Best Practice Advice AC2-10

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Best Practice Advice

Internal combustion engine flows for motored operation

Application Challenge AC2-10   © copyright ERCOFTAC 2020

Best Practice Advice

Key Fluid Physics

Application Uncertainties, Computational Domain and Boundary Conditions

Physical Modeling, Numerics and Recommendations

From experience and previous studies in the literature, it is quite obvious that the intrinsic unsteadiness and anisotropy of the flow field in IC engines renders scale-resolving approaches such as LES more suitable than RANS for the numerical simulation of such configurations, even though the high computational costs remain a real issue for routine design and optimization procedures.

Three simulations of the same test case were presented. However, a direct comparison is challenging because

  • there are many differences between the simulations concerning physical models and numerical approaches and
  • the in-cylinder flow processes are coupled non-linearly.

Numerics and physical modeling are closely intertwined with each other in scale-resolving simulations. In the view of the authors, dissipation should be prescribed by the subgrid scale model and not by the numerical scheme. This implies that scale separation through implicit filtering is defined by the turbulent viscosity rather than the numerical diffusivity. Other approaches and viewpoints consider a much closer coupling of numerics and physical modeling, which might make it even more difficult to quantify the individual contributions and draw general conclusions.

In the following, we give some recommendations concerning the numerics and the physical models. Finally, some general considerations concerning the more practical aspects of IC engine simulations and the analysis of results are given.

Numerics

The use of second or higher order schemes minimizes numerical dissipation and this allows to accurately resolve the large-scale flow structures. Further, dissipation is exclusively controlled by the SGS model. This approach can be compromised by any upwind contribution that increases the amount of numerical dissipation. Formally, schemes with upwinding (QUICK, TVD, ENO, ...) can still achieve high orders of accuracy, but this order is mainly relevant for grid convergence - something that is (almost) never achieved with LES, where the underlying solution is typically poorly resolved, as the underlying solution will gain smaller scales with a smaller filter-scale or grid-refinement. Similarly, time-integration schemes that induce dissipation (e.g. non-central schemes with a bias to an implicit scheme) should be avoided. For engine cases where higher Ma numbers occur (e.g. detonation waves), robust schemes are required, and one option is Ma-number dependent blending as demonstrated by \cite{Janas2017}.

At the present time, only very few engine LES have shown the ability of achieving robust momentum transport with central schemes without artificial stabilization (which in turn increases the amount of numerical dissipation) - but these tend to reduce accuracy near the walls instead \cite{Nguyen2017}. This means that there is still a clear need for further research on numerical schemes for engine LES!

Quality estimators, for example by comparing the resolved to the modeled turbulent kinetic energy, cannot be trusted. Much work has shown, e.g. \cite{Klein2005,Klein2008} or \cite{Nguyen2017} that the criteria tend to be optimistic, and that they are usually misleading if used with (dissipative) non-central schemes: in these cases, numerical dissipation will reduce the turbulent kinetic energy on the smallest resolved scales, so that a reduced sub grid-energy is predicted - falsely implying that the unresolved part of the turbulent kinetic energy is low, whereas it is only underestimated due to poor numerics.

Depending on the amount of numerical dissipation, combustion models may need to be adjusted, in particular the modelling of wrinkling factors, and effective grid filter-widths may need to be considered, which may be higher than the “ideal” (Schumann) case where the filter width is proportional to the cell-size \cite{Mercier2015}.

Physical models

Many models for e.g. spray and combustion were originally developed for RANS applications and were subsequently adapted for LES. This also applies for many turbulence subgrid scale models, especially the ones based on an eddy viscosity. Especially for scale-resolving engine calculations, it is not yet possible to give clear recommendations which models or model combinations are best suited. This would require a thorough comparison of physical models using the same numerical approach and the same CFD solver, an example of such an investigation can be found in \cite{BuhlDietzsch2017}. Such detailed one-by-one comparisons are rare in the literature for IC engine simulations. However, a few general recommendations are summarized instead, which have been addressed in more detail in the ERCOFTAC "Best Practice of IC Engines" (Chapter 5) \cite{Amsini2016}.

General considerations

Experience has shown that a minimum mesh resolution can be estimated from RANS simulations (conducted on either a moving or static mesh) by cautiously (see above) considering quality indices under motored conditions.

Subsequent to any verification, a comprehensive validation of the CFD approach (which includes both the numerics and the physical models) represents an indispensable prerequisite. This is essentially achieved by comparing the simulation results with experimental data in order to demonstrate the simulation accuracy and to deliver the main characteristics of the system under study.

Besides all the classical requirements for well-qualified RANS and LES, there is a special challenge for IC engine simulations related to cycle-to-cycle variation processes. To ensure that meaningful, fully converged flow statistics are collected, it is recommended to extend the statistical sample by carrying out consecutive multi-cycle computations. Appropriate parallelization strategies are available \cite{Amsini2016}. The required number of cycles strongly depends on the quantity of interest. For lower order moments, only a limited number of cycles need to be computed while for rare events, the number of cycles can increase dramatically. In general, it is good practice to provide error bars from simulation results, e.g. based on the estimated deviation of the average and the variance \cite{Janas2017}. Furthermore, when performing scale-resolving simulations, the first 5\,-\,10 cycles should be discarded to ensure statistical independence of the flow structures from the initial conditions.

