Reactive Structures of Ammonia MILD Combustion in Diffusion Ignition Processes

Reactive structures have been analyzed, when ammonia is used as a fuel, in a steady 1D counterflow diffusion flame layer, mimicking diffusion ignition processes. The characterization has been carried out in a wide range of feeding parameters under Moderate or Intense Low-oxygen Dilution (MILD) combustion conditions. Both the Hot-Fuel-Diluted-Fuel (HFDF) and Hot-Oxidant-Diluted-Fuel (HODF) configurations were studied to analyze the main effects of the inlet feeding conditions on the oxidative structures. The reaction zone has been analyzed in terms of temperature and heat release profiles in the mixture fraction space for various ranges of inlet parameters, using a standard code and a validated chemical kinetic scheme. Several features of the reaction zone have been recognized as reported also in previous works for hydrocarbon flames. They were used as discriminative for the achievement of various combustion regimes. In particular, the flame thickening process and the absence of correlation between the maximum heat release and the stoichiometric mixture fraction were analyzed to build maps of behaviors. The latter were reported on an inlet preheating level-temperature increase plane for fixed values of the bulk strain rate and system pressures. Another relevant feature previously reported with hydrocarbons in the literature, in Hot Diluted Diffusion Ignition (HDDI) processes under MILD conditions, was the pyrolysis depression. The latter characteristic has been not observed when ammonia is used as a fuel, for the operative conditions here investigated. Indeed, the heat release profiles do not show the presence of negative heat release regions. The results obtained for the HFDF configuration are strongly dependent on the system pressure level. Finally, the HODF condition has been also analyzed for ammonia at the atmospheric pressure. Boundaries of the combustion regimes and reactive structure features showed several differences between HFDF and HODF cases with respect to the inlet parameters.

Classification of Microchannel Flame Regimes Based on Convolutional Neural Networks

Micro-combustion has shown significant potential to study and characterize the combustion behavior of hydrocarbon fuels. Among several experimental approaches based on this method, the most prominent one employs an externally heated micro-channel. Three distinct combustion regimes are reported for this device namely, weak flames, flames with repetitive extinction and ignition (FREI), and normal flames, which are formed at low, moderate, and high flow rate ranges, respectively. Within each flame regime, noticeable differences exist in both shape and luminosity where transition points can be used to obtain insights into fuel characteristics. In this study, flame images are obtained using a monochrome camera equipped with a 430 nm bandpass filter to capture the chemiluminescence signal emitted by the flame. Sequences of conventional flame photographs are taken during the experiment, which are computationally merged to generate high dynamic range (HDR) images. In a highly diluted fuel/oxidizer mixture, it is observed that FREI disappear and are replaced by a gradual and direct transition between weak and normal flames which makes it hard to identify different combustion regimes. To resolve the issue, a convolutional neural network (CNN) is introduced to classify the flame regime. The accuracy of the model is calculated to be 99.34, 99.66, and 99.83% for “training”, “validation”, and “testing” data-sets, respectively. This level of accuracy is achieved by conducting a grid search to acquire optimized parameters for CNN. Furthermore, a data augmentation technique based on different experimental scenarios is used to generate flame images to increase the size of the data-set.

A Data-Driven Methodology for the Simulation of Turbulent Flame Speed across Engine-Relevant Combustion Regimes

Turbulent combustion modelling in internal combustion engines (ICEs) is a challenging task. It is commonly synthetized by incorporating the interaction between chemical reactions and turbulent eddies into a unique term, namely turbulent flame speed sT. The task is very complex considering the variety of turbulent and chemical scales resulting from engine load/speed variations. In this scenario, advanced turbulent combustion models are asked to predict accurate burn rates under a wide range of turbulence–flame interaction regimes. The framework is further complicated by the difficulty in unambiguously evaluating in-cylinder turbulence and by the poor coherence of turbulent flame speed (sT) measurements in the literature. Finally, the simulated sT from combustion models is found to be rarely assessed in a rigorous manner. A methodology is presented to objectively measure the simulated sT by a generic combustion model over a range of engine-relevant combustion regimes, from Da = 0.5 to Da = 75 (i.e., from the thin reaction regime to wrinkled flamelets). A test case is proposed to assess steady-state burn rates under specified turbulence in a RANS modelling framework. The methodology is applied to a widely adopted combustion model (ECFM-3Z) and the comparison of the simulated sT with experimental datasets allows to identify modelling improvement areas. Dynamic functions are proposed based on turbulence intensity and Damköhler number. Finally, simulations using the improved flame speed are carried out and a satisfactory agreement of the simulation results with the experimental/theoretical correlations is found. This confirms the effectiveness and the general applicability of the methodology to any model. The use of grid/time resolution typical of ICE combustion simulations strengthens the relevance of the proposed dynamic functions. The presented analysis allows to improve the adherence of the simulated burn rate to that of literature turbulent flames, and it unfolds the innovative possibility to objectively test combustion models under any prescribed turbulence/flame interaction regime. The solid data-driven representation of turbulent combustion physics is expected to reduce the tuning effort in ICE combustion simulations, providing modelling robustness in a very critical area for virtual design of innovative combustion systems.