Reduced-order modeling of turbulent reacting flows using data-driven approaches

Turbulent multicomponent reacting flows are described by a large number of coupled partial differential equations. With such large systems of equations, the current computational capabilities are insufficient for detailed simulations. At the same time, accurate simulations are crucial to support the rapidly developing combustion technologies. Dimensionality reduction and machine learning approaches appear well-suited for building reduced-order models (ROMs) of complex systems with many degrees of freedom. Dimensionality reduction techniques project a high-dimensional system onto a lower-dimensional basis. Projections can be computed from the available training data and are referred to as low-dimensional manifolds (LDMs). Dimensionality reduction is often coupled with nonlinear regression to bypass the errors associated with the inverse basis transformation. Regression allows to reconstruct the target thermo-chemical state quantities from the LDM parameters. A data-driven reduced-order modeling workflow provides substantial reduction to the number of transport equations solved in combustion simulations, but the quality of the manifold topology is one of the decisive aspects in successful modeling. Numerous manifold challenges of turbulent combustion have been reported in the literature and ought to be addressed. The present work advances the performance of ROMs of reacting flows. Our main focus is in addressing the outstanding manifold challenges. We provide novel tools and algorithms that can help further reduce the order, and improve the predictive capabilities of the model.The original contribution of this work is the development of tools to quantify the quality of LDMs from the perspective of reduced-order modeling. We propose a metric that reduces the LDM topology to a single number, based on two aspects that affect modeling in particular: (1) steep gradients and (2) non-uniqueness in dependent quantities of interest (QoIs). Such quantitative tool was not availa
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished