arXiv subjects

Xiangyu Hu

Publications and source records attributed to Xiangyu Hu.

At least 73 records · Page 4Linked to original sources

arXiv2021

Generalised and efficient wall boundary condition treatment in GPU-accelerated smoothed particle hydrodynamics

This paper presents a generalised and efficient wall boundary treatment in the smoothed particle hydrodynamics (SPH) method for 3-D complex and arbitrary geometries with single- and multi-phase flows to be executed on graphics processing units (GPUs). Using a force balance between the wall and fluid particles with a novel penalty method, a pressure boundary condition is applied on the wall dummy particles which effectively prevents non-physical particle penetration into the wall boundaries also in highly violent impacts and multi-phase flows with high density ratios. A new density reinitialisation scheme is also presented to enhance the accuracy. The proposed method is very simple in comparison with previous wall boundary formulations on GPUs that enforces no additional memory caching and thus is ideally suited for heterogeneous architectures of GPUs. The method is validated in various test cases involving violent single- and multi-phase flows in arbitrary geometries and demonstrates very good robustness, accuracy and performance. The new wall boundary condition treatment is able to improve the high accuracy of its previous version \citep{ADAMI2012wall} also in complex 3-D and multi-phase problems, while it is efficiently executable on GPUs with single precision floating points arithmetic which makes it suitable for a wide range of GPUs, including consumer graphic cards. Therefore, the method is a reliable solution for the long-lasting challenge of the wall boundary condition in the SPH method for a broad range of natural and industrial applications.

physics.comp-ph↗

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↗

arXiv2021

Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates

Efficiently predicting the flowfield and load in aerodynamic shape optimisation remains a highly challenging and relevant task. Deep learning methods have been of particular interest for such problems, due to their success for solving inverse problems in other fields. In the present study, U-net based deep neural network (DNN) models are trained with high-fidelity datasets to infer flow fields, and then employed as surrogate models to carry out the shape optimisation problem, i.e. to find a drag minimal profile with a fixed cross-section area subjected to a two-dimensional steady laminar flow. A level-set method as well as Bezier-curve method are used to parameterise the shape, while trained neural networks in conjunction with automatic differentiation are utilized to calculate the gradient flow in the optimisation framework. The optimised shapes and drag force values calculated from the flowfields predicted by DNN models agree well with reference data obtained via a Navier-Stokes solver and from the literature, which demonstrates that the DNN models are capable of predicting not only flowfield but also yield satisfactory aerodynamic forces. This is particularly promising as the DNNs were not specifically trained to infer aerodynamic forces. In conjunction with the fast runtime, the DNN-based optimisation framework shows promise for general aerodynamic design problems.

physics.flu-dyn↗

arXiv2021

A splitting random-choice dynamic relaxation method for smoothed particle hydrodynamics

For conventional smoothed particle hydrodynamics (SPH), obtaining the static solution of a problem is time-consuming. To address this drawback, we propose an efficient dynamic relaxation method by adding large artificial-viscosity-based damping into the momentum conservation equation. Then, operator splitting methods are introduced to discretize the added viscous term for relaxing the time-step limit. To further improve the convergence rate, a random-choice strategy is adopted, in which the viscous term is imposed randomly rather than at every time step. In addition, to avoid the thread-conflict induced by applying shared-memory parallelization to accelerate implicit method, a splitting cell-linked list scheme is devised. A number of benchmark tests suggest that the present method helps systems achieve equilibrium state efficiently.

cs.CE↗

arXiv2021

A simple artificial damping method for total Lagrangian smoothed particle hydrodynamics

In this paper, we present a simple artificial damping method to enhance the robustness of total Lagrangian smoothed particle hydrodynamics (TL-SPH). Specifically, an artificial damping stress based on the Kelvin-Voigt type damper with a scaling factor imitating a von Neumann-Richtmyer type artificial viscosity is introduced in the constitutive equation to alleviate the spurious oscillation in the vicinity of the sharp spatial gradients. After validating the robustness and accuracy of the present method with a set of benchmark tests with very challenging cases, we demonstrate its potentials in the field of bio-mechanics by simulating the deformation of complex stent structures.