Recommendations for future work

The presented experimental and simulation work revealed several open issues within IC-engine simulations as discussed in subsections \ref{sec:uncertainties} and \ref{sec:recommendations} which can be summarized as follows:

  • Further research is needed on robust and accurate numerical schemes for engine LES.
  • Many models were originally developed for RANS applications and were subsequently adapted for LES. A thorough comparison of such physical models is needed to give clear recommendations which models or model combinations are best suited.
  • There is still a lack of accurate boundary layer models for momentum and heat transfer.

References

  1. A. Amsden. KIVA3V. A Block-Structured KIVA Program for Engines with Vertical or Canted Valves. Tech. rep. Los Alamos National Laboratory, 1997.
  2. A. Amsden, P. O'Rourke, and T. Butler. KIVA-II: A Computer Program for Chemically Reactive Flows with Sprays. Tech. rep. Los Alamos National Laboratory, 1989.
  3. S. Amsini, F. di Mare, K. Nishad, P. Keller, S. Buhl, E. Hartmann and C. Hasse. “Internal Combustion Engine”. In: ERCOFTAC Best Practice Guidelines for Computational Fluid Dynamics of Turbulent Combustion. Ed. by L. Vervisch and D. Roekaerts. 2016.
  4. E. Baum, B. Peterson, B. Böhm, and A. Dreizler. “On The Validation of LES Applied to Internal Combustion Engine Flows: Part 1: Comprehensive Experimental Database”. In: Flow, Turbulence and Combustion 92.1 (2014), pp. 269–297.
  5. E. Baum, B. Peterson, C. Surmann, D. Michaelis, B. Böhm, and A. Dreizler. “Investigation of the 3D flow field in an IC engine using tomographic PIV”. In: Proceedings of the Combustion Institute 34.2 (2013), pp. 2903–2910.
  6. M. Baumann, F. di Mare, and J. Janicka. “On the Validation of Large Eddy Simulation Applied to Internal Combustion Engine Flows Part II: Numerical Analysis”. In: Flow, Turbulence and Combustion 92.1 (2014), pp. 299–317.
  7. J. Borée and P. Miles. “In-Cylinder Flow”. In: Encyclopedia of Automotive Engineering. John Wiley and Sons, Ltd, 2014.
  8. S. Buhl, F. Dietzsch, C. Buhl, and C. Hasse. “Comparative study of turbulence models for scale-resolving simulations of internal combustion engine flows”. In: Computers & Fluids 156 (2017). Ninth International Conference on Computational Fluid Dynamics (ICCFD9), pp. 66–80.
  9. S. Buhl, F. Gleiss, M. Köhler, F. Hartmann, D. Messig, C. Brücker, and C. Hasse. “A Combined Numerical and Experimental Study of the 3D Tumble Structure and Piston Boundary Layer Development During the Intake Stroke of a Gasoline Engine”. In: Flow, Turbulence and Combustion 98.2 (2017), pp. 579–600.
  10. S. Buhl, D. Hain, F. Hartmann, and C. Hasse. “A comparative study of intake and exhaust port modeling strategies for scale-resolving engine simulations”. In: International Journal of Engine Research (). In press, doi: 10.1177/1468087417707452.
  11. S. Bürkle, L. Biondo, C. Ding, R. Honza, V. Ebert, B. B&oumlhm, and C. Wagner. “In-Cylinder Temperature Measurements in a Motored IC Engine using TDLAS”. In: Flow, Turbulence and Combustion (submitted 2017).
  12. M. Buschbeck, N. Bittner, T. Halfmann, and S. Arndt. “Dependence of combustion dynamics in a gasoline engine upon the in-cylinder flow field, determined by high-speed PIV”. In: Experiments in Fluids 53.6 (2012), pp. 1701–1712.
  13. L. Davidson. ‘Using isotropic synthetic fluctuations as inlet boundary conditions for unsteady simulations”. In: Advances and Applications in Fluid Mechanics 1 (2007), pp.1–35
  14. I. Demirdžić, Ž. Lilek, and M. Perić. “A collocated finite volume method for predicting flows at all speeds”. In: International Journal for Numerical Methods in Fluids 16.12 (1993), pp. 1029–1050.
  15. I. Demirdžić and M. Perić. “Space conservation law in finite volume calculations of fluid flow”. In: Internationa; Journal for Numerical Methods in Fluids 8.9 (1988), pp. 1037–1050.
  16. J. Ferziger and M. Perić. Computational Methods for Fluid Dynamics. Springer-Verlag Berlin Heidelberg, 2002.
  17. D. Freudenhammer, E. Baum, B. Peterson, B. Böhm, B. Jung and S. Grundmann. “Volumetric intake flow measurements of an IC engine using magnetic resonance velocimetry”. In: Experiments in Fluids 55.5 (2014), p. 1724.
