# Study of an ultra-lean Hydrogen Spark-Ignition Engine combining Large-Eddy Simulation, Experiments and Statistical Analysis

Julien Steib, Karine Truffin, Delphine Sinoquet, Rajesh Kumar, Vincent Giuffrida, Caio Leite, Stéphane Jay

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Julien Steib, Karine Truffin, Delphine Sinoquet, Rajesh Kumar, Vincent Giuffrida, et al. Study of an ultra-lean Hydrogen Spark-Ignition Engine combining Large-Eddy Simulation, Experiments and Statistical Analysis. 12th European Combustion Meeting, The Combustion Institute, Apr 2025, Édimbourg, United Kingdom. hal-05497666

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Study of an ultra-lean Hydrogen Spark-Ignition Engine
combining Large-Eddy Simulation, Experiments and
Statistical Analysis

STEIB Julien<sup>a,*</sup>, TRUFFIN Karine<sup>a</sup>, SINOQUET Delphine<sup>a</sup>, KUMAR Rajesh<sup>a</sup>, GIUFFRIDA Vincent<sup>a</sup>, RAMALHO LEITE Caio<sup>b</sup>, JAY Stéphane<sup>a</sup>

<sup>a</sup> IFP Energies nouvelles, 1 et 4 av. de Bois-Préau, F-92852 Rueil-Malmaison, France

<sup>b</sup> Université d'Orléans, INSA CVL, Laboratoire PRISME - EA 4229, F-45072 Orléans, France

**Abstract**

Hydrogen-fueled internal combustion engine (ICE) offers a promising carbon-free alternative to conventional en-
gines. To accurately predict engine’s behavior and assess its reliability, a dedicated validation framework involving
experimental data and Large-Eddy Simulation (LES) is presented. Experimental results from high-speed Particle
Image Velocimetry (HS-PIV) are used to evaluate the number of cycles needed to ensure ensemble averages con-
vergence, and to validate LES aerodynamic results. the LES version of the Extended Coherent Flame Model
(LES-ECFM) is calibrated using experimental data. This study paves the way to future uncertainty analysis of the
simulation boundary conditions.

Keywords: Spark ignition engine; LES; Hydrogen; Bootstrap; Particle Image Velocimetry; Combustion model

1. Introduction

Hydrogen offers a promising, climate-neutral energy carrier for eco-friendly transport, particularly for medium to heavy-duty vehicles requiring high efficiency (> 45%) and long-range capabilities. Hydrogen internal combustion engines (H2ICEs) differ from conventional fuels due to hydrogen's properties, such as a wide ignition range (4-76% vol) enabling lean combustion and flame speeds up to ten times faster than conventional fuels. While hydrogen combustion eliminates carbon-based emissions (CO2, CO, HC and soot), high combustion temperatures can still produce nitrogen oxides (NOx), which must be managed, in particular by reducing drastically the Fuel/Air (F/A) equivalence ratio with some consequences explained hereafter. The burning rate of lean hydrogen-air mixtures is highly sensitive to thermodynamic conditions, and differential diffusion effects impact local flame speed and structure under strain rate and curvature. These factors can contribute to the cyclic variability in spark-ignition engines

(especially with lean and ultra-lean mixture),
which remains insufficiently understood for hy-
drogen. Comprehensive studies are critical to
ensure hydrogen engine stability and reliabil-
ity. In the past, Large-Eddy Simulation (LES)
has been successfully used to account for and
investigate the cycle-to-cycle variations in non-
hydrogen-fueled engines [1]. The combustion
system's sensitivity to small perturbations im-
plies that inaccuracies in parameter values, in-
cluding boundary conditions, engine parame-
ters and model parameters, can significantly af-
fect simulation results [2]. Therefore, reduc-
ing the uncertainties of these parameters is es-
sential to ensure accurate and reliable LES re-
sults. The spatial resolution required to capture
the physics of turbulent reactive flows, and the
number of cycles required to capture the non-
stationary phenomena of engine statistics, lead
to expensive simulations. A methodology to
deal with the uncertainties in this context has
been developed. Firstly, a validation and calibra-
tion phase of the simulations must be performed.
Experimental data, provided by Prisme Labora-
tory [3] from an optical single-cylinder port in-
jection hydrogen engine, is used for this task.
This data is exploited to 1) Estimate the num-
ber of cycles necessary to be simulated in LES,
2) Validate LES aerodynamic results on a non-
fired case, 3) Calibrate the combustion model on
a lean hydrogen case.

