  The entry phase constitutes a design driver for aerospace systems that
include such a critical step. This phase is characterized by hypersonic flows
encompassing multiscale phenomena that require advanced modeling capabilities.
However, since high fidelity simulations are often computationally prohibitive,
simplified models are needed in multidisciplinary analyses requiring fast
predictions. This work proposes data-driven surrogate models to predict the
flow, and mixture properties along the stagnation streamline of hypersonic
flows past spherical objects. Surrogate models are designed to predict
velocity, pressure, temperature, density and air composition as a function of
the object's radius, velocity, reentry altitude and surface temperature. These
models are trained with data produced by numerical simulation of the
quasi-one-dimensional Navier-Stokes formulation and a selected Earth
atmospheric model. Physics-constrained parametric functions are constructed for
each flow variable of interest, and artificial neural networks are used to map
the model parameters to the model's inputs. Surrogate models were also
developed to predict surface quantities of interest for the case of nonreacting
or ablative carbon-based surfaces, providing alternatives to semiempirical
correlations. A validation study is presented for all the developed models, and
their predictive capabilities are showcased along selected reentry trajectories
of space debris from low-Earth orbits.
