arXiv subjects

Georgios Fourtakas

Publications and source records attributed to Georgios Fourtakas.

8 recordsLinked to original sources

arXiv2026

Memory Layouts for GPU-Data Transfer Buffering in SPH

The rise in GPU compute speed has outpaced improvements in host-to-device memory transfer speeds, despite the advent of shared-memory superchips. Consequently, memory transfer times now constitute an increasingly large fraction of total time-to-solution, compelling developers to compress GPU kernel input and output data into compact, minimal formats prior to GPU-offloading. This complements existing work on GPU- and compute-friendly data arrangements. We study a Smoothed Particle Hydrodynamics solver and propose memory layout strategies for host-side particle data that are particularly well-suited to GPU-offloading. Specifically, we advocate splitting classic array-of-struct data structures into a split array-of-struct arrangement, in which each logical struct decomposes into substructs determined by kernel read/write access patterns and attribute types. Splitting a monolithic particle struct into several bespoke, finer-grained structs can reduce the time required to pack data to and from buffers by ~20% - 40%, lowering total time spent on GPU-offloading by ~12% - 25%.

cs.DC↗

arXiv2026

A high-order Fourier Continuation (FC)-based spectral incompressible Smoothed Particle Hydrodynamics (ISPH) scheme for general boundary conditions in wall-bounded domains

In this paper, a high-order Fourier Continuation (FC) algorithm is introduced into the spectral smoothed particle hydrodynamics (SPH) scheme to simulate the wall-bounded incompressible flows. This work aims to extend the spectral ISPH scheme towards the high-order simulation of flows with non-periodic wall boundary conditions. Herein, a polynomial-based Fourier continuation technique is applied to the velocity and pressure to make the domain both periodic and Cp smooth. The spatial SPH discretisation is performed subsequently in the frequency space on the FC-extended domain by building upon the convolution theorem using fast Fourier transform (FFT). The incorporation of Neumann boundary conditions is straightforward, and more generally, the FC method enforces periodicity across the domain regardless of the boundary condition type. The convergence order, additional computational cost, and implementation technique of the FC method are also discussed. Combined with a projection-based time integration scheme and a spectral PPE solver, the FC-based spectral ISPH framework is validated against several classical CFD benchmarks. The principal finding of this work is that the incorporation of FC techniques enables the spectral ISPH scheme to simulate wall-bounded flows with high-order convergence, and accurately capturing complex vortex dynamics. This work therefore represents a step towards a fully high-order spectral Lagrangian SPH solver with complex geometries

physics.flu-dyn↗

arXiv2026

Exploring Neural Network Surrogates for High-Order Mesh-Free Interpolants

Mesh-free numerical methods offer flexibility in the discretisation of complex geometries, showing significant potential for problems where mesh-based methods struggle. Although high-order approximations can be obtained through consistency-correction linear systems, such approaches remain prohibitively expensive for Lagrangian formulations, which commonly exhibit low-order convergence. Here we investigate the use of machine learning (ML) to bridge this gap, developing two strategies to couple multilayer perceptrons (MLPs) with the Local Anisotropic Basis Function Method (LABFM) as an exemplar high-order mesh-free method. In the first strategy, neural networks are trained to directly surrogate the high-order kernel; in the second, surrogate models are developed to compute the solution of the dense, low-rank linear systems arising in high-order mesh-free discretisations. The first strategy yields qualitative agreement with validation data but only marginally outperforms inconsistent smoothed particle hydrodynamics (SPH) kernels, with divergent behaviour observed for the Laplacian operator. The second strategy produces solution vectors with mean absolute errors of $\mathcal{O}(10^{-4}$--$10^{-5})$, replicating LABFM second-order convergence rate up to a resolution-dependent limiting accuracy and achieving up to a $5\times$ speedup at equivalent accuracy. However, sensitivity analyses reveal that higher-order approximations impose increasingly stringent accuracy requirements on the predicted solution vector, representing a fundamental challenge for current neural network architectures.

physics.comp-ph↗

arXiv2026

Learning Mesh-Free Discrete Differential Operators with Self-Supervised Graph Neural Networks

Mesh-free numerical methods provide flexible discretisations for complex geometries; however, classical meshless discrete differential operators typically trade low computational cost for limited accuracy or high accuracy for substantial per-stencil computation. We introduce a parametrised framework for learning mesh-free discrete differential operators using a graph neural network trained via polynomial moment constraints derived from truncated Taylor expansions. The model maps local stencils relative positions directly to discrete operator weights. The current work demonstrates that neural networks can learn classical polynomial consistency while retaining robustness to irregular neighbourhood geometry. The learned operators depend only on local geometry, are resolution-agnostic, and can be reused across particle configurations and governing equations. We evaluate the framework using standard numerical analysis diagnostics, showing improved accuracy over Smoothed Particle Hydrodynamics, and a favourable accuracy-cost trade-off relative to a representative high-order consistent mesh-free method in the moderate-accuracy regime. Applicability is demonstrated by solving the weakly compressible Navier-Stokes equations using the learned operators.