## 2. Engine presentation

Experiments were done in a single-cylinder
spark-ignition optical engine with a volume dis-
placement of 0.4 L and two intake and two
exhaust valves. The experimental setup and
diagnostics were specifically designed to sup-
ply boundary conditions and validation data for
the numerical study. The sensor positions are
chosen to coincide with the location of the in-
flow/outflow boundaries of the computational
domain. There is a Bowditch piston with a 20
mm radius quartz crown, and a quartz liner for
the sake of PIV measurements. For the same
purpose, two optical accesses have been created
in the cylinder head. The main geometrical pa-
rameters are listed in Table 1. Hydrogen is in-
jected continuously 700 mm away from the in-
take valves, to ensure an homogeneous mixture
in the combustion chamber at spark-timing. A
temperature regulation loop set at about 80°C
circulates inside the crankcase and in the cylin-
der head to stabilize the engine's temperature.
The studied fuel-air equivalence ratio varies be-
tween 0.2 and 0.5, with engine speeds set at 900,
1200 and 1500 rpm. These limited speeds min-
imize the risk of engine overheating, as the oil
cannot be used with optical equipment. In addi-
tion, the transparent pieces are more fragile than
metal, and gas leaks from the cylinder are more
likely to occur.

<table><caption>Table 1: Engine specifications and operating conditions.</caption><thead><tr><td>Description</td><td>Value</td></tr></thead><tbody><tr><td>Bore</td><td>77 mm</td></tr><tr><td>Stroke</td><td>85.8 mm</td></tr><tr><td>Connecting rod</td><td>138.5 mm</td></tr><tr><td>Piston-to-crank offset</td><td>0.8 mm</td></tr><tr><td>Geometrical CR</td><td>10.0</td></tr><tr><td>Intake valve open</td><td>17 cad</td></tr><tr><td>Intake valve close</td><td>213 cad</td></tr><tr><td>Exhaust valve open</td><td>521 cad</td></tr><tr><td>Exhaust valve close</td><td>715 cad</td></tr></tbody></table>

Here, a single operating point is investigated,

with an engine speed of 1200 rpm and an aver-
age intake pressure of 0.7 bar. The equivalence
ratio is 0.40. The Indicated Mean Effective Pres-
sure (IMEP) obtained is 2.5 bar.

## 3. Exploitation of experimental High speed PIVs

### 3.1. HS-PIV Data

PIV experiments were conducted using a
high-speed 532 nm Nd:YAG laser (Quantronix
Dual Hawk-HP) and a Complementary Metal-
Oxide-Semiconductor (CMOS) high-speed
camera (Phantom v1610) with a full resolution
of 1280x800 pixels², equipped with a 100 mm
objective lens and a band-pass filter (Central
Wave Length: 532 nm; Full Width to Half Max-
imum: 10 nm). The pulse energy is measured at
11 mJ and inter-pulsed time between both laser
pulses was optimized to 30 µs. The images
were taken every 5 CAD in the central portion
of the vertical tumble plane. 150 consecutive
cycles are acquired from early intake to late
compression stroke (300 to 45 °bTDC). The
field of view is 67.8 x 61.8 mm² with a pixel
size of 1.5 mm. PIV analyses were done over
an interrogation window of 32 x 32 pixels²,
with a 50 % overlap. The experimental setup is
described with more details in [3].

