Accurate identification of vortical structures is central to the analysis of complex fluid flows. While the Q-criterion remains one of the most widely used vortex identification methods, it is known to suffer from sensitivity in shear-dominated regions and a lack of robustness in threshold selection. In this study, a novel vortex identification technique is proposed that enhances the classical Q-criterion by incorporating a local measure of rotational coherence and applying a data-driven thresholding approach. Specifically, the method modulates Q by a swirl coherence metric derived from spatial variations in the vorticity field, emphasizing rotationally consistent regions. A Gaussian mixture model is then employed to determine an adaptive threshold that separates vortical regions from background flow structures. The resulting framework enables automatic and physically interpretable vortex segmentation. The method is tested on numerical simulations of flow over a cambered plate, on particle image velocimetry data of colliding vortex rings, and in geophysical flow. Comparisons with conventional criteria demonstrate that the proposed method yields more accurate results and avoids false identifications in high-shear zones. This hybrid physical–statistical approach offers a robust and generalizable tool for vortex identification across a wide range of flow conditions.

This paper investigates the limitations of traditional Reynolds-averaged Navier–Stokes (RANS) models, particularly their inability to accurately predict Kolmogorov flow, a fundamental flow driven by sinusoidally varying forces. Despite being known for over 70 years, the Kolmogorov flow has not been extensively utilized in the development of turbulence models. Existing RANS models, such as the standard k−ε and k−ω shear stress transport, fail to capture key flow characteristics of the Kolmogorov flow, leading to an unbounded growth (inflation) of the turbulent length scale. This paper proposes a new eddy viscosity formulation to address these issues. The proposed model introduces a geometry-informed (GI) eddy viscosity formulation, which incorporates a power function dependent on the geometric filter size to avoid the unphysical turbulent inflation in Kolmogorov flow. The GI eddy viscosity model was coupled with the k−ε transport equation and tested in channel flow, the Kolmogorov flow, and flow over a backward-facing step. The results demonstrated that the new model outperforms traditional models in some tests, providing better predictions of turbulent kinetic energy, and it also mitigates turbulent inflation. The study concludes by highlighting the potential for further model improvements and future validation using the time-varying Kolmogorov flow data.

We present a curated dataset comprising three-dimensional direct numerical simulations of Kolmogorov flow across multiple distinct parameter regimes. These simulations cover a wide range of Reynolds numbers and excitation modes, capturing diverse flow behaviours. The dataset is designed to support research in turbulence modelling, particularly for machine learning-based model development. Each case includes raw velocity fields and a Python-based interpolation code is provided for the interpolation of the velocity fields onto a structured grid in a format compatible with ParaView, accompanied by comprehensive metadata for ease of use. This open-access dataset aims to facilitate advances in turbulence modelling, a continuing and vital challenge in fluid mechanics research.

The main aim of the current study is the detection of vortices in three‐dimensional fluid flow around a circular cylinder and an axial fan with the Lagrangian‐averaged vorticity deviation vortex identification method. Important parameters of the method include the size of the sought vortices and their convexity. Previous research has been conducted for the circular cylinder by the authors, however, only with respect to two‐dimensional geometries. The authors intend to compare the recommendations for the vortex detection parameters (in three dimensions) with those of the previously found results (in two dimensions). The capabilities of the LAVD technique are also tested on a practical example of a flow around an axial fan. With respect to the axial fan, the goal to be achieved is the detection and visualization of the so‐called tip leakage vortex. Comparison with the result of another detection method (Banks' vorticity predictor‐pressure corrector vortex core identification method), provided by a different author will also be performed.

Approximating the three-dimensional radial energy spectrum based on one-dimensional directional data is challenging, as experiments and reduced datasets provide limited spatial information. Additionally, spectral analysis is often restricted directionally by rapid spatial variations in flow properties. Classical relations, such as Batchelor's, formally connect one-dimensional and three-dimensional spectra, but their practical application requires numerical differentiation, which heavily amplifies noise in discrete spectral data. In addition, Batchelor's formula relies on the assumption of isotropy. To avoid these limitations, this study assesses a simple algebraic mapping for radial spectrum estimation. The parametrization and validation of the proposed function are based on direct numerical simulation data of Kolmogorov flow. The approximate radial spectrum is compared against the exact spectrum computed from the full three-dimensional velocity field. In addition, the resulting turbulent kinetic energy and dissipation values are also examined. The results show that the approximate spectrum reproduces the energy-containing and intermediate scales well, preserving the total turbulent kinetic energy. Larger discrepancies appear at high wavenumbers, indicating reduced accuracy near the smallest resolved scales. The conversion is extended to wall-bounded flows by introducing a local mapping function dependent on the nondimensional wall distance. Furthermore, the practical utility of the approximation is demonstrated by fitting the data to Pope's model spectrum to extract the integral length scale across the channel height. Overall, the proposed approach provides a simple, robust, and differentiation-free method for radial spectrum approximation.