# Codes

## Contents

# Code Description

This page is dedicated to a short presentation of the three **low-Mach number** codes used for the rest of this benchmarl: YALES2, DINO and Nek5000.

Since these codes have already been the subject of many publications and are not completely new, **only the most relevant features are discussed in what follows**, with suitable references for those readers needing more details.

## YALES2

YALES2 is a **massively parallel multiphysics platform**^{[1]} developed since 2009 by Moureau, Lartigue and co-workers at CORIA (Rouen, France).
It is dedicated to the high-fidelity simulation of **low-Mach number flows in complex geometries**.
It is based on the **Finite Volumes formulation** of the **Navier-Stokes equations** and it can solve both **non-reacting** and **reacting flows**.
It can actually solve various physical problems thanks to its original structure which is composed of a main numerical library accompanied with tens of dedicated solvers (for acoustics, multiphase flows, heat transfer, radiation\ldots) which can be coupled with one another.
YALES2 relies on **unstructured meshes** and a **fully parallel dynamic mesh adaptation technique** to improve the resolution in physically-relevant zones to mitigate the computational cost^{[2]}.
As a result it can easily handle meshes composed of billions of tetrahedra, thus enabling the **Direct Numerical Simulation** of laboratory and semi-industrial configurations.
It is now composed of nearly 500,000 lines of object-oriented Fortran and the parallelism is currently ensured by a pure **MPI** paradigm, although a hybrid **OpenMP/MPI** as well as a GPU version are under development.

As most low-Mach number codes, the time-advancement is based on a **projection-correction method** following the pioneering work of^{[3]}.
The prediction step uses a method which is a blend between a **4th-order Runge-Kutta method** and a **4th-order Lax-Wendroff-like method**^{[4]}, combined with a **4th-order node-based centered finite-volume discretization** of the convective and diffusive terms.
Moreover, to improve the performance of the correction step, the pressure of the previous iteration is included in the prediction step to limit the **splitting errors**.
The correction step is required to ensure **mass conservation**, using a pressure that arises from a **Poisson equation**.
This is performed numerically by solving a linear system on the pressure at each node of the mesh thanks to a dedicated in-house version of the deflated conjugate gradient algorithm, which has been optimized for solving elliptic equations on massively parallel machines^{[5]}.

When considering **multispecies** and **non-isothermal flows**, the extension of the classical projection method proposed by Pierce et al.^{[6]} is used to account for **variable-density flows**.
All the thermodynamic and transport properties are provided by the **Cantera software**^{[7]}, which has been fully re-implemented in **Fortran** to avoid any performance issues.
Each species thermodynamic property is specified by **5th-order polynomials** on two temperature ranges (below and above 1000 K).
The mixture is supposed to be both thermally and mechanically perfect.

Regarding transport properties, several type of models are implemented in YALES2:

1) the default approach is based on the computation of transport properties for each species (using tabulated molecular potentials), then combining those to obtain mixture-averaged coefficients for **viscosity**, **conductivity** and **diffusion velocities** (Hirschfelder and Curtiss approximation^{[8]};

2) alternatively, simplified laws (for example the Sutherland law^{[9]} can be used for the **viscosity**, while fixed values for **Prandtl** and **Schmidt numbers** allow the computation of **conductivity** and **diffusion coefficients**;

3) a mix of both approaches, for example computing **viscosity** and **conductivity** with a mixture-averaged approximation and then imposing a **Lewis number** for each species.
This last approach has been retained in the present benchmark.

Finally, the source terms used in the **reacting simulations** are modeled with an **Arrhenius law** with the necessary modifications needed to take into account three-body or pressure-dependent reactions.

From a numerical point of view, it must be noticed that both the **diffusion** and **reaction processes** occur at time scales which can be orders of magnitude smaller than the convective time scale.
Solving these phenomena with explicit methods would thus drastically limit the **global timestep** of the whole simulation and induce an overwhelming CPU cost.
To mitigate these effects when dealing with **multi-species reacting flows**, the classical operator **splitting technique** is used.
The **diffusion process** is solved with a fractional timestep method inside each convective iteration, each substep being limited by a **Fourier condition** to ensure stability.
This method gets activated only when the mesh is very fine (typically when performing DNS with very diffusive species like or , as in the present project); otherwise an explicit treatment is sufficient.
The chemical source terms are then integrated with a dedicated stiff solver, namely the **CVODE** library from SUNDIALS^{[10]}^{[11]}^{[12]}.
To that purpose, each control volume is considered as an isolated reactor with both constant pressure and enthalpy.

