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Internal combustion engine flows for motored operation
Application Challenge AC210 © copyright ERCOFTAC 2023
CFD Simulations
Introduction
The TU Darmstadt engine has been investigated by three different groups using LES (Large Eddy Simulation) and hybrid URANS (unsteady Reynoldsaveraged NavierStokes)/LES. These research groups are located at Technische Universität Bergakademie Freiberg (TUBF), Universität DuisburgEssen (UDE) and Technische Universität Darmstadt (TUD). In the follow, the different approaches will be presented, including general information about the code and the physical modelling, computational domain and mesh treatment, initial and boundary conditions and computational requirements. Additional and detailed information can be found in the scientific papers listed in Table 3.
Overview of simulation
Test data for the motored case is available at three different operating points. A test case considering an engine speed of 800 RPM is investigated in the numerical studies, which are summarized in Table 3.
Group  Approach  CFD Code / Publication 

TUBF  URANS / LES  Ansys CFX R16.0 / Buhl et al. [9, 10] 
UDE  LES  OpenFoam2.3.x / Janas et al. [21] 
TUD  LES  KIVA4mpi / He et al. [20] 
Further, simulations were carried out by Nguyen et al. [31] and Baumann et al. [6], which are not part of this comparison.
CFD codes
In the following, the governing equations, turbulence modelling and solver are described for the three different CFD codes.
Ansys CFX R16.0
For this compressible case, the filtered governing equations for mass, momentum and total enthalpy are solved. They are defined as:
with the thermal conductivity and the turbulent Prandtl number (set to 0.9 in the following). A nondensityweighted filtered variable is denoted by , while is the densityweighted filtered variable. This system is complemented by the filtered equation of state for perfect gases, defined as:
with the molecular weight and the universal gas constant . The eddy viscosity () is calculated based on the scaleadaptive simulation (SASSST) turbulence model [27]. In case of insufficient spatial or temporal resolution (for a SRS), it reverts to the SST model [26] and maintains a valid base of modelling. Its key element is the von Kármàn length scale [38]. (), introduced into the scaledetermining equation. In unstable flows, adjusts the eddy viscosity to a level which allows the formation of a turbulent spectrum
[27, 45, 39, 25].
is defined as
with
and the von Kármán constant .
ANSYS CFX R16.0 is a nodebased, conservative and timeimplicit finitevolume code [36, 35, 46]. The mass flow is evaluated such that a pressurevelocity coupling is achieved [37]. The discrete systems of equations are solved using a coupled algebraic multigrid method [36]. To minimize numerical diffusion, a secondorder scheme in space [45] and a secondorder backward scheme in time are used.
OpenFOAM2.3.x
The following governing equations for mass, momentum and absolute internal energy are solved on a moving grid:
Here, the overline denotes LES filtered and the tilde Favre filtered quantities. Further, denotes the viscous stress tensor. Detailed information regarding equations 4.7–4.9 and the utilized variables can be found in
[21, 22].
The standard Smagorinsky model [40] with a model constant of 0.062 is used to account for the unresolved subgrid stresses τ_{sgs} and the Sutherland law is used to calculate the molecular viscosity. The pressurevelocitydensity coupling for flows at arbitrary Machnumber, proposed by Demirdžić, is employed to calculate the pressure [14, 16]. Since the time step size is not limited according to the speed of sound, this approach allows bigger time steps, using an implicit secondorder scheme. The momentum equation is solved using a central differencing scheme (CDS). In regions with Machnumbers greater than 0.3 a TVDscheme is used to avoid numerical problems [29] and unacceptable numerical dissipation. A total variation diminishing scheme (TVDscheme) is utilized to discretize the convective scalarfluxes, using the Sweby limiter [43].
The time step size employed is calculated from the maximum CFL number of 2. The smallest time step occurs when the valves are opening and closing, since the smallest mesh resolution can be found inside the valve gap and the highest velocities. This leads to time steps smaller than one micro second. After the valves are closed, the time step size increases to almost 50 micro seconds during the compression and expansion stroke.
