scholarly journals Real-Time Incompressible Fluid Simulation on the GPU

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiao Nie ◽  
Leiting Chen ◽  
Tao Xiang
Keyword(s):  
Incompressible Fluid ◽  
Real Time ◽  
Optimization Techniques ◽  
Fluid Simulation ◽  
Global Memory ◽  
Time Step ◽  
Neighbor Search ◽  
Particle Hydrodynamics ◽  
On Chip ◽  
Nvidia Gpu

We present a parallel framework for simulating incompressible fluids with predictive-corrective incompressible smoothed particle hydrodynamics (PCISPH) on the GPU in real time. To this end, we propose an efficient GPU streaming pipeline to map the entire computational task onto the GPU, fully exploiting the massive computational power of state-of-the-art GPUs. In PCISPH-based simulations, neighbor search is the major performance obstacle because this process is performed several times at each time step. To eliminate this bottleneck, an efficient parallel sorting method for this time-consuming step is introduced. Moreover, we discuss several optimization techniques including using fast on-chip shared memory to avoid global memory bandwidth limitations and thus further improve performance on modern GPU hardware. With our framework, the realism of real-time fluid simulation is significantly improved since our method enforces incompressibility constraint which is typically ignored due to efficiency reason in previous GPU-based SPH methods. The performance results illustrate that our approach can efficiently simulate realistic incompressible fluid in real time and results in a speed-up factor of up to 23 on a high-end NVIDIA GPU in comparison to single-threaded CPU-based implementation.

A Proposal of a Haptic System for Real-Time Interaction with Clay-Like Objects

2014 ◽  
Vol 1079-1080 ◽  
pp. 631-637
Author(s):  
Lan Hai Liu ◽  
Satoshi Miyake ◽  
Katsuhito Akahane ◽  
Makoto Sato
Keyword(s):  
Real Time ◽  
Particle System ◽  
High Viscosity ◽  
Fluid Simulation ◽  
Time Interaction ◽  
Time Simulation ◽  
Particle Hydrodynamics ◽  
Smoothed Particle ◽  
Novel Method ◽  
The Stability

People often interact with deformable objects when they are kneading clay or making traditional desserts, either directly with their hands and fingers or through tools. Haptic interactions with virtual clay-like objects would significantly make the simulations more interesting and more real. However, to achieve a stable and real-time simulation of a clay-like particle system with high viscosity is challenging. In this research, we propose a novel method that allows real-time haptic interaction with clay-like objects. The particle system is based on a SPH(Smoothed-Particle Hydrodynamics) model, and the procedure of the conventional SPH method for fluid simulation is improved for simulating a particle system especially of high viscosity. The haptic rendering is done by a string-based haptic interface SPIDAR-G. We evaluate the performance and the stability of the proposed method in the end.

scholarly journals Particle-Based Dynamic Water Drops with High Surface Tension in Real Time

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1265
Author(s):  
Ki-Hoon Kim ◽  
Jung Lee ◽  
Chang-Hun Kim ◽  
Jong-Hyun Kim
Keyword(s):  
Surface Tension ◽  
Real Time ◽  
Liquid Metals ◽  
High Surface ◽  
Computational Cost ◽  
Great Influence ◽  
Fluid Simulation ◽  
Control Surface ◽  
Time Step ◽  
High Surface Tension

Surface tension has a great influence on the shape of the fluid interface, and is an important physical characteristic in expressing not only liquids but also liquid metals such as mercury and gallium. In the field of physics-based particle fluid simulations, it is a challenging problem to express the high surface tension generated by fluid-air or fluid-solid interaction in real time. The main reasons for this are (1) The magnitude of the force that can be stably expressed in real-time fluid simulation is limited, so when the magnitude of the surface tension increases at a large time-step, the simulation stability decreases, and (2) If we use a small time-step, a stronger force can be expressed. However, it becomes difficult to operate in real time because the computational cost increases. Techniques were proposed to solve this problem for a few specific scenes, but there has not yet been a general approach that can reliably express high surface tension in various scenarios. In this paper, we propose a real-time particle-based fluid simulation framework that can efficiently and stably express high surface tension. Unlike the previous methods, we newly model the surface tension so that the strong surface tension force generated in the droplet area with a large curvature is applied evenly in the normal and tangent directions regardless of the size of the droplet. We also propose new pressure constraints that converge quickly and accurately using this force. Our method can be effectively used in various physics-based simulation scenarios because it can easily express and control surface tension effects that appear in materials such as liquid metal as well as water.

A PCISPH implementation using distributed multi-GPU acceleration for simulating industrial engineering applications

2020 ◽  
Vol 34 (4) ◽  
pp. 450-464
Author(s):  
Kevin Verma ◽  
Christopher McCabe ◽  
Chong Peng ◽  
Robert Wille
Keyword(s):  
Smoothed Particle Hydrodynamics ◽  
High Performance ◽  
Optimization Techniques ◽  
Test Case ◽  
Time Step ◽  
Water Splash ◽  
Particle Hydrodynamics ◽  
Order Of Magnitude ◽  
Smoothed Particle ◽  
Incompressible Smoothed Particle Hydrodynamics

