Given that conventional Lagrangian-Eulerian (LE) computations of sprays are typically performed with the near-field region almost completely unresolved, key physics associated with radial dispersion are not well captured. A new Near-field Lagrangian Dispersion Model (NFLDM) is presented to improve stochastic radial dispersion of the liquid phase. This Langevin-based model employs self-similar representations of mean velocity and Reynolds stress fields obtained from Volume-of-Fluid (VoF) simulations. A data-fitted self-similarity model is developed using VoF results from a range of spray simulations employing 2–4.5 MPa ambient pressure, multiple injection velocities, injections of n-dodecane and methanol, and the Engine Combustion Network Spray-A and Spray-D nozzle geometries. The NFLDM is first compared against the conventional LE model, where a given spray angle is typically imposed. In addition to correctly predicting the variation of liquid dispersion as a function of distance downstream of the injector, the NFLDM yields an approximate 50% reduction of error in comparison to the conventional LE treatment. It is shown that the radial transport of liquid in the near field is governed by stochastic fluid motions rather than by the mean radial flow. The NFLDM is further assessed over a range of conditions yielding satisfactory agreement with experimentally-validated VoF results.
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