Xiaojuan Ban

Efficient extracting surfaces approach employing anisotropic kernels for SPH fluids

AbstractParticles are disordered throughout the entire process of fluid simulation using particle-based methods, extracting surfaces through following up particles is unlikely to achieve. Therefore, it is reasonably necessary to extract fluid surfaces called surface reconstruction which has been research focus in particle-based fluid simulation for decades. To construct more smooth surfaces and enhance reconstruction efficiency in fluid simulation, this paper addresses an efficient anisotropic surface reconstruction method for particle-based fluid simulation. First, we simplify and modify the construction of traditional anisotropic kernel function. Second, we divide particles into near-surface particles and internal particles according to the analysis of particles’ eigenvectors. Finally, near-surface particles are involved in the calculation of surface reconstruction while internal particles are directly assigned color field values through the number of neighbor particles. Experimental results show that this algorithm ensures smoothness and geometric characteristics of fluid surfaces reconstructed. Compared to existing algorithms, this approach is simple and easy to implement and greatly improves the operation efficiency.Graphical Abstract

Adaptively stepped SPH for fluid animation based on asynchronous time integration

Abstract We present a novel adaptive stepping scheme for SPH fluids, in which particles have their own time steps determined from local conditions, e.g. courant condition. These individual time steps are constrained for global convergence and stability. Fluid particles are then updated asynchronously. The approach naturally allocates computing resources to visually complex regions, e.g. regions with intense collisions, thereby reducing the overall computational time. The experiments show that our approach is more efficient than the standard method and the method with globally adaptive time steps, especially in highly dynamic scenes.

A Symmetry Particle Method towards Implicit Non‐Newtonian Fluids

In this paper, a symmetry particle method, the smoothed particle hydrodynamics (SPH) method, is extended to deal with non‐Newtonian fluids. First, the viscous liquid is modeled by a non‐Newtonian fluid flow and the variable viscosity under shear stress is determined by the Carreau‐Yasuda model. Then a pressure correction method is proposed, by correcting density error with individual stiffness parameters for each particle, to ensure the incompressibility of fluid. Finally, an implicit method is used to improve efficiency and stability. It is found that the nonNewtonian behavior can be well displayed in all cases, and the proposed SPH algorithm is stable and efficient.

Surface Tension Model Based on Implicit Incompressible Smoothed Particle Hydrodynamics for Fluid Simulation

In order to capture stable and realistic microscopic features of fluid surface, a surface tension and adhesion method based on implicit incompressible SPH (smoothed particle hydrodynamics) is presented in this paper. It gives a steady and fast tension model and can solve the problem of not considering adhesion. Molecular cohesion and surface minimization are considered for surface tension, and adhesion is added to show the microscopic characteristics of the surface. To simulate surface tension and adhesion stably and efficiently, the surface tension and adhesion model is integrated to an implicit incompressible SPH method. The experimental results show that the method can better simulate surface features in a variety of scenarios compared with previous methods and meanwhile ensure stability and efficiency.

A Tongue Image Segmentation Method Based on Enhanced HSV Convolutional Neural Network

In the procedure of the Chinese medical tongue diagnosis, it’s necessary to carry out the original tongue image segmentation to reduce interference to the tongue feature extraction caused by the non-tongue part of the face. In this paper, we propose a new method based on enhanced HSV color model convolutional neural network for tongue image segmentation. This method can get a better in tongue image segmentation results compared with others. This method also has a great advantage over other methods in the processing speed.

Adaptiving Time Steps for SPH Cloth-Fluid Coupling

We propose a new cloth-fluid coupling scheme which takes the advantages of the position-based method. With the constraint to distance and angle, deformable sheet could be implemented and coupled with fluid particles. Furthermore, an adaptive time-stepping method is adopted for the cloth-fluid coupling, which increases and decreases the required time step automatically according to the scenario. While comparatively large time steps can be used, the efficiency of the simulation is significantly improved compared to the constant time-stepping.

A Density-Correction Method for Particle-Based Non-Newtonian Fluid

We propose a novel non-Newtonian fluid simulation method for SPH. The variable viscosity under shear stress is achieved using a viscosity model known as Cross model. By adopting a density-correction scheme, larger time step is available for simulation. The achieved results show that both Newtonian fluid and non-Newtonian fluid could be achieved by our model. Furthermore, density-correction scheme improves the stability and efficiency of simulation significantly.

Iron and Steel Enterprises Big Data Visualization Analysis Based on Spark

Given the difficulty of browsing and analyzing big data via web browsers, using Spark technology, we took the big-data analysis of steel companies as an example to propose a framework for big-data visualization technology. We used HDFS for data storage, Spark for data analysis, Django for web systems, and ECharts for data visualization, ultimately providing a complete visualization solution. Using visualization, we realized price forecasting, sales analysis and production process quality traceability, help enterprises to make decisions and provide support for technological process improvement.

A Symmetric Particle-Based Simulation Scheme towards Large Scale Diffuse Fluids

We present a symmetric particle simulation scheme for diffuse fluids based on the Lagrangian Smoothed Particle Hydrodynamics (SPH) model. In our method, the generation of diffuse particles is determined by the entropy of fluid particles, and it is calculated by the velocity difference and kinetic energy. Diffuse particles are generated near the qualified diffuse particle emitters whose diffuse material generation rate is greater than zero. Our method fits the laws of physics better, as it abandons the common practice of adding diffuse materials at the crest empirically. The coupling between diffuse materials and fluid is a post-processing step achieved by the velocity field, which enables the avoiding of the time-consuming process of cross finding neighbors. The influence weights of the fluid particles are assigned based on the degree of coupling. Therefore, it improved the accuracy of the diffuse particle position and made the simulation results more realistic. The approach is appropriate for large scale diffuse fluid, as it can be easily integrated in existing SPH simulation methods and the computational overhead is negligible.

Fluid-Solid Boundary Handling Using Pairwise Interaction Model for Non-Newtonian Fluid

In order to simulate fluid-solid boundary interaction for non-Newtonian Smoothed Particle Hydrodynamics (SPH) fluids, we present a steady and realistic fluid-solid boundary handling method using symmetrical interaction forces. Firstly, we use the improved SPH method to model the non-Newtonian fluid. Secondly, the density of boundary particle is created into the calculation of fluid-solid interaction forces. Besides, we apply friction conditions to constrain the fluid particles at the boundary. Finally, we apply the predictive-corrective scheme to correct the density deviation and improve boundary computing efficiency. The experiment confirms the feasibility for the interaction between non-Newtonian fluid and solid objects with this method. At the same time, it reflects the viscous characteristics and ensures the physical properties of non-Newtonian fluid. In addition, compared to existing methods, this method is more stable and easier to implement.