cs.CE↗

arXiv2021

Reformulated dissipation for the free-stream preserving of the conservative finite difference schemes on curvilinear grids

In this paper, we develop a new free-stream preserving (FP) method for high-order upwind conservative finite-difference (FD) schemes on the curvilinear grids. This FP method is constrcuted by subtracting a reference cell-face flow state from each cell-center value in the local stencil of the original upwind conservative FD schemes, which effectively leads to a reformulated dissipation. It is convenient to implement this method, as it does not require to modify the original forms of the upwind schemes. In addition, the proposed method removes the constraint in the traditional FP conservative FD schemes that require a consistent discretization of the mesh metrics and the fluxes. With this, the proposed method is more flexible in simulating the engineering problems which usually require a low-order scheme for their low-quality mesh, while the high-order schemes can be applied to approximate the flow states to improve the resolution. After demonstrating the strict FP property and the order of accuracy by two simple test cases, we consider various validation cases, including the supersonic flow around the cylinder, the subsonic flow past the three-element airfoil, and the transonic flow around the ONERA M6 wing, etc., to show that the method is suitable for a wide range of fluid dynamic problems containing complex geometries. Moreover, these test cases also indicate that the discretization order of the metrics have no significant influences on the numerical results if the mesh resolution is not sufficiently large.

math.NA↗

arXiv2020

An efficient fully Lagrangian solver for modeling wave interaction with oscillating wave energy converter

In this paper, we present an efficient, accurate and fully Lagrangian numerical solver for modeling wave interaction with oscillating wave energy converter (OWSC). The key idea is to couple SPHinXsys, an open-source multi-physics library in unified smoothed particle hydrodynamic (SPH) framework, with Simbody which presents an object-oriented Application Programming Interface (API) for multi-body dynamics. More precisely, the wave dynamics and its interaction with OWSC is resolved by Riemann-based weakly-compressible SPH method using SPHinXsys, and the solid-body kinematics is computed by Simbody library. Numerical experiments demonstrate that the proposed solver can accurately predict the wave elevations, flap rotation and wave loading on the flap in comparison with laboratory experiment. In particularly, the new solver shows optimized computational performance through CPU cost analysis and comparison with commercial software package ANSYS FLUENT and other SPH-based solvers in literature. Furthermore, a linear damper is applied for imitating the power take-off (PTO) system to study its effects on the hydrodynamics properties of OWSC and efficiency of energy harvesting. In addition, the present solver is used to model extreme wave condition using the focused wave approach to investigate the extreme loads and motions of OWSC under such extreme wave conditions. It worth noting that though the model validation used herein is a bottom hinged oscillating Wave Energy Converter (WEC), the obtained numerical results show promising potential of the proposed solver to future applications in the design of high-performance WECs.

physics.flu-dyn↗

arXiv2020

Perceptual Evaluation of Liquid Simulation Methods

This paper proposes a novel framework to evaluate fluid simulation methods based on crowd-sourced user studies in order to robustly gather large numbers of opinions. The key idea for a robust and reliable evaluation is to use a reference video from a carefully selected real-world setup in the user study. By conducting a series of controlled user studies and comparing their evaluation results, we observe various factors that affect the perceptual evaluation. Our data show that the availability of a reference video makes the evaluation consistent. We introduce this approach for computing scores of simulation methods as visual accuracy metric. As an application of the proposed framework, a variety of popular simulation methods are evaluated.

cs.GR↗

arXiv2020

SPHinXsys: an open-source multi-physics and multi-resolution library based on smoothed particle hydrodynamics

In this paper, we present an open-source multi-resolution and multi-physics library: SPHinXsys (pronunciation: s'finksis) which is an acronym for \underline{S}moothed \underline{P}article \underline{H}ydrodynamics (SPH) for \underline{in}dustrial comple\underline{X} \underline{sys}tems. As an open-source library, SPHinXsys is developed and released under the terms of Apache License (2.0). Along with the source code, a complete documentation is also distributed to make the compilation and execution easy. SPHinXsys aims at modeling coupled multi-physics industrial dynamic systems including fluids, solids, multi-body dynamics and beyond, in a multi-resolution unified SPH framework. As an SPH solver, SPHinXsys has many advantages namely, (1) the generic design provides a C++ API showing a very good flexibility when building domain-specific applications, (2) numerous industrial or scientific applications can be coupled within the same framework and (3) with the open-source philosophy, the community of users can collaborate and improve the library. SPHinXsys presently (v0.2.0) includes validations and applications in the fields of fluid dynamics, solid dynamics, thermal and mass diffusion, reaction-diffusion, electromechanics and fluid-structure interactions (FSI).

