Schübeler Technologies

Turbomachinery Blades Optimization

About Schübeler Technologies

Schübeler Technologies, founded in 1997, is a German company that develops and produces fiber-reinforced plastic parts. In its early years, it focused specifically on carbon-fiber-reinforced-polymer (CFRP) axial fans. Through repetitive testing of its designs, the team has deepened its theoretical and practical knowledge of these systems. In 2002, a new factory was open in Paderborn, Germany where a wide range of products including CFRP axial, mixed flow and radial fans, compressors, lightweight components, industrial parts, and more are being manufactured.

In 2005, Schübeler Technologies entered the Unmanned Aerial Vehicles (UAV) market and has brought new challenges, especially in structural design and optimization. Sandro Pinent is a managing partner together with the company founder Daniel Schübeler. Besides organizational tasks, he is also in charge of numerical calculations (CFD and FEA) and project management.

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Sandro Pinent

The main advantage for us to use SimScale is to have a fast and simple way to get FEM calculation results. We don’t really need experience with Code_Aster but still get really quick and reliable results. Also, the subscription price is reasonable.

Sandro Pinent

Managing Partner


Testing Turbomachinery Blades with Numerical Analysis

While getting good analytical results from conventional data analysis, Sandro and his team quickly decided to go one step further with numerical analysis.

For finite element analysis, they first relied on Code_Aster but putting effort into the understanding of this complex program was a bottleneck and shouted for alternatives. Knowing the advantages of the solver itself, Sandro was looking for an easier way to use it. He came across the SimScale platform by reading one of the technical newsletters and decided to test it.

Although paper calculations gave a promising efficiency and the physical test also showed good results, it was still important to deeper understand the behavior of water through the turbine construction; for example, how the pressure is distributed on the turbine blades.

The project then moved to a critical stage, where modifications and improvements to the design had to be implemented in the technical drawings for the construction of the final turbine version.

Soon Sandro realized that SimScale makes Code_Aster accessible to every other engineer in the team. The original code is very hard to handle, let alone reading all the documentations in French.

With the help of the SimScale user interface, Sandro and his colleagues at Schübeler Technologies could easily set up a simulation and work efficiently.


From Code_Aster to SimScale for Stress Analysis

The first project performed on the SimScale platform was an analysis of the stress acting on a turbomachinery blade for model airplanes, which can also be used in motorsports or UAV engines.

The goal was to prove that the blades and mounting could withstand the operating conditions and that the design decisions taken in order to reduce the weight of the elements would not hinder the safety of the equipment.

Using the step-by-step approach, at the beginning, only the blade was simulated. Static, linear analysis of the stress response of the blade to high rotation speed was analyzed. Having obtained satisfactory results, Sandro was able to begin the investigation of the blade assembly.

This stage required using non-linear static analysis with physical contacts. The simulations measured the stress response and the system’s deformation in working conditions of 50 000 rounds per minute revolution speed.

In order to ensure a quick analysis workflow, the first simulation was performed on a coarse mesh. After proving that the setup is correct, a full-scale analysis on the refined mesh was done.


Optimized Blade Assembly with Reduced Weight

The nonlinear simulation of the blade assembly on the coarse mesh of 20 000 nodes took 24 minutes to run on 4 core machine.

A refined full study was done using 95 000 nodes and took 21 minutes on 32 cores.

This clearly shows the advantage of having easy access to supercomputer resources. Results obtained thanks to the simulations have proven that the weight-optimized design is safe and can operate properly under working conditions. Using the CAE approach, Sandro could save time and money that would be required to make iterative prototype testing of the fan.