• Parametric Stage Definition: How to rapidly define and optimize the shape of an industrial radial compressor stage, and how to give the system enough design freedom with just a handful of controlling input parameters.
• Unified Aerodynamic Optimization: How a Physics-Enhanced Machine Learning system goes about creating the most efficient 3D blade shapes in the given meanline outline, and matches diffuser and volute size to the flow coming from the rotor.
• Rapid Local Deployment: How to build and operate these Intelligent, physics-bound systems to run on your own local hardware in a matter of hours.
Complete the short form to register for the webinar.
ADT’s Physics-Enhanced Machine Learning system can be tasked with creating full stage turbomachinery, including rotors, diffusers and volutes.
By designing these components as a fully matched system we can exploit and extract the maximum flow performance and the highest efficiency.
We consider surge and choke margin so that map width becomes an optimisation objective, along with other multi-point performance considerations.
This webinar will demonstrate the workflow to set up and run this study - showing how easy it is to create a Physics-Enhanced Machine Learning system for an application that has proven stubbornly resistant to performance gains when using direct design techniques and separate component design considerations.
The event addresses all engineers, developers or researchers dealing with Turbomachinery Design.
TURBOdesign Suite Toolkits
3D Inverse design is used as the geometry engine to produce physics-enhanced design candidates
A range of possible diffuser widths and heights is allowed in order to maximise the stage efficiency


80-86 Gray's Inn Road, London, WC1X 8NH
+44 (0) 20 7299 1178