cs.LG↗

arXiv2026

Task-parallelism in SWIFT for heterogeneous compute architectures

This paper highlights first steps towards enabling graphics processing unit (GPU) acceleration of the task-parallel smoothed particle hydrodynamics (SPH) solver SWIFT. Novel combinations of algorithms are presented, enabling SWIFT to function as a truly heterogeneous software leveraging task-parallelism on CPUs for memory-bound computations concurrently with GPUs for compute-bound computations while minimising the effects of CPU-GPU communication latency. The proposed algorithms are validated in extensive testing. The GPU acceleration methodology is shown to deliver up to 3.5 and 7.5 speedups for the offloaded computations when including and excluding the time required to prepare and post-process data transfers on the CPU side, respectively. The overall performance of the GPU-accelerated hydrodynamic solver for a full simulation on a single Grace-Hopper superchip is 1.8 times faster compared to the superchips fully parallelised CPU capabilities. This constitutes an improvement from 8 million particle updates/s for the full CPU-only baseline (115,000 updates per CPU core) to 15 million updates/s for the GPU-accelerated SPH solver. Moreover, it displays near-perfect strong scaling on 4 Grace-Hopper nodes. The GPU-acceleration is also demonstrated to give a 29 percent improvement in energy efficiency in comparison to CPU-only baselines. Finally, inter-influential bottlenecks in the prototype solver presented in this work are identified: A significant amount of time (up to 80 percent) of a GPU-offloading cycle is spent on preparing and post-processing particle data on the CPU for the transfer to and from the GPU, respectively. Approaches are suggested to minimise their effects and maximise the solver's performance in our future work.

cs.PF↗

arXiv2024

Divergence cleaning for weakly compressible smoothed particle hydrodynamics

This paper presents a divergence cleaning formulation for the velocity in the weakly compressible smoothed particle hydrodynamics (SPH) scheme. The proposed hyperbolic/parabolic divergence cleaning, ensures that the velocity divergence, $div(\mathbf{u})$, is minimised throughout the simulation. The divergence equation is coupled with the momentum conservation equation through a scalar field $ψ$. A parabolic term is added to the time-evolving divergence equation, resulting in a hyperbolic/parabolic form, dissipating acoustic waves with a speed of sound proportional to the local Mach number in order to maximise dissipation of the velocity divergence, preventing unwanted diffusion of the pressure field. The $div(\mathbf{u})$-SPH algorithm is implemented in the open-source weakly compressible SPH solver DualSPHysics. The new formulation is validated against a range of challenging 2-D test cases including the Taylor-Green vortices, patch impact test, jet impinging on a surface, and wave impact in a sloshing tank. The results show that the new formulation reduces the divergence in the velocity field by at least one order of magnitude which prevents spurious numerical noise and the formation of unphysical voids. The temporal evolution of the impact pressures shows that the $div(\mathbf{u})$-SPH formulation virtually eliminates unwanted acoustic pressure oscillations. Investigation of particle resolution confirms that the new $div(\mathbf{u})$-SPH formulation does not reduce the spatial convergence rate.

physics.flu-dyn↗

arXiv2024

Framework for uncertainty quantification of wave-structure interaction in a flume

In this paper we propose a numerical procedure for the quantification of uncertainties in wave-structure interaction. We utilise the smoothed particle hydrodynamics (SPH) scheme for modelling the wave mechanics, coupled one-way with a finite element method (FEM) for the structural response. Physical wave flumes are extensively used in the study of hydrodynamics especially in wave-structure interaction (WSI) and prediction of forces to near-shore structures in disaster mitigation and offshore structures in the oil and gas, and more recently renewable energy sector. Over the years, numerical wave flumes have been developed extensively to enable the modelling of complex wave-structure interaction. However, most of these studies are deterministic and limited to using either simple flexible beams or rigid monolithic structures to model the structural part in the WSI. Additionally, uncertainties are commonly observed in both wave and structural parameters and need to be accounted for. This work presents a numerical framework to enable uncertainty quantification for wave-structure interaction problems in terms of the forces experienced by the structure. A one-way coupling between SPH with the FEM and uncertainty quantification (UQ) is proposed. We employ the so-called Tokyo wave flume geometry, which has potential for future surrogate modelling in WSI. The developed model is validated using numerical and experimental results from the literature and is used to demonstrate the prediction of probabilistic responses of structures under breaking and non-breaking wave scenarios.

math.NA↗

arXiv2021

State-of-the-art SPH solver DualSPHysics: from fluid dynamics to multiphysics problems

DualSPHysics is a weakly compressible smoothed particle hydrodynamics (SPH) Navier-Stokes solver initially conceived to deal with coastal engineering problems, especially those related to wave impact with coastal structures. Since the first release back in 2011, DualSPHysics has shown to be robust and accurate for simulating extreme wave events along with a continuous improvement in efficiency thanks to the exploitation of hardware such as graphics processing units (GPUs) for scientific computing or the coupling with wave propagating models such as SWASH and OceanWave3D. Numerous additional functionalities have also been included in the DualSPHysics package over the last few years which allow the simulation of fluid-driven objects. The use of the discrete element method (DEM) has allowed the solver to simulate the interaction among different bodies (sliding rocks, for example), which provides a unique tool to analyse debris flows. In addition, the recent coupling with other solvers like Project Chrono or MoorDyn has been a milestone in the development of the solver. Project Chrono allows the simulation of articulated structures with joints, hinges, sliders and springs and MoorDyn allows simulating moored structures. Both functionalities make DualSPHysics one of the meshless model world leaders in the simulation of offshore energy harvesting devices. Lately, the present state of maturity of the solver goes beyond single phase simulations, allowing multi-phase simulations with gas-liquid and a combination of Newtonian and non-Newtonian models expanding further the capabilities and range of applications for the DualSPHysics solver. These advances and functionalities make DualSPHysics a state-of-the-art meshless solver with emphasis on free-surface flow modelling.

physics.flu-dyn↗