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Internal combustion engine flows for motored operation
Application Challenge AC210 © copyright ERCOFTAC 2020
Best Practice Advice
Key Fluid Physics
Flows within internal combustion engines are still challenging for numerical simulations. The key fluid physics which need to be captured are:
 Location, size and intensity of the recirculation zone between the intake valve and the cylinder wall (left of the intake valve in Figure 19)
 Orientation, size and strength of the intake jet (right of the intake valve in Figure 19)
 Location and strength of the tumble vortex
Application Uncertainties, Computational Domain and Boundary Conditions
The boundary conditions have an influence on the incylinder pressure especially on the peakpressure. The influence of following boundary conditions on the peak pressure was evaluated:
 The compression ratio and valve timing were verified experimentally. The uncertainties were found to be negligible.
 The influence of blowby was found to be negligible.
 A large uncertainty was found for the total incylinder mass at intakevalve closing. Because the temperature of the incoming air is 23° C and the cylinder head was set to 60° C the temperature of the intake air is uncertain. A variation of the intake temperature in the range of 23–60° C has a significant influence on peak pressure. Air temperature measurements within the cylinder have therefore been performed recently [11].
 An uncertainty remains for the heat transfer over the walls because it is difficult to measure and difficult to assess in an LES, where accurate boundary layer modeling for momentum and heat transfer is still not fully solved.
 For optically accessible engines the piston crevice has a strong contribution to heat losses because it is typically very deep to prevent the piston to ride over the glass liner. This results in a large surfacetovolume ratio. Therefore it is not advisable to delete the piston crevice and to adjust the compression ratio through geometrical changes elsewhere. It is important to take into account the influence of the piston crevice on heat transfer as well.
The computational domain should include parts of the intake and exhaust pipe. A comparison of simulations using the entire intake and exhaust port pipe with a length of 1680 and 1080mm (measured from the flange of the cylinder head up to the pressure plenum) and a shortened version with a length of 192 and 122mm revealed similar results for the incylinder pressure, mass and velocity field [10]. For this test case the short ports were found to be a good compromise between accuracy and computational costs.
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 scaleresolving 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 incylinder flow processes are coupled nonlinearly.
Numerics and physical modeling are closely intertwined with each other in scaleresolving 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 largescale 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 filterscale or gridrefinement. Similarly, timeintegration schemes that induce dissipation (e.g. noncentral 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 Manumber dependent blending as demonstrated by [21].
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 [30]. 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. [23, 24] or [30] that the criteria tend to be optimistic, and that they are usually misleading if used with (dissipative) noncentral schemes: in these cases, numerical dissipation will reduce the turbulent kinetic energy on the smallest resolved scales, so that a reduced sub gridenergy 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 filterwidths may need to be considered, which may be higher than the “ideal” (Schumann) case where the filter width is proportional to the cellsize [28].
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 scaleresolving 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 [8]. Such detailed onebyone 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) [3].
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 wellqualified RANS and LES, there is a special challenge for IC engine simulations related to cycletocycle variation processes. To ensure that meaningful, fully converged flow statistics are collected, it is recommended to extend the statistical sample by carrying out consecutive multicycle computations. Appropriate parallelization strategies are available [3]. 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 [21]. Furthermore, when performing scaleresolving 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 in ICengine simulations as discussed in the uncertainties section and the physical modelling section 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.
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Contributed by: Carl Philip Ding, Rene Honza, Elias Baum, Benjamin Böhm, 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, 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 DuisburgEssen, Germany
Contributed by: Stefan Buhl, Christian Hasse — Fachgebiet Simulation reaktiver ThermoFluid Systeme (STFS), Technische Universität Darmstadt, Germany; former: Professur Numerische Thermofluiddynamik (NTFD), Technische Universität Bergakademie Freiberg, Germany
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