# TOWARD REDUCED ORDER MODELING OF HYDROGEN-AIR COMBUSTION IN THE PRESENCE OF INSTABILITIES WITH HIGH-FREQUENCY CONTENT

F.M. Sinjelo<fnref n="1" />, V. Morgenthaler<fnref n="2" />, L.F. Figueira Da Silva<fnref n="1" />

## BACKGROUND-AIM

This study aims to develop a methodology for learning reduced-order models based on numerical modeling results, particularly focusing on hydrogen-air combustion in the presence of intrinsic instabilities with rich frequency content. Using computational fluid dynamics, we have developed a model for the propagation of one-dimensional detonations in a transient regime to permit a detailed analysis of these instabilities. We examine the influence of the composition of the fuel mixture and the initial pressure and temperatures on instabilities that occur downstream of the detonation shock front. Calculations cover an equivalence ratio from 0.4 to 4.0, pressure ranging from 0.2 to 1.0 bar.

## METHODS

Steady-state Analysis of detonation structure using balance equations to assess the sensitivity of induction and reaction lengths to changes in equivalence ratio and initial pressure, providing crucial parameters for the transient model setup.

Transient simulation involves solving 1D reactive Euler equations using an explicit, density-based solver with Roe flux-difference splitting. The hydrogen-air combustion model uses a 9-species, 24-step reaction mechanism integrated through a stiff solver.

## RESULTS

Preliminary investigations of transient 1D pulsating detonations in a hydrogen-air mixture confirmed an unstable behavior with rich frequency content and highlighted the importance of kinetic mechanism choice. Computations are underway to be compared with results from Yungster & Radhakrishnan 2004.

## CONCLUSIONS

Using the database of 1D transient simulation results that will be constructed, we expect to use Neural Ordinary Differential Equations integrated with physical principles to enhance model accuracy by ensuring adherence to known physical laws, improving generalization across different datasets, and reducing computational demands.

<footnote n="1">¹Institut Pprime, CNRS, ISAE-ENSMA, Université de Poitiers</footnote>

<footnote n="2">²Ansys France, Villeurbanne</footnote>