Fast and realistic coupling of blood flow and vessel wall is of great importance to virtual surgery. In this paper, we propose a novel data-driven coupling method that formulates physics-based blood flow simulation as a regression problem, using an improved periodic-corrected neural network (PcNet), estimating the acceleration of every particle at each frame to obtain fast, stable and realistic simulation. We design a particle state feature vector based on smoothed particle hydrodynamics (SPH), modeling the mixed contribution of neighboring proxy particles on the blood vessel wall and neighboring blood particles, giving the extrapolation ability to deal with more complex couplings. We present a semi-supervised training strategy to improve the traditional BP neural network, which corrects the error periodically to ensure long term stability. Experimental results demonstrate that our method is able to implement stable and vivid coupling of blood flow and vessel wall while greatly improving computational efficiency.
Particle-based methods are widely used in fluid simulation with borderline adaptability and good computational performance. @cite_2 @cite_15 @cite_1 @cite_7 @cite_8 However, the research on the direction of blood flow simulation is scarce. M " simplified the blood flow to Newtonian fluid for coupling simulation. @cite_13 implemented the method of M " using PPU parallel acceleration. @cite_5 coupled SPH and particle spring models to simulate blood flow. @cite_4 The above method lacks visual realism due to neglecting the non-Newtonian fluid characteristics of blood flow, and at the same time the simulation is small in scale and the real-time performance is not sufficient. implemented GPU-assisted blood flow simulation using the Compute Unified Device Architecture (CUDA), achieving 50 fps simulation at 9,000 particles, but did not consider the interaction of blood flow with blood vessels. @cite_11 proposed a GPU-accelerated mixed particle-based coupling method for blood flow and vessel wall that real-time simulated at 100,000 particle size. @cite_17 Although these studies have made some improvements in computational efficiency, a more efficient method is urgently needed due to the multi-phase coupling involved in the virtual surgery scene with flexible organs and surgical instruments.
@cite_4 @cite_7 @cite_8 @cite_1 @cite_17 @cite_2 @cite_5 @cite_15 @cite_13 @cite_11
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We present a fast, petaflop-scalable algorithm for Stokesian particulate flows. Our goal is the direct simulation of blood, which we model as a mixture of a Stokesian fluid (plasma) and red blood cells (RBCs). Directly simulating blood is a challenging multiscale, multiphysics problem. We report simulations with up to 200 million deformable RBCs. The largest simulation amounts to 90 billion unknowns in space. In terms of the number of cells, we improve the state-of-the art by several orders of magnitude: the previous largest simulation, at the same physical fidelity as ours, resolved the flow of O(1,000-10,000) RBCs. Our approach has three distinct characteristics: (1) we faithfully represent the physics of RBCs by using nonlinear solid mechanics to capture the deformations of each cell; (2) we accurately resolve the long-range, N-body, hydrodynamic interactions between RBCs (which are caused by the surrounding plasma); and (3) we allow for the highly non-uniform distribution of RBCs in space. The new method has been implemented in the software library MOBO (for “Moving Boundaries”). We designed MOBO to support parallelism at all levels, including inter-node distributed memory parallelism, intra-node shared memory parallelism, data parallelism (vectorization), and fine-grained multithreading for GPUs. We have implemented and optimized the majority of the computation kernels on both Intel AMD x86 and NVidia's Tesla Fermi platforms for single and double floating point precision. Overall, the code has scaled on 256 CPU-GPUs on the Teragrid's Lincoln cluster and on 200,000 AMD cores of the Oak Ridge national Laboratory's Jaguar PF system. In our largest simulation, we have achieved 0.7 Petaflops s of sustained performance on Jaguar. The vessel wall and the blood flow interact and influence each other, and real-time coupling between them is of great importance to the virtual surgery as well as the research and diagnosis of vascular disease. On the basis of smoothed particle hydrodynamics SPH, we present a new approach to solve non-Newtonian viscous force of blood and a parallel mixed particles-based coupling method for blood flow and vessel wall. Meanwhile, we also design a proxy particle-based vessel wall force visualization method. Our method is as follows. Firstly, we solve the non-Newtonian viscous forces of blood through the SPH method to discretize the Casson equation. Secondly, in each time step, we combine blood particles and sampling proxy particles on the blood vessel wall to form mixed particles and calculate the interaction forces through the SPH method between every pair of the neighboring mixed particles inside the graphics processing unit. Thirdly, the forces of the proxy particles will be mapped to the color display of the proxy particle. Experimental results demonstrate that our method is able to implement real-time sizeable coupling of blood flow and vessel wall while mainly ensuring physical authenticity and it can also provide real-time and obvious information about vessel wall force distribution. Copyright © 2015 John Wiley & Sons, Ltd. In this paper, we propose an interactive method based on Smoothed Particle Hydrodynamics (SPH) to simulate blood as a fluid with free surfaces. While SPH was originally designed to simulate astronomical objects, we gear the method towards fluid simulation by deriving the force density fields directly from the Navier-Stokes equation and by adding a term to model surface tension effects. In contrast to Eulerian grid-based approaches, the particle-based approach makes mass conservation equations and convection terms dispensable which reduces the complexity of the simulation. In addition, the particles can directly be used to render the surface of the fluid. Our method can be used in interactive surgical training systems with models of up to 3000 particles. Abstract This paper reports large-scale direct numerical simulations of homogeneous-isotropic fluid turbulence, achieving sustained performance of 1.08 petaflop s on gpu hardware using single precision. The simulations use a vortex particle method to solve the Navier–Stokes equations, with a highly parallel fast multipole method ( fmm ) as numerical engine, and match the current record in mesh size for this application, a cube of 4096 3 computational points solved with a spectral method. The standard numerical approach used in this field is the pseudo-spectral