Browsing by Subject "Lattice Boltzmann method"

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    Accuracy improvement of the immersed boundary–lattice Boltzmann coupling scheme by iterative force correction
    (Elsevier, 2016-01) Zhang, Chunze; Cheng, Yongguang; Zhu, Luoding; Wu, Jiayang; Department of Mathematical Sciences, School of Science
    The non-slip boundary condition at solid walls cannot be accurately achieved by the conventional immersed boundary–lattice Boltzmann (IB–LB) coupling schemes due to insufficient interpolation accuracy. To solve this problem, an iterative force correction procedure for the IB–LB coupling scheme is proposed. Cheng’s external forcing term in the LB equation is selected to properly incorporate the present and the next time step effects. The unknown IB force and the corresponding force on fluid at the next time step are calculated by iterative correction, based on the known immersed boundary speed, flow velocity, and the relationship between the IB speed and the IB force. Instead of the Dirac delta function, the Lagrange interpolation polynomial is used to obtain the IB speed from nearby fluid velocity. Typical cases, including the flow around a circular cylinder, shearing flow near a non-slip wall, and circular Couette flow between two inversely rotating cylinders, are simulated to verify and validate the method. It is shown that the present method guarantees the non-slip boundary condition and maintain the overall first-order spatial convergence rate of the conventional immersed boundary method (IBM). The accuracy improvement is obvious for both stationary and moving solid boundaries in both viscous flows and strong shearing flows. To demonstrate application possibility, a mechanical heart valve flow is also simulated, and better agreements with experimental data are achieved compared to those by commercial software.
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    Designing a Parallel Memory-Aware Lattice Boltzmann Algorithm on Manycore Systems
    (IEEE, 2018-09) Fu, Yuankun; Li, Feng; Song, Fengguang; Zhu, Luoding; Computer and Information Science, School of Science
    Lattice Boltzmann method (LBM) is an important computational fluid dynamics (CFD) approach to solving the Naiver-Stokes equations and simulating complex fluid flows. LBM is also well known as a memory bound problem and its performance is limited by the memory access time on modern computer systems. In this paper, we design and develop both sequential and parallel memory-aware algorithms to optimize the performance of LBM. The new memory-aware algorithms can enhance data reuses across multiple time steps to further improve the performance of the original and fused LBM. We theoretically analyze the algorithms to provide an insight into how data reuses occur in each algorithm. Finally, we conduct experiments and detailed performance analysis on two different manycore systems. Based on the experimental results, the parallel memory-aware LBM algorithm can outperform the fused LBM by up to 292% on the Intel Haswell system when using 28 cores, and by 302 % on the Intel Skylake system when using 48 cores.
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    Fully parallelized Lattice Boltzmann scheme for fast extraction of biomedical geometry
    (Elsevier, 2019-06) Wang, Zhiqiang; Zhao, Ye; Yu, Huidan (Whitney); Lin, Chen; Sawchuck, Alan P.; Mechanical and Energy Engineering, School of Engineering and Technology
    We develop a fully parallel numerical method which quickly performs 2D and 3D segmentation on GPU to extract anatomical structures from medical images. The algorithm solves the level set equations completely within a Lattice Boltzmann model (LBM). Compared with existing LBM-based segmentation approaches, a parallel distance field regularization is added to the LBM computing scheme to keep computation stable with large time step iteration. This approach also avoids external regularization which has been a major impediment to direct parallelization of level set evolution with LBM. It allows the whole computing process to be efficiently executed on GPU. Moreover, the method can be incorporated with different image features to adopt in various image segmentation tasks. Therefore, our method enables fully GPU accelerated geometric extraction from medical images, leading to high computing performance which is demanded in many practical applications. This method is used to exactly accurate 2D and 3D anatomical structures from many real world CT and MRI images. The achieved results can also directly feed required boundary information to LBM-based hemodynamics simulation.