Peng E S Chen | Searcharxiv

arXiv2026

Coherence does not always imply causality in wall-bounded turbulence

Compact structures of intense tangential Reynolds stress (Q events) are well-known components of wall-bounded turbulence, and have been shown to be coherent because they approximately govern their own evolution. It has therefore often been assumed that they also are causally significant, in the sense that they explain the evolution of the incoherent component of the flow, which could thus be modeled as a superposition of structures. Since strong events typically only fill a small percentage of the total flow volume, this is also cited as a reason for considering structures as targets for efficient engineering flow control. This paper shows that the causality assumption does not hold in general. Only about half of the structures identified in the flow are causally more significant than an equivalent volume of incoherent turbulence. To explain this variability, feature-based analysis and conditional averaging are performed. For wall-attached Q2 events and wall-detached structures, causally enhanced events are characterized by elevated strain rate and spanwise vorticity. These signatures are traced to intense upstream strain regions generated by the interaction of Q4-like motions impinging on Q2 events. For wall-attached Q4 events, the dominant indicators are instead the wall-normal and streamwise vorticity components; enhanced causal significance is associated with strong wall-normal vorticity. These findings show that quadrant events cannot be treated as a dynamically homogeneous class in causal analyses. Their causal significance depends strongly on the local flow environment, emphasizing the need to interpret coherent structures in terms of their interactions with the surrounding turbulence.

physics.flu-dyn↗

arXiv2023

A priori screening of data-enabled turbulence models

Assessing the compliance of a white-box turbulence model with known turbulent knowledge is straightforward. It enables users to screen conventional turbulence models and identify apparent inadequacies, thereby allowing for a more focused and fruitful validation and verification. However, comparing a black-box machine-learning model to known empirical scalings is not straightforward. Unless one implements and tests the model, it would not be clear if a machine-learning model, trained at finite Reynolds numbers preserves the known high Reynolds number limit. This is inconvenient, particularly because model implementation involves retraining and re-interfacing. This work attempts to address this issue, allowing fast a priori screening of machine-learning models that are based on feed-forward neural networks (FNN). The method leverages the mathematical theorems we present in the paper. These theorems offer estimates of a network's limits even when the exact weights and biases are unknown. For demonstration purposes, we screen existing machine-learning wall models and RANS models for their compliance with the log layer physics and the viscous layer physics in a priori manner. In addition, the theorems serve as essential guidelines for future machine-learning models.

physics.flu-dyn↗