arXiv subjects

Oskar J. Haidn

Publications and source records attributed to Oskar J. Haidn.

16 recordsLinked to original sources

arXiv2025

A GPU-Accelerated Fully Coupled Fluid-Solid-Thermal SPH Solver for Industrial Gearboxes: Application to Lubricant Flow and Heat Transfer in a Bevel-Helical Reducer

This study presents a GPU-accelerated, fully coupled fluid-solid-thermal Smoothed Particle Hydrodynamics (SPH) framework for high-fidelity analysis of splash-lubricated gearboxes. A series of thermo-fluid simulations of a bevel-helical gear reducer were conducted by varying shaft speed, oil immersion depth, and lubricant viscosity to evaluate their influence on splash dynamics, churning losses, and lubricant temperature rise. The results show that churning losses increase by nearly an order of magnitude as the speed rises from 150 to 600 rad/s, while the corresponding lubricant temperature rise becomes approximately three to four times smaller. Variations in immersion depth and viscosity adjust the heating rate only modestly-typically within 10-20%-with their influence reversing between low- and high-speed regimes. The GPU backend provides a 7-9 speedup over a high-performance desktop CPU, enabling multi-million-particle, full-gearbox thermo-fluid simulations without specialized hardware. These findings demonstrate the feasibility of high-fidelity thermal analysis of industrial gearboxes and provide quantitative insight into the coupled splash, churning, and heat-transfer mechanisms that govern gearbox thermal performance.

physics.flu-dyn↗

arXiv2025

Toward Efficient FSI Modeling in Patient-Specific Arteries: SPH Simulation of Blood Flow in Thin Deformable Vessels

Accurate simulation of blood flow in deformable vessels is critical in cardiovascular research for understanding disease progression and informing clinical decision-making. However, due to the thin-walled nature of arteries, traditional smoothed particle hydrodynamics (SPH) approaches based on full-dimensional volume modeling often require extremely fine particle spacing to ensure numerical convergence for the solid mechanics. This, in turn, leads to redundant resolution in the fluid domain to maintain sufficient kernel support near the fluid-solid interface in fluid-structure interaction (FSI) simulations. To address this limitation, we propose an efficient reduced-dimensional shell-based SPH method for modeling thin-walled deformable arteries, and conduct FSI for capturing hemodynamics and arterial wall mechanics. Through a series of validation cases, the proposed shell model demonstrates comparable accuracy in fluid dynamics to the volume model, while achieving faster convergence in solid mechanics and reduced computational cost. We further investigate the influence of wall compliance on flow transitions and key hemodynamic indices, highlighting the necessity of FSI modeling over rigid-wall assumptions. Finally, the method is applied to two patient-specific vascular geometries, i.e. the carotid artery and the aorta, which demonstrates its robustness, efficiency and physiological relevance in realistic cardiovascular simulations.

cs.CE↗

arXiv2025

Sloshing suppression with a controlled elastic baffle via deep reinforcement learning and SPH simulation

This study employed smoothed particle hydrodynamics (SPH) as the numerical environment, integrated with deep reinforcement learning (DRL) real-time control algorithms to optimize the sloshing suppression in a tank with a centrally positioned vertical elastic baffle. Compared to rigid baffle movement and active strain control methods, the active-controlled movable elastic baffle, which remains undeformed at its base, achieved the best performance with an 81.63% reduction in mean free surface amplitude. A cosine-based expert policy derived from DRL data is also extracted, resulting in a comparable 76.86% reduction in a three-dimensional (3D) numerical simulation. Energy analyses showed that elastic baffle motion effectively decreased system energy by performing negative work on the fluid, reducing kinetic and potential energy. The DRL-based and expert policies also demonstrated robust performance across varying excitation frequencies and water depths. Specifically, rigid baffles proved more effective at frequencies below the system's first natural frequency, while elastic baffles exhibited superior performance at higher frequencies. Changes in water depth minimally affected the effectiveness of control policies, though they significantly influenced elastic baffle deformation behavior. Overall, the sloshing suppression efficiency consistently ranged between 70% and 80%, confirming DRL-informed control methods' versatility and effectiveness under diverse operating conditions.

