Learn about how the 3D inverse design method enables the key pillars of a Machine Learning system for turbomachinery design and optimization. You will see how to exploit a powerful but manageable set of performance based parameters to create a wide range of complex 3D blade shapes covering a vast design space.
Discover how turbomachinery optimization is a multi-point process that requires the efficient integration and blending of algebraic tools with low- and high-fidelity simulation. Then see our Machine Learning algorithm set about the task of improving the performance and range of a well known compressor design.
See our demonstration of simulation data management, managed as a simple, integrated process, where connections between the geometry-optimizer engine and the simulation tools are controlled in a couple of mouse clicks.
