Matching Items (2)
Description
This dissertation describes a process for interface capturing via an arbitrary-order, nearly quadrature free, discontinuous Galerkin (DG) scheme for the conservative level set method (Olsson et al., 2005, 2008). The DG numerical method is utilized to solve both advection and reinitialization, and executed on a refined level set grid (Herrmann, 2008) for effective use of processing power. Computation is executed in parallel utilizing both CPU and GPU architectures to make the method feasible at high order. Finally, a sparse data structure is implemented to take full advantage of parallelism on the GPU, where performance relies on well-managed memory operations.
With solution variables projected into a kth order polynomial basis, a k+1 order convergence rate is found for both advection and reinitialization tests using the method of manufactured solutions. Other standard test cases, such as Zalesak's disk and deformation of columns and spheres in periodic vortices are also performed, showing several orders of magnitude improvement over traditional WENO level set methods. These tests also show the impact of reinitialization, which often increases shape and volume errors as a result of level set scalar trapping by normal vectors calculated from the local level set field.
Accelerating advection via GPU hardware is found to provide a 30x speedup factor comparing a 2.0GHz Intel Xeon E5-2620 CPU in serial vs. a Nvidia Tesla K20 GPU, with speedup factors increasing with polynomial degree until shared memory is filled. A similar algorithm is implemented for reinitialization, which relies on heavier use of shared and global memory and as a result fills them more quickly and produces smaller speedups of 18x.
With solution variables projected into a kth order polynomial basis, a k+1 order convergence rate is found for both advection and reinitialization tests using the method of manufactured solutions. Other standard test cases, such as Zalesak's disk and deformation of columns and spheres in periodic vortices are also performed, showing several orders of magnitude improvement over traditional WENO level set methods. These tests also show the impact of reinitialization, which often increases shape and volume errors as a result of level set scalar trapping by normal vectors calculated from the local level set field.
Accelerating advection via GPU hardware is found to provide a 30x speedup factor comparing a 2.0GHz Intel Xeon E5-2620 CPU in serial vs. a Nvidia Tesla K20 GPU, with speedup factors increasing with polynomial degree until shared memory is filled. A similar algorithm is implemented for reinitialization, which relies on heavier use of shared and global memory and as a result fills them more quickly and produces smaller speedups of 18x.
ContributorsJibben, Zechariah J (Author) / Herrmann, Marcus (Thesis advisor) / Squires, Kyle (Committee member) / Adrian, Ronald (Committee member) / Chen, Kangping (Committee member) / Treacy, Michael (Committee member) / Arizona State University (Publisher)
Created2015
Description
Identifying and tracking the location of the fluid interface is a fundamental aspect of multiphase flows. The Volume of Fluid (VOF) and Level Set methods are widely used to track the interface accurately. Analyzing the liquid structures such as sheets, ligaments, and droplets helps understand the flow physics and fluid breakup mechanism, aids in predicting droplet formation, improves atomization modeling and spray combustion. The thesis focuses on developing a new method to identify these liquid structures and devise a sphere model for droplet size prediction by augmenting concepts of linear algebra, rigid body dynamics, computational fluid mechanics, scientific computing, and visualization. The first part of the thesis presents a new approach to classify the fluid structures based on their length scales along their principal axes. This approach provides a smooth tracking of the structures' generation history instead of relying on high-speed video imaging of the experiment. A droplet is observed to have three equal length scