arXiv subjects

Lionel Agostini

Publications and source records attributed to Lionel Agostini.

6 recordsLinked to original sources

arXiv2026

Pressure-strain redistribution as the mechanism for dissimilar heat transfer under spanwise wall oscillation waveforms

Spanwise wall oscillation can enhance convective heat transfer disproportionately to its drag penalty, a departure from the Reynolds analogy termed dissimilar heat transfer (DHT). The companion study of Gu'erin et al. (2026) established that an optimised quasi-plateau waveform attains an analogy factor $\overline{A}n \approx 1.09$ at $Pr = 1$ and attributed this preferential thermal enhancement to the absence of a pressure-strain redistribution channel in the temperature variance equation, but the mechanism had not been quantitatively verified. The present study addresses this gap through phase-resolved variance transport budget analysis from direct numerical simulation of turbulent channel flow at $Reτ= 200$, $Pr = 1$. Two complementary pressure-mediated mechanisms are identified. At the Stokes-strain reversal, the pressure-strain redistribution $Π_{uu}$ imposes a pronounced drain on the streamwise velocity variance with no counterpart in the temperature variance equation: the divergence-free constraint redistributes momentum variance among velocity components but has no scalar analogue. During the quasi-steady plateau phases, the pressure-temperature-gradient correlation $Π_{vθ}$ preferentially enhances the wall-normal scalar flux relative to the momentum flux. The concentration of both mechanisms within the reversal and plateau phases, rather than at the Stokes-layer penetration maxima, identifies the duration of the quasi-steady phases as the controlling parameter for DHT enhancement, resolving the paradox whereby increased penetration depth does not produce increased dissimilarity.

physics.flu-dyn↗

arXiv2026

Duty-cycle modulation of the self-sustaining process by spanwise wall oscillation

Direct Numerical Simulation of turbulent channel flow at friction Reynolds number around 200 is performed with spanwise wall actuation to achieve drag reduction. A quasi-square-wave waveform, featuring impulsive transitions and constant-velocity plateaus, separates the actuation cycle into distinct Reversal and Displacement Phases, thereby permitting direct observation of the underlying physics. Phase-resolved analysis reveals that the actuation modulates the self-sustaining process (SSP): during the Reversal Phase, the Stokes strain passes through zero, the SSP resumes, and streaks regenerate; during the Displacement Phase, sustained Stokes strain diverts wall-normal vorticity spanwise via vortex tilting, depleting SSP precursors and suppressing streaks. A stochastic enstrophy-budget analysis confirms this mechanism at the governing-equation level: competition between mean-shear production of wall-normal enstrophy and Stokes-driven spanwise diversion, drawing from a shared reservoir, reflects directed, phase-opposed switching. The quasi-square wave improves the gross drag-reduction margin by 2.5 percentage points over the optimal sinusoidal baseline, solely via temporal Stokes-strain redistribution, and the waveform renders duty-cycle switching directly observable, thus elucidating the causal chain of drag reduction.

physics.flu-dyn↗

arXiv2026

A Hybrid Generative Reduced-Order Model for the Minimal Flow Unit

A data-driven reduced-order modelling framework is proposed for wall-bounded turbulent flows to forecast the intermittent near-wall dynamics over extended time horizons from sparse sensor measurements. The approach combines a $β$-VAE-GAN, which compresses high-dimensional flow fields into a low-dimensional latent space, with a sensor-conditioned Transformer that forecasts the evolution of the latent variables. The temporal module employs Easy Attention, a static time-mixing operator that replaces the learnable query-key mechanism of standard self-attention at reduced computational cost, combined with an adapted AdaLN-Zero modulation mechanism for sensor-based conditioning. Evaluated on the Minimal Flow Unit ($Re_τ= 200$) at $y^+ = 14$, the compression stage recovers $87\%$ of the turbulent kinetic energy within a four-dimensional latent space, exceeding the standard $β$-VAE baseline by more than $10\%$. The latent dimensions autonomously encode the characteristic timescales of the flow, with specific coordinates capturing the low-frequency signature of the near-wall regeneration cycle ($T^+ \approx 1724$), establishing the physical interpretability of the learnt representation. The sensor-conditioned Transformer maintains accurate forecasts over $17{,}288\,t^+$ from an initialisation window of only $128\,t^+$, whilst end-to-end inference reconstructs $82\%$ of the turbulent kinetic energy. The principal limitation is the attenuation of rare, extreme-amplitude events, a consequence of the encoder prioritising the most statistically recurrent flow states within the low-dimensional bottleneck. Nevertheless, the framework accurately reproduces the alternating active and quiescent phases of the regeneration cycle, demonstrating its suitability as a surrogate model for the intermittent dynamics of wall-bounded turbulence.

