Turbulent Details Simulation for SPH Fluids via Vorticity Refinement

A major issue in Smoothed Particle Hydrodynamics (SPH) approaches is the numerical dissipation during the projection process, especially under coarse discretizations. High-frequency details, such as turbulence and vortices, are smoothed out, leading to unrealistic results. To address this issue, we introduce a Vorticity Refinement (VR) solver for SPH fluids with negligible computational overhead. In this method, the numerical dissipation of the vorticity field is recovered by the difference between the theoretical and the actual vorticity, so as to enhance turbulence details. Instead of solving the Biot-Savart integrals, a stream function, which is easier and more efficient to solve, is used to relate the vorticity field to the velocity field. We obtain turbulence effects of different intensity levels by changing an adjustable parameter. Since the vorticity field is enhanced according to the curl field, our method can not only amplify existing vortices, but also capture additional turbulence. Our VR solver is straightforward to implement and can be easily integrated into existing SPH methods

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

Discovering Sentimental Interaction via Graph Convolutional Network for Visual Sentiment Prediction

With the popularity of online opinion expressing, automatic sentiment analysis of images has gained considerable attention. Most methods focus on effectively extracting the sentimental features of images, such as enhancing local features through saliency detection or instance segmentation tools. However, as a high-level abstraction, the sentiment is difficult to accurately capture with the visual element because of the “affective gap”. Previous works have overlooked the contribution of the interaction among objects to the image sentiment. We aim to utilize interactive characteristics of objects in the sentimental space, inspired by human sentimental principles that each object contributes to the sentiment. To achieve this goal, we propose a framework to leverage the sentimental interaction characteristic based on a Graph Convolutional Network (GCN). We first utilize an off-the-shelf tool to recognize objects and build a graph over them. Visual features represent nodes, and the emotional distances between objects act as edges. Then, we employ GCNs to obtain the interaction features among objects, which are fused with the CNN output of the whole image to predict the final results. Experimental results show that our method exceeds the state-of-the-art algorithm. Demonstrating that the rational use of interaction features can improve performance for sentiment analysis

Effects of External Environments on the Fixed Elongation and Tensile Properties of the VAE Emulsion–Cement Composite Joint Sealant

Joint sealant is affected by various environmental factors in service, such as different temperatures, water soaking, ultraviolet and so on. In this paper, the VAE emulsion–cement compositejoint sealant was pretreated under multiple simulation environments. Thereafter, the degradation rules of fixed elongation and tensile properties of joint sealants at different mix proportions were systemically investigated under the action of external environments (temperature, water soaking and ultraviolet), and the influence mechanisms of diverse environmental factors were analyzed. The research results suggested that, under the action of external environments, the VAE emulsion–cement composite joint sealants exhibited degradation effects to varying degrees. After the addition of plasticizer, the joint sealants had reduced cohesion strength in low temperature environment and enhanced flexible deformability. The addition of water repellent improved the water resistance of joint sealants. Meanwhile, adding ultraviolet shield agent partially improved the ultraviolet radiation aging resistance. A greater powder–liquid ratio led to the lower flexibility of joint sealants, but superior water resistance and ultraviolet radiation aging resistance

Stability Analysis and Clinic Phenomenon Simulation of a Fractional-Order HBV Infection Model

In this paper, a fractional-order HBV model was set up based on standard mass action incidences and quasisteady assumption. The basic reproductive number R0 and the cytotoxic T lymphocytes’ immune-response reproductive number R1 were derived. There were three equilibrium points of the model, and stable analysis of each equilibrium point was given with corresponding hypothesis about R0 or R1. Some numerical simulations were also given based on HBeAg clinical data, and the simulation showed that there existed positive logarithmic correlation between the number of infected cells and HBeAg, which was consistent with the clinical facts. The simulation also showed that the clinical individual differences should be reflected by the fractional-order model

A Unified Multiple-Phase Fluids Framework Using Asymmetric Surface Extraction and the Modified Density Model

Multiple-phase fluids’ simulation and 3D visualization comprise an important cooperative visualization subject between fluid dynamics and computer animation. Interactions between different fluids have been widely studied in both physics and computer graphics. To further the study in both areas, cooperative research has been carried out; hence, a more authentic fluid simulation method is required. The key to a better multiphase fluid simulation result is surface extraction. Previous works usually have problems in extracting surfaces with unnatural fluctuations or detail missing. Gaps between different phases also hinder the reality of simulation. In this paper, we propose a unified surface extraction approach integrated with a modified density model for the particle-based multiphase fluid simulation. We refine the original asymmetric smoothing kernel used in the color field and address a binary tree scheme for surface extraction. Besides, we employ a multiphase fluid framework with modified density to eliminate density deviation between different fluids. With the methods mentioned above, our approach can effectively reconstruct the fluid surface for particle-based multiphase fluid simulation. It can also resolve the issue of overlaps and gaps between different fluids, which has widely existed in former methods for a long time. The experiments carried out in this paper show that our approach is able to have an ideal fluid surface condition and have good interaction effects

Discovering Sentimental Interaction via Graph Convolutional Network for Visual Sentiment Prediction

With the popularity of online opinion expressing, automatic sentiment analysis of images has gained considerable attention. Most methods focus on effectively extracting the sentimental features of images, such as enhancing local features through saliency detection or instance segmentation tools. However, as a high-level abstraction, the sentiment is difficult to accurately capture with the visual element because of the “affective gap”. Previous works have overlooked the contribution of the interaction among objects to the image sentiment. We aim to utilize interactive characteristics of objects in the sentimental space, inspired by human sentimental principles that each object contributes to the sentiment. To achieve this goal, we propose a framework to leverage the sentimental interaction characteristic based on a Graph Convolutional Network (GCN). We first utilize an off-the-shelf tool to recognize objects and build a graph over them. Visual features represent nodes, and the emotional distances between objects act as edges. Then, we employ GCNs to obtain the interaction features among objects, which are fused with the CNN output of the whole image to predict the final results. Experimental results show that our method exceeds the state-of-the-art algorithm. Demonstrating that the rational use of interaction features can improve performance for sentiment analysis