physics.comp-ph↗

arXiv2020

Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows

With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and evaluate a large number of trained neural networks with respect to their accuracy for the calculation of pressure and velocity distributions. In particular, we illustrate how training data size and the number of weights influence the accuracy of the solutions. With our best models we arrive at a mean relative pressure and velocity error of less than 3% across a range of previously unseen airfoil shapes. In addition all source code is publicly available in order to ensure reproducibility and to provide a starting point for researchers interested in deep learning methods for physics problems. While this work focuses on RANS solutions, the neural network architecture and learning setup are very generic, and applicable to a wide range of PDE boundary value problems on Cartesian grids.

cs.LG↗

arXiv2020

An integrative smoothed particle hydrodynamics framework for modeling cardiac function

Mathematical modeling of cardiac function can provide augmented simulation-based diagnosis tool for complementing and extending human understanding of cardiac diseases which represent the most common cause of worldwide death. As the realistic starting-point for developing an unified meshless approach for total heart modeling, herein we propose an integrative smoothed particle hydrodynamics (SPH) framework for addressing the simulation of the principle aspects of cardiac function, including cardiac electrophysiology, passive mechanical response and electromechanical coupling. To that end, several algorithms, e.g., splitting reaction-by-reaction method combined with quasi-steady-state (QSS) solver , anisotropic SPH-diffusion discretization and total Lagrangian SPH formulation, are introduced and exploited for dealing with the fundamental challenges of developing integrative SPH framework for simulating cardiac function, namely, (i) the correct capturing of the stiff dynamics of the transmembrane potential and the gating variables , (ii) the stable predicting of the large deformations and the strongly anisotropic behavior of the myocardium, and (iii) the proper coupling of electrophysiology and tissue mechanics for electromechanical feedback. A set of numerical examples demonstrate the effectiveness and robustness of the present SPH framework, and render it a potential and powerful alternative that can augment current lines of total cardiac modeling and clinical applications.

cs.CE↗

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↗

arXiv2020

A Feature-aware SPH for Isotropic Unstructured Mesh Generation

In this paper, we present a feature-aware SPH method for the concurrent and automated isotropic unstructured mesh generation. Two additional objectives are achieved with the proposed method compared to the original SPH-based mesh generator (Fu et al., 2019). First, a feature boundary correction term is introduced to address the issue of incomplete kernel support at the boundary vicinity. The mesh generation of feature curves, feature surfaces and volumes can be handled concurrently without explicitly following a dimensional sequence. Second, a two-phase model is proposed to characterize the mesh-generation procedure by a feature-size-adaptation phase and a mesh-quality-optimization phase. By proposing a new error measurement criterion and an adaptive control system with two sets of simulation parameters, the objectives of faster feature-size adaptation and local mesh-quality improvement are merged into a consistent framework. The proposed method is validated with a set of 2D and 3D numerical tests with different complexities and scales. The results demonstrate that high-quality meshes are generated with a significant speedup of convergence.

cs.GR↗

arXiv2019

A multi-resolution SPH method for fluid-structure interactions

In this paper, we present a multi-resolution smoothed particle hydrodynamics (SPH) method for modeling fluid-structure interaction (FSI) problems. By introducing different smoothing lengths and time steps, the spatio-temporal discretization is applied with different resolutions for fluid and structure. To ensure momentum conservation at the fluid-structure coupling, a position-based Verlet time integration scheme is introduced. Furthermore, the time-averaged velocity and acceleration of solid particles are introduced to enhance force matching in the fluid and solid equations. A set of numerical examples including several bio-mechanical problems are considered to demonstrate the efficiency, accuracy and robustness of the present method. A open-source code for all the examples is also provided.

