• Aucun résultat trouvé

Particle tracking methodology for Lagrangian numerical simulations

N/A
N/A
Protected

Academic year: 2021

Partager "Particle tracking methodology for Lagrangian numerical simulations"

Copied!
2
0
0

Texte intégral

(1)

HAL Id: hal-01931714

https://hal.inria.fr/hal-01931714

Submitted on 24 Nov 2018

HAL is a multi-disciplinary open access

archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Particle tracking methodology for Lagrangian numerical

simulations

Marcos Di Iorio, Mireille Bossy, Cyril Mokrani, Antoine Rousseau

To cite this version:

Marcos Di Iorio, Mireille Bossy, Cyril Mokrani, Antoine Rousseau. Particle tracking methodology for Lagrangian numerical simulations. Wave and Tidal - 3rd International Workshop, Nov 2018, Valdivia, Chile. �hal-01931714�

(2)

Particle tracking methodology for

Lagrangian numerical simulations

Advanced modeling in Marine Energy Research Group

M. Di Iorio, M. Bossy, C. Mokrani & A. Rousseau

Numerical tool diagram

Output Features

Conclusions

1. 3D image and animation automated generation;

2. Exportable .vtk files for other visualization software

(Paraview, Blender, etc.);

3. Eulerian velocity distribution with streamlines;

4. Particle field colored with instant velocity;

5. Select particle tracking starting points, seed-cells and

particle trajectory detection region, “net-region”;

6. Particle trajectory visualization and post-treatment

(fluctuation reduction, resolution, etc.)

Reproduce the fluid particle trajectories in order

to describe the physical processes involved in the

flow.

Provide a trajectory visualization tool useful for

SDM-OceaPos developers and users.

Create a plug-in version that can be implemented

independently from the existing code.

Automate the methodology customizable to

different study cases.

It was possible to extract and reproduce particle trajectories from

the SDM-Ocepos simulation results.

The tool allows the interpretation of the particle’s behavior and

helps to understand how it is affected by the Lagrangian

algorithm.

Intuitive parameters, such as trajectory length and particle

velocity at the region of interest, can be implemented to

customize the image generation.

contact:

marcos.diiorio@meric.cl

Research group:

L1P6

meric.cl/proyecto-6/

3D visualization of a Porous-disk simulation

(Meyers-Roc benchmark case)

Exported particle position

data into Paraview

(Meyers-Roc benchmark case)

Particle tracking starting points

located on the disk’s center line.

6

1

1

2

2

3

3

4

4

5

5

6

Particle trajectory treatment

(Meyers-Roc benchmark case)

Recirculation region trajectory

detection (Ameida benchmark case)

Particle trajectory

“smoothing”

Objectives

Références

Documents relatifs

(ii) similarly, the second graphic shows the exact trajectory of the score function, displayed in black solid line, as provided by the Kalman lter, and the approximate

• A failure probability estimator based on uncertain trajectory prediction using mixtures of box kernels, and its integration in a differentiable Chance Constrained optimisation

En 1814, l’avenir de l’Évêché suscite de multiples projets souvent contradictoires : retour à la France, restauration de la principauté épiscopale, création d’un nouveau

Experiments with different sequences show that a particle filter based tracker with adaptive feature selection outperforms other established color based tracker in difficult

In order to understand the channel growth parameters, with the goal of producing low resistivity channels, the conductive channels produced with a different

We have compared the velocity spectra of light particles detected in coincidence with target like and projectile like fragments with the predictions of this

III. 2 shows the distribution of charge signals measured on the solid test electrode in one of the diamond planes using electrons from a ƒ†„ Sr …C‡ source. The signal in the

The particle filter framework in our approach handles this level of data association in an implicit way because the co- variance matrices are extracted from the regions specified by