Multifidelity domain-aware learning for the design of re-entry vehicles

  title={Multifidelity domain-aware learning for the design of re-entry vehicles},
  author={Francesco Di Fiore and Paolo Maggiore and L. Mainini},
  journal={Structural and Multidisciplinary Optimization},
  pages={3017 - 3035}
The multidisciplinary design optimization (MDO) of re-entry vehicles presents many challenges associated with the plurality of the domains that characterize the design problem and the multi-physics interactions. Aerodynamic and thermodynamic phenomena are strongly coupled and relate to the heat loads that affect the vehicle along the re-entry trajectory, which drive the design of the thermal protection system (TPS). The preliminary design and optimization of re-entry vehicles would benefit from… 
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