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Machine Learning for the Design and Optimization of a Axial Fan

Starting from the 3D inverse design method, we identify the key pillars of a Machine Learning system for turbomachinery design and optimization. We explain how we can 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. 


Recognising that high-fidelity simulation will always be required to evaluate complex flow fields, especially when aiming for objectives across multiple operating points, the blade design space is explored and managed by a reactive optimization and search algorithm that judiciously applies high-fidelity simulation only where and when it is required. 


Simulation data management is shown as a simple, integrated process, where connections between the geometry-optimizer engine and the simulation tools are managed in a couple of mouse clicks.

 

This webinar introduces and discusses the concepts of:

Don't miss this opportunity to see how TURBOdesign Suite is expanding its reach and providing you with the advanced tools needed to design and analyze a wider variety of high-performing turbomachinery applications.


Who is it for?

The event addresses all engineers, developers or researchers dealing with Turbomachinery Design.

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Full rotor optimized design 
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Flow vectors on baseline design
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Pareto front of candidate solutions, for multi-point objectives 
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Significant efficiency gains across all operating points with the optimized design over the baseline

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TURBOdesign Suite Toolkits

Meet the Speakers

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Richard Evans

Applications Engineer

 
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Lorenzo Bossi

Chief Operating Officer