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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.

Using CFD to predict flow-generated noise and other aeroacoustic effects

Flow-generated noise can have significantly adverse effects on our everyday lives. Product designers and engineers at the world’s most innovative and successful companies have recognised this fact, and are increasingly using CFD to incorporate noise mitigation strategies into their product design process.

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