direct numerical simulations (dns) in simplified ice...

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LL

LES4ICE, Rueil-Malmaison, December 2018

Direct Numerical Simulations (DNS) in simplified ICE configurations

using immersed boundaries and detailed chemistry

Dominique Thévenin

Institute of Fluid Dynamics & Thermodynamics, Univ. of Magdeburg „Otto von Guericke“, Germany

[email protected] http://www.lss.ovgu.de

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Ø Turbulent flows with reactions are of fundamental interest and are found in uncountable practical applications, due to §  Changing energy supplies, §  Increased environmental pressure, §  Need for a better control of process outcome, §  More stringent safety regulations, §  ...

Introduction

Ø In order to get an accurate representation of turbulent ignition and propagation, the present study relies exclusively on Direct Numerical Simulation (DNS)

Ø Taking into account real geometrical features becomes increasingly necessary

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Research relevant for ICE DNS study LES-based optimization of injection

[Hellmann et al., ICLASS, Chicago, 2018]

[Abdelsamie & Thévenin, Proc. Combust. Inst. 37, (2018) in press]

Spray ignition and combustion

[Theile et al., SAE Int. J. Engines 9(4), (2016) 1-17]

LES of cyclic variability

[Chittipotula et al., Chem. Eng. Sci. 70, (2012) 62-76]

Nanoparticle generation

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Needed building blocks?

•  Taking into account detailed reaction networks, •  and suitable schemes (from literature)

DNS in simplified ICE configurations using immersed boundaries and detailed chemistry

•  Able to treat complex, possibly moving geometries

A (validated and efficient)

DNS code: DINO

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DNS code: DINO



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DINO in a nutshell Ø 3rd-generation DNS tool in the group Ø High-order finite-difference solver Ø  with Immersed Boundary Method (IBM) Ø  Involving 3 co-developers (GIT), started 2012 Ø  Core in Fortran 2003, external layer of C++ Ø  Dimensional code Ø  Currently: 7 users in 4 countries Ø  Low Mach number or incompressible formulation Ø  Semi-implicit time integration (Rosenbrock approach with direct Jacobian computation for reactions) Ø  Up to 6th order in space, 4th order in time Ø  Adapted but static tensor-product grid

[Abdelsamie et al., Comput. Fluids 131, (2016) 123-141] Intro

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Parallelization

Ø  Parallelization: domain decomposition using MPI Ø  Based on 2D x-pencil (2Decomp&FFT, open source)

Ø  Including efficient 2D parallel FFT solver

Ø Taking further advantage of this to solve Poisson equation with FFT

Ø  Even for non-periodic boundary conditions: then use in-house pre- & post-processing

Ø All simulations on SuperMUC (Leibniz Supercomputing Center in Munich) with nx1000 processors

yx

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Validation: turbulent flow Excellent agreement with experimental data

Excellent agreement with DNS of Moser et al., Vreman & Kuerten...

[Abdelsamie et al., Comput. Fluids 131, (2016) 123-141]

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Detailed reaction networks

Oxidation mechanism involving many species and elementary reactions, providing accurate results also for complex fuels and configurations

∂ρ

∂t+

∂(ρuj)

∂xj= 0

∂(ρui)

∂t+

∂(ρuiuj)

∂xj= −

∂p

∂xi+

∂τij∂xj

(i = 1, 2, 3)

∂et∂t

+∂ (et + p)uj

∂xj=

∂ (ujτkj)

∂xk−

∂qj∂xj

∂ (ρYl)

∂t+

∂ (ρYluj)

∂xj= −

∂ (ρYlVDlj)

∂xj+Wlω̇l (l = 1 . . .Ns)

p = ρrTNs!

l=1

Yl = 1

τ = 2µd−2

3µ(∇.v) I or

τ = 2µd+ κ(∇.v) I

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Successfully tested up to 352 species and 13,264 elementary reactions in DINO (butanol)

from literature…

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Physicochemical models Ø Chemical kinetics described by:

§  CANTERA (default) §  (CHEMKIN) §  Tabulated chemistry

Ø  Transport processes described by: §  CANTERA (default) §  EGlib §  (TRANSPORT)

Ø  Thermodynamics described by: §  CANTERA (default) §  (CHEMKIN)

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Kin

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E

12 sL =

ω k1

2

∫ dx + ρD∇Yk2 − ρD∇Yk

1

ρ1(Yk2 −Yk

1)

