COUPLED 2023

An HPC Multi-Physics Framework for Next-Generation Industrial Aircraft Simulations

  • Fadiga, Ettore (Leonardo Labs, Leonardo S.p.A)
  • Rondina, Francesco (Leonardo Labs, Leonardo S.p.A)
  • Oliani, Stefano (Leonardo Labs, Leonardo S.p.A.)
  • Benacchio, Tommaso (Leonardo Labs, Leonardo S.p.A.)
  • Malacrida, Daniele (Leonardo Labs, Leonardo S.p.A.)
  • Capone, Luigi (Leonardo Labs, Leonardo S.p.A.)

Please login to view abstract download link

The aerodynamic performance of industrial aircraft is strongly coupled with the structural deformation under aerodynamic load. Consequently, multi-physics simulations represent an important asset during the design and optimization phases. Most of the Fluid-Structure Interaction (FSI) coupling approaches adopted by researchers and engineers can be divided into monolithic and partitioned techniques. The monolithic coupling, which consists of a unique system of equations for the coupled problem, is usually characterized by higher robustness and easier scalability. However, this approach is inherently customized for specific applications and requires a significant development effort. The partitioned coupling links existing software on a higher level, benefitting from higher flexibility and lower time-to-solution. As the computational world marches towards the exascale, black-box coupling libraries such as preCICE [1] aim to combine the flexibility and user-friendliness of partitioned approaches with complete and efficient usage of the available computational power. This talk focuses on aeroelasticity analyses currently performed at the Leonardo Labs facilities, exploiting the recently installed davinci-1 supercomputer [2]. Open-source CFD and structural dynamics software applications are coupled using preCICE to conduct fully three-dimensional FSI analyses of aeroelastic test cases of industrial interest. The activities are part of a broader Digital Innovation industrial strategy centred on Digital Twins combining high-fidelity, highly scalable numerical simulations with data-driven AI models.