### 3.2. Estimation of statistical uncertainty

The large number of instantaneous fields
available results in well-converged ensemble-
averaged velocity fields, with no discontinuities.
However, computing this quantity with less cy-
cles produces a less accurate result. It intro-
duces sampling uncertainty, which can affect
the quantitative analysis of cycle-to-cycle vari-
ability (CCV), particularly when using flow de-
composition methods such as triple decomposi-
tion [4]. Unfortunately, in LES, the computa-
tional cost limits the number of simulated cy-
cles. Therefore, finding the optimal balance be-
tween computational expense and simulation ac-
curacy is of particular interest. Given the avail-
ability of 150 instantaneous fields, analyses are
first conducted on experimental data to support
this investigation.
In turbulence statistics, different options ex-
ist to estimate the statistical uncertainty asso-
ciated with sampling [5]. The bootstrap al-
algorithm [6] is a resampling algorithm suitable for the estimation of statistics of any order. Starting from the original dataset (here the N=150 instantaneous velocity fields) $X = (U_1, ..., U_N)$, in bootstrap, B independent bootstrap samples $X_{boot,1}, X_{boot,2}, ..., X_{boot,B}$ are generated. Each sample is created by choosing randomly N fields with replacement from the original dataset. For each bootstrap sample, the statistic of interest $\hat{\theta}_{boot,i}, i = 1, ..., B$ is computed (here, the ensemble average of the velocity, $\overline{U}(n)$, for various number of cycles n). The bootstrap estimate of variance is:

$$var(\hat{\theta})_{boot} = \frac{1}{B-1} \sum_{i=1}^{B} (\hat{\theta}_{boot,i} - \overline{\hat{\theta}}_{boot})^2 \quad (1)$$

with

$$\overline{\hat{\theta}}_{boot} = \frac{1}{B} \sum_{i=1}^{B} \hat{\theta}_{boot,i} \quad (2)$$

Here, the bootstrap technique is applied to the experimental sample of instantaneous velocity fields with different sizes for the original dataset (n=10, n=20), to quantify the influence of the number of cycles on the estimation of sampling uncertainty. The chosen n are small on purpose, representative of the possible budget of LES. These 2 datasets are sub-samples of the 150 cycles dataset. Fig. 1 (n=10) and Fig. 2 (n=20) show estimation results using functional box-plots [7], obtained with bootstrap (on the left) vs estimations computed from the entire dataset of 150 cycles (on the right), for 1D lines extracted from the 2D ensemble-average velocity magnitude, at 180 CAD (end of intake stroke). They cover the most important features of the flow field. The horizontal line is positioned at the location where the two intake valve flows converge. The vertical line is placed within the tumbles structure where velocities reach their maximum values.

The estimations from the entire dataset is computed as follows: B samples are generated by picking randomly n cycles (n=10 or n=20) without replacement from the original 150 cycles dataset. As in bootstrap, the statistics of interest is computed B times and statistics are extracted from these B estimations. It results in an estimation of the sampling uncertainty associated with datasets of size n=10 or n=20, and is used as a

reference to evaluate the quality of Bootstrap results (Fig. 1 and 2, right).

<figure><img src="image_2.png" /><figcaption>Fig. 1: Bootstrap estimation of mean velocity magnitude (n=10) applied to lines extracted from the 2D magnitude of ensemble average velocity field at 180 crank angle degrees (left), compared with reference estimations computed with more cycles (right)</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2: Bootstrap estimation of mean velocity magnitude (n=20) applied to lines extracted from the 2D magnitude of ensemble average velocity field at 180 crank angle degrees (left), compared with reference estimations computed with more cycles (right)</figcaption></figure>

Bootstrap and reference have been computed with B=1000. A minimum of B=200 is ad-
vised in the literature [5]. To represent statistics, functional box-plots are used. It is an adaptation of classical box-plots to functional data, like curves. The gray envelope corresponds to the 50% central region (the interquantile range in classical box-plots). Outliers (colored lines) are detected by inflating by 1.5 the size of the 50% central region The light gray envelope correspond to the maximum non-outlying envelope. The median curve is shown in black. For each graph, in white, the estimated mean associated with the bootstrap or the reference is shown. In orange, the ensemble average velocity computed from the 150 instantaneous velocity fields is shown. On Fig. 1, for both lines, the estimated mean (white) follows the same trend as the reference, despite some oscillations. Concerning outliers, some are extremely far from the means. It is important to note that in LES, only one computation of the ensemble average mean is made with the simulated sample, leading to only one curve, which can be in the 50% central region, close to the median, or in the worst case, an outlier. It can be observed that the estimated bootstrap envelopes for both curves are perturbed in comparison to the reference estimation on the left. These envelopes can be under-predicted or over-predicted. In Fig. 2, for n=20, significative improvements are visible compared to n=10. The average size of the 50% central region is reduced from 3.79 m/s to 2.50 m/s for the horizontal curve (-34%) and from 3.35 m/s to 2.28 m/s for the vertical curve (-32%). Outliers are closer to mean curves. Envelopes are of similar scales when compared to the reference curves. Estimated mean in white is closer to ensemble mean for N=150 in orange. Concerning the amplitude of sampling uncertainty, it is greater in zones where variability is high, like for the horizontal line, in the valve flow interaction zone, and not necessarily where velocities are high, like in the tumble structure (vertical line) where velocities can reach 8 m/s. To conclude, this study shows that a compromise between quality of convergence and number of cycles simulated may be found. The spatial resolution of the PIVs fields being different from the LES resolution, the convergence quality obtained for 10 to 20 LES cycles could be different from the current experimental results.