Luo et al., 2012: 32 species & 206 reactions UCSD, 2003: 39 species & 173 reactions UCSD, 2005: 46 species & 235 reactions

For example for ethylene (C2H4):

Validation: reacting flows

Flame speed Ignition delay

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Ghost-cell Immersed Boundary Ø Force term not computed directly, but enforced indirectly by the fictitious velocity at ghost points

Ø Conventional IBM: boundary-normal projection

[Chi et al., Int. J. Numer. Meth. Fluids 83, (2017) 132-148]

Φbp = 0.5(Φip +Φgp )+o(Δl2 )

Δl

Δl = o(Δx)

Poor assumption!

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New higher-order ghost-cell IBM

Flapping wing, Re=157

Ø Novelty 1: local directional extrapolation Accurate boundary representation, avoiding extra

iterations in the extrapolation scheme (better parallelization) Ø Novelty 2: always a single layer of ghost points

Sharp interface representation Ø Novelty 3: Δl = o(Δx) is not any more an assumption

Convergence rate ensured

[Chi et al., J. Comput. Phys., under review] Intro

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Resulting spatial order of IBM

2nd order

DINO/IBM

Test case: flow around cylinder Ref St = f D/U∞

Tseng et al. 0.164

Lai et al. 0.165

Kim et al. 0.165

Dias et al. 0.171

Williamson (Exp.) 0.166

Present IBM 0.166

IBM-Wall

IBM-Wall

flow

Test case: tilted Poiseuille flow Intro

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DNS code: DINO

Application examples




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Simple ICE model

wall geometry/piston represented by IBM level-set function = 0

Compression ratio: 12Piston speed: 560 rpm (just enough to get turbulence, Remax=2990)

Ø Reference experiment of Morse et al. (1978) Ø Later investigated in a number of URANS and LES studies Ø First cold-flow DNS documented in Schmitt (2014), followed by numerous investigations, in particular by the same group (ETH Zürich) Ø Prescribed piston movement (sinus) Ø Prescribed inflow velocity Ø Isothermal no-slip walls Ø Mesh resolution down to 7,5 µm

moving piston

top dead center (TDC)

bottom dead center (BDC)

75 mm 60 mm

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Velocity of the piston reaches maximum. Jets impact side walls and separate. Rupture of symmetry.

90oCA45oCA

Gas is pushed in, and jets/vortex ring form.

Velocity magnitude

Cold-flow intake stroke

Flow dominated by flapping jets and small-scale structures.

135oCA

Piston reaches BDC. Large recirculating vortices still visible.

180oCA

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Mixing during intake stroke (2D) In

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Res

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Velocity magnitude Temperature Y(H2)

Inside cylinder: Y(O2) = 0.115, Y(N2) = 0.756, Y(H2O) = 0.129 Injection: homogeneous H2/air mixture at φ = 0.5

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Compression stroke (2D) In

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Res

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Temperature Y(H2)Zoom on self-ignition

Mechanism of Williams (9 species, 12 reversible reactions)

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Ø  Progress in numerical techniques as well as computer power now allow DNS investigations of flows with reactions in complex, moving geometries

Ø DNS is a perfect tool to gain a deeper understanding, and investigate ignition and flame propagation,

Ø  but many challenges remain, for instance: §  Correct description of all transport and thermodynamic

properties for all relevant conditions; §  Deriving from DNS simple but sufficiently accurate

models; §  Efficient (on-the-fly) analysis of the results (no full

storage possible!) §  …

Conclusions

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Next steps

[Eshghinejadfard et al., J. Fluid Mech. 849, (2018) 510-540]

[Hosseini et al., Physica A 499, (2018) 40-57]

Ø  Fine-tune IBM implementation Ø  4th-generation DNS tool as hybrid with Lattice-Boltzmann? -  Velocity/pressure (LB); Thermochemistry (Finite-Diff.)

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Dr. Abouelmagd Abdelsamie

Cheng Chi

Financing: DFG (Deutsche Forschungsgemeinschaft), Max Planck Society, German Ministry of Research, Volkswagen AG, Bosch GmbH Computing time: Leibniz Supercomputing Center, Munich

Dr. Martin Theile

Assoc. Prof. Gábor Janiga

Robin Hellmann Ali Hosseini

direct numerical simulations (dns) in simplified ice...

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