Reconstructing shock front of unstable detonations based on multi-layer perceptron

TL;DR

This paper introduces a machine learning-based method using Multi-Layer Perceptron to reconstruct the shock front in unstable detonations, aiding in understanding complex detonation dynamics from reactive front data.

Contribution

It presents a novel MLP-based framework for predicting shock front evolution in detonations, demonstrating good generalization across different activation energies.

Findings

The method accurately predicts shock front evolution in 1D and 2D detonations.

Prediction accuracy depends on the activation energy and cellular detonation instability.

The approach shows strong generalization to different reactive mixture conditions.

Abstract

The dynamics of frontal and transverse shocks in gaseous detonation waves is a complex phenomenon bringing many difficulties to both numerical and experimental research. Advanced laser-optical visualization of detonation structure may provide certain information of its reactive front, but the corresponding lead shock needs to be reconstructed building the complete flow field. Using the Multi-Layer Perceptron (MLP) approach, we propose in this study a shock front reconstruction method which can predict evolution of the lead shock wavefront from the state of the reactive front. The method is verified through the numerical results of one- and two-dimensional unstable detonations based on the reactive Euler equations with a one-step irreversible chemical reaction model. Results show that the accuracy of the proposed method depends on the activation energy of the reactive mixture, which influences prominently the cellular detonation instability and hence, the distortion of the lead shock surface. To select the input variables for training and evaluate their influence on the effectiveness of the proposed method, five groups, one with six variables and the other with four variables, are tested and analyzed in the MLP model. The trained MLP is tested in the cases with different activation energies, demonstrates the inspiring generalization capability. This paper offers a universal framework for predicting detonation frontal evolution and provides a novel way to interpret numerical and experimental results of detonation waves.