Vers une modélisation eulérienne unifiée pour les écoulements diphasiques : phénomènes géométriques à petite échelle et stratégies de calcul flexibles associées
Ruben Di Battista · theses.fr (ABES) · 2021
In current times we are witnessing a “second space race”: private companies like SpaceX are paving the way to a new generation of space launcher systems optimized for cost effectiveness and extreme performances that will bring humankind to Mars for the first time in its existence. A key aspect of those systems is to provide a high level of reusability leading to a drastic drop of launch costs. This translates into propulsion systems that need to operate on wider flight envelopes, with more advantageous propellant pairs like cryogenic methane and liquid oxygen, therefore requiring tighter designs for the injection systems. The injectors are responsible for the correct nebulization of fuel and oxidizer and they have a direct impact on the performance of the engines. These kind of problems are shared across different applications and are somehow generic The current state of the art modeling strategies fail at predicting the correct distributions of droplets in the combustion chamber. Therefore, the target of this thesis is to contribute to the design of a unified modeling framework addressing the derivation of system of equations governing two-phase flow systems characterized by a sound mathematical structure via a variational approach named Stationary Action Principle (SAP) coupled to the second principle of thermodynamics. This effort is backed by a tailored computational toolset that allows the rational choice of modeling assumptions and the effective simulations of the developed models, possibly on modern computing architectures. This work identifies three main points of improvement: the development of reduced-order models via a variational procedure named the Stationary Action Principle (SAP) featuring a set of equations that include geometrical properties such as the interfacial surface density and the mean and Gauss curvatures; the implementation of a geometric DNS post-processing tool that is used to collect useful insight from high-fidelity simulations in order to craft an accurate reducedorder model, and the development of a Python library that acts as a prototyping playbook aimed at quickly testing ideas in the context of numerical schemes, boundary conditions, domain configurations, with the potential ability of leveraging modern computational architectures such as GPUs.