The Influence of Carrier Air Preheating on Autoignition of Inline-Injected Hydrogen–Nitrogen Mixtures in Vitiated Air of High Temperature

The minimization of autoignition risk is critical to premixer design. Safety factors based on ignition delays of homogeneous mixtures, are generally used to guide the choice of a residence time for a given premixer. However, autoignition chemistry at aeroderivative conditions is fast (0.5-2 milliseconds) and can be initiated within typical premixer residence times. The analysis of what takes place in this short period involves the study of low-temperature precursor chemistry. By coupling the evolution of the Chemical Explosive Modes to turbulence, it is possible to obtain a measure of spatial autoignition risk where both chemical (e.g. ignition delay) and aerodynamic (e.g. local residence time) influences are unified. In this article, we describe a method that couples Large Eddy Simulation to newly developed, reduced autoignition chemical kinetics to study autoignition precursors in an example premixer representative of real life geometric complexity. A blend of pure methane and dimethyl ether (DME), a common fuel used for experimental autoignition studies, was transported using the reduced mechanism (38 species / 238 reactions) at engine conditions at increasing levels of DME concentration until exothermic autoignition kernels were formed. The Chemical Explosive Mode analysis closely follows the large thermochemical changes in the premixer as a function of DME concentration and identifies where the premixer is sensitive and flame anchoring is likely to occur.

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Prediction of the Autoignition of a Fuel Jet in a Confined Turbulent Hot Coflow Using Machine Learning Methods

Qian

Liu

et al. 2020

Volume 4A: Combustion, Fuels, and Emissions

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For advanced lean premixed gas turbine combustors that have high inlet air temperatures, autoignition may occur during the fuel/air mixing process, which can cause flame-holing inside the premixing device and burn the hardware. An experimental study was performed using a setup that mimics the fuel/air mixing process of lean-premixed combustors. In the present experiment, the preheated air was injected into a quartz tube, and a fuel jet was injected concentrically into the hot turbulent air coflow. The quartz tube allows for direct observation of the autoignition behavior, which develops when the fuel and air mix as they flow inside the tube. This paper presents a study combining machine learning methods and physical analysis that is aimed at predicting autoignition in such flows. A model for the prediction of autoignition of a fuel jet in a flow configuration referred to as a ‘confined turbulent hot coflow’ (CTHC) is developed using machine learning techniques based on binary logistic regression and support vector machine. Key factors that impact the autoignition phenomenon are identified by analyzing the underlying physics and are used to form the feature vector of the model. The model is trained using data from experiments and is validated by an additional set of data, which are selected randomly. The results show that the model predicts the autoignition event with satisfactory accuracy and quick turnaround. The trained model parameters in turn provide insights into the quantitative contribution of different factors that impact the autoignition event. Thus, the machine-learning based method can form an alternative to CFD modeling in some cases.

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The Influence of Carrier Air Preheating on Autoignition of Inline-Injected Hydrogen–Nitrogen Mixtures in Vitiated Air of High Temperature

Cited by 4 publications

References 24 publications

Prediction of Flame Type and Liftoff Height of Fuel Jets in Turbulent Hot Coflow Using Machine Learning Methods

Prediction of Flame Type and Liftoff Height of Fuel Jets in Turbulent Hot Coflow Using Machine Learning Methods

Analysis of Auto-Ignition Chemistry in Aeroderivative Premixers at Engine Conditions

Prediction of the Autoignition of a Fuel Jet in a Confined Turbulent Hot Coflow Using Machine Learning Methods

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