KIVA4mpi
The Favrefiltered governing equations for mass, momentum and internal energy are discretized on a moving mesh according to the Arbitrary LagrangianEulerian (ALE) approach [2, 44]. The overline characterizes LES filtered and the tilde Favre filtered quantities. Further information regarding the different terms of equations 4.10–4.13 can be found in [2, 44, 20].
The internal energy is defined as follows:
The classical Smagorinsky model [40] with a Smagorinsky constant set to 0.1 is utilized to model the turbulent kinematic viscosity. The quasisecondorder upwind (QSOU) scheme [2] is used as the spatial and the firstorder implicit Euler as the temporal discretization scheme. A staggered arrangement is employed to store the variables and the time step size is adjusted during the computation according to accuracy requirements and to retain a CFL number smaller than one.
Computational Domain and Mesh Treatment
Ansys CFX R16.0
As illustrated in Figure 12, the numerical domain is characterized by temporarily disabled ports for the time periods when the corresponding valves are closed. When the valves reopen, the ports are added to the numerical domain. Due to this disabling and enabling strategy, the flow field of each port has to be reinitialized within each individual operating cycle. In this study the reinitialization is done by one (previously calculated) flow field for each port, to minimize the computational costs.
Figure 12: Ansys CFX  Numerical domain during the entire operation cycle. 
Figure 13 illustrates the mesh topology within the combustion chamber, shown on the valve middle plane. The hybrid, and bodyconformal mesh consists of tetrahedral, prism and hexahedral elements with a maximum cell size of 1mm. Mesh refinements of 0.25mm are defined at the intake jet region, the valve stem and the separation edge within the intake port. The boundary layer is resolved by 10 prism layers with a minimum height of 25μm next to the wall. From 310 to 36° bTDC and from 37 to 314° bTDC an extrusion volume is generated to reduce the number of cells. A key grid approach [9] is used, while the effects of mesh morphing and interpolation were investigated in a previous study [19]. The time step width is set to 0.1 CAD (≈ 2.08×10^{5}s) for all simulations.
Figure 13: Ansys CFX  General mesh topology within the combustion chamber. Mesh refinement at the intake jet region, valve stem and intake port separation edge are highlighted by orange box. 
OpenFOAM2.3.x
The numerical grid is built up of equidistant hexahedral cells, locally refined at the spark plug and the valve seat region (see left of Figure 14. Inside the combustion chamber an average cell size of 0.5mm is employed, whereas inside the valve seat region, the cell size is 0.125 to 1mm. At the spark plug the cell size is 0.25mm and inside the inlet and outlet manifolds, at a distance of the valves, the mesh resolution is reduced to 2mm. In total, the used grids contain 2.0 million cells at TDC and up to 5.7 million cells at BDC. To close the valves, so called 'curtains' are used, which are internal walls around the valve seat.
Figure 14: OpenFoam  Left: Engine grid on a cut through the valves with opened intake valve. Middle: Top view of the cylinder head. Right: Full computational domain.} 
The mesh motion strategy involves 144 target grids for every 5 CAD for one full cycle, which are created prior to the simulation. Here, 5 CAD correspond to a piston motion of less than 4mm, where the mesh quality stays high. Every 5 CAD the results are mapped to a new grid. The mesh motion is realized by shifting each mesh point by a distance resulting from the multiplication of the time step size and deformation velocity, which is obtained by a Laplace equation for a grid velocity field. Further, all convective fluxes are calculated relative to the moving grid [15]. Further Information can be found in [22].
KIVA4mpi
The computational domain involves the complete geometry of the IC engine including intake and exhaust till the measuring locations (in case of intake second measurement location), see Figure 2. The mesh consists of hexahedral elements with a varying resolution within the engine geometry which is displayed in Figure 15. At BDC, the averaged cell width around the intake valves is approximately 0.4mm and 0.72mm in the cylinder body. Due to the mesh movement, the total amount of cells varies between 0.41 million cells at TDC and 1.76 million cells at BDC. Details about the mesh motion strategy can be found in [1].