# 4. Numerical setup

## 4.1. LES setup

Designed for engine applications, the finite volume commercial CONVERGE-CFD solver is used. It allows automatic generation of orthogonal structured grids at runtime using a Cartesian cut-cell method and a triangulated surface from the engine geometry. Parallel [8] and consecutive multi cycles LES are performed. A second order Crank-Nicolson scheme is used for temporal integration and a second order central difference scheme is used for spatial discretization. To limit the computational cost, the mesh size varies in time and space using embedded zones and adaptive mesh refinement in the domain. The mesh size is more refined at intake during intake stroke, always of size 0.5 mm in the cylinder, and less refined in the exhaust pipe.

## 4.2. Combustion modeling

To model combustion, the Extended Coherent Flame model (ECFM-LES) is used [9]. In this version, the progress variable is not directly transported but computed from the transported species mass fractions following:

$$ \tilde{c} = \frac{\sum_i \tilde{Y}_i^b}{\sum_i \tilde{Y}_i^u + \sum_i \tilde{Y}_i^b} \qquad (3) $$

Where $\tilde{c}$ is the filtered progress variable, $\tilde{Y}_i$ is the filtered species mass-fraction, with u for unburned and b for burned. In the species and energy equations, the source terms related to combustion need a closure. To model these terms, represented here by the reaction rate of the progress variable $\tilde{\omega}_c$, the concept of flame surface density (FSD) is introduced:

$$ \tilde{\omega}_c = \bar{\rho}^u S_L^0 \bar{\Sigma} \tilde{c} \qquad (4) $$

Where $\bar{\rho}^u$ is the unburned gases density, $S_L$ is the laminar flame speed, and $\bar{\Sigma} \tilde{c}$ is the filtered flame surface density. A transport equation is used for the filtered flame surface density:

$$ \frac{\partial \bar{\Sigma} \tilde{c}}{\partial t} + T_{res} + T_{sgs} = S_{res} + S_{sgs} + C_{res} + C_{sgs} + P \qquad (5) $$

Where $T_{res}$, $S_{res}$ and $C_{res}$ and P are respectively the resolved transport, strain, curvature
<figure><img src="image_4.png" /><figcaption>Fig. 3: Comparison of trapped mass (A) and cylinder pressure for LES, 0D Amesim and experiments, during intake stroke (B) and compression and expansion stroke (C) on a non reactive case (Pintake=0.7 bar, N=1200rpm)</figcaption></figure>

and propagation terms due to resolved flow mo-
tions and $T_{sgs}$, $S_{sgs}$ and $C_{sgs}$ are respectively
the subgrid-scale transport, strain and curvature
terms. In particular, the subgrid-scale strain
$S_{sgs}$ is modeled as :

$$
S_{sgs} = \alpha_{cfm} \Gamma \left( \frac{\hat{u}'}{S_L^0}, \frac{\hat{\Delta}}{\delta_L^0} \right) \frac{\hat{u}'}{\hat{\Delta}} \quad (6)
$$

Where $S_L$, $\delta_L$, are respectively the unstretched
laminar flame speed and laminar flame thick-
ness tabulated following the same methodology
as in [10]. $\hat{u}'$ is an estimate of the sgs flow ve-
locity fluctuation at the LES combustion scale
$\hat{\Delta}$, which is approximately 8 times the cell size
in the flame front [11]. $\Gamma$ is the efficiency func-
tion computed using the formulation proposed
by [12], which has been shown to accurately
predict the turbulent strain rate across various
Karlovitz numbers (flamelet to TRZ regime).
In the present formulation, thermo-diffusive and
Markstein effects are not considered in the mod-
eling of either the flame surface density or the
laminar flame speed. The calibration coefficient
$\alpha_{CFM}$ allows to adjust the combustion speed.