physics.flu-dyn↗

arXiv2024

The efficient implementation of transport velocity formulation

The standard smoothed particle hydrodynamics (SPH) method suffers from tensile instability, resulting in particle clumping and void regions under negative pressure conditions. In this study, we extend the transport-velocity formulation of Adami et al. (2013) \cite{adami2013transport} in the weakly-compressible SPH (WCSPH) framework to address this long-standing issue. Rather than relying on background pressure, our modified and improved transport-velocity correction scales directly to the smoothing length, making it suitable for variable-resolution flows. Additionally, we introduce a limiter to the new formulation to prevent overcorrection, especially for flow with small velocities. These modifications enhance the general applicability of the transport velocity in fluid dynamics. Numerical tests involving low-velocity and variable-resolution cases demonstrate that the new formulation offers a general and accurate solution for multi-physics SPH simulations.

physics.flu-dyn↗

arXiv2024

Adaptive optimization of wave energy conversion in oscillatory wave surge converters via SPH simulation and deep reinforcement learning

The nonlinear damping characteristics of the oscillating wave surge converter (OWSC) significantly impact the performance of the power take-off system. This study presents a framework by integrating deep reinforcement learning (DRL) with numerical simulations of OWSC to identify optimal adaptive damping policy under varying wave conditions, thereby enhancing wave energy harvesting efficiency. Firstly, the open-source multiphysics libraries SPHinXsys and Simbody are employed to establish the numerical environment for wave interaction with OWSCs. Subsequently, a comparative analysis of three DRL algorithms-proximal policy optimization (PPO), twin delayed deep deterministic policy gradient (TD3), and soft actor-critic (SAC)-is conducted using the two-dimensional (2D) numerical study of OWSC interacting with regular waves. The results reveal that artificial neural networks capture the nonlinear characteristics of wave-structure interactions and provide efficient PTO policies. Notably, the SAC algorithm demonstrates exceptional robustness and accuracy, achieving a 10.61% improvement in wave energy harvesting. Furthermore, policies trained in a 2D environment are successfully applied to the three-dimensional (3D) study, with an improvement of 22.54% in energy harvesting. Additionally, the study shows that energy harvesting is improved by 6.42% for complex irregular waves. However, for the complex dual OWSC system, optimizing the damping characteristics alone is insufficient to enhance energy harvesting.

cs.CE↗

arXiv2024

DRLinSPH: An open-source platform using deep reinforcement learning and SPHinXsys for fluid-structure-interaction problems

Fluid-structure interaction (FSI) problems are characterized by strong nonlinearities arising from complex interactions between fluids and structures. These pose significant challenges for traditional control strategies in optimizing structural motion, often leading to suboptimal performance. In contrast, deep reinforcement learning (DRL), through agent interactions within numerical simulation environments and the approximation of control policies using deep neural networks (DNNs), has shown considerable promise in addressing high-dimensional FSI problems. Additionally, smoothed particle hydrodynamics (SPH) offers a flexible and efficient computational approach for modeling large deformations, fractures, and complex interface movements inherent in FSI, outperforming traditional grid-based methods. In this work, we present DRLinSPH, an open-source Python platform that integrates the SPH-based numerical environment provided by the open-source software SPHinXsys with the mature DRL platform Tianshou to enable parallel training for FSI problems. DRLinSPH has been successfully applied to four FSI scenarios: sloshing suppression using rigid and elastic baffles, optimization of wave energy capture through an oscillating wave surge converter (OWSC), and muscle-driven fish swimming in vortices. The results demonstrate the platform's accuracy, stability, and scalability, highlighting its potential to advance industrial solutions for complex FSI challenges.

