Ion-Currents in Oxyfuel Cutting Flames Exposed to External Bias Voltages

Computational Fluid Dynamics (CFD) and predictive models are presented in this dissertation that illustrates the detailed electrical characteristics, and the current-voltage (i-v) relationship throughout the preheating process of premixed methane-oxygen (CH4-O2) oxyfuel cutting flame subject to electric bias voltages. As such, the equations describing combustion, electrochemical transport for charged species, and potential are solved through a commercially available finite-volume CFD code. The reactions of the methane-oxygen (CH4 – O2) flame were combined with the GRI 3.0 mechanism and a 25-species reduced mechanism, respectively, and additional ionization reactions that generate three chemi-ions, H3O+, HCO+, and e– , to describe the chemistry of ions in flames. The electrical characteristics such as ion migrations and ion distributions are investigated for a range of electric potential, V ∈ [−10V, +10V ]. Since the physical flame is comprised of twelve Bunsen-like conical flame, inclusion of the third dimension imparts the resolution of fluid mechanics and the interaction among the individual cones. As for developing the predictive models, four different supervised machine learning (ML) algorithms, decision tree (DT), random forest (RF), K-nearest neighbors (KNN), and artificial neural network (ANN), were employed to predict the i-v relationship. An experimental dataset of ≈ 10050 was utilized where a 60:20:20 split was adopted, allocating 60% for training, 20% for validation, and 20% for testing. It was concluded that charged 'sheaths' are formed at both torch and workpiece surfaces, subsequently forming three distinct regimes in the i-v relationship. The i-v characteristics obtained have been compared to the previous experimental study for premixed flame. In this way, the overall model generates a better understanding of the physical behavior of the oxyfuel cutting flames, along with a more validated i-v characteristics. Such understanding might provide critical information towards achieving an autonomous oxyfuel cutting process.