scholarly journals A Coupled Grid-Particle Method for Fluid Animation on GPU

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Fengquan Zhang ◽  
Qiuming Wei ◽  
Zhaohui Wu
Keyword(s):  
Free Surface ◽  
Real Time ◽  
Large Scale ◽  
Grid Method ◽  
Particle Method ◽  
Distribution Functions ◽  
Fluid Simulation ◽  
Reconstruction Method ◽  
Fluid Surface ◽  
Fluid Animation

In digital production environments, high-quality visual effects play a key role in our mobile device such as game and film. The simulation of fluid animation with free surface is an important area in computer graphic. However, the tracking of fluid surface is a challenging problem because of its instability. In this paper, a coupled grid-particle method for fluid animation surface tracking and detail preserving is proposed. Firstly, based on the nonequilibrium extrapolation method, we design a novel method for reconstructing distribution functions (DFs) of interface grids of lattice Boltzmann method (LBM) and couple the reconstruction method with LBM and volume of fluid (VOF) to track the free surface, which can obtain the accurate surface. Secondly, in order to avoid the loss of details caused by weaknesses in the traditional LBM-VOF method, we design a coupled grid-particle method that not only makes full use of the advantages of the coupled grid-particle method but also realizes the two-way coupling between grid method and particle method. Furthermore, for achieving the real-time requirements of fluid animation, we use GPU parallel computing to accelerate the simulation and use an improved screen space fluid (SSF) rendering method for realistic rendering. The various experiments show that this work can track the fluid surface with high precision and preserve the details of the fluid surface, and it also achieves good real-time performance in large-scale fluid simulation.

scholarly journals A Poisson Equation-Based Method for 3D Reconstruction of Animated Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ziang Lei
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Large Scale ◽  
Least Square ◽  
Reconstruction Method ◽  
Feature Points ◽  
Computing Power ◽  
To Come ◽  
Point Detection ◽  
Considerable Period

3D reconstruction techniques for animated images and animation techniques for faces are important research in computer graphics-related fields. Traditional 3D reconstruction techniques for animated images mainly rely on expensive 3D scanning equipment and a lot of time-consuming postprocessing manually and require the scanned animated subject to remain in a fixed pose for a considerable period. In recent years, the development of large-scale computing power of computer-related hardware, especially distributed computing, has made it possible to come up with a real-time and efficient solution. In this paper, we propose a 3D reconstruction method for multivisual animated images based on Poisson’s equation theory. The calibration theory is used to calibrate the multivisual animated images, obtain the internal and external parameters of the camera calibration module, extract the feature points from the animated images of each viewpoint by using the corner point detection operator, then match and correct the extracted feature points by using the least square median method, and complete the 3D reconstruction of the multivisual animated images. The experimental results show that the proposed method can obtain the 3D reconstruction results of multivisual animation images quickly and accurately and has certain real-time and reliability.

scholarly journals Real-Time Smoke Simulation based on Vortex Method

2021 ◽  
Author(s):  
Fei Wan ◽  
Jingpu Zhang ◽  
Lizheng Guo ◽  
Yunchang Liu
Keyword(s):  
Fluid Dynamics ◽  
Real Time ◽  
Hybrid Method ◽  
Grid Method ◽  
Particle Method ◽  
Vortex Method ◽  
Experimental Methods ◽  
Numerical Dissipation ◽  
Simulation Based ◽  
Smoke Simulation

In this paper, we use three different experimental methods (particle method, grid method and hybrid method) to model and simulate the smoke from the perspective of fluid dynamics. Through the comparison of different methods, we conclude: The particle method can avoid the numerical dissipation problem caused by grid calculation, but it also brings problems such as the distortion of the trajectory of the example. The grid method is accurate in calculation, but it is prone to numerical dissipation and loss of details. Finally, we choose the hybrid method to store the vorticity in the form of particles in vortex particles, avoiding the numerical dissipation problem caused by the use of grids, and including rich turbulence, which perfectly shows the simulation effect of smoke.

scholarly journals A 3D Reconstruction Method Using Multisensor Fusion in Large-Scale Indoor Scenes

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Panlong Gu ◽  
Fengyu Zhou ◽  
Dianguo Yu ◽  
Fang Wan ◽  
Wei Wang ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Large Scale ◽  
Reconstruction Method ◽  
Loop Closure ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Indoor Scenes ◽  
Slam Algorithm ◽  
Robot Position

RGBD camera-based VSLAM (Visual Simultaneous Localization and Mapping) algorithm is usually applied to assist robots with real-time mapping. However, due to the limited measuring principle, accuracy, and distance of the equipped camera, this algorithm has typical disadvantages in the large and dynamic scenes with complex lightings, such as poor mapping accuracy, easy loss of robot position, and much cost on computing resources. Regarding these issues, this paper proposes a new method of 3D interior construction, which combines laser radar and an RGBD camera. Meanwhile, it is developed based on the Cartographer laser SLAM algorithm. The proposed method mainly takes two steps. The first step is to do the 3D reconstruction using the Cartographer algorithm and RGBD camera. It firstly applies the Cartographer algorithm to calculate the pose of the RGBD camera and to generate a submap. Then, a real-time 3D point cloud generated by using the RGBD camera is inserted into the submap, and the real-time interior construction is finished. The second step is to improve Cartographer loop-closure quality by the visual loop-closure for the sake of correcting the generated map. Compared with traditional methods in large-scale indoor scenes, the proposed algorithm in this paper shows higher precision, faster speed, and stronger robustness in such contexts, especially with complex light and dynamic objects, respectively.

Expanding Slice Grid Method for Large Scale Tsunami Simulation Based on the Particle Method

2018 ◽  
Vol 2018.31 (0) ◽  
pp. 293
Author(s):  
Naoki NAKAYA ◽  
Mitsuteru ASAI ◽  
Keita OGASAWARA ◽  
Mikito FURUICHI ◽  
Daisuke NISHIURA
Keyword(s):  
Large Scale ◽  
Grid Method ◽  
Particle Method ◽  
Tsunami Simulation ◽  
Simulation Based

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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