Figure 15: KIVA4mpi  Left: Mesh on valve middle plane (z=19mm) at 270° bTDC.Right: Isometric view of cylinder head. 
Initial and Boundary Conditions
Ansys CFX R16.0
The boundaries are illustrated in Figure 16. For the exhaust port, timeresolved pressure and temperature curves were chosen. The intake port boundary condition consists of a timeresolved temperature and mass flow. The fluid within the fireland crevice (clearance volume between cylinder and piston) is modelled based on the timeresolved mass flow and temperature at the annular gap derived from the piston clearance (0.53mm), see right of Figure 16. Please note that blowby gases are neglected in this work. If not available in the experiment, the boundary conditions were generated by previous 0D/1D simulations, which will be described next.
Figure 17 illustrates the general workflow to achieve the boundary conditions and initialization data. First (S1), a 0D/1D engine model up to the pressure plenum was created using the software GTPower (v7.2). This model was calibrated with the available experimental data (e.g. incylinder pressure) and timeresolved boundary conditions were extracted at defined positions (indicated in Figure 17. In a second step (S2), an URANS simulation was performed to obtain the necessary port flow fields. Synthetic turbulence [13] was used to generate 6 different initial fields for SRS. Afterwards, 3 consecutive cycles were simulated for each of these 6 initial fields. In a last step (S3), the flow fields obtained were used to initialize the consecutive operating cycles.
Figure 16: Ansys CFX  Boundary condition imposed on the intake and exhaust ports. Right: Blue area on piston indicates the piston clearance volume used for fireland crevice modeling. 
Figure 17: Ansys CFX  Workflow to obtain boundary conditions and initialization data. 
OpenFOAM2.3.x
At 360° bTDC the intake and exhaust valves are open and the fluid is assumed to be at rest in the entire domain. At this point the simulation is started and 20 consecutive engine cycles are calculated. From the measurements, the timevarying absolute pressure is known and imposed at positions of 530mm upstream of the intake valves and 360mm downstream of the exhaust valves. The piston topland crevice volume was blocked and the geometric compression ratio adapted, accordingly. At the walls a noslip boundary condition is applied. The temperature is set to 295K at the inlet and to a fixed value of 333K at the walls. No additional wall modelling is used considering the heat transfer.
KIVA4mpi
The initial temperature and pressure in the intake and exhaust pipe as well as the timedependent pressure boundary condition are set to the experimentally determined values at the second intake and exhaust measuring location shown in Figure 2. The piston topland crevice volume is included in the computational domain wherefore no additional boundary condition is needed here. The valve temperature is set to 338K and the wall temperature of all other walls to 300K. Further, a noslip condition is applied and no special treatment of the walls is performed.
Computational Requirements
Ansys CFX R16.0
The simulations were performed on INTEL E52680v2 processors connected by InfiniBand FDR. Table 4 summarizes the computational effort for the simulated 60 cycles. Accordingly, approximately 5.79 days were required for the simulation of one full cycle.
Minimum number of finite volumes  1.8×10^{6} 
Maximum number of finite volumes  8.7×10^{6} 
Average number of finite volumes  4.9$×10^{6} 
CPU  120 
CPUh  1.0×10^{6} 
OpenFOAM2.3.x
The simulations were performed on 16 CPUs (AMD Opteron  Type 6234) with a total of 192 cores. Table 5 summarizes the computational time required for the different phases of the cycle.
Interval  days  % of total time  CAD 

Full cycle  6.5  100  720 
Intake valve closing  1.1  16  30 
Exhaust valve opening  2.4  38  45 
Overlapping  0.5  7  50 
Rest  2.4  37  595 
KIVA4mpi
In general, the simulation were performed on an Intel Xeon Processor E52680 v3 using 12 CPU. A cycleparallelization technique was used to calculate several runs of consecutive cycles, further described in He et al. [20]. Considering this, approximately 78.23 hours were required to calculate one cycle using the described mesh.
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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