Using the stiff integration technique results in a very strong load imbalance between the various regions of the flow: the fresh gases are solved in a very small number of integration steps while the inner flame region may require tens or even hundreds of integration steps.
To overcome this difficulty, a dedicated **MPI** dynamic scheduler based on a work-sharing algorithm ensures global load-balancing.

## DINO

DINO is a **3-D DNS code** used for **incompressible** or **low-Mach number flows**, the latter approach being used in this project.
The development of DINO started in the group of D. Thévenin (Univ. of Magdeburg) in 2013.
DINO is a **Fortran-90 code**, written on top of a 2-D pencil decomposition to enable efficient large-scale parallel simulations on distributed-memory supercomputers by coupling with the open-source library 2-DECOMP&FFT^{[13]}.
The code offers different features and algorithms in order to investigate different physicochemical processes.
Spatial derivative are computed by default using **sixth-order central finite differences**.
Time integration relies on **several Runge-Kutta solvers**.
In what follows, an explicit **4th-order Runge-Kutta approach** has been used.
A **3rd-order semi-implicit Runge-Kutta integration** can be activated as needed, when considering stiff chemistry.
In this case, non-stiff terms are still computed with explicit Runge-Kutta, while the **PyJac package**^{[14]} is used to integrate in an implicit manner all chemistry terms with an analytical Jacobian computation.
All thermodynamic, chemical and transport properties are computed using the open-source library **Cantera 2.4.0**^{[15]}.

The transport properties can be computed based on three different models:

1) Constant Lewis numbers,

2) mixture-averaged,

3) full multicomponent diffusion, by coupling either again with **Cantera** or with the open-source library EGlib^{[16]}.

The **Poisson equation** is solved using Fast Fourier Transform (**FFT**) for **periodic** as well as for **non-periodic boundary conditions**, relying in the latter case on an in-house pre- and post-processing technique.
The I/O operations are implemented using two different approaches: (1) binary **MPI-I/O** using 2-DECOMP&FFT for check-points and restart files; (2) HDF5 files used for analysis and visualization.

**Multi-phase flows** can be simulated in DINO using resolved or non-resolved (point) particles and droplets using a **Lagrangian approach**^{[17]}^{[18]}. Complex boundaries are represented by a novel second-order immersed boundary method implementation (**IBM**) based on a directional extrapolation scheme^{[19]}.
More details about the implemented algorithms can be found in particular in^{[20]}.
Since DINO has been developed as a multi-purpose code for analyzing many different **reacting** and **non-reacting flows**^{[21]}^{[22]}^{[23]}, a detailed verification and validation is obviously essential.

## Nek5000

This **spectral element low-Mach number reacting flow solver** is based on the highly-efficient open-source solver Nek5000^{[24]} extended by a plugin developed at ETH implementing a **high-order splitting scheme** for **low-Mach number reacting flows**^{[25]}.
The spectral element method (**SEM**) is a **high-order weighted residual technique** for spatial discretization that combines the accuracy of spectral methods with the geometric flexibility of the finite element method allowing for accurately representation of complex geometries^{[26]}.
The computational domain is decomposed into conforming elements, which are **quadrilaterals** (**in 2-D**) or **hexahedra** (in **3-D**) that conform to the domain boundaries.
Within each element, functions are expanded as th-order polynomials so that resolution can be increased either by decreasing the element size (-type refinement) or by increasing the polynomial order (-type refinement; typically ).
The grids can be unstructured and allow for **static local refinement**, while **adaptive mesh refinement** has been recently developed^{[27]}.
By casting the **polynomial approximation** in tensor-product form, the differential operators on gridpoints per element can be evaluated with only work and storage.

