arXiv subjects

Samuel Draycott

Publications and source records attributed to Samuel Draycott.

5 recordsLinked to original sources

arXiv2026

Drawing with water waves

The deterministic reproduction of complex 3D wave fields remains a significant challenge in ocean engineering. This study proposes a novel methodology for drawing arbitrary 2D curves and 3D volumetric shapes on a water surface using transient multi-directional focused waves. To overcome the limitations of conventional discrete-point focusing methods, our framework integrates B\'ezier curve parametrisation, equal arc-length sampling, and an Iterative Amplitude Correction (IAC) algorithm. This effectively mitigates wave height overshoot and enables precise spatial superposition of spectral components. The method's effectiveness was validated through linear wave theory and Smoothed Particle Hydrodynamics (SPH) simulations, successfully reproducing 2D characters and a 3D human face. Physical experiments in the FloWave circular wave basin further demonstrated target shape generation, such as a 2D star and 3D pyramid. Although further integration of nonlinear wave theories is necessary for high-amplitude accuracy, this technique establishes a deterministic methodology for creating arbitrary 2D and 3D surface geometries. It represents a significant advantage in wave field control for ocean engineering applications.

physics.flu-dyn↗

arXiv2025

Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models

We investigate the statistical accuracy of temporally interpolated spatiotemporal flow sequences between sparse, decorrelated snapshots of turbulent flow fields using conditional Denoising Diffusion Probabilistic Models (DDPMs). The developed method is presented as a proof-of-concept generative surrogate for reconstructing coherent turbulent dynamics between sparse snapshots, demonstrated on a 2D Kolmogorov Flow, and a 3D Kelvin-Helmholtz Instability (KHI). We analyse the generated flow sequences through the lens of statistical turbulence, examining the time-averaged turbulent kinetic energy spectra over generated sequences, and temporal decay of turbulent structures. For the non-stationary Kelvin-Helmholtz Instability, we assess the ability of the proposed method to capture evolving flow statistics across the most strongly time-varying flow regime. We additionally examine instantaneous fields and physically motivated metrics at key stages of the KHI flow evolution.

physics.flu-dyn↗

arXiv2025

Dynamics of jet formation and collapse for axisymmetric surface gravity waves: coupled 3D potential flow and SPH simulations

Axisymmetric waves occur across a wide range of scales. This study analyses large-scale gravity-dominated axisymmetric waves, with jet heights of up to 6 m, for which surface-tension effects are negligible. The Bond number is O(10^5) and the Weber number ranges from O(10^4) to O(10^6). Our aim is to clarify the dynamics of highly nonlinear axisymmetric jet formation, cavity collapse and the consequent generation of secondary jets. The newly developed three-dimensional framework OceanSPHysics3D, combining unsteady potential flow with smoothed particle hydrodynamics, enables full simulation of jet initiation and collapse. The computed free-surface elevations and jet evolution agree well with the experiments of McAllister et al. (Journal of Fluid Mechanics, 2022) and with an analytical jet-tip-angle formulation by Longuet-Higgins (Journal of Fluid Mechanics, 1983). The simulations elucidate how the falling primary jet induces a secondary jet. The mechanisms forming the pre-jet trough and the post-jet cavity are fundamentally different. The pre-jet trough arises geometrically from directional focusing of the constituent waves, yielding a self-similar shape when appropriately scaled. In contrast, the post-jet cavity is formed inertially by the falling continuous jet and lacks both spatial and temporal self-similarity. Its collapse also differs: the cavity pinches off at the neck to generate upward and downward secondary jets, with local accelerations reaching approximately 150 times gravity. The primary jet scale governs the ensuing secondary-jet dynamics, including vortex-ring formation and strong vertical mixing. These findings illustrate the complexity of axisymmetric jet dynamics and demonstrate the ability of the present framework to reproduce the key coupled processes in such extreme free-surface events.

physics.flu-dyn↗

arXiv2024

Concerning the Use of Turbulent Flow Data for Machine Learning

This article describes some common issues encountered in the use of Direct Numerical Simulation (DNS) turbulent flow data for machine learning. We focus on two specific issues; 1) the requirements for a fair validation set, and 2) the pitfalls in downsampling DNS data before training. We attempt to shed light on the impact these issues can have on machine learning and computer vision for turbulence. Further, we include statistical and spectral analysis for the homogenous isotropic turbulence from the John Hopkins Turbulence Database, a Kolmogorov flow, and a Rayleigh-B\'enard Convection Cell using data generated by the authors, to concretely demonstrate these issues.

physics.flu-dyn↗

arXiv2023

Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery

We investigate the statistical recovery of missing physics and turbulent phenomena in fluid flows using generative machine learning. Here we develop a two-stage super-resolution method using spectral filtering to restore the high-wavenumber components of a Kolmogorov flow. We include a rigorous examination of generated samples through the lens of statistical turbulence. By extending the prior methods to a combined super-resolution and conditional high-wavenumber generation, we demonstrate turbulence recovery on a 8x upsampling task, effectively doubling the range of recovered wavenumbers.

physics.flu-dyn↗