Laboratório de Computação de Alto Desempenho e Aprendizado de Máquina em Engenharia (L'CADAME)
The high performance computing (HPC) and machine learning in the engineering laboratory (L'CADAME) is part of the Mechanical Engineering Program in collaboration with Applied Mathematics Department, at Federal University of Rio de Janeiro (UFRJ).
L’CADAME is mainly focused on developing advanced numerical techniques and modeling complicated phenomena (such as inorganic scaling) for flow assurance applications. Our current projects are related to developing a new unconditionally hyperbolic “Slug Capturing” method to solve compositional flows (n-phase). The code developed for this purpose could handle as many phases, mixtures and components which is necessary and the whole process will be accelerated using the GPU platform. To simulate the thermodynamical behaviour of the multi-phase/multi-component flow, a new PVT solver is also under development. This solver can handle 3-phase settings (liquid-CO2 liquid-gas), will be data aware by using machine learning to re-calibrate the coefficients on fly, and is boosted using GPU computation.
Unlock the future of porous media analysis with a groundbreaking research project led by L'Cadame in partnership with ExxonMobil. The central challenge facing our field today is the accurate reconstruction of 3D porous media images from low-resolution 2D or 3D samples that may contain defects. More
Recently, L'CADAME, in partnership with other universities, was accredited as the National Institute of Science, Technology and Innovation in Rheology (INCT-RHE9). More
Direct numerical simulation (DNS) of the non-Newtonian fluids and developing models.
Theoretical study of the Newtonian turbulent flow and development of a new friction equation and mean velocity profile for the High Reynolds numbers (after Blasius range).
Crowd counting in the photos and videos has become an important topic in the field of intelligent surveillance, public security. In this project a machine learning model has been trained to accurately estimate the number of people in images.
Daniel CruzProfessor (Mechanical Engineering)
Fabio RamosProfessor (Applied Mathematics)
Hamidreza AnbarlooeiProfessor (Applied Mathematics)
Roney ThompsonProfessor (Mechanical Engineering)
Adriano CortesProfessor (Applied Mathematics)
Gabriel SanfinsPhD Student
Bernardo BrenerPhD Student
Camila Nunes de Lima LeitePhD Student
Matheus MacedoPhD Student
Siavosh samoodiPhD Student
Aida MaroofMaster Student
- F. Nikfarjam, H. R. Anbarlooei, and D. O. A. Cruz, A Mechanistic Model for the Two-Phase Slug Flow of the Purely Viscous Non-Newtonian Liquids through Pipes, SPE Prod & Oper 1-14 (2022). [Link]
- H. R. Anbarlooei, D. O. A. Cruz, and F. Ramos, Connection between attached eddies, friction factor and mean-velocity profile, Physical Review Fluids 7, 024602 (2022). [Link]
- G. Sanfins, H. R. Anbarlooei, D. O. A. Cruz, and F. Ramos, Complete and incomplete similarity for the mean velocity profile of turbulent pipe and channel flows at extreme Reynolds number, Physics of Fluids 33, 085118 (2021). [Link]
- H.R.Anbarlooei, D.O.A.Cruz, and F.Ramos, New power-law scaling for friction factor of extreme Reynolds number pipe flows, Physics of Fluids 32, 095121 (2020). [Link]
- H.R.Anbarlooei, D.O.A.Cruz, F.Ramos, and C.M.M.Santos, On the connection between Kolmogorov microscales and friction in pipe flows of viscoplastic fluids, Physica D: Nonlinear Phenomena , 376–377 (2018). [Link]
- H.R.Anbarlooei, D.O.A.Cruz, F.Ramos, and C.M.M.Santos, Phenomenological friction equation for turbulent flow of Bingham fluids, Physical Review E, 96 (2017). [Link]