Overview
Research leveraging explainable artificial intelligence (AI) has characterized the three-dimensional organization of coherent structures within turbulent flow over aircraft wings. This approach utilizes deep neural networks to predict short-term flow evolution, identifying dynamically significant regions based on their predictive importance rather than relying on predefined kinematic criteria.
Research Context
The three-dimensional organization of coherent structures in turbulent flow over aircraft wings has historically been elusive. This lack of understanding limits efforts to reduce fuel consumption in aircraft. Traditional methods for characterizing these structures often rely on classical, predefined kinematic criteria. The current work aimed to move beyond these traditional paradigms by using a predictive organization approach.
Approach
The study employed explainable artificial intelligence (XAI) to analyze turbulent wing flow dynamics. The methodology involved two primary stages:
- Deep Neural Network Training: A deep neural network was trained to predict the short-term evolution of the turbulent flow. This network learned the dynamic behavior of the flow without explicit prior definitions of coherent structures.
- Shapley-value Attribution for Relevance Identification: Following network training, Shapley-value attribution methods were applied. These methods were used to identify the regions of the flow that exhibited the highest relevance or predictive importance for the network's short-term flow evolution predictions. This allowed for the characterization of coherent structures based on their dynamic influence rather than their static kinematic properties.
Findings
The application of explainable deep learning revealed specific dynamics and characteristics of coherent structures in turbulent wing flow:
- Shift in Predictive Importance: As the flow decelerated toward the trailing edge of the wing, a significant shift in predictive importance was observed. Initially, predictive importance was associated with near-wall low-speed structures. This importance then transitioned to three-dimensional pairs of high-speed and low-speed fluid regions.
- Dominance of New Structure Pairs: These identified three-dimensional pairs of high- and low-speed fluid regions progressively dominated the dynamically relevant flow. The study noted that these pairs did not match any single classical structure family previously identified in turbulence research.
- Spatial Configuration of Pairs: Most of these newly identified pairs were observed to stand spanwise side-by-side. This arrangement enclosed a near-vertical momentum interface. This interface was characterized by the strongest velocity jump within the flow.
- Geometrical and Volumetric Properties: The geometry of these pairs remained invariant when measured in viscous units. Concurrently, their volume was observed to expand as the flow approached separation.
- Predictive Organization: The results suggest a predictive organization of turbulent flow that traditional paradigms, based on predefined kinematic criteria, have previously overlooked.
Why This Matters
The identification of a predictive organization in turbulent flows, different from that captured by traditional paradigms, offers new avenues for control strategies. A better understanding of these dynamics, specifically related to the three-dimensional organization of coherent structures over aircraft wings, is relevant for efforts aimed at reducing fuel consumption in aviation.