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 accurate high-fidelity aerothermodynamic analysis, which are usually expensive computational fluid dynamic simulations.
We propose an original formulation for multifidelity active learning that considers both the information extracted from
data and domain-specific knowledge. Our scheme is developed for the design of re-entry vehicles and is demonstrated for
the case of an Orion-like capsule entering the Earth atmosphere. The design process aims to minimize the mass of propellant
burned during the entry maneuver, the mass of the TPS, and the temperature experienced by the TPS along the re-entry.
The results demonstrate that our multifidelity strategy allows to achieve a sensitive improvement of the design solution with
respect to the baseline. In particular, the outcomes of our method are superior to the design obtained through a single-fidelity
framework, as a result of the principled selection of a limited number of high-fidelity evaluations
