Fluent-Based Simulation of TIG Arc Combustion Using UDF and UDS

Literature Overview

This study by Peng Xiaofei and colleagues from Nanchang University and the University of Kentucky, published in Hot Working Technology in 2016, presents a comprehensive computational model for TIG arc combustion using ANSYS Fluent software. Funded by the National Natural Science Foundation (Project 61165008) and the Ministry of Education Return Fund (Project 13006199), the work demonstrates the implementation of custom physical models through User-Defined Functions (UDF) and User-Defined Scalars (UDS) to accurately capture the complex physics of the TIG welding arc.

Model Architecture and Implementation

The model is based on the Navier-Stokes equations, energy conservation equation, and Maxwell's equations, formulated as a two-dimensional axisymmetric problem. The implementation requires custom programming through Fluent's UDF and UDS capabilities to incorporate physics that are not available in the standard solver.

The UDF commands are used to dynamically connect the temperature-dependent physical properties of argon gas, the momentum equation source terms representing electromagnetic forces, and the current density distribution at the tungsten electrode boundary to the computational model. The UDS command adds the current continuity equation as an additional transport equation solved simultaneously with the standard CFD equations.

Simulation Results and Physical Interpretation

The simulation results confirm several well-established characteristics of TIG arc behavior while providing quantitative detail through the computational model. The arc temperature field exhibits the expected bell-shaped distribution, with maximum temperature at the arc center axis. The arc pressure is maximum along the axial direction, which drives the outward plasma flow at the arc boundaries. The physical properties of argon gas vary significantly with the arc temperature field, as expected for a high-temperature plasma.

The current density distribution reveals that axial current density decreases continuously along the axial direction from cathode to anode. The radial current density exhibits a Gaussian distribution, which is consistent with the observed bell-shaped arc geometry. These results provide quantitative data that can be used to validate and refine simplified heat source models used in weld pool simulation.

Practical Applications and Study Insights

The value of this computational approach extends to multiple areas of welding engineering. The detailed arc physics model can serve as a foundation for developing more accurate heat source models for weld pool simulation, which in turn improves predictions of weld geometry, residual stress, and microstructural evolution. For pipeline welding applications, understanding the arc current density distribution helps explain variations in penetration depth and weld width under different process conditions.

The methodology demonstrated in this study is particularly relevant for process development and optimization. By adjusting boundary conditions and parameters in the simulation, engineers can predict how changes in current, electrode geometry, or shielding gas composition affect arc behavior without conducting extensive physical experiments. This computational approach reduces development costs and accelerates the process optimization cycle, especially for new welding applications or specialized materials where experimental data is limited.

The integration of electromagnetic forces through UDF source terms is critical for accurately capturing arc stability and shape. In engineering practice, arc stability directly affects weld quality and process consistency. The ability to simulate these effects computationally provides valuable insight into process parameter interactions that are difficult to isolate through experimental methods alone. This study demonstrates that with appropriate custom programming, general-purpose CFD software can be configured to model the complex multi-physics phenomena inherent in arc welding, providing engineers with a powerful tool for process understanding and optimization.