4.3. Boundary conditions

The computational domain is composed of the
combustion chamber and parts of the intake and
exhaust pipes (Fig. 4). It stops where experi-
mental rapid pressure sensors are located on the
test bench, so that they can be used as bound-
ary conditions. For validations, additional data
like wall heat fluxes and trapped mass is ob-
tained with a three pressure traces analysis (us-
ing averaged rapid pressures at intake, cylinder
and exhaust from acquisition of 150 experimen-
tal consecutive cycles) with 1D modeling. In
1D, to take into account for complex intake phe-
nomena like mass flow rate fluctuations coming

from acoustic waves, the whole intake system is
modeled (plenum + pipe) and 1D fluid dynamics
equations for pipes are applied. The boundary
condition at intake is the slow pressure from the
plenum. The gas leaks from the cylinder are sup-
posed to be negligible for these operating condi-
tions where the charge is low, and are not mod-
eled.

<figure><img src="image_5.png" /><figcaption>Fig. 4: Boundary conditions position on the simulation do-
main (Rapid mass flow rate at inlet and rapid pressure at
outlet)</figcaption></figure>

5. Validations of LES results with experiments on
non-reactive case

5.1. Perturbations methodology

For the motored case, LES simulations were
performed in parallel. The computational cost
is the same but the simulation time is highly
reduced. As shown in [13], the interaction be-
tween the flows from the two intake valves dur-
ing the intake stroke can significantly influence
cyclic variability. Hence, perturbation is intro-
duced before this interaction occurs, at the start
of the intake stroke. An isotropic perturbation is
applied using the Fourier method [14]. A length
scale parameter is required. It corresponds to
the shortest allowed wavelength in the Fourier
modes and is set to 5 mm (10 times the cell size
and 1/8 of the pipe diameter). The velocity per-
turbation is applied after two consecutive cycles
of initialization. This operation is repeated and 12 individual cycles are simulated in parallel.

## 5.2. Validation on thermodynamic results

Resulting thermodynamic quantities are presented in Fig. 3. The LES in-cylinder mass is close to the result from 1D three pressure trace analysis during intake, compression and expansion strokes. Cylinder pressure during intake stroke (B) is affected by pressure oscillations due to acoustic waves. LES and experimental curves exhibit similar frequency and amplitude, validating the 1D-based mass flow rate methodology. At the end of the intake stroke, around -200 CAD before top dead center, the LES and experimental pressures align closely, ensuring accurate pressure evolution during compression stroke, as shown in (C).

## 5.3. Comparison with experimental high speed PIVs

Comparison of the LES results and the experimental PIVs are presented in Fig. 5. Even if the simulated field is not fully converged, an overall good agreement between experiments and simulations is observed. However, velocity are globally higher in LES. During intake stroke, at CAD 80, 120 and 160 CAD, the intake valve flow is visible and more intense in LES. The secondary jet is more important for experiments than LES (CAD 80). The most important difference occurs after the tumble motion impinges the piston, on the left side of the field, where LES velocities are way more important than experiments. It is visible for angles 200 and 240. At the end of the compression stroke, a better agreement between the fields is visible.

# 6. Validations of LES results with experiments on lean hydrogen reactive case

## 6.1. Combustion calibration

Combustion has been calibrated with the alpha parameter $\alpha_{CFM}$ in Eq. 6 from the CFM model. Although the equivalence ratio at intake is 0.4, the equivalence ratio inside the combustion chamber is 0.37 because of the residual gases from the previous cycle. The calibrated value of alpha is set here to 0.75. 12 cycles have

been simulated. The resulting LES mean pressure and sigma envelope is compared to experiments in Fig. 6. The simulated pressures are in good agreement with the experimental mean pressure at the end of the compression stroke (combustion is initiated at 345 crank angle degree). The simulated mean pressure and envelope are below the experimental ones during the first half of the combustion, between 355 and 370 crank angles. Simulated standard deviation is superior to experimental one, meaning that the LES predicts higher variability in the early flame stage. It reaches a maximum of 1 bar at 366 CAD, whereas exp. sigma is 0.9 bar at 371 CAD. It seems that the under prediction of the mean pressure leads to more frequent slow combustions, which introduces more variability. After 370 crank angle degrees, the experimental variability becomes more important than the LES variability. This could be explained by: 1) in the simulations, only the aerodynamic and combustion variabilities are represented. For instance, the variability in the experimental trapped mass, resulting from intake pressure fluctuation, does not appear, because the same instantaneous mass flow rate is imposed at the intake boundary condition. 2) according to the study carried out in section 3, a result obtained on 12 cycles may not be sufficient to ensure statistical convergence in representing the variability of 150 cycles.

