Powder transport in directed energy deposition (DED) is governed by complex interactions among high-speed gas flow, turbulent structures, particle inertia, and the presence of the bottom wall boundary (substrate). In this study, an Eulerian–Lagrangian (EL) framework is developed to investigate powder transport in a four-nozzle directed energy deposition system. The baseline LES–EL configuration is first validated against measured powder concentration profiles. The validated framework is then used to compare large-eddy simulation and Reynolds-averaged Navier–Stokes predictions under matched conditions. The results show that large-eddy simulation captures the jet-merging process and the wall-induced recirculation near the impingement region more accurately, whereas the Reynolds-averaged model produces overly broad turbulence dissipation and substantially underpredicts the turbulence kinetic energy. The results also show that the presence of the wall alters the near-wall flow structure, but this feature is not captured well by the RANS model compared with LES. Because particle motion is primarily inertia-dominated in the present configuration, a particle-only model can predict the overall powder focusing, namely the convergence of the powder stream toward the deposition region, with acceptable accuracy while reducing the computational time by about 95% relative to the LES–EL model. However, resolved gas-phase flow remains necessary to capture the near-wall flow structure and the associated thermal transport. In particular, removal of the inflow produces a much larger hot-fluid region and a significantly higher fluid temperature above the prescribed molten region (melt pool) on the bottom wall, whereas the corresponding increase in mean particle temperature remains limited because of the short particle residence time. These results clarify that simplified models are useful for rapid estimation of global powder-focusing characteristics, while predictive simulations of near-wall flow and thermal behavior in powder-fed DED require resolved flow-field information.
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Yeh et al. (2026) studied this question.
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