The thesis Structural Shape Optimization Using BGM at the Root of Thermal Turbine Blades, presented by Simone Putzu at the University of Rome Tor Vergata, offers a rigorous exploration of innovative methodologies for structural optimization. This research focuses on axial turbine blade roots, a critical component in thermal turbines, and integrates advanced computational tools to enhance their mechanical performance.

Central to this study is the application of the RBF Morph ACT Extension within ANSYS Mechanical, a state-of-the-art tool leveraging Radial Basis Functions (RBF) for precise and adaptable mesh morphing. By controlling displacement-imposed point clouds with unparalleled accuracy, RBF Morph ensures seamless updates to geometries while preserving the original topology. This capability is integrated with Finite Element Method (FEM) analyses, resulting in a streamlined and robust optimization workflow.

A cornerstone of the research is the Biological Growth Method (BGM), a bio-inspired approach that mimics natural growth processes to refine structural shapes. When applied to turbine blade roots, this method effectively reduces stress concentrations and achieves isotensional surfaces, significantly enhancing the fatigue life of the components. Such improvements are critical in ensuring the reliability and durability of turbine systems under demanding operational conditions.

The RBF Morph tool, originally introduced for ANSYS Fluent in 2009 and extended to Mechanical in 2015, plays a pivotal role in advancing both automated and semi-automated workflows. Its versatility accommodates modern manufacturing technologies, such as Additive Manufacturing, while addressing traditional machining constraints. This ensures that optimized geometries are not only computationally feasible but also practical for industrial production.

Putzu’s work demonstrates the transformative potential of combining cutting-edge simulation tools with bio-inspired design methodologies. The findings provide valuable insights into the future of structural optimization in turbine technology, highlighting the practical benefits of integrating innovative computational methods with traditional engineering practices. This research serves as a compelling case for the broader adoption of such methodologies in industrial applications, promising enhanced performance, reliability, and efficiency in turbine components.