![]() ![]() 34, 133–147 (2003)ĭupuis, A., Chopard, B.: Lattice gas modeling of scour formation under submarine pipelines. Przekop, R., Moskal, A., Grado, L.: Lattice-Boltzmann model approach for description of the structure of deposited particulate matter in fibers filters. Masselot, A., Chopard, B.: A lattice Boltzmann model for particle transport and deposition. 41, 5236–5248 (2007)Īl-Fulaij, H., Cipollina, A., Micale, G., Ettouney, H., Bogle, D.: Eulerian-Lagrangian modeling and computational fluid dynamics simulation of wire mesh demisters in MSF plants. ![]() Zhang, Z., Chen, Q.: Comparison of the Eulerian and Lagrangian methods for predicting particle transport in enclosed spaces. Lobovský, L., Bublík, O., Heidler, V., Vimmr j.: Numerical and experimental prediction of free surface flow of shear-thinning fluids. In: Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018, 2020, 2211–2222 (2018) Vimmr J., Lobovský L., Bublík, Mandys T.: Experimental validation of numerical approach for free surface flows modeling based on lattice Boltzmann method. Kharmiani, S.F., Passandideh-Fard, M.: A two-phase lattice Boltzmann study on injection filling of cavities with arbitrary shapes. Szucki, M., Suchy, J.S., Lelito, J., Malinowski, P., Sobczyk, J.: Application of the lattice Boltzmann method for simulation of the mold filling process in the casting industry. Zhang Y.J., Qian X.W., Zhou X.J., Yin Y.J., Shen X.: Simulation of casting filling process using the lattice Boltzmann method. Ginzburg, I., Steiner, K.: Lattice Boltzmann model for free-surface flow and its application to filling process in casting. Vander Hoef, M.A., Van Sint, A.M., Deen, N.G.: Numerical simulation of dense gas-solid fluid beds. Ladd, A.J., Varberg, R.: Lattice-Boltzmann simulations of particle-fluid suspensions. Koji, M.: Numerical simulation for gas micro-flows using Boltzmann equation. Zhang, T.: Inversely tracking indoor airborne particles to locate their release sources. Zhang, T.: Detection and mitigation of contaminants in commercial aircraft cabins. Zhang, T., Chen, Q.: Identification of contaminant sources in enclosed spaces by using a single sensor. Zhang, T., Chen, Q.: Identification of contaminant sources in enclosed environments by inverse CFD modeling. Liu, X., Zhai, Z.: Prompt tracking of indoor airborne contaminants source location with probability-base inverse multi-zone modeling. Liu, X., Zhai, X.: Location identification for indoor instantaneous point contaminant source by probability-based inverse computational fluid dynamics modeling. We encourage applications from First Nations people, culturally and linguistically diverse people, people with disabilities, neurodiverse people, and people of all genders, sexualities and age groups.Liu, X., Zhai, Z.: Inverse modeling methods for indoor airborne pollutant tracking: literature review and fundamentals. Applicants must have a strong background in the theoretical analyses of Navier-Stokes equations (e.g., linear and nonlinear stability analyses, asymptotic analysis, dynamical systems theory) and expertise in the numerical calculations related to the analyses.ĭiversity is one of our greatest strengths at Monash. The successful candidate will work on an ARC Discovery Project led by Dr Kengo Deguchi, involving mathematical analysis of multiscale coherent structures in various shear flows and engage in developing related computational tools to be enabled by the international interdisciplinary collaboration between the Mathematics and Engineering communities in Australia and Japan.Īpplicants must have a PhD in Mathematics, Physics, Engineering or a related field. The School of Mathematics at Monash University invites applicants for the position of Research Fellow in Theoretical and Computational Fluid Dynamics. ![]()
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