WAKESET: A Large-Scale, High-Reynolds Number Flow Dataset for Machine Learning of Turbulent Wake Dynamics

2026-02-01

AI Summary

This study addresses the scarcity of large-scale, high-fidelity datasets for machine learning in high-Reynolds-number turbulent flows. To bridge this gap, the authors construct a high-quality CFD dataset focused on turbulent wakes generated during underwater vehicle recovery, with Reynolds numbers reaching up to 1.09×10⁸ and encompassing a range of speeds and rudder angles. Leveraging high-fidelity RANS simulations, parametric case design, and data augmentation techniques, the dataset comprises 4,360 flow field instances derived from 1,091 base simulations. This work provides the first benchmark dataset of turbulent wakes tailored to real-world engineering scenarios, thereby enabling machine learning applications such as flow field prediction, surrogate modeling, and autonomous navigation in high-Reynolds-number regimes.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Intelligent Robots: State Estimation
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Machine learning (ML) offers transformative potential for computational fluid dynamics (CFD), promising to accelerate simulations, improve turbulence modelling, and enable real-time flow prediction and control-capabilities that could fundamentally change how engineers approach fluid dynamics problems. However, the exploration of ML in fluid dynamics is critically hampered by the scarcity of large, diverse, and high-fidelity datasets suitable for training robust models. This limitation is particularly acute for highly turbulent flows, which dominate practical engineering applications yet remain computationally prohibitive to simulate at scale. High-Reynolds number turbulent datasets are essential for ML models to learn the complex, multi-scale physics characteristic of real-world flows, enabling generalisation beyond the simplified, low-Reynolds number regimes often represented in existing datasets. This paper introduces WAKESET, a novel, large-scale CFD dataset of highly turbulent flows, designed to address this critical gap. The dataset captures the complex hydrodynamic interactions during the underwater recovery of an autonomous underwater vehicle by a larger extra-large uncrewed underwater vehicle. It comprises 1,091 high-fidelity Reynolds-Averaged Navier-Stokes simulations, augmented to 4,364 instances, covering a wide operational envelope of speeds (up to Reynolds numbers of 1.09 x 10^8) and turning angles. This work details the motivation for this new dataset by reviewing existing resources, outlines the hydrodynamic modelling and validation underpinning its creation, and describes its structure. The dataset's focus on a practical engineering problem, its scale, and its high turbulence characteristics make it a valuable resource for developing and benchmarking ML models for flow field prediction, surrogate modelling, and autonomous navigation in complex underwater environments.

Problem

• turbulent wake dynamics
• high-Reynolds number flow
• machine learning for CFD
• large-scale dataset
• turbulence modelling

Innovation

• high-Reynolds number turbulence
• CFD dataset
• machine learning for fluid dynamics
• wake dynamics
• RANS simulations