Requirements Towards Predictive Simulations of Turbulent Reacting Flows

TL;DR

This paper reviews the current state of turbulent reacting flow modeling, identifies existing challenges such as transient processes and multiphase flows, and discusses recent approaches including data assimilation for improved predictive capabilities.

Contribution

It provides a comprehensive review of combustion modeling approaches, highlights key challenges, and introduces recent concepts like data assimilation for enhanced model evaluation and prediction.

Findings

Current models predict temperature and major species well

Challenges include transient processes and multiphase flows

Recent methods incorporate time-resolved measurements for model improvement

Abstract

Significant progress has been made on the model development for simulating turbulent reacting flows. As a consequence, we are currently in a position where key-physical aspects of fairly complex combustion processes are well understood at a qualitative and -- in many cases -- also at a quantitative level. Examples are the prediction of temperature and major species, statistically stationary flames, gas-phase combustion, turbulent transport, and turbulence/flame coupling. However, current challenges lie in capturing transient processes and stability boundaries, minor species and emissions, multiphase flows and phase-transition, as well as multidimensional flame/flow interactions that may involve flame curvature effects, stratification, partially premixing, or flame-wall interaction. With this, the question arises what steps need to be taken to elevate the current state of modeling capabilities in order to address these deficiencies? This paper seeks to address this question. We begin by reviewing the current state of combustion model approaches, our quest for improving existing models, the separation of errors arising from numerical discretization and physical models, and ideas on model evaluations. We then proceed by examining concepts on quantitative model evaluations, requirements on predictability, quantities of interest, and cost/accuracy trade-offs. We close by introducing recent concepts that assimilate time-resolved measurements into numerical simulations for state estimation, model evaluation, and parameter determination.