CFD modeling predicts critical depth variations in non‐uniform channel flows by simulating a range of channel configurations and flow regimes that have been validated against experimental data (Abril and Knight, 2004). In studies of trapezoidal, compound, curved, contraction, expansion, and Venturi channels—with flow conditions varying from supercritical to turbulent and two‐phase flows—CFD methods such as 2D finite element, 3D finite volume, and volume‐of‐fluid approaches (often using k‑ε turbulence models) reproduce key hydrodynamic features (Abril and Knight, 2004). For example, one investigation of two‐phase flow recorded mean absolute relative errors in depth prediction of 0.007 using CFD (versus 0.004–0.011 by alternative machine‐learning techniques), and a study on supercritical contraction flows reported a maximum depth prediction error of 5.3% along with significant vertical accelerations(Gholami et al., 2017). In analyses of gradual open channel expansions, a minimum expansion length of 0.35 m was required to maintain supercritical flow, and reliable predictions were supported by R² values above 0.95 (Hassan, 2020). These findings illustrate that CFD modeling, when calibrated with experimental data, can effectively capture the influence of channel geometry and flow conditions on critical depth variations (Abril and Knight, 2004).
Atakulov et al. (Wed,) studied this question.