Research Cluster
Solid and Fluid Simulation for Industrial Processes
Contact point
Michele Chiumenti
Academic Leaders
Luís M. Cervera, Michele Chiumenti, Ramon Codina, Narges Dialami, Joan Baiges
External Advisors
Pär Jonsen, Karen Willcox
Research groups
- Industrial Manufacturing Processes
Michele Chiumenti - Fluid Mechanics
Ramón Codina Rovira
Overview
Staff
Projects
Publications
This Cluster develops advanced computational models for fluid mechanics and thermo-mechanical applications, specializing in additive manufacturing, friction stir welding, and complex multiphysics industrial processes.
The Solid and Fluid Simulation for Industrial Processes Research Cluster at CIMNE specializes in developing advanced computational models for complex industrial manufacturing processes. The cluster’s expertise spans two major domains: fluid mechanics simulation and thermo-mechanical simulation, employing cutting-edge numerical methods to solve challenging engineering problems.
The Fluid Mechanics Group focuses on the numerical modeling of turbulent, compressible, and multiphysics flows, including fluid-structure interaction (FSI), magnetohydrodynamics (MHD), and biofluids. Their work integrates advanced finite element techniques, embedded mesh refinement, adaptive meshing, and reduced-order modeling (ROM), enabling high-fidelity simulations for aerospace design, hydraulic systems, and biomedical applications. Emphasis is placed on computational efficiency, simulation accuracy, and the transfer of fundamental research to real-world engineering problems through high-performance computing.
The Industrial Manufacturing Processes Group develops advanced simulation tools to model complex manufacturing operations such as casting, welding, additive manufacturing, and sheet forming. Through the implementation of high-fidelity finite element models in platforms like COMET and FEMUSS, the group enables process optimization, defect prediction, and improved material performance. Their software solutions—such as VULCAN, WELDPACK, ADD2MAN, HP4FSW, and STAMPACK—translate academic innovation into industry-ready tools used across sectors including automotive, aerospace, and heavy machinery.
Research priorities include the development of digital twins, ROM-based shape optimization using AI, and real-time control strategies for advanced manufacturing. The cluster pursues innovation through the integration of HPC, machine learning, and multiscale modeling to address process reliability, performance prediction, and sustainability in industrial applications.
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