physics.flu-dyn↗

arXiv2026

VIVALDy: A Hybrid Generative Reduced-Order Model for Turbulent Flows, Applied to Vortex-Induced Vibrations

Developing reduced-order models applicable to fluid-dynamics problems involving complex geometries and different flow conditions remains a critical challenge for turbulent flows. This study introduces VIVALDy, a novel machine-learning framework that employs a hybrid $β$-Variational Autoencoder-Generative Adversarial Network ($β$-VAE-GAN) architecture with masked convolutions to extract dominant flow features into a compact latent space while preserving fidelity at solid-fluid interfaces. A bidirectional transformer then models the temporal evolution of these features, learning to predict flow trajectories from minimal sensor inputs. This two-stage approach enables the transformer to map sensor measurements to dominant flow variables identified by the autoencoder, advancing reduced-order modeling capabilities for real-time flow prediction. The effectiveness of the framework is demonstrated through application to a problem relevant to vortex-induced vibration (VIV) energy harvesting systems, reconstructing the turbulent flow around a one-degree-of-freedom moving cylinder. Validated against experimental data spanning fluid-structure interaction regimes of interest, VIVALDy accurately predicts different flow states using only the cylinder displacement. The framework demonstrates adequate performance in both reconstruction accuracy and statistical fidelity across diverse operating conditions, enabling efficient prediction of the turbulent flow phenomena governing vortex-induced vibration.

physics.flu-dyn↗

arXiv2026

End-to-end data-driven prediction of urban airflow and pollutant dispersion

Climate change and the rapid growth of urban populations are intensifying environmental stresses within cities, making the behavior of urban atmospheric flows a critical factor in public health, energy use, and overall livability. This study targets to develop fast and accurate models of urban pollutant dispersion to support decision-makers, enabling them to implement mitigation measures in a timely and cost-effective manner. To reach this goal, an end-to-end data-driven approach is proposed to model and predict the airflow and pollutant dispersion in a street canyon in skimming flow regime. A series of time-resolved snapshots obtained from large eddy simulation (LES) serves as the database. The proposed framework is based on four fundamental steps. Firstly, a reduced basis is obtained by spectral proper orthogonal decomposition (SPOD) of the database. The projection of the time series snapshot data onto the SPOD modes (time-domain approach) provides the temporal coefficients of the dynamics. Secondly, a nonlinear compression of the temporal coefficients is performed by autoencoder to reduce further the dimensionality of the problem. Thirdly, a reduced-order model (ROM) is learned in the latent space using Long Short-Term Memory (LSTM) netowrks. Finally, the pollutant dispersion is estimated from the predicted velocity field through convolutional neural network that maps both fields. The results demonstrate the efficacy of the model in predicting the instantaneous as well as statistically stationary fields over long time horizon.

cs.LG↗

arXiv2023

Breaking the Reynolds Analogy: Decoupling Turbulent Heat and Momentum Transport via Spanwise Wall Oscillation in Wall-Bounded Flow

This work investigates spanwise wall oscillation (SWO) as a method to preferentially enhance heat transfer over drag in turbulent channel flow. Direct numerical simulations at $Re_τ=180$ and $\Pr=1$ show set of wall-oscillation parameters reducing drag also decrease heat transfer similarly, maintaining coupled transport. However, large period ($T^+=500$) and amplitude ($W^+=30$) induce substantially greater heat transfer intensification, increasing 15 % versus only 7.7 % drag rise. This Reynolds analogy breaking enables preferential elevation of heat transport over momentum. FIK identity analysis reveals negligible impact of forcing terms on dissimilarity. Instead, differences arise from the solenoidal velocity and linear temperature equations. Both the turbulent shear stress and heat flux are amplified near the wall under oscillation. However, the heat flux intensifies more substantially, especially at its peak. This preferential enhancement of the near-wall heat flux, exceeding the shear stress amplification, facilitates greater thermal transport augmentation relative to the friction increase. Results demonstrate that spanwise wall oscillation can preferentially intensify heat transfer beyond drag, providing a promising technique for improving heat exchanger. Further work should optimize the period and amplitude of the oscillation and elucidate the underlying physics of this dissimilar heat transfer control.

physics.flu-dyn↗