cs.CE↗

arXiv2024

Physics-driven complex relaxation for multi-body systems of SPH method

In the smoothed particle dynamics (SPH) method, the characteristics of a target particle are interpolated based on the information from its neighboring particles. Consequently, a uniform initial distribution of particles significantly enhances the accuracy of SPH calculations. This aspect is particularly critical in Eulerian SPH, where particles are stationary throughout the simulation. To address this, we introduce a physics-driven complex relaxation method for multi-body systems. Through a series of two-dimensional and three-dimensional case studies, we demonstrate that this method is capable of achieving a globally uniform particle distribution, especially at the interfaces between contacting bodies, and ensuring improved zero-order consistency. Moreover, the effectiveness and reliability of the complex relaxation method in enhancing the accuracy of physical simulations are further validated.

cs.CE↗

arXiv2024

Liquid sloshing behaviours in an elastic tank and suppression effect of baffles

In this paper, a fluid-structure interaction (FSI) framework based on the smoothed particle hydrodynamics (SPH) method is employed to investigate the forces and deformations experienced by LNG tanks during liquid sloshing. As a Lagrangian approach, the SPH method offers the advantage of accurately modelling free-surface flow. The fluid phase consisting of water and air is modelled as a multi-phase system for getting closer to real transport situations. Additionally, the application of FSI within a single framework reduces data transfer discrepancies between fluid dynamics and solid mechanics. To validate the reliability of the numerical methodology, the simulation results about the free surface elevation and wave profiles are compared with experimental data. Subsequently, ring baffles and vertical baffles are introduced separately. While the degree of force acting on the tanks is assessed, the anti-sloshing effectiveness of baffles on sloshing suppression and the variations in stress and strain distributions are evaluated. Further, to compare the influence of the material properties of baffles on sloshing phenomena, the rigid baffle and elastic baffle with different Young's moduli are immersed in the liquid. The results indicate that in this LNG tank configuration, the closer the baffle properties align with rigidity, the more effective the sloshing inhibition.

physics.flu-dyn↗

arXiv2024

An efficient truncation scheme for Eulerian and total Lagrangian SPH methods

In smoothed particle hydrodynamics (SPH) method, the particle-based approximations are implemented via kernel functions, and the evaluation of performance involves two key criteria: numerical accuracy and computational efficiency. In the SPH community, the Wendland kernel reigns as the prevailing choice due to its commendable accuracy and reasonable computational efficiency. Nevertheless, there exists an urgent need to enhance the computational efficiency of numerical methods while upholding accuracy. In this paper, we employ a truncation approach to limit the compact support of the Wendland kernel to 1.6h. This decision is based on the observation that particles within the range of 1.6h to 2h make negligible contributions, practically approaching zero, to the SPH approximation. To address integration errors stemming from the truncation, we incorporate the Laguerre-Gauss kernel for particle relaxation due to the fact that this kernel has been demonstrated to enable the attainment of particle distributions with reduced residue and integration errors \cite{wang2023fourth}. Furthermore, we introduce the kernel gradient correction to rectify numerical errors from the SPH approximation of kernel gradient and the truncated compact support. A comprehensive set of numerical examples including fluid dynamics in Eulerian formulation and solid dynamics in total Lagrangian formulation are tested and have demonstrated that truncated and standard Wendland kernels enable achieve the same level accuracy but the former significantly increase the computational efficiency.

cs.CE↗

arXiv2023

Incorporating the algorithm for the boundary condition from FVM into the framework of Eulerian SPH

