Vivien LORIDAN, Gabriel PRIGENT, Fabien CHOPIN, Simon PELUCHON
DOI Number: N/A
Conference number: HiSST-2025-015
In the context of atmospheric entry, a numerical platform that couples a computational fluid dynamics module and a material thermal response solver has been developed over the last decades and has been enriched over time with different ablation strategies, such as the historical ablation model that relies on B’ tabulation, or more recently the nonequilibrium multi-species ablation approach. The purpose of this work is to focus on a third strategy: the multi-element ablation model. It aims at taking the advantages from both the aforementionned frameworks, as it is shown to be more predictive than the usual B’ tabulations and to be more computationally competitive than the exhaustive multi-species
ablation paradigm. For this new formulation, the Navier-Stokes equations have been rewritten in terms of chemical elements, allowing to deal with a reduced number of chemical entities (i.e. only the atomic components of the chemical species) and thus shrinking the size of the whole Navier-Stokes system to solve. Under the assumption of chemical equilibrium, the proportions of chemical species that govern the flow properties are retrieved at each iterative step by using the equilibrium solver of the open-source
Mutation++ library. Computing efficiency has additionally be gained by replacing the successive calls to Mutation++ with a neural network that has been trained to emulate its behavior. As a validating framework, 2D axisymmetric simulations are carried out on three different configurations. The first two rely on the arc-jet tests conducted respectively in the VKI’s Plasmatron facility and in the Interactive Heating Facility at NASA Ames Research Center, and the third one is related to the atmospheric entry of the IRV-2 vehicle. The multi-element approach using the solver from Mutation++ is shown to be reliable and efficient compared with its multi-species counterpart. With the use of the neural network,
the results remain accurate with a gain in computational time up to a factor of 4.