The principal advantage of the **SEM** is that convergence is exponential in , yielding minimal **numerical dispersion** and **dissipation**, so that significantly fewer grid points per wavelength are required in order to accurately propagate a turbulent structure over the extended time required in high Reynolds number simulations.
Nek5000 uses locally **structured basis coefficients** ( arrays), which allow direct addressing and tensor-product-based derivative evaluation that can be cast as efficient matrix-matrix products involving operators applied to data values for each element.
As a result, data movement per grid point is the same as for **low-order methods**.
It uses scalable domain-decomposition-based iterative solvers with efficient preconditioners.
Communication is based on the Message Passing Interface (**MPI**) standard, and the code has proven scalability to over one million ranks.
Nek5000 provides balanced I/O latency among all processors and reduces the overhead or even completely hides the I/O latency by using dedicated I/O communicators in the optimal case.

**Time advancement** is performed using the **splitting scheme** proposed in^{[25]} to decouple the highly non-linear and stiff thermochemistry (species and energy governing equations) from the hydrodynamic system (continuity and momentum).
Species and energy equations are integrated without further **splitting using the implicit stiff integrator solver CVODE from the SUNDIALS package**^{[10]}^{[11]}^{[12]} that uses backward differentiation formulas (BDF).
The **continuity** and **momentum equations** are integrated using a **second-** or **third-order semi-implicit formulation** (EXT/BDF) treating the non-linear advection term explicitly^{[26]}.
The thermodynamic properties, detailed chemistry, and transport properties are provided by optimized subroutines compatible with **Chemkin**^{[28]}.

The reacting flow solver can handle complex time-varying geometries and has been used for instance to simulate laboratory-scale internal combustion engines^{[29]}^{[30]}.
It can account for conjugate fluid-solid heat transfer and detailed gas phase as well as surface kinetics^{[31]}.

## General comments on the codes

The three **aforementioned solvers** are all **unsteady**, **high-fidelity codes** based on the **low-Mach number** formulation of the **Navier-Stokes equations**.
They can perform both **Direct Numerical Simulations** or **Large Eddy Simulations** of reacting flows, only **DNS** being considered here.
However, they differ in a certain number of points, mainly from the numerical point of view.
The aim of this section is to emphasize those major differences.
First, **YALES2** is an **unstructured code**, designed to handle any type of elements; its main application field pertains to **LES** of industrially relevant flows, though **DNS** is possible as well.
On the other hand, both **DINO** and **Nek5000** are mostly dedicated to **DNS** of configurations found in fundamental research.
Both **DINO** and **Nek5000** can only deal with **quads** or **hexas**, with the major difference that **DINO** is based on a **structured connectivity** while **Nek5000** can use **unstructured meshes** (pavings).
As a consequence, both **DINO** and **Nek5000** employ **higher-order numerical schemes** compared to **YALES2**, limited at best to a **4th-order scheme**.
All codes rely on dedicated libraries to compute the thermo-chemical properties of the flow, either **Chemkin**, **Cantera**, or in-house versions of those.
Moreover, they also rely at least to some extent on external software to perform the temporal integration of the stiff chemical source terms.
Regarding the Poisson equation for pressure which must be solved by all codes, **DINO** relies on a spectral formulation by performing direct and inverse Fourier transforms, which is possible thanks to its structured mesh.
On the other hand, both **YALES2** and **Nek5000** use an iterative solver with an efficient preconditioning technique; this method is more versatile and should be computationally more efficient for large and complex geometrical configurations.
**Nek5000** employs CVODE to integrate the thermochemical equations without further **splitting** of the different terms accounting for convection, diffusion and chemistry.
The main differences between the three codes are summarized in the table below.
Please note that the presented values are those used for the benchmark, even though some other options are available in each codes.

Code | YALES2 | DINO | Nek5000 |
---|---|---|---|

Connectivity | Unstructured | Structured | Unstructured |

Discretization Type | Finite Volumes | Finite Differences | Spectral Elements |

Grid point distribution | Regular hexahedra | Regular hexahedra | Regular hexahedra with GLL points |

Spatial order | 4th | 6th | 7th - 15th (typically) |

Temporal method | expl. RK4 | expl. RK4 / semi-impl. RK3 | semi-impl. BDF3 |

Pressure solver | CG with Deflation MPrec. | FFT-based | CG/GMRES with Jacobi/Schwartz Prec. |

Thermo-chemistry | Cantera (re-coded) | Cantera | Chemkin interface |

Chemistry integration | CVODE | expl. RK4 | CVODE |

Operator splitting | Yes | No | No |

Parallel paradigm | MPI | MPI | MPI |

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