CETIM is a research and study center that supports many industries (aerospace, automotive, agriculture, construction, energy, oil & gas, process) in their current and future technological challenges.
- Collective studies (link between academia and industry)
- Study common problems for a large set of industry players to share/capitalize on knowledge/know-how
- Commercial studies (service offering)
- Specific engineering services for companies who do not necessarily have the tools or expertise in-house
CETIM aims at improving the competitiveness of the companies that benefit from its services/studies thanks to mechanical engineering, innovation transfer and advanced manufacturing solutions.
The range of skills and expertise offered by CETIM is very wide (see image below + “metallic and composite materials, surface treatments, manufacturing processes, assembly, sealing, fluid and flows, NDT, …”).
A simulation poll bringing together about thirty simulation engineers was created in 2017 under the direction of Thierry Raphenne to meet the needs of analysis in the product development phase, which are more and more prevalent in the industry.
Part 1 – Simulation team: Experts to support industry engineering challenges
Thierry Raphenne and his team ran a simulation poll on a community of experts. Thierry supervises that team of 30 people who work full time on simulation analysis projects (mechanics, finite element, CFD, DEM…). He has worked in this field since his engineering study. At CEA, his thesis established a material behavior model with finite element method.
Expectations from the industry on requesting expert support from CETIM:
- Validate product sizing
- Understand the source of a failure. (Failure analysis, simulation helps to understand and explain the phenomena)
“What simulation brings is a better understanding of what’s going on and an acceleration of the implementation / release of products. For example, the design of a car today is done in less than 18 months versus 5 years ago. The use of simulation accelerates the process of validation and design of industrial products. Without simulation > more recurrent breakage because many fatigue situations are not considered, which are at the origin of part breakage.” – Thierry Raphenne
Simulation allows us to analyze more use-case and consequently, therefore can reduce development costs.
Without simulation, the work was done more on simplified hypotheses. This led to the oversizing of certain parts of the machine to ensure that they fitted and did not break.
Part 2: A strong link between academia and industry to meet common needs – with a particular focus on co-simulation
Collaboration among academia and industry worlds
CETIM offers the opportunity to bridge the gap between the industry players and academics, based on real-world cases coming from the industry. Those work groups allow the development of strong skill sets for the company benefiting from CETIM simulation expertise. Those transversal projects assemble up to 80 industry representatives who are then split into subgroups to work on given topics. One of the projects co-led among CETIM, the industry and the academic world is to focus on simulation and coupling.
Focus on co-simulation
For industrial players involved in that collaborative working group, it’s key to focus simulation and coupling among various types of simulation (from 1D to 3D, to couple Multiphysics with multibody type of simulation..). The aim is to verify and validate the design of a complete (and complex) system using simulation.
After this working group, the idea is to establish what are the know-how that can be easily transferred to the industrial players, based on a real demonstrator built by the team.
Part 3: The strength of Simcenter simulation solutions
The CETIM simulation team opted for Simcenter
The team chose simulation software from the Simcenter portfolio because these solutions “provide a wide range of physics, mechanical, fluidic, thermal, which allows multi-physics simulations to be performed with a single environment. Simcenter solutions are open to input/output from/to other simulation solutions.” – Thierry Raphenne
The development of a methodology to spread the use of software
“we have developed templates on the way to use Simcenter software to save time for post-processing of results, for combining constraint values. Available to all users of the software.”
“There is still a lot of work to do to internally to create a strong/solid simulation community. We still need to convince non-users of the simulation of the usefulness and create a dynamic team.”
“People are more eager to trust simulation analysis when it cannot be visualized by tests (in the case of CFD for example). Highlighting of very complex, very coupled phenomena, which cannot be done in tests / allows us to popularize more the utility of simulation tools” – Thierry Raphenne
Part 4: Application cases – simulation to answer very complex engineering challenges application cases – almost impossible to test.
The industry needs is to get things done right the first time, to reduce time to market delivery with a reliable and durable product.
Application case 1 :
Increase the lifespan of machine parts. In the context of energy transition, being able to predict lifespan of machine parts and increase the durability of equipment.
Application case – Heavy construction machine
Application case 2:
“Thanks to Simcenter STAR-CCM+ technical feature, especially on DEM application we could win new projects to support the agricultural industry”
Part 5 : Machine learning and artificial intelligence – future of simulation
- The future of simulation involves the use of AI with the implementation of machine learning methods. This will be increasingly used by industry and will boost the use of simulation. Simulation will help refine and develop models in complex contexts. Benefits from studies and related results achieved on previous projects will be used to nurture new projects (part of the transverse project simulation and coupling.)
- Coupling data from testing and simulations campaign will naturally follow thanks to an increasingly important use of machine learning > integration of data coming from the test bench.