Building upon the powerful multi-point, multi-objective optimization framework introduced in TURBOdesign Suite, the 2025.1 release takes a significant leap forward by dramatically enhancing its CAE integration capabilities. While the 2024.2 release laid the groundwork for tackling complex design challenges using our unique machine learning system, leveraging the efficient 3D inverse design parameterization, the intelligent Reactive Response Surface (RRS), and the automated coupling with industry-leading CAE solvers like Ansys CFX and Simcenter STAR-CCM+, the 2025.1 release extends this powerful integration to a wider spectrum of radial turbomachinery.
In this webinar, we will look into how the enhanced CAE integration in TURBOdesign Suite 2025.1 now seamlessly connects with your preferred CAE tools for the design and analysis of centrifugal fans, Sirocco fans, radial inflow turbines, and Francis turbines, in addition to the previously supported axial and mixed flow machines. This expansion allows you to capitalize on the speed and efficiency of our machine learning platform to tackle the unique multi-point and multi-objective design challenges inherent in these diverse applications.
We will showcase how the core strengths of TURBOdesign Suite, the ability to parameterize complex 3D blade shapes with minimal parameters while maintaining crucial performance targets, and the rapid generation of highly accurate surrogate models using the Reactive Response Surface, are now more readily applicable to these radial machine types through improved CAE workflows.
Imagine effortlessly exploring design spaces, optimizing for multiple operating conditions, and achieving superior performance for your centrifugal fans requiring specific pressure rise and flow rate characteristics, or for your radial inflow turbines demanding high efficiency across a variable load range. Similarly, the intricate design considerations for Sirocco fans and the complex flow physics within Francis turbines can now be tackled with greater automation and insight through this enhanced integration.
