MG- local-PCA method for the reduction of collisional radiative argon plasma mechanism.

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SPARK: Simplified plasma models based on reduced kinetics

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SPARK: Simplified plasma models based on reduced kinetics

info:eu-repo/semantics/nonPublishe

Simplified plasma models based on reduced kinetics

Performing high-fidelity plasma simulations remains computationally expensive because of their large dimension and complex chemistry. Atmospheric re-entry plasmas for instance, involve hundreds of species in thousands of reactions used in detailed physical models. These models are very complex as they describe the non-equilibrium phenomena due to finite-rate processes in the flow. Chemical non-equilibrium arises because of the many dissociation, ionization and excitation reaction at various time-scales. Vibrational, rotational, electronic and translational temperatures characterize the flow and exchange energy between species, which leads to thermal non-equilibrium.With the current computational resources, detailed three-dimensional simulations are still out of reach. Detailed calculations using the full dynamics are often restricted to a zero- or one-dimensional description. A trade-off has to be made between the level of accuracy of the model and its computational cost. This thesis presents various methods to develop accurate reduced kinetic models for plasma flows. Starting from detailed chemistry, high-fidelity reductions are achieved through the application of either physics-based techniques, such as presented by the binning methods and time-scale based reductions, either empirical techniques given by principal component analysis. As an original contribution to the existing methods, the physics-based techniques are combined with principal component analysis uniting both communities. The different techniques are trained on a 34 species collisional-radiative model for argon plasma by comparing shock relaxation simulations.The best performing method is applied on the large N-N2 mechanism containing 9391 species and 23 million reactions calculated by the NASA Ames Research Center. As a preliminary step, the system dynamics is analyzed to improve our understanding of the various processes occurring in plasma flows. The reactions are analyzed and classified according to their importance. A deep investigation of the kinetics enables finding the main variables and parameters characterizing the plasma, which can thereafter be used to develop or improve existing reductions.As a result, a novel coarse grain model has been developed for argon by binning the electronic excited levels and the ionized species into 2 Boltzmann averaged energy bins. The ground state is solved individually together with the free electrons, reducing the species mass conservation equations from 34 to 4. Principal component analysis has been transferred from the combustion community to plasma flows by investigating the Manifold-Generated and Score-PCA techniques. PCA identifies low dimensional manifolds empirically, projecting the full kinetics to its base of principal components. A novel approach combines the binning techniques with PCA, finding an optimized model for reducing the N3 rovibrational collisional model.Doctorat en Sciences de l'ingénieur et technologieinfo:eu-repo/semantics/nonPublishe

Calculation of collision integrals for ablation species

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Reduction of a collisional- radiative Argon model comparing a modified binning method with principal component analysis

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Electron Gas in a Lattice of Positive Charges

SCOPUS: ch.binfo:eu-repo/semantics/publishe

Calculation of collision integrals for ablation species

info:eu-repo/semantics/publishe

Ignition of methane and ethylene via nanosecond pulsed discharges

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Principal component analysis acceleration of rovibrational coarse-grain models for internal energy excitation and dissociation

The present work introduces a novel approach for obtaining reduced chemistry representations of large kinetic mechanisms in strong non-equilibrium conditions. The need for accurate reduced-order models arises from compression of large ab initio quantum chemistry databases for their use in fluid codes. The method presented in this paper builds on existing physics-based strategies and proposes a new approach based on the combination of a simple coarse grain model with Principal Component Analysis (PCA). The internal energy levels of the chemical species are regrouped in distinct energy groups with a uniform lumping technique. Following the philosophy of machine learning, PCA is applied on the training data provided by the coarse grain model to find an optimally reduced representation of the full kinetic mechanism. Compared to recently published complex lumping strategies, no expert judgment is required before the application of PCA. In this work, we will demonstrate the benefits of the combined approach, stressing its simplicity, reliability, and accuracy. The technique is demonstrated by reducing the complex quantum N2(Σg+1)-N(Su4) database for studying molecular dissociation and excitation in strong non-equilibrium. Starting from detailed kinetics, an accurate reduced model is developed and used to study non-equilibrium properties of the N2(Σg+1)-N(Su4) system in shock relaxation simulations.SCOPUS: ar.jinfo:eu-repo/semantics/publishe