  The placement of temperature sensitive and safety-critical components is
crucial in the automotive industry. It is therefore inevitable, even at the
design stage of new vehicles that these components are assessed for potential
safety issues. However, with increasing number of design proposals, risk
assessment quickly becomes expensive. We therefore present a parameterized
surrogate model for the prediction of three-dimensional flow fields in
aerothermal vehicle simulations. The proposed physics-informed neural network
(PINN) design is aimed at learning families of flow solutions according to a
geometric variation. In scope of this work, we could show that our
nondimensional, multivariate scheme can be efficiently trained to predict the
velocity and pressure distribution for different design scenarios and geometric
scales. The proposed algorithm is based on a parametric minibatch training
which enables the utilization of large datasets necessary for the
three-dimensional flow modeling. Further, we introduce a continuous resampling
algorithm that allows to operate on one static dataset. Every feature of our
methodology is tested individually and verified against conventional CFD
simulations. Finally, we apply our proposed method in context of an exemplary
real-world automotive application.
