The numerical modeling of nonequilibrium mechanisms is computationally demanding due to the onerous number of degrees of freedom coupled with wide flow spatiotemporal scales, resulting in highly nonlinear and stiff chemical kinetics. To accelerate computing of reacting flow simulations without losing fidelity, data-driven strategies, such as principal component analysis, and physics-based models, such as computational singular perturbation, are typically implemented. In the present work, a novel reduced-order model (ROM) framework is proposed to combine the two strategies with nonlinear regression fitting into a surrogate model. First, it is shown that the equations governing the evolution in space and time of a small disturbance around an equilibrium state can be formulated as a generalized eigenvalue problem. The solution to this problem can be used to represent the evolution of disturbances relaxing toward equilibrium downstream of an initial perturbation, such as a shock. The eigenvectors of the system distinguish between groups of slow nonequilibrium processes and fast, near-equilibrium processes. The calculation of the system eigenbasis, however, is the most computationally expensive operation of the numerical algorithm. This paper proposes the substitution of this operation with nonlinear regression fitting, namely Gaussian process regression, to map the eigenvalues and eigenvectors onto a truncated basis of principal components. The method is applied to a two-temperature plasma evolving downstream of a shock. The feasibility and fidelity of the novel ROM framework are presented, demonstrating the viable application of computational learning methods for eigenbasis prediction.


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    Titel :

    Surrogate Eigenbasis Reconstruction for Nonequilibrium Thermochemical Kinetics in Hypersonic Flows


    Beteiligte:


    Erscheinungsdatum :

    01.04.2025




    Medientyp :

    Aufsatz (Konferenz) , Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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