## 6.2. Combustion results

The fastest and slowest combustion cycles, along with a cycle exhibiting combustion close to the experimental average, have been selected. The flame shape is illustrated for these cases in Fig. 7. An iso-volume of the progress variable at $c=0.8$ is shown, colored by the magnitude of the velocity in the combustion chamber. At 354 CAD, for the average and fast combustion, the flame is convected towards the exhaust valves, increasing the flame surface and the combustion speed, unlike the slow combustion. At 360 CAD, the average and fast flames both expanded towards the exhaust valves. On the bottom of these flames, velocities going towards the other side of the combustion chamber convey the flame in the opposite direction. Because the aerodynamic is weaker or because the flame is late, this effect is not visible for the slow combustion. The higher velocity magni-
<figure><img src="image_6.png" /><figcaption>Fig. 5: Ensemble average velocity magnitude in the tumble plane for crank angle degrees 80, 120, 160, 200, 240, 280. LES (from 12 instantaneous velocity fields) VS Experimental High-speed PIV (150 instantaneous velocity fields). Combustion top dead center is at 360 CAD. Note that the width of the optical window size is shorter than the cylinder diameter which does not permit to observe the tumble velocity near the walls.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6: Mean cylinder pressure and sigma envelope over combustion stroke, LES (12 cycles) vs EXP (100 cycles), for a reactive case (Pintake=0.7 bar, N=1200rpm, Equivalence ratio = 0.4)</figcaption></figure>

tudes can be observed on the fast combustion.

The influence of the aerodynamic field on the

combustion development is highlighted.

## 7. Conclusion

In this paper an LES set-up and methodology

were designed to address the issue of cyclic vari-

ability analysis of a research engine fed with hy-

drogen. In view of further investigating the ori-

gins of variabilities and to manage the simula-

tion cost, the question of the number of cycles

required to assess the quality of the results was

tackled based on the analysis of the ensemble

average velocity magnitude. A strategy to deal

with sampling uncertainty was presented using

bootstrap methodology. The variability, repre- sentative of sampling uncertainty, is close to the reference for n=20.

Non-reactive engine simulations were per- formed. The comparison between experimental HS-PIV and LES results showed an overall good agreement but some discrepancies are identified at specific locations (secondary jet during intake stroke, tumble front after impingement with the piston).

Reactive lean hydrogen engine simulations were also performed. The combustion model relies on the ECFM-LES approach originally designed for gasoline spark-ignition engine. Combustion calibration were made using an ensemble of 12 cycles. Resulting mean pressure was in good agreement with the experimental measurements, although an overestimation of the pressure vari- ability in the early stages of combustion. The impact of in-cylinder aerodynamic field at com- bustion early stages was also highlighted.

In future work under lean conditions, the bootstrap method will be applied to both ex- periments and LES, on first and second order quantities (mean and RMS velocity fields), to strengthen the analysis. In addition, the impact of parameter uncertainties, such as cylinder wall temperatures, spark advance, and combustion model parameters, on combustion results will be investigated. The sensitivity of LES predictions to variations in these parameters will be further analyzed using uncertainty analysis approaches.

## Declaration of competing interest
<figure><img src="image_8.png" /><figcaption>Fig. 7: Iso-volume of the progress variable at 0.8 colored by the magnitude of velocity, for a slow, average and fast combustion, and for two crank angles (354 and 360). Intake valves are on the left and exhaust valves on the right.</figcaption></figure>

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This work is supported by the ALEK-CIA project of the Agence Nationale de la Recherche (ANR-20-CE05-0007), in partnership with Prisme Laboratory.
This work was granted access to the HPC resources from the GENCI (Allocation N° A0142B10763).

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