Model Reduction in Chemical Reaction Networks: A Data-Driven Sparse-Learning Approach

The reduction of large kinetic mechanisms is a crucial step for fluid dynamics simulations of com- bustion systems. In this paper, we introduce a novel approach for mechanism reduction that presents unique features. We propose an unbiased reaction-based method that exploits an optimization-based sparse-learning approach to identify the set of most influential reactions in a chemical reaction network. The problem is first formulated as a mixed-integer linear program, and then a relaxation method is leveraged to reduce its computational complexity. Not only this method calculates the minimal set of reactions subject to the user-specified error tolerance bounds, but it also incorporates a bound on the propagation of error over a time horizon caused by reducing the mechanism. The method is unbiased toward the optimization of any characteristic of the system, such as ignition delay, since it is assembled based on the identification of a reduced mechanism that fits the species concentrations and reaction rate generated by the full mechanisms. Qualitative and quantitative validations of the sparse encoding approach demonstrate that the reduced model captures important network structural properties.

A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks

In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of reactions is then employed to construct a reduced chemical reaction mechanism, which is relevant to chemical interaction network modeling. The problem of identifying influential reactions is first formulated as a mixed-integer quadratic program, and then a relaxation method is leveraged to reduce the computational complexity of our approach. Qualitative and quantitative validation of the sparse encoding approach demonstrates that the model captures important network structural properties with moderate computational load.