  18. D. Goryntsev, A. Sadiki, M. Klein and J. Janicka. “Large eddy simulation based analysis of the effects of cycle-to-cycle variations on air-fuel mixing in realistic DISI IC-engines”. In: Proceedings of the Combustion Institute 32.2 (2009), pp. 2759–2766.
  19. C. Hasse, V. Sohm, and B. Durst. “Numerical investigation of cyclic variations in gasoline engines using a hybrid URANS/LES modeling approach”. In: Computers and Fluids 39.1 (2010), pp. 25–48.
  20. C. He, W. Leudesdorff, F. di Mare, A. Sadiki, and J. Janicka. “Analysis of In-cylinder Flow Field Anisotropy in IC Engine using Large Eddy Simulation”. In: Flow, Turbulence and Combustion 99.2 (2017), pp. 353–383.
  21. P. Janas, I. Wlokas, B. Böhm, and A. Kempf. “On the Evolution of the Flow Field in a Spark Ignition Engine”. In: Flow, Turbulence and Combustion 98.1 (2017), pp. 237–264.
  22. P. Janas. “Large Eddy Simulation of In-Cylinder Phenomena in Spark Ignition Engines”. PhD thesis. Universität Duisburg-Essen, 2017, pp. 237–264.
  23. M. Klein. “An Attempt to Assess the Quality of Large Eddy Simulations in the Context of Implicit Filtering”. In: Flow, Turbulence and Combustion 75.1 (2005), pp. 131–147.
  24. M. Klein, J. Meyers, and B. Geurts. “Assessment of LES Quality Measures Using the Error Landscape Approach”. In: Quality and Reliability of Large-Eddy Simulations. Ed. by J. Meyers, B. J. Geurts, and P. Sagaut. Dordrecht: Springer Netherlands, 2008, pp. 131–142.
  25. A. Lucius and G. Brenner. “Unsteady CFD simulations of a pump in part load conditions using scale-adaptive simulation”. In: International Journal of Heat and Fluid Flow 31.6 (2010), pp. 1113–1118.
  26. F. Menter. “Two-equation eddy-viscosity turbulence models for engineering applications”. In: AIAA Journal 32.8 (1994), pp. 1598–1605.
  27. F. Menter and Y. Egorov. “The Scale-Adaptive Simulation Method for Unsteady Turbulent Flow Predictions. Part 1: Theory and Model Description”. In: Flow, Turbulence and Combustion 85.1 (2010), pp. 113–138.
  28. R. Mercier, V. Moureau, D. Veynante, and B. Fiorina. “{LES of turbulent combustion: On the consistency between flame and flow filter scales”. In: Proceedings of the Combustion Institute 35.2 (2015), pp. 1359–1366.
  29. T. Nguyen, P. Janas, T. Luccini, G. D'Errico, S. Kaiser, and A. Kempf. “LES of Flow Processes in an SI Engine Using Two Approaches: OpenFoam and PsiPhi”. In: SAE 2014 World Congress and Exhibition. SAE International, 2014.
  30. T. Nguyen and A. Kempf. “Investigation of Numerical Effects on the Flow and Combustion in LES of ICE”. In: Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles 72.4 (2017).
  31. T. Nguyen, F. Proch, I. Wlokas, and A. Kempf. “Large Eddy Simulation of an Internal Combustion Engine Using an Efficient Immersed Boundary Technique”. In: Flow, Turbulence and Combustion 97.1 (2016), pp. 191–230.
  32. B. Peterson, E. Baum, C.-P. Ding, D. Michaelis, A. Dreizler, and B. Böhm. “Early flame propagation in a spark-ignition engine measured with quasi 4D-diagnostics”. In: Proceedings of the Combustion Institute 35.3 (2015), pp. 3829–3837.





Contributed by: Carl Philip Ding,Rene Honza, Elias Baum, Andreas Dreizler — Fachgebiet Reaktive Strömungen und Messtechnik (RSM),Technische Universität Darmstadt, Germany


Contributed by: Brian Peterson — School of Engineering, University of Edinburgh, Scotland UK


Contributed by: Chao He , Wibke Leudesdorff, Guido Kuenne, Benjamin Böhm, Amsini Sadiki, Johannes Janicka — Fachgebiet Energie und Kraftwerkstechnik (EKT), Technische Universität Darmstadt, Germany


Contributed by: Peter Janas, Andreas Kempf — Institut für Verbrennung und Gasdynamik (IVG), Lehrstuhl für Fluiddynamik, Universität Duisburg-Essen, Germany


Contributed by: Stefan Buhl, Christian Hasse — Fachgebiet Simulation reaktiver Thermo-Fluid Systeme (STFS), Technische Universität Darmstadt, Germany; former: Professur Numerische Thermofluiddynamik (NTFD), Technische Universität Bergakademie Freiberg, Germany

Front Page

Description

Test Data

CFD Simulations

Evaluation

Best Practice Advice


© copyright ERCOFTAC 2018