Predicting lean blowout limit of swirl stabilized combustor based on hybrid approach

Abstract

Given the significance of LBO phenomena for practical combustors, plenty of work can be found with different methodologies. However, based on the conceptual approaches, one may classify them into the following four categories: semi-empirical methods, numerical simulations, Damkohler method, and hybrid methods. Hybrid methods are comparatively recent and are based on combination of semi-empirical and numerical simulations. Prediction of lean blowout (LBO) is imperative in the safety perspective of aero engines. LBO predictions based on semi-empirical correlations are the simplest and the most economical so far. Owing to insufficient modelling capability and depth of these correlations, the prediction accuracy is limited and needs improvement. Though, introduction of flame volume concept in semi-empirical correlation has brought improvement by taking into account the effects of geometric configuration, complex spatial interaction of mixing, turbulence, heat transfer and combustion processes within in primary combustion zone, its estimation at preliminary design stage is a difficult and subsequently poses challenge to LBO prediction loop. This work extends the hybrid LBO prediction previously based on cold flow simulations to reactive flow simulations. Reynolds-averaged Navier-Stokes based simulations were carried out in Fluent 15.0 to maintain the robustness in prediction loop. Based on criterion defined for identification of flame in solution domain, flame volume for each configuration was estimated and subsequently utilized to predict LBO. It was proved that flame volume near lean limit is a small region in inner recirculation region and is a strong function of combustor geometric configuration. Moreover, by the comparison with experimental data of 11 combustors, the lean blowout fit well with that obtained by experiments, having maximum and average errors of ±20 and ±6% between predictions and measurements. The improvement in prediction can be attributed to inclusion of hot flow physics and criterion defined for flame identification in reactive flow simulations.