IU Indianapolis ScholarWorks :: Browsing by Author "Zhu, Luoding"

Browsing by Author "Zhu, Luoding"

Now showing 1 - 10 of 23

3D simulation of a viscous flow past a compliant model of arteriovenous-graft annastomosis
(Elsevier, 2019-03) Bai, Zengding; Zhu, Luoding; Mathematical Sciences, School of Science
Hemodialysis is a common treatment for end-stage renal-disease patients to manage their renal failure while awaiting kidney transplant. Arteriovenous graft (AVG) is a major vascular access for hemodialysis but often fails due to the thrombosis near the vein-graft anastomosis. Almost all of the existing computational studies involving AVG assume that the vein and graft are rigid. As a first step to include vein/graft flexibility, we consider an ideal vein-AVG anastomosis model and apply the lattice Boltzmann-immersed boundary (LB-IB) framework for fluid-structure-interaction. The framework is extended to the case of non-uniform Lagrangian mesh for complex structure. After verification and validation of the numerical method and its implementation, many simulations are performed to simulate a viscous incompressible flow past the anastomosis model under pulsatile flow condition using various levels of vein elasticity. Our simulation results indicate that vein compliance may lessen flow disturbance and a more compliant vein experiences less wall shear stress (WSS).
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.
An efficient immersed boundary-lattice Boltzmann method for the hydrodynamic interaction of elastic filaments
(Elsevier, 2011) Tian, Fang-Bao; Luo, Haoxiang; Zhu, Luoding; Liao, James C.; Lu, Xi-Yun; Mathematical Sciences, School of Science
We have introduced a modified penalty approach into the flow-structure interaction solver that combines an immersed boundary method (IBM) and a multi-block lattice Boltzmann method (LBM) to model an incompressible flow and elastic boundaries with finite mass. The effect of the solid structure is handled by the IBM in which the stress exerted by the structure on the fluid is spread onto the collocated grid points near the boundary. The fluid motion is obtained by solving the discrete lattice Boltzmann equation. The inertial force of the thin solid structure is incorporated by connecting this structure through virtual springs to a ghost structure with the equivalent mass. This treatment ameliorates the numerical instability issue encountered in this type of problems. Thanks to the superior efficiency of the IBM and LBM, the overall method is extremely fast for a class of flow-structure interaction problems where details of flow patterns need to be resolved. Numerical examples, including those involving multiple solid bodies, are presented to verify the method and illustrate its efficiency. As an application of the present method, an elastic filament flapping in the Kármán gait and the entrainment regions near a cylinder is studied to model fish swimming in these regions. Significant drag reduction is found for the filament, and the result is consistent with the metabolic cost measured experimentally for the live fish.
Application of Machine Learning to GPU Optimization, Deep Q-Networks and Computational Fluid Dynamics
(2025-05) Zigon, Robert J.; Song, Fengguang; Zhu, Luoding; Tuceryan, Mihran; Fang, Shiaofen
Throughout society today, machine learning has been catapulted to a transformative problem solving approach across various domains, ranging from natural language processing to computer vision to engineering optimization. The fundamental principle is the ability of algorithms to learn patterns and make decisions based on data, rather than relying on explicitly programmed instructions. This dissertation addresses the research question: “How can machine learning techniques be applied to improve computational efficiency and prediction accuracy in high-performance scientific computing tasks, including GPU kernel optimization, Deep Q-Networks, and computational fluid dynamics?” To answer the question, we devised three distinct problems, each of which is orthogonal to the next to represent a wide breadth of exploration. The problems focus on the two paradigms of supervised learning and reinforcement learning.
Computational Methods and Models in Circulatory and Reproductive Systems
(Hindawi, 2016) Tian, Fang-Bao; Sui, Yi; Zhu, Luoding; Shu, Chang; Sung, Hyung J.; Department of Mathematical Sciences, School of Science
A deformable plate interacting with a non-Newtonian fluid in three dimensions
