mathematical Models CD-adapco does not choose to be an Inventor of Models We see our tasks as: Model adopter Implementation - Refinement, Generalization and Industrialization Validation Development of Recommended Usage & Best Practices Dissemination Benchmarking Support Further refinement in the light of experience

6 Engineering-Level vs High Fidelity Models Engineering-Level spray-breakup model High-Fidelity spray-breakup model Why do we support High-Fidelity Models? We learn a lot about fundamental physics If we can t solve the problem in a HF model we certainly won t solve it in a EL model Our objective is to deliver both HF and EL models

7 University Research & Research Clubs We actively support and fund a number of universities around the world where there are strong ICE Research groups We support and engage with many others the following slides are not a comprehensive list just a brief summary We also members of and/or participate in various research clubs : FVV, Germany Support of industry-initiated university projects DERC Direct-Injection Engine Research Consortium Univ of Wisconsin research club focused on experimental and theoretical ICE fundamentals

8 University Research University of Modena, Italy Principal Investigator: Prof S Fontanesi Development and Application of LES and improved RANS models for flow, mixture distribution and knock University of Connecticut, US Principal Investigators: Professors Tianfeng Lu and Zhuyin Ren Chemistry mechanisms, solver speedup, reduced mechanisms, and mechanism reduction tools

9 Penn State University, US University Research Principal Investigator: Prof D Haworth Validation of in-cylinder Flow and Turbulence with experimental data, soot model development Doshisha University, Japan, Principal Investigator: Prof Senda Spray-Wall impingement modeling. fuel injection, spray heat & mass transfer, flash boiling

10 University Research University of Darmstadt, Germany Principal Investigator: Prof Janicka Validation of Flow and Turbulence with in-cylinder measurements University of Vienna Principal Investigator: Prof Lauer Modeling of multicomponent fuels, wall effects, autoignition in gasoline engines

11 University Research Seoul National University (SNUAL) Principal Investigator: Prof Min Combustion and emissions modeling: flamelets, dual-fuel, level-set, knock ETH Zurich (Technical University) Principal Investigator: Dr Yuri Wright Combustion and emissions modeling: CMC, level-set, fuels, LES and DES in-cylinder flows

12 Fuel Injection and Sprays Current injection systems and operating conditions can lead to complex spray phenomena Pinj = 20 Mpa Pamb = 120 kpa Tinj = 100 C Pamb = 40 kpa Ref: Parrish & Zink, GM R&D, Ilass 2008

13 Non-flashing vs flashing analysis Non-flashing Flashing Y is the mass fraction of non-condensible gas Courtesy of Prof David Schmidt, University of Massachusetts

14 Pintle Nozzle Pintle nozzle jet-string breakup

15 LES/VOF calculation of jet-string breakup ATOMIC 5 deg sector

16 Spray Modeling Strategy Support of HF and EL projects to understand better and improve physics modeling Develop numerical and meshing technologies to support new Engineering Level models that can be embodied into in-cylinder calculations

17 In-Cylinder LES, mixture formation and Cyclic Variability Ensemble or cycle-averaged CFD and measurements are an approximation to reality

18 In-Cylinder LES, mixture formation and Cyclic Variability Small-scale information lost in the averaging process

19 LES Intake Flow Structure

20 Cycled-Averaged LES

21 LES multicycle flame development

22 LES - 3D Results Insight:

23 Prediction of COV

24 In-Cylinder LES, mixture formation and Cyclic Variability The Challenges: Can we predict this successfully using High-Fidelity modeling? Can we derive an Engineering-Level model from the lessons learned from a High-Fidelity model? Are these modeling additions to our code or different ways of processing existing solutions? Are there other flow features, such as instabilities, that we ignore at our peril? Is there a first-mover advantage for an OEM in being an earlyadopter of high-end technology?

25 Combustion Chemistry Strengthened Team: Graham Goldin Karin Fröjd DARS v2.10 release: ECFM and PVM library generation incl dual-fuel Extended Range Soot library University Collaborations Complex fuel chemistry (DME, biodiesel etc) Soot modeling Dual Fuel

26 Meshing Technology Meshing is important! Automatic o Efficiency & consistency in a production environment o Optimization and automated shape-change Accurate o The ability to capture boundary details and small-scale phenomena and gradients, for example, sprays, spark ignition etc Robust & Fast o Must be successful 99% of the time and add minimal overhead

27 Meshing Technology We have/will have 3 meshing options: 1) Existing template/trimmed meshing es-ice today, being developed for STAR-CCM+ 2) XMesher based on multiple morphed meshes and solution mapping STAR-CD and STAR-CCM+ 3) Overset Mesh Attachment of individual meshes to components moving through a stationary background mesh STAR-CCM+ only

28 Overset Mesh

29 XMesher Automated Meshing for ICE Developed over the past ~3 years Stage 1: Fully automatic, full-cycle meshing Tested successfully on ~30 different real engine cases Close comparison of results with es-ice meshing Will be released in 2015 Stage 2 Local mesh refinement linked to specific events Embedded spray-adapted meshing

30 Morphing Solution Mapping Solution Continues XMesher Solution Strategy Constrained Polyhedra Aligned valve-seat mesh Prism Layers Core Cartesian Mesh Solution mapped to next mesh Mesh morphed in - time Mesh morphed in + time Mesh generated at this time

31 XMesher

32 Illustration of XMesher process Initial Meshes are inserted automatically

33 Illustration of XMesher process Meshes are morphed with piston and valve motions, tested and then additional meshes inserted as required

34 Illustration of XMesher process This cycle is repeated until meshes for the entire calculation have been generated.

35 Performance Concurrent meshing/morphing is used. Typical total time ~ ½ to 1 hour Time (mins) # cores

36 Summary of Test Cases Case Total Mesh Generation time (mins) Number of Grids Base Mesh (mm) Max No. of cells Min No. of cells #1 2v gasoline #2 4v gasoline #3 4v diesel #4 4v gasoline #5 4v gasoline #6 4v gasoline Further Cases

37 Cell Count through an engine cycle 1.5M 1.2M

38 Vector Field

39 Objective is: XMesher Stage 2 Physics-dependent local refinement General spray-oriented meshing, including the possibility if meshing inside the injector if required In prototype stage now

40 Physics-driven Locally-refined meshing

41 Physics-driven Locally-refined meshing

42 General Spray-Adapted Mesh

43 General Spray-Adapted Mesh

44 Summary Major Commitment to developing a STAR-CCM+ ICE Solution Continued Support for STAR-CD for as long as required by our Customers v4.24 in 2015 New automated accurate meshing coming in both STAR-CD and STAR-CCM+ Focused physics research leading to an improved predictive capability in key areas Continued Support to deliver Best-in-Class Engineering Solutions for ICE of all our Customers