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.

UFRJ, Mechanica, Matematica Applicada


Diffusion models in Porous Media (Diff-Twins)

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

GPU Accelerated Multi-Phase Flow Solver

Development of a new always hyperbolic slug capturing solver for the multiphase compositional flow simulations in flow assurance applications. More

PVT & Machine Learning

Developing a specialized PVT solver to handle high percentage of CO2 and presence of the three phase (gas-CO2 liquid-oil) configuration. Machine learning techniques will be used to calibrate empirical constants on fly. More

Rheology of Complex Materials (applications in advanced technologies)

Recently, L'CADAME, in partnership with other universities, was accredited as the National Institute of Science, Technology and Innovation in Rheology (INCT-RHE9). More

Non-Newtonian Turbulence

Direct numerical simulation (DNS) of the non-Newtonian fluids and developing models.

High Reynolds Turbulence

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).

Multiphase Flow Simulatior for the Wells (SEMPO)

Development of models and solution methods to study two-phase flows with presence of the non-Newtonian liquid phase. More

Crowd Counting

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.

High Speed Multi-Phase Flows

Development of a numerical solver to study shock wave propagation in multi-phase and/or multi-material domains. The result could be used to study shock-structure interaction, detonation wave, combustion and the related saftey issues. More


Daniel Cruz

Professor (Mechanical Engineering)

Fabio Ramos

Professor (Applied Mathematics)

Hamidreza Anbarlooei

Professor (Applied Mathematics)

Roney Thompson

Professor (Mechanical Engineering)

Adriano Cortes

Professor (Applied Mathematics)

Valter Aibe


Cecilia SANTOS


Farhad Nikfarjam


Gustavo Celis


Reza Arefi-Damghani


Gabriel Sanfins

PhD Student

Bernardo Brener

PhD Student

Camila Nunes de Lima Leite

PhD Student

Matheus Macedo

PhD Student

Siavosh samoodi

PhD Student

Aida Maroof

Master Student

Partners and Sponsors


  • 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]