(AIP, 2017-08) Zhu, Luoding; Yu, Xijun; Liu, Nansheng; Cheng, Yongguang; Lu, Xiyun; Mathematical Sciences, School of Science
We consider a deformable plate interacting with a non-Newtonian fluid flow in three dimensions as a simple model problem for fluid-structure-interaction phenomena in life sciences (e.g., red blood cell interacting with blood flow). A power-law function is used for the constitutive equation of the non-Newtonian fluid. The lattice Boltzmann equation (the D3Q19 model) is used for modeling the fluid flow. The immersed boundary (IB) method is used for modeling the flexible plate and handling the fluid-plate interaction. The plate drag and its scaling are studied; the influences of three dimensionless parameters (power-law exponent, bending modulus, and generalized Reynolds number) are investigated.
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.
An IB Method for Non-Newtonian-Fluid Flexible-Structure Interactions in Three-Dimensions
(Tech Science Press, 2019) Zhu, Luoding; Mathematical Sciences, School of Science
Problems involving fluid flexible-structure interactions (FFSI) are ubiquitous in engineering and sciences. Peskin’s immersed boundary (IB) method is the first framework for modeling and simulation of such problems. This paper addresses a three-dimensional extension of the IB framework for non-Newtonian fluids which include power-law fluid, Oldroyd-B fluid, and FENE-P fluid. The motion of the non-Newtonian fluids are modelled by the lattice Boltzmann equations (D3Q19 model). The differential constitutive equations of Oldroyd-B and FENE-P fluids are solved by the D3Q7 model. Numerical results indicate that the new method is first-order accurate and conditionally stable. To show the capability of the new method, it is tested on three FFSI toy problems: a power-law fluid past a flexible sheet fixed at its midline, a flexible sheet being flapped periodically at its midline in an Oldroyd-B fluid, and a flexible sheet being rotated at one edge in a FENE-P fluid.
Interactive 3D simulation for fluid–structure interactions using dual coupled GPUs
(Springer, 2018-01) Zigon, Bob; Zhu, Luoding; Song, Fengguang; Mathematical Sciences, School of Science
The scope of this work involves the integration of high-speed parallel computation with interactive, 3D visualization of the lattice-Boltzmann-based immersed boundary method for fluid–structure interaction. An NVIDIA Tesla K40c is used for the computations, while an NVIDIA Quadro K5000 is used for 3D vector field visualization. The simulation can be paused at any time step so that the vector field can be explored. The density and placement of streamlines and glyphs are adjustable by the user, while panning and zooming is controlled by the mouse. The simulation can then be resumed. Unlike most scientific applications in computational fluid dynamics where visualization is performed after the computations, our software allows for real-time visualizations of the flow fields while the computations take place. To the best of our knowledge, such a tool on GPUs for FSI does not exist. Our software can facilitate debugging, enable observation of detailed local fields of flow and deformation while computing, and expedite identification of ‘correct’ parameter combinations in parametric studies for new phenomenon. Therefore, our software is expected to shorten the ‘time to solution’ process and expedite the scientific discoveries via scientific computing.
IUPUI Center for Mathematical Biosciences
(Office of the Vice Chancellor for Research, 2010-04-09) Boukai, Benzion; Chin, Ray; Dziubek, Andrea; Fokin, Vladimir; Ghosh, Samiran; Kuznetsov, Alexey; Li, Fang; Li, Jiliang; Rader, Andrew; Rubchinsky, Leonid; Sarkar, Jyotirmoy; Guidoboni, Giovanna; Worth, Robert; Zhu, Luoding
At-Large Mission: “to serve as an umbrella center for spearheading research and programmatic activities in the general bio-mathematics area” • promote and facilitate faculty excellence in mathematical and Computational research in the biosciences; • provide a mechanism and an environment that fosters collaborative research activities across the mathematical sciences and the life and health sciences schools at IUPUI— specifically with the IUSOM; • provide foundations and resources for further strategic development in targeted areas of mathematical and computational biosciences research; and • create greater opportunities and increase competitiveness in seeking and procuring extramural funding.

Browsing by Author "Zhu, Luoding"

Results Per Page

Sort Options