Chemical kinetics mechanisms are essential for understanding, analyzing, and simulating
complex combustion phenomena. In this study, a neural ordinary differential equation …

Experimental data under a wide range of conditions are essential for the optimization of
combustion kinetic models. However, some laboratory measurements under given …

The binary diffusion coefficients of fuel molecules in bath gasses are key parameters for
accurate predictions of flame properties such as extinction strain rates. In combustion …

Response-surface (RS) surrogate approaches permit efficient inverse uncertainty
quantification (UQ) of combustion kinetic models, wherein the uncertainty of reaction rates is …

In this work, we present a framework for the analysis and reduction of plasma mechanisms
by means of weighted directed graphs, in which reactions and species are both treated as …

This study introduces a novel algorithm that combines deep learning with active subspace
method to address the challenge of quantitatively analyzing the effects of various …

The uncertainty in soot kinetics parameters will lead to uncertainty in the prediction of soot
characteristics, including particle size distribution (PSD) and soot volume fraction (SVF) …

Chemical kinetics mechanisms are essential for understanding, analyzing, and simulating
complex combustion phenomena. In this study, a Neural Ordinary Differential Equation …

Combustion kinetic modeling is an integral part of combustion simulation, and extensive
studies have been devoted to developing both high fidelity and computationally affordable …

Simulations of chemically reacting flows are particularly sensitive to kinetic models, which in
turn depend on a large number of parameters. Therefore, quantifying parametric …