cs.CE↗

arXiv2019

A L2-norm regularized incremental-stencil WENO scheme for compressible flows

For the simulation of compressible flow with a broadband of length scales and discontinuities, the WENO schemes using incremental stencil sizes other than uniform ones are promising for more robustness and less numerical dissipation. However, in smooth region, large weights may be assigned to smaller stencils due to the lack of high-order derivatives in the smoothness indicator compared with that of larger stencils, and may degrade the order accuracy and lead too much numerical dissipation to resolve fine flow structures. In order to cope with this drawback, based on the stencil selection of WENO-IS [Wang et al., IJMF 104 (2018): 20-31], we propose a L2-norm regularized incremental-stencil WENO scheme. In this method, a new L2-norm regularization is introduced into the WENO weighting strategy to modulate the weights of incremental-width stencils by taking account the L2-norm error term. A high-order non-dimensional discontinuity detector is then utilized as the regularization parameter for adaptive control. In addition, a hybrid method is adopted to further improve the performance and the computational efficiency. A number of benchmark cases suggest that the present scheme achieves very good robustness and fine-structure resolving capabilities.

physics.comp-ph↗

arXiv2019

Spot the Difference: Accuracy of Numerical Simulations via the Human Visual System

Comparative evaluation lies at the heart of science, and determining the accuracy of a computational method is crucial for evaluating its potential as well as for guiding future efforts. However, metrics that are typically used have inherent shortcomings when faced with the under-resolved solutions of real-world simulation problems. We show how to leverage crowd-sourced user studies in order to address the fundamental problems of widely used classical evaluation metrics. We demonstrate that such user studies, which inherently rely on the human visual system, yield a very robust metric and consistent answers for complex phenomena without any requirements for proficiency regarding the physics at hand. This holds even for cases away from convergence where traditional metrics often end up inconclusive results. More specifically, we evaluate results of different essentially non-oscillatory (ENO) schemes in different fluid flow settings. Our methodology represents a novel and practical approach for scientific evaluations that can give answers for previously unsolved problems.

physics.data-an↗

arXiv2019

A Consistent Parallel Isotropic Unstructured Mesh Generation Method based on Multi-phase SPH

In this paper, we propose a consistent parallel unstructured mesh generator based on a multi-phase SPH method. A set of physics-motivated modeling equations are developed to achieve the targets of domain decomposition, communication volume optimization and high-quality unstructured mesh generation simultaneously. A unified density field is defined as the target function for both partitioning the geometry and distributing the mesh-vertexes. A multi-phase Smoothing Particle Hydrodynamics (SPH) method is employed to solve the governing equations. All the optimization targets are achieved implicitly and consistently by the particle relaxation procedure without constructing triangulation/tetrahedralization explicitly. The target of communication reduction is achieved by introducing a surface tension model between distinct partitioning sub-domains, which are characterized by colored SPH particles. The resulting partitioning diagram features physically localized sub-domains and optimized interface communication. The target of optimizing the mesh quality is achieved by introducing a tailored equation-of-state (EOS) and a smooth isotropic kernel function. The mesh quality near the interface of neighboring sub-domains is improved by gradually removing the surface-tension force once a steady state is achieved. The proposed method is developed basing on a new parallel environment for multi-resolution SPH to exploit both coarse- and fine-grained parallelism. A set of benchmarks are conducted to verify that all the optimization targets are achieved consistently within the current framework.

physics.comp-ph↗

arXiv2019

Dual-criteria time stepping for weakly compressible smoothed particle hydrodynamics

Implementing particle-interaction configuration and time integration are performance intensive essentials of particle-based methods. In this paper, a dual-criteria time-stepping method is proposed to improve the computational efficiency of the weakly-compressible smoothed particle hydrodynamic (WCSPH) method for modeling incompressible flows. The key idea is to introduce an advection time criterion, which is based on fluid velocity field, for recreating the particle-interaction configuration. Within this time criterion, several steps of pressure relaxation determined by the acoustic time criterion, based on the artificial speed of sound, can be carried out without updating the particle interaction configuration and much larger time-step sizes compared with the conventional counterpart. The method has shown optimized computational performance through CPU cost analysis. Good accuracy and performance is obtained for the presented benchmarks implying promising potential of the proposed method for incompressible flow and fluid-structure interaction simulations.

physics.comp-ph↗