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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Alphonius, A., Barbeau, L., Blais, B., Gaboriault, O., Guévremont, O., Lamouche, J., Laurentin, P. J. É., Marquis, O., Münch, P., Oliveira Ferreira, V., Papillon Laroche, H., Patience, P. A., Prieto Saavedra, L., & Vaillant, M. (2025). Lethe 1.0: An open-source parallel high-order computational fluid dynamics software framework for single and multiphase flows. Computer Physics Communications, 109880. External link
Guévremont, O., Barbeau, L., Moreau, V., Galli, F., Virgilio, N., & Blais, B. (2025). Robust pore-resolved CFD through porous monoliths reconstructed by micro-computed tomography: From digitization to flow prediction. Chemical Engineering Journal, 504, 158577 (17 pages). Available
Barbeau, L., Étienne, S., Béguin, C., & Blais, B. (2024). Solid–fluid force modeling: insights from comparing a reduced order model for a pair of particles with resolved CFD-DEM. International Journal of Multiphase Flow, 178, 104882 (23 pages). Available
Barbeau, L. (2024). Vers une approche CFD-DEM résolue d'ordre élevé pour la simulation directe d'écoulements autour d'essaims de particules [Ph.D. thesis, Polytechnique Montréal]. Available
Barbeau, L., Golshan, S., Deng, J., Étienne, S., Béguin, C., & Blais, B. (2024). High-order moving immersed boundary and its application to a resolved CFD-DEM model. Computers & Fluids, 268, 106094 (14 pages). External link
Bibeau, V., Barbeau, L., Boffito, D. C., & Blais, B. (2023). Artificial neural network to predict the power number of agitated tanks fed by CFD simulations. Canadian Journal of Chemical Engineering, 101(10), 5992-6002. Available
Barbeau, L., Étienne, S., Béguin, C., & Blais, B. (2022). Development of a high-order continuous Galerkin sharp-interface immersed boundary method and its application to incompressible flow problems. Computers & Fluids, 239, 105415 (13 pages). Available
Blais, B., Barbeau, L., Bibeau, V., Gauvin, S., Geitani, T. E., Golshan, S., Kamble, R., Mikahori, G., & Chaouki, J. (2020). Lethe: An open-source parallel high-order adaptative CFD solver for incompressible flows. SoftwareX, 12, 9 pages. External link