Finite volume method (FVM) is a widely used mesh-based technique, renowned for its computational efficiency and accuracy but it bears significant drawbacks, particularly in mesh generation and handling complex boundary interfaces or conditions. On the other hand, smoothed particle hydrodynamics (SPH) method, a popular meshless alternative, inherently circumvents the mesh generation and yields smoother numerical outcomes but at the expense of computational efficiency. Therefore, numerous researchers have strategically amalgamated the strengths of both methods to investigate complex flow phenomena and this synergy has yielded precise and computationally efficient outcomes. However, algorithms involving the weak coupling of these two methods tend to be intricate, which has issues pertaining to versatility, implementation, and mutual adaptation to hardware and coding structures. Thus, achieving a robust and strong coupling of FVM and SPH in a unified framework is imperative. Due to differing boundary algorithms between these methods in Wang's work, the crucial step for establishing a strong coupling of both methods within a unified SPH framework lies in incorporating the FVM boundary algorithm into the Eulerian SPH method. In this paper, we propose a straightforward algorithm in the Eulerian SPH method, algorithmically equivalent to that in FVM, grounded in the principle of zero-order consistency. Moreover, several numerical examples, including fully and weakly compressible flows with various boundary conditions in the Eulerian SPH method, validate the stability and accuracy of the proposed algorithm.

cs.CE↗

arXiv2023

A fourth-order kernel for improving numerical accuracy and stability in Eulerian and total Lagrangian SPH

The error of smoothed particle hydrodynamics (SPH) using kernel for particle-based approximation mainly comes from smoothing and integration errors. The choice of kernels has a significant impact on the numerical accuracy, stability and computational efficiency. At present, the most popular kernels such as B-spline, truncated Gaussian (for compact support), Wendland kernels have 2nd-order smoothing error and Wendland kernel becomes mainstream in SPH community as its stability and accuracy. Due to the fact that the particle distribution after relaxation can achieve fast convergence of integration error respected to support radius, it is logical to choose kernels with higher-order smoothing error to improve the numerical accuracy. In this paper, the error of 4th-order Laguerre-Wendland kernel proposed by Litvinov et al. \cite{litvinov2015towards} is revisited and another 4th-order truncated Laguerre-Gauss kernel is further analyzed and considered to replace the widely used Wendland kernel. The proposed kernel has following three properties: One is that it avoids the pair-instability problem during the relaxation process, unlike the original truncated Gaussian kernel, and achieves much less relaxation residue than Wendland and Laguerre-Wendland kernels; One is the truncated compact support size is the same as the non-truncated compact support of Wendland kernel, which leads to both kernels' computational efficiency at the same level; Another is that the truncation error of this kernel is much less than that of Wendland kernel. Furthermore, a comprehensive set of $2D$ and $3D$ benchmark cases on Eulerian SPH for fluid dynamics and total Lagrangian SPH for solid dynamics validate the considerably improved numerical accuracy by using truncated Laguerre-Gauss kernel without introducing extra computational effort.

physics.comp-ph↗

arXiv2023

Extended Eulerian SPH and its realization of FVM

Eulerian smoothed particle hydrodynamics (Eulerian SPH) is considered as a potential meshless alternative to a traditional Eulerian mesh-based method, i.e. finite volume method (FVM), in computational fluid dynamics (CFD). While researchers have analyzed the differences between these two methods, a rigorous comparison of their performance and computational efficiency is hindered by the constraint related to the normal direction of interfaces in pairwise particle interactions within Eulerian SPH framework. To address this constraint and improve numerical accuracy, we introduce Eulerian SPH extensions, including particle relaxation to satisfy zero-order consistency, kernel correction matrix to ensure first-order consistency and release the constraint associated with the normal direction of interfaces, as well as dissipation limiters to enhance numerical accuracy and these extensions make Eulerian SPH rigorously equivalent to FVM. Furthermore, we implement mesh-based FVM within SPHinXsys, an open-source SPH library, through developing a parser to extract necessary information from the mesh file which is exported in the MESH format using the commercial software ICEM. Therefore, these comprehensive approaches enable a rigorous comparison between these two methods.

