Transitioning a Phase-Change Lattice Boltzmann Solver for High-Fidelity Melt Pool Dynamics in Metal Additive Manufacturing
Faculty Mentor
Hayri Sezer
Location
Russell Union Ballroom
Type of Research
Proposed
Session Format
Poster Presentation
College
Allen E. Paulson College of Engineering & Computing
Department
Mechanical Engineering
Abstract
The stability and morphology of the melt pool are critical factors in determining the structural integrity and quality of parts produced through metal additive manufacturing (AM). This research focuses on the adaptation of a validated Lattice Boltzmann Method (LBM) phase-change solver to capture the high-energy fluid dynamics and thermal transients inherent in laser-based metallic processing. Originally developed and benchmarked for ice-melting scenarios driven by Marangoni and natural convection, the solver is transitioned here to model the extreme temperature gradients and rapid phase transitions found in the 3D printing of metals.
The computational model integrates surface-tension-driven Marangoni flow and buoyancy-induced natural convection to simulate the complex fluidic behavior within the melt zone. By simultaneously solving for the liquid-solid interface and the evolving velocity fields, the model provides a detailed prediction of melt pool depth, width, and overall stability under various laser power and scanning speed parameters.
This LBM solver serves as a high-fidelity sub-model within a comprehensive computational additive manufacturing framework. Its role is to provide the localized thermal and fluidic data necessary for understanding defect formation and the evolution of the final bead geometry. The transition from low-temperature benchmarks to high-energy industrial applications demonstrates the versatility of the LBM framework in handling multi-physics phase-change problems. Ultimately, this work offers a robust numerical tool for optimizing process parameters and advancing the predictive capabilities of digital manufacturing architectures.
Program Description
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Start Date
4-23-2026 2:00 PM
End Date
4-23-2026 4:00 PM
Recommended Citation
Ozen, Eray, "Transitioning a Phase-Change Lattice Boltzmann Solver for High-Fidelity Melt Pool Dynamics in Metal Additive Manufacturing" (2026). GS4 Student Scholars Symposium. 198.
https://digitalcommons.georgiasouthern.edu/research_symposium/2026/2026/198
Transitioning a Phase-Change Lattice Boltzmann Solver for High-Fidelity Melt Pool Dynamics in Metal Additive Manufacturing
Russell Union Ballroom
The stability and morphology of the melt pool are critical factors in determining the structural integrity and quality of parts produced through metal additive manufacturing (AM). This research focuses on the adaptation of a validated Lattice Boltzmann Method (LBM) phase-change solver to capture the high-energy fluid dynamics and thermal transients inherent in laser-based metallic processing. Originally developed and benchmarked for ice-melting scenarios driven by Marangoni and natural convection, the solver is transitioned here to model the extreme temperature gradients and rapid phase transitions found in the 3D printing of metals.
The computational model integrates surface-tension-driven Marangoni flow and buoyancy-induced natural convection to simulate the complex fluidic behavior within the melt zone. By simultaneously solving for the liquid-solid interface and the evolving velocity fields, the model provides a detailed prediction of melt pool depth, width, and overall stability under various laser power and scanning speed parameters.
This LBM solver serves as a high-fidelity sub-model within a comprehensive computational additive manufacturing framework. Its role is to provide the localized thermal and fluidic data necessary for understanding defect formation and the evolution of the final bead geometry. The transition from low-temperature benchmarks to high-energy industrial applications demonstrates the versatility of the LBM framework in handling multi-physics phase-change problems. Ultimately, this work offers a robust numerical tool for optimizing process parameters and advancing the predictive capabilities of digital manufacturing architectures.
