-
behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
-
skills include: Interest or background in composite materials, particularly in modelling and/or testing Basic understanding of finite element methods (FEM); any exposure to impact or burst mechanics is a
-
. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess the structural performance of composite sleeves under operational conditions
-
dioxide (SO2) are commonly measured. Each pollutant is produced and destroyed by different processes, and the levels of the various pollutants are correlated with each other, for example, and increase in
-
the potential to accelerate materials design and optimization. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and
-
corrosion-fatigue conditions by integrating multiscale physics-based models combined with mesoscale experimental tests. This research will study the effects of corrosion-induced changes in composition
-
to scrap-based EAF steelmaking, by using a high percentage of scrap supplemented with ore based metallics (OBMs), is an attractive route to decarbonise the steelmaking process. However, residual elements