physics.flu-dyn↗

arXiv2022

Level-set based pre-processing algorithm for particle-based methods

Obtaining high quality particle distribution representing clean geometry in pre-processing is essential for the simulation accuracy of the particle-based methods. In this paper, several level-set based techniques for cleaning up `dirty' geometries automatically and generating homogeneous particle distributions are presented. First, a non-resolved structure identifying method based on level-set field is employed to detect the tiny fragments which make the geometry `dirty' under a given resolutions. Second, a re-distance algorithm is proposed to remove the tiny fragments and reconstruct clean and smooth geometries. Third, a `static confinement' boundary condition is developed in the particle relaxation process. By complementing the kernel support for the particles near the geometric surface, the boundary condition achieves better body-fitted particle distribution on the narrow region with high curvature. Several numerical examples include a 2D airfoil 30P30N, 3D SPHinXsys symbol, a skyscraper with a flagpole and an inferior vena cava demonstrate that the present method not only cleans up the `dirty' geometries efficiently, but also provides better body-fitted homogeneous particle distribution for complex geometry.

cs.CE↗

arXiv2021

Physics-driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks

Recently, physics-driven deep learning methods have shown particular promise for the prediction of physical fields, especially to reduce the dependency on large amounts of pre-computed training data. In this work, we target the physics-driven learning of complex flow fields with high resolutions. We propose the use of \emph{Convolutional neural networks} (CNN) based U-net architectures to efficiently represent and reconstruct the input and output fields, respectively. By introducing Navier-Stokes equations and boundary conditions into loss functions, the physics-driven CNN is designed to predict corresponding steady flow fields directly. In particular, this prevents many of the difficulties associated with approaches employing fully connected neural networks. Several numerical experiments are conducted to investigate the behavior of the CNN approach, and the results indicate that a first-order accuracy has been achieved. Specifically for the case of a flow around a cylinder, different flow regimes can be learned and the adhered "twin-vortices" are predicted correctly. The numerical results also show that the training for multiple cases is accelerated significantly, especially for the difficult cases at low Reynolds numbers, and when limited reference solutions are used as supplementary learning targets.

physics.flu-dyn↗

arXiv2020

Generative Adversarial Networks with Physical Evaluators for Spray Simulation of Pintle Injector

Due to the adjustable geometry, pintle injectors are specially suitable for the liquid rocket engines which require a widely throttleable range. While applying the conventional computational fluid dynamics approaches to simulate the complex spray phenomena in the whole range still remains to be a great challenge. In this paper, a novel deep learning approach used to simulate instantaneous spray fields under continuous operating conditions is explored. Based on one specific type of neural networks and the idea of physics constraint, a Generative Adversarial Networks with Physics Evaluators (GAN-PE) framework is proposed. The geometry design and mass flux information are embedded as the inputs. After the adversarial training between the generator and discriminator, the generated field solutions are fed into the two physics evaluators. In this framework, mass conversation evaluator is designed to improve the training robustness and convergence. And the spray angle evaluator, which is composed of a down-sampling CNN and theoretical model, guides the networks generating the spray solutions more according with the injection conditions. The characterization of the simulated spray, including the spray morphology, droplet distribution and spray angle, is well predicted. The work suggests a great potential of the prior physics knowledge employment in the simulation of instantaneous flow fields.

physics.flu-dyn↗

arXiv2020

A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networks

With several advantages and as an alternative to predict physics field, machine learning methods can be classified into two distinct types: data-driven relying on training data and physics-driven using physics law. Choosing heat conduction problem as an example, we compared the data- and physics-driven learning process with deep Convolutional Neural Networks (CNN). It shows that the convergences of the error to ground truth solution and the residual of heat conduction equation exhibit remarkable differences. Based on this observation, we propose a combined-driven method for learning acceleration and more accurate solutions. With a weighted loss function, reference data and physical equation are able to simultaneously drive the learning. Several numerical experiments are conducted to investigate the effectiveness of the combined method. For the data-driven based method, the introduction of physical equation not only is able to speed up the convergence, but also produces physically more consistent solutions. For the physics-driven based method, it is observed that the combined method is able to speed up the convergence up to 49.0\% by using a not very restrictive coarse reference.

physics.comp-ph↗