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A working example of a Simulation-enabled Digital Twin

Is your company developing a Digital Twin strategy? LEAP’s engineers have created working examples of a Simulation-based Digital Twin in action - a real-time, virtual replica of your equipment constantly updated through IoT data that provides you with:
- insight into real-time performance using extra Virtual Sensor outputs
- data to assess machine health & identify possible failure conditions
- actionable data to enable predictive maintenance & avoid costly downtime.

Shape Optimisation with the Adjoint solver, Part 2: Useful new features

In our 2nd part of a series on the Adjoint solver, learn how recent developments have focused solely on improving the usefulness of the Adjoint solver for real-world customer problems - allowing for definition of multiple objectives; expanding to include more physics; and allowing more user control over geometry modifications and constraints.

Shape Optimisation without constraints - How to use the Adjoint Solver Part 1

Engineers are continually under pressure to improve the performance of their products and often look to gain an edge using optimisation techniques - trying to reduce drag, increase lift (or downforce), or reduce pressure drop. Rather than relying on intuition to make geometry changes that are often constrained (using a parametric CAD approach), you can now use the new Adjoint solver to compute localised sensitivity data (related to your objectives) and optimize your design semi-automatically.

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