method, relying on the fft algorithm as the numerical engine. The particle-based simulations presented in this paper quantitatively match the kinetic energy spectrum obtained with a pseudo-spectral method, using a trusted code. In terms of parallel performance, weak scaling results show the fmm -based vortex method achieving 74 parallel efficiency on 4096 processes (one gpu per mpi process, 3 gpu s per node of the tsubame -2.0 system). The fft -based spectral method is able to achieve just 14 parallel efficiency on the same number of mpi processes (using only cpu cores), due to the all-to-all communication pattern of the fft algorithm. The calculation time for one time step was 108 s for the vortex method and 154 s for the spectral method, under these conditions. Computing with 69 billion particles, this work exceeds by an order of magnitude the largest vortex-method calculations to date. In this paper we introduce a novel parallel and interactive SPH simulation and rendering method on the GPU using CUDA which allows for high quality visualization. The crucial particle neighborhood search is based on Z-indexing and parallel sorting which eliminates GPU memory overhead due to grid or hierarchical data structures. Furthermore, it overcomes limitations imposed by shading languages allowing it to be very flexible and approaching the practical limits of modern graphics hardware. For visualizing the SPH simulation we introduce a new rendering pipeline. In the first step, all surface particles are efficiently extracted from the SPH particle cloud exploiting the simulation data. Subsequently, a partial and therefore fast distance field volume is rasterized from the surface particles. In the last step, the distance field volume is directly rendered using state-of-the-art GPU raycasting. This rendering pipeline allows for high quality visualization at very high frame rates. Petascale plasma physics simulations have recently entered the regime of simulating trillions of particles. These unprecedented simulations generate massive amounts of data, posing significant challenges in storage, analysis, and visualization. In this paper, we present parallel I O, analysis, and visualization results from a VPIC trillion particle simulation running on 120,000 cores, which produces 30TB of data for a single timestep. We demonstrate the successful application of H5Part, a particle data extension of parallel HDF5, for writing the dataset at a significant fraction of system peak I O rates. To enable efficient analysis, we develop hybrid parallel FastQuery to index and query data using multi-core CPUs on distributed memory hardware. We show good scalability results for the FastQuery implementation using up to 10,000 cores. Finally, we apply this indexing query-driven approach to facilitate the first-ever analysis and visualization of the trillion particle dataset. Lattice Boltzmann Methods (LBM) are used for the computational simulation of Newtonian fluid dynamics. LBM-based simulations are readily parallelizable; they have been implemented on general-purpose processors, field-programmable gate arrays (FPGAs), and graphics processing units (GPUs). Of the three methods, the GPU implementations achieved the highest simulation performance per chip. With memory bandwidth of up to 141 GB s and a theoretical maximum floating point performance of over 600 GFLOPS, CUDA-ready GPUs from NVIDIA provide an attractive platform for a wide range of scientific simulations, including LBM. This paper improves upon prior single-precision GPU LBM results for the D3Q19 model by increasing GPU multiprocessor occupancy, resulting in an increase in maximum performance by 20 , and by introducing a space-efficient storage method which reduces GPU RAM requirements by 50 at a slight detriment to performance. Both GPU implementations are over 28 times faster than a single-precision quad-core CPU version utilizing OpenMP. Traditional fluid simulations require large computational resources even for an average sized scene with the main bottleneck being a very small time step size, required to guarantee the stability of the solution. Despite a large progress in parallel computing and efficient algorithms for pressure computation in the recent years, realtime fluid simulations have been possible only under very restricted conditions. In this paper we propose a novel machine learning based approach, that formulates physics-based fluid simulation as a regression problem, estimating the acceleration of every particle for each frame. We designed a feature vector, directly modelling individual forces and constraints from the Navier-Stokes equations, giving the method strong generalization properties to reliably predict positions and velocities of particles in a large time step setting on yet unseen test videos. We used a regression forest to approximate the behaviour of particles observed in the large training set of simulations obtained using a traditional solver. Our GPU implementation led to a speed-up of one to three orders of magnitude compared to the state-of-the-art position-based fluid solver and runs in real-time for systems with up to 2 million particles. We present the first large-scale simulation of blood flow in the coronary artieries and other vessels supplying blood to the heart muscle, with a realistic description of human arterial geometry at spatial resolutions from centimeters down to 10 microns (near the size of red blood cells). This multiscale simulation resolves the fluid into a billion volume units, embedded in a bounding space of 300 billion voxels, coupled with the concurrent motion of 300 million red blood cells, which interact with one another and with the surrounding fluid. The level of detail is sufficient to describe phenomena of potential physiological and clinical significance, such as the development of atherosclerotic plaques. The simulation achieves excellent scalability on up to 294, 912 Blue Gene P computational cores. Recent advances in the parallelizability of fast N-body algorithms, and the programmability of graphics processing units (GPUs) have opened a new path for particle based simulations. For the simulation of turbulence, vortex methods can now be considered as an interesting alternative to finite difference and spectral methods. The present study focuses on the efficient implementation of the fast multipole method and pseudo-particle method on a cluster of NVIDIA GeForce 8800 GT GPUs, and applies this to a vortex method calculation of homogeneous isotropic turbulence. The results of the present vortex method agree quantitatively with that of the reference calculation using a spectral method. We achieved a maximum speed of 7.48 TFlops using 64 GPUs, and the cost performance was near $9.4 GFlops. The calculation of the present vortex method on 64 GPUs took 4120 s, while the spectral method on 32 CPUs took 4910 s.