Self-consistent, high-order spatial profiles in a model for two-fluid turbulent mixing.

Area of Science:

• Fluid Dynamics
• Computational Physics

Background:

• Turbulent mixing layers are crucial in many fluid dynamics phenomena.
• Existing Reynolds-averaged Navier-Stokes models often struggle to accurately capture high-order spatial profiles due to assumptions like linear mixing profiles.

Purpose of the Study:

• To present a novel Reynolds-averaged Navier-Stokes model capable of admitting self-consistent, high-order spatial profiles in two-fluid turbulent mixing layers.
• To relax the limiting assumption of linear mixing profiles found in previous models.

Main Methods:

• Development of a new turbulence model, termed the k-ϕ-L-a-V model.
• Application of similarity analysis to derive constraints on model coefficients for self-similar growth rates.
• Simulation of Rayleigh-Taylor, Richtmyer-Meshkov, and Kelvin-Helmholtz mixing layers using the developed model.

Main Results:

• The new model achieves significantly better agreement with experimental profiles compared to previous models.
• Similarity analysis successfully derived constraints to ensure consistency between growth rates and high-order spatial profiles.
• Simulations confirmed the recovery of expected growth parameters and maintenance of high-order spatial profiles.

Conclusions:

• The k-ϕ-L-a-V model offers improved accuracy in simulating turbulent mixing layers.
• Careful model construction is essential to avoid unconstrained growth rates.
• The model demonstrates robust performance across different types of mixing layer instabilities.