dc.contributor.author	Rahman, S. M. Mahbobur	en
dc.contributor.committeechair	Untaroiu, Alexandrina	en
dc.contributor.committeemember	Qiao, Rui	en
dc.contributor.committeemember	Iliescu, Traian	en
dc.contributor.committeemember	Martin, Christopher Reed	en
dc.contributor.committeemember	Sandu, Corina	en
dc.contributor.department	Mechanical Engineering	en
dc.date.accessioned	2025-01-03T09:00:56Z	en
dc.date.available	2025-01-03T09:00:56Z	en
dc.date.issued	2025-01-02	en
dc.description.abstract	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.	en
dc.description.abstractgeneral	Oxyfuel flame cutting is a century-old technique having widespread applications in heavy industries, including, but not limited to, building construction, defense, shipyards, etc. However, the mechanized oxyfuel cutting process has never benefited from the degree of autonomy due to contemporary sensing technologies' limitations at high-temperature working conditions. As a result, an experienced labor force is required to operate the system, thereby lowering the efficacy associated with this cutting process. A potential solution to this problem is motivated by preliminary measurements demonstrating that electrical events called 'ion currents' associated with the flame itself can reliably indicate vital process states. Provided that an autonomous process is achieved, this work could realize reliable cost-effective control of the oxyfuel cutting process, a capability of great interest to many core US industries involved in construction, and major equipment manufacture for defense and energy applications. Critical parameters (standoff, F/O ratio, flow rate, etc.) must be detected during operation to ensure an autonomous oxyfuel cutting process. The motivation stems from the fact that by measuring such co-dependence between critical parameters and electrical characteristics through a data acquisition unit (DAQ) and power supply, the shortcomings of sensing suites in a harsh operating environment can be compromised. Experimental data in the literature indicated the current-voltage (i-v) relationship with different critical parameters of oxyfuel flame to be the salient electrical characteristic in the preheating process when cutting steel. A comprehensive two-dimensional computational simulation using StarCCM+ only with the reduced combustion chemical mechanism with ion-exchange reactions has already been completed to elucidate the experimental results and to investigate the electrical characteristics such as ion migrations and ion distributions. Nonetheless, the findings exhibit some magnitude of differences compared to the experimental results. Thereby to further improve the results and better understand the underlying physics, further computational models using ANSYS FLUENT are proposed herein, having the reduced surface chemical mechanism considered. In addition, predictive models were developed based on machine learning (ML) algorithms. Four supervised ML algorithms - decision tree (DT), random forest (RF), Knearest neighbors (KNN), and artificial neural network (ANN) - were adopted to predict the current-voltage (i-v) relationship at different process states. ML offers a more data-driven, adaptable, and scalable approach to prediction compared to traditional methods. Its ability to handle large, noisy, and complex data makes it especially powerful for tasks that are challenging for conventional analytical techniques. The results of this study illustrate the detailed electrical characteristics of premixed methane-oxygen (CH4 – O2) oxyfuel cutting flame subject to an electric field, for both the computational fluid dynamics (CFD) and ML models. Since the physical flame is comprised of twelve Bunsen-like conical flame, inclusion of the third dimension will impart the resolution of fluid mechanics and the interaction among the individual cones. Moreover, the chemical activity at the work surface will also be considered, however, with a substantial simplification of the three-dimensional model as a cost. The overall model will generate a better understanding of the physical behavior of the oxyfuel cutting flames, along with a more validated currentvoltage (i-v) relationship. Consequently, this relationship could then be embedded into a control algorithm to detect the critical process parameters that may facilitate a step towards achieving an autonomous oxyfuel cutting process.	en
dc.description.degree	Doctor of Philosophy	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:42127	en
dc.identifier.uri	https://hdl.handle.net/10919/123882	en
dc.language.iso	en	en
dc.publisher	Virginia Tech	en
dc.rights	Creative Commons Attribution 4.0 International	en
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	en
dc.subject	Oxyfuel Flame	en
dc.subject	Premixed Mixture	en
dc.subject	Electric Field	en
dc.subject	Secondary Ions	en
dc.subject	CFD	en
dc.subject	ML	en
dc.subject	Electrical Characteristics	en
dc.subject	i-v Curve	en
dc.title	Ion-Currents in Oxyfuel Cutting Flames Exposed to External Bias Voltages	en
dc.type	Dissertation	en
thesis.degree.discipline	Mechanical Engineering	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Doctor of Philosophy	en

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

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