Predictive–corrective incompressible smoothed particle hydrodynamics (PCISPH) is a promising variant of the particle-based fluid modeling technique smoothed particle hydrodynamics (SPH). In PCISPH, a dedication prediction–correction scheme is employed which allows for using a larger time step and thereby outperforms other SPH variants by up to one order of magnitude. However, certain characteristics of the PCISPH lead to severe synchronization problems that, thus far, prevented PCISPH from being applied to industrial scenarios where high performance computing techniques need to leveraged in order to simulate in appropriate resolution. In this work, we are for the first time, presenting a highly accelerated PCISPH implementation which employs a distributed multi-GPU architecture. To that end, dedicated optimization techniques are presented that allow to overcome the drawbacks caused by the algorithmic characteristics of PCISPH. Experimental evaluations on a standard dam break test case and an industrial water splash scenario confirm that PCISPH can be efficiently employed to model real-world scenarios involving a large number of particles.

scholarly journals Multi-Resolution SPH Simulation of a Laser Powder Bed Fusion Additive Manufacturing Process

2021 ◽  
Vol 11 (7) ◽  
pp. 2962
Author(s):  
Mohamadreza Afrasiabi ◽  
Christof Lüthi ◽  
Markus Bambach ◽  
Konrad Wegener
Keyword(s):  
Search Algorithm ◽  
Computational Effort ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Neighbor Search ◽  
Particle Hydrodynamics ◽  
Sph Simulation ◽  
Particle Refinement ◽  
Search Approach

This paper presents an efficient mesoscale simulation of a Laser Powder Bed Fusion (LPBF) process using the Smoothed Particle Hydrodynamics (SPH) method. The efficiency lies in reducing the computational effort via spatial adaptivity, for which a dynamic particle refinement pattern with an optimized neighbor-search algorithm is used. The melt pool dynamics is modeled by resolving the thermal, mechanical, and material fields in a single laser track application. After validating the solver by two benchmark tests where analytical and experimental data are available, we simulate a single-track LPBF process by adopting SPH in multi resolutions. The LPBF simulation results show that the proposed adaptive refinement with and without an optimized neighbor-search approach saves almost 50% and 35% of the SPH calculation time, respectively. This achievement enables several opportunities for parametric studies and running high-resolution models with less computational effort.

scholarly journals On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 689
Author(s):  
Tom Springer ◽  
Elia Eiroa-Lledo ◽  
Elizabeth Stevens ◽  
Erik Linstead
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Operating Systems ◽  
System On Chip ◽  
Low Latency ◽  
Management Framework ◽  
On Chip ◽  
Specialized Hardware ◽  
Deterministic Behavior

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can provide the low-latency, deterministic execution required for embedded, and potentially safety-critical, applications at the edge. Despite this, studies considering the integration of real-time operating systems, specialized hardware, and machine learning/deep learning algorithms remain limited. In particular, better mechanisms for real-time scheduling in the context of machine learning applications will prove to be critical as these technologies move to the edge. In order to address some of these challenges, we present a resource management framework designed to provide a dynamic on-device approach to the allocation and scheduling of limited resources in a real-time processing environment. These types of mechanisms are necessary to support the deterministic behavior required by the control components contained in the edge nodes. To validate the effectiveness of our approach, we applied rigorous schedulability analysis to a large set of randomly generated simulated task sets and then verified the most time critical applications, such as the control tasks which maintained low-latency deterministic behavior even during off-nominal conditions. The practicality of our scheduling framework was demonstrated by integrating it into a commercial real-time operating system (VxWorks) then running a typical deep learning image processing application to perform simple object detection. The results indicate that our proposed resource management framework can be leveraged to facilitate integration of machine learning algorithms with real-time operating systems and embedded platforms, including widely-used, industry-standard real-time operating systems.

scholarly journals Design of a Real-Time Breakdown Voltage and On-Chip Temperature Monitoring System for Single Photon Avalanche Diodes

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 25
Author(s):  
Shijie Deng ◽  
Alan P. Morrison ◽  
Yong Guo ◽  
Chuanxin Teng ◽  
Ming Chen ◽  
...  
Keyword(s):  
Real Time ◽  
Breakdown Voltage ◽  
Count Rate ◽  
Single Photon ◽  
Photon Counting ◽  
Temperature Monitoring ◽  
Dark Count ◽  
Chip Temperature ◽  
Avalanche Diodes ◽  
On Chip

The design and implementation of a real-time breakdown voltage and on-chip temperature monitoring system for single photon avalanche diodes (SPADs) is described in this work. In the system, an on-chip shaded (active area of the detector covered by a metal layer) SPAD is used to provide a dark count rate for the breakdown voltage and temperature calculation. A bias circuit was designed to provide a bias voltage scanning for the shaded SPAD. A microcontroller records the pulses from the anode of the shaded SPAD and calculates its real-time dark count rate. An algorithm was developed for the microcontroller to calculate the SPAD’s breakdown voltage and the on-chip temperature in real time. Experimental results show that the system is capable of measuring the SPAD’s breakdown voltage with a mismatch of less than 1.2%. Results also show that the system can provide real-time on-chip temperature monitoring for the range of −10 to 50 °C with errors of less than 1.7 °C. The system proposed can be used for the real-time SPAD’s breakdown voltage and temperature estimation for dual-SPADs or SPAD arrays chip where identical detectors are fabricated on the same chip and one or more dummy SPADs are shaded. With the breakdown voltage and the on-chip temperature monitoring, intelligent control logic can be developed to optimize the performance of the SPAD-based photon counting system by adjusting the parameters such as excess bias voltage and dead-time. This is particularly useful for SPAD photon counting systems used in complex working environments such as the applications in 3D LIDAR imaging for geodesy, geology, geomorphology, forestry, atmospheric physics and autonomous vehicles.

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