arXiv subjects

Jiashun Guan

Publications and source records attributed to Jiashun Guan.

4 recordsLinked to original sources

arXiv2026

Turbulence Physics Governs a Scaling Law for the Machine-Learning Predictability Ceiling in Chaotic Flow

For centuries, the intrinsic chaos of unsteady fluid motion has stood as a formidable barrier to long-term forecasting. While machine learning (ML) has recently emerged as a transformative paradigm for predicting flow evolution, it encounters a pervasive yet unexplained "performance wall": an inevitable deterioration in accuracy as the forecast horizon extends. Here, we demonstrate that this deterioration is not a deficiency of model architecture, no matter how state-of-the-art, but a fundamental constraint imposed by the underlying system, which can be understood through turbulence theory established decades ago. In the setting of bluff body flow, a canonical phenomenon for spatiotemporal complexity in fluid mechanics, we reveal a scaling law governing the deterioration of ML predictability, derived from a Kolmogorov-inspired framework and validated through high-fidelity simulations. Our findings establish a closed loop between the predictability ceiling and its interpretation, bridging the gap between transparent physical theories and modern black-box inference. More broadly, this work provides a theoretical compass for constructing trustworthy ML in complex dynamical systems across the physical sciences.

physics.flu-dyn↗

arXiv2025

Distinct Lifetime Scaling Laws of Turbulent Puff in Duct Flow

The spatio-temporal dynamics of localized turbulent puffs $-$ the characteristic transitional structures in square duct flows $-$ are investigated through direct numerical simulations and theoretical analyses. It is revealed that the turbulent puffs are transient structures, exhibiting distinct relaminarization regimes bifurcated at a critical Reynolds number $Re_c\simeq1450$. Puff's mean lifetimes at the subcritical regime ($Re Re_c$). By implementing pattern preservation approximation, the Reynolds-Orr kinetic energy equation is reduced to a noisy saddle-node bifurcation equation, which explains the observed scaling laws in terms of the deterministic decay governed by the critical slowing down at the subcritical regime, and the abrupt decay activated by the stochastic fluctuations. Despite geometric confinement inducing unique secondary flows, e.g., corner-localized streamwise vortex pairs, corner-aligned high-speed streaks, and forked low-speed streaks, the puff lifetime statistics remain analogous to those in pipe flows, suggesting geometric invariance in decay mechanisms for transitional wall-surrounded turbulence.

physics.flu-dyn↗

arXiv2025

Vortex-Induced Drag Forecast for Cylinder in Non-uniform Inflow

In this letter, a physics-based data-driven strategy is developed to predict vortex-induced drag on a circular cylinder under non-uniform inflow conditions - a prevalent issue for engineering applications at moderate Reynolds numbers. Traditional pressure-signal-based models exhibit limitations due to complex vortex dynamics coupled with non-uniform inflow. To address this issue, a modified fully connected neural network (FCNN) architecture is established that integrates upstream velocity measurements (serving as an inflow calibration) with pressure-signal-based inputs to enhance predictive capability (R^2 ~ 0 to 0.75). Direct numerical simulations (DNS) at Reynolds number Re = 4000 are implemented for model training and validation. Iterative optimizations are conducted to derive optimized input configurations of pressure sensor placements and velocity components at upstream locations. The optimized model achieves an R^2 score of 0.75 in forecasting high-amplitude drag coefficient fluctuations (C_d=0.2 - 1.2) within a future time window of one time unit. An exponential scaling between model performance and optimized pressure signal inputs is observed, and the predictive capability of sparsely distributed but optimized sensors is interpreted by the scaling. The optimized sensor placements correspond to the physical mechanism that the flow separation dynamics play a governing role in vortex-induced drag generation. This work advances machine learning applications in fluid-structure interaction systems, offering a scalable strategy for forecasting statistics in turbulent flows under real-world engineering conditions.

physics.flu-dyn↗

arXiv2025

The Onset of Metastable Turbulence in Pipe Flow

The onset of turbulence in pipe flow has been a fundamental challenge in physics, applied mathematics, and engineering for over 140 years. To date, the precursor of this laminar-turbulent transition is recognized as transient turbulent spots or puffs, but their defining characteristics - longevity, abrupt relaminarization, and super-exponential lifetime scaling - have been lack of first-principles explanations. By combining extensive computer simulations, theory, and verifications with experimental data, we identify distinct puff relaminarizations separated by a critical Reynolds number, which are defined by a noisy saddle-node bifurcation derived from the Navier-Stokes equations. Below the critical number, the mean lifetime of puff follows a square-root scaling law, representing an intrinsically deterministic decay dominated by the critical slowing down. Above the critical value, the bifurcation's node branch creates a potential well stabilizing the turbulence, while the saddle branch mediates stochastic barrier-crossing events that drive memoryless decay - a hallmark of metastable states. Accordingly, the mean lifetimes are solved theoretically and can be fitted super-exponentially. By quantifying the deterministic and stochastic components in the kinetic energy equation, the lifetime statistics of puff are analyzed in a unified framework across low-to-moderate Reynolds number regimes, uncovering the mechanisms governing the transition to metastable turbulence in pipe flows.

physics.flu-dyn↗