Pareto frontier
This example shows a trade-off between drag coefficient and bending stiffness for a family of optimised airfoil sections. Each point represents a design that is non-dominated with respect to the chosen objectives.
Multi-objective design
Optimization
ACE develops optimisation workflows that improve engineering designs when several objectives must be balanced at once. Typical toolchains use Dakota, OpenMDAO and custom automation so that aerodynamic, structural or system-level parameters can be explored efficiently and compared on a consistent basis.
Dakota
OpenMDAO
Pareto frontiers
Why multi-objective optimisation
Real engineering design rarely has a single best answer. Better aerodynamic efficiency may conflict with stiffness, mass, manufacturability or cost. Multi-objective optimisation identifies the set of best compromises so that design choices can be made with a clear view of the trade-offs.
Optimised airfoil family
Each candidate section expresses a different balance between aerodynamic efficiency and structural capability. Together they form a practical design space rather than a single rigid answer.