arXiv subjects

Tomek Jaroslawski

Publications and source records attributed to Tomek Jaroslawski.

4 recordsLinked to original sources

arXiv2026

Effect of free-stream turbulence on a moderate adverse pressure gradient turbulent boundary layer developing over an airfoil

Turbulent boundary layers (TBLs) subjected to adverse pressure gradients (APGs) are common to industrial aerodynamic applications, yet the effect of freestream turbulence (FST) on TBLs developing under moderate APGs remains insufficiently understood. Wind-tunnel experiments were conducted to investigate the effects of FST on a developing TBL over a NACA 0015 airfoil. Varying the angle of attack (2 and 4$^\circ$) adjusted the pressure gradient, and hotwire anemometry measured boundary layer properties at different chordwise positions ($x/c$ = 0.400--0.625, with $β= δ^*/τ_0 dP/dx$ = 0.2-1.5). The FST level was increased using static grids, resulting in levels ranging from 0.15 to 6$\%$. The chord-based Reynolds number was kept constant at around 250,000 for all configurations. The results show that increasing FST systematically modifies the mean-flow development of the APG boundary layer. Higher FST levels reduce the shape factor and partially suppress the APG-induced wake in the mean velocity profile, while increasing the skin-friction coefficient towards values closer to canonical ZPG behaviour. The streamwise velocity variance is amplified in both the inner and outer regions, and spectral analysis shows that this increase is associated with energetic large-scale motions introduced by the freestream turbulence, with characteristic wavelengths of order $λ_x/δ\approx 13$. These large scale structures penetrate into the boundary layer and contribute to the near-wall variance, with a stronger effect observed as the adverse pressure gradient increases. The results show that FST is a governing parameter in developing APG TBLs over airfoils and that its influence is amplified by the pressure gradient. It must therefore be considered when interpreting mean-flow evolution, turbulence statistics, and scale interactions in realistic aerodynamic environments.

physics.flu-dyn↗

arXiv2025

Uncertainty Quantification in Resolvent Analysis of Experimental Wall-Bounded Turbulent Flows

Experimental mean flows are commonly used to study wall-bounded turbulence. However, these measurements are often unable to resolve the near-wall region and thus introduce ambiguity in the velocity closest to the wall. This poses a source of uncertainty in equation-based approaches that rely on these mean flow measurements such as resolvent analysis. Resolvent analysis provides a scale-dependent decomposition of the linearized Navier Stokes equations that identifies optimal gains, response modes and forcing modes that has been used to great effect in turbulent wall-bounded flows. Its potential in the development of predictive tools for a variety of wall-bounded flows is high but the limitations of the input data must be addressed. Here, we quantify the sensitivity of resolvent analysis to common sources of experimental uncertainty and show that this sensitivity can be quantified with minimal additional computational cost. This approach is applied to both local and biglobal resolvent analysis by using artificial disturbances to mean profiles in the former and particle image velocimetry measurements with differing near-wall fits in the latter. We also highlight an example where poor near-wall resolution can lead to erroneous conclusions compared to the full-resolution data in an adverse pressure gradient turbulent boundary layer.

physics.flu-dyn↗

arXiv2025

Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling

The high dimensionality and complex dynamics of turbulent flows in urban street canyons present significant challenges for wind and environmental engineering, particularly in addressing air quality, pollutant dispersion, and extreme wind events. This study introduces a deep learning framework to predict spatio-temporal flow behavior in street canyons with varying geometric configurations and upstream roughness conditions. A convolutional encoder-decoder transformer model, trained on particle image velocimetry (PIV) data from wind tunnel experiments, is employed with autoregressive training to predict flow characteristics. The training dataset contains diverse flow regimes, with a focus on the wall-parallel plane near the canyon roof, a critical region for pollutant exchange between the outer flow and the canyon interior. The model accurately reproduces key flow features, including mean turbulent statistics, two-point correlations, quadrant analysis, and dominant flow structures, while demonstrating strong agreement with experimental data in capturing the temporal evolution of flow dynamics. These findings demonstrate the potential of deep learning models to enhance predictive capabilities for urban canyon flows, offering a pathway toward improved urban design, sustainable environmental management, and more effective pollutant dispersion modeling.

physics.flu-dyn↗

arXiv2024

Basset-Boussinesq history force and inertia are relevant for unsteady particle settling dynamics

Our experiments on a sphere falling under gravity in Stokes flow show significant history effects. We observe an algebraic, not exponential, relaxation rate to the terminal velocity, validating the solution to the Basset-Boussinesq-Oseen equation. Unlike in steady Stokes theory, our experiments and theory reveal a vortex ring forming around the sphere and drifting away. As the Reynolds number nears unity, the vortex ring lags behind the sphere, departing from Stokesian theory, though the sphere's algebraic response persists. These findings are critical for interactions in the Stokes limit.

physics.flu-dyn↗