An efficient method based on the evolutionary center algorithm for optimizing chemical-diffusive models for flame acceleration and DDT

TL;DR

This paper introduces a hybrid optimization method combining Evolutionary Center Algorithm and Nelder-Mead to efficiently calibrate chemical-diffusive models for simulating flame acceleration and DDT, achieving high accuracy and reduced computational cost.

Contribution

The paper develops a novel hybrid ECA-NM method that significantly improves the efficiency and accuracy of optimizing chemical-diffusive models for combustion simulations.

Findings

ECA-NM optimizes model parameters with four orders of magnitude lower error.

The method reduces computational cost by two orders of magnitude.

Simulations match experimental data for flame and detonation properties.

Abstract

This paper presents an efficient method based on Evolutionary Center Algorithm (ECA) for accurately and efficiently determining the optimal reaction and diffusion parameters for Chemical-Diffusive Models (CDM) to simulate flame acceleration (FA) and deflagration-to-detonation transition (DDT). The proposed method leverages the global search capability of the ECA and the local optimization strength of the Nelder-Mead (NM) algorithm. The hybrid approach (ECA-NM) can efficiently optimize CDM parameters that are capable of accurately reproducing the major properties of combustion waves. The CDMs for premixed flames and detonations of hydrogen in air or oxygen were developed using the present ECA-NM method and validated against canonical tests of combustion waves and previous experiments of FA and DDT. The results show that the major flame and detonation properties calculated using the developed CDMs match those obtained from detailed chemical reaction mechanisms over a wide range of equivalence ratio. The simulated FA and DDT in a channel also agree qualitatively and quantitatively with experiments in terms of complex flame instabilities (e.g., tulip and distorted tulip flames), flame displacement speed, and detonation occurrence. In addition, detailed comparisons to the traditional genetic algorithm demonstrate that the developed ECA-NM method diminishes the global error by four orders of magnitude while reducing the computational cost by two orders of magnitude. This work provides a significantly efficient method for developing chemical-diffusive models that allows quantitative multi-scale simulations of transient flames and detonations in complex scenarios.