A machine-learning framework for plasma-assisted combustion using principal component analysis and Gaussian process regression
International Conference on Uncertainty Quantification & Optimisation, 2020•Springer
The intricate nature of detailed kinetic mechanisms for plasma-assisted combustion
motivates the development of high-fidelity surrogates to simplify their use in extensive
numerical simulations. This chapter presents a machine-learning approach based on the
coupling of Principal Component Analysis (PCA) with Gaussian Process Regression (GPR)
to produce a reliable reduced-order representation of the detailed plasma-combustion
physics. The entire state-space is expressed in function of a selected number of principal …
motivates the development of high-fidelity surrogates to simplify their use in extensive
numerical simulations. This chapter presents a machine-learning approach based on the
coupling of Principal Component Analysis (PCA) with Gaussian Process Regression (GPR)
to produce a reliable reduced-order representation of the detailed plasma-combustion
physics. The entire state-space is expressed in function of a selected number of principal …
Abstract
The intricate nature of detailed kinetic mechanisms for plasma-assisted combustion motivates the development of high-fidelity surrogates to simplify their use in extensive numerical simulations. This chapter presents a machine-learning approach based on the coupling of Principal Component Analysis (PCA) with Gaussian Process Regression (GPR) to produce a reliable reduced-order representation of the detailed plasma-combustion physics. The entire state-space is expressed in function of a selected number of principal components using a non-linear Gaussian regression model. This machine-learning framework allows for a superior dimensionality compression compared to conventional data-driven reduction strategies based solely on principal component analysis. The performance of the present technique is assessed for the simulation of ethylene-air ignition by nanosecond repetitive pulsed discharges at conditions relevant to supersonic combustion and flame holding in scramjet cavities: temperatures from 600 K to 1000 K, and a pressure of 0.5 atm.
Springer
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