arXiv subjects

Junlin Yuan

Publications and source records attributed to Junlin Yuan.

4 recordsLinked to original sources

arXiv2025

Modeling and Computational Fluid Dynamics Validation of a Nonholonomically Constrained Two-Rigid-Body Swimming System

A simple nonholonomic dynamics model is developed as a low-order model for generating undulatory swim-like motions, validated through computational fluid dynamics (CFD) simulations. The rigid-body-dynamics model generates swimming motion by imposing a nonholonomic (NH) constraint on the tail of a two-body system, requiring that tail-fin velocity aligns with the tail angle, while the head moves in a straight line through a slot constraint. The system has one degree of freedom, with equations of motion derived using Lagrange multipliers. Two-dimensional CFD simulations validate the model in an incompressible Newtonian fluid, where the resolved tail fin interacts with fluid through the immersed boundary method until steady-state swimming is achieved. The validation demonstrates excellent quantitative agreement between CFD and model predictions for body orientation angle and normal fluid force across variations in fin motion amplitude, period, and Reynolds number. While an exact NH constraint point does not exist, an effective period-averaged NH location can be identified for successful model predictions. At higher Reynolds numbers, the two-body kinematics displays independence from the Reynolds number variation. The CFD data reveal that the two-body model captures the type of power-law relationship between Reynolds and Strouhal numbers governing undulatory swimming from tadpoles to whales, indicating that the simplified two-link model is representative of swimming dynamics in continuous geometries at various scales. A key limitation is that the drag force model requires a priori CFD calibration to match steady-swim velocity, limiting standalone predictive capability. The results demonstrate that the low-order NH constraint-based model effectively captures essential swimming dynamics, offering a robust alternative to existing fluid-force models.

physics.flu-dyn↗

arXiv2023

A generalized wall-pressure spectral model for non-equilibrium boundary layers

This study uses high-fidelity simulations (DNS or LES) and experimental datasets to analyse the effect of non-equilibrium streamwise mean pressure gradients (adverse or favourable), including attached and separated flows, on the statistics of boundary layer wall-pressure fluctuations. The datasets collected span a wide range of Reynolds numbers ($Re_\theta$ from 300 to 23,400) and pressure gradients (Clauser parameter from $-0.5$ to 200). The datasets are used to identify an optimal set of variables to scale the wall pressure spectrum: edge velocity, boundary layer thickness, and the peak magnitude of Reynolds shear stress. Using the present datasets, existing semi-empirical models of wall-pressure spectrum are shown unable to capture effects of strong, non-equilibrium adverse pressure gradients, due to inappropriate scaling of wall pressure using wall shear stress, calibration with limited types of flows, and dependency on model parameters based on friction velocity, which reduces to zero at the detachment point. To address these short-comings, a generalized wall-pressure spectral model is developed with parameters that characterize the extent of the logarithmic layer and the strength of the wake. Derived from the local mean velocity profile, these two parameters inherently carry effect of the Reynolds number, as well as those of the non-equilibrium pressure gradient and its history. Comparison with existing models shows that the proposed model behaves well and is more accurate in strong-pressure-gradient flows and in separated-flow regions.

physics.flu-dyn↗

arXiv2022

Supersonic turbulent flows over sinusoidal rough walls

Direct numerical simulations were performed to characterize fully developed supersonic turbulent channel flows over isothermal rough walls. The effect of roughness was incorporated using a level-set/volume-of-fluid immersed boundary method. Turbulence statistics of five channel flows are compared, including one reference case with both walls smooth and four cases with smooth top walls and bottom walls with two-dimensional (2D) and three-dimensional (3D) sinusoidal roughnesses. Results reveal a strong dependence of the turbulence on the roughness topography and the associated shock patterns. Specifically, the 2D geometries generate strong oblique shock waves that propagate across the channel and are reflected back to the rough-wall side. These strong shocks are absent in the smooth-wall channel and are significantly weaker in cases with 3D roughness geometries, replaced by weak shocklets. At the impingement locations of the shocks on the top wall in the 2D roughness cases, localized augmentations of turbulence shear production are observed. Such regions of augmented production also exist for the 3D cases, at a much weaker level. The oblique shock waves are thought to be responsible for a more significant entropy generation for cases with 2D surfaces than those with 3D ones, leading to a higher irreversible heat generation and consequently higher temperature values in 2D roughness cases. In the present supersonic channels, the effects of roughness extend beyond the near-wall layer due to the shocks. This suggests that outer layer similarity may not fully apply to a rough-wall supersonic turbulent flow.

physics.flu-dyn↗

arXiv2020

Data-driven prediction of the equivalent sand-grain height in rough-wall turbulent flows

This paper investigates a long-standing question about the effect of surface roughness on turbulent flow: what is the equivalent roughness sand-grain height for a given roughness topography? Deep Neural Network (DNN) and Gaussian Process Regression (GPR) machine learning approaches are used to develop a high-fidelity prediction approach of the Nikuradse equivalent sand-grain height $k_s$ for turbulent flows over a wide variety of different rough surfaces. To this end, 45 surface geometries were generated and the flow over them simulated at $\hbox{Re}_τ=1000$ using direct numerical simulations. These surface geometries differed significantly in moments of surface height fluctuations, effective slope, average inclination, porosity and degree of randomness. Thirty of these surfaces were considered fully-rough and they were supplemented with experimental data for fully-rough flows over 15 more surfaces available from previous studies. The DNN and GPR methods predicted $k_s$ with an average error of less than 10% and a maximum error of less than 30%, which appears to be significantly more accurate than existing prediction formulas. They also identified the surface porosity and the effective slope of roughness in the spanwise direction as important factors in drag prediction.

physics.flu-dyn↗