1. Improved Near Wall Treatment for CI Engine CFD Simulations Mika Nuutinen Helsinki University of Technology, Internal Combustion Engine Technology

2. 2 Conjugate Heat Transfer in CFD  Continuous heat flux across surface  Simultaneous determination of heat flow and temperature within a fluid and its adjacent solid e.g. Cylinder charge and piston Engine block and coolant

3. 3 Why conjugate heat transfer?  Primarily: Designer needs accurate temperature data in/on solid part Maximum temperature (melting) Temperature distribution (thermal loads)  Secondarily: Produces transient, more accurate boundary condition for temperature More accurate heat loss prediction More accurate overall temperature/pressure fields

4. 4 CFD problems in heat transfer  Inaccuracy of RANS turbulence models (k-ε, k-ω)  Extreme field gradients near walls  Standard wall treatment (wall functions) omits the effects of temperature induced density variations near walls

5. 5 New wall function formalism  Derived similarly to standard wall functions, but with smooth turbulent viscosity transition (Mellor) and variable near wall turbulent Pr (Kays)  Sensitive to temperature induced density variation near the walls unlike standard wall functions +Improves heat transfer and temperature predictions +Easy to include other temperature variable effects to e.g. heat capacity, μ, k… -No analytical solution -> computational burden

6. 6 Essential equations

7. 7 Velocity wall functions (hot gas case)

8. 8 Temperature wall functions (hot gas)

9. 9 Wall Heat flux prediction (hot gas)

10. 10 Wall function comparison, typical CI engine simulation case Simulations were made with 4 combinations of turbulence models and near wall treatments: 1) High Reynolds number k-e model with standard wall functions. 2) High Reynolds number k-e model with the new variable density wall functions 3) High Reynolds number RNG k-e model with standard wall functions. 4) Low Reynolds number k-e model with hybrid wall treatment.

11. 11 Spray and Combustion modeling  Lagrangian particle tracking  Transfer of mass, momentum and heat modeled  Droplet break up models: Reitz-Diwakar etc.  Turbulent dispersion, collisions, coalescence  EBU LaTCT (laminar and turbulent characteristic time) combustion model

12. 12 Computational grid, fluid & solid zones

13. 13 Cylinder pressure

14. 14 Piston heat transfer

15. 15 Piston peak temperature

16. 16 Piston surface temperature

17. 17 Concluding remarks  The new wall function formalism works well in practical simulations  Enhances the predicted wall heat transfer in CI engine simulations when the gas is hot (and vice versa)  Further improvements easy to implement  Computational burden can be minimized by selecting a smaller boundary where the heat transfer is critical