🧭 How can you predict wear before the prototype even exists?
In motorsport and automotive engineering, simulation has traditionally focused on performance: power unit output, lap time, fuel economy, thermal management…
But performance means little if components don’t survive real operating conditions. This is where multiscale tribology and wear modelling come in.
By combining surface roughness measurements, contact mechanics (e.g. Greenwood–Tripp models), and system-level digital twins, it’s possible to predict wear behaviour before any physical testing.
This line of research is a core part of my work, integrating contact mechanics models, CFD/FEM submodels, and vehicle-scale simulations to anticipate efficiency-durability trade-offs.
🔍 What we developed:
• Advanced wear prediction algorithms applicable to multiple components and operating conditions.
• A multiscale roughness evolution methodology to capture surface transformations over time.
• Dynamic wear evolution models capable of handling complex, transient regimes.
🧪 The key insight:
Microscopic surface phenomena drive macroscopic system behaviour, and digital twins can bridge that gap.
📌 Why this matters:
• In racing, we can evaluate component durability for aggressive duty cycles long before track testing.
• In road vehicles, it helps optimize lubricants and components to meet durability and CO₂ targets simultaneously.
• In aerospace, energy, or heavy industry, the same methodology enables predictive maintenance and design optimization in high-load, high-reliability systems.
👉 Question for the community:
Where do you see the greatest potential for predictive wear modelling: durability, efficiency, or cost reduction?
🤝 This work has been developed in collaboration with CINTECX – Universidade de Vigo, repsol, and Escola Politécnica da USP. Specially with Francisco Profito, who has been a key contributor to this research.
#DigitalTwin #Tribology #WearModelling #MultiscaleSimulation #Motorsport #VehicleEngineering #Aerospace #Energy #PredictiveMaintenance #CFD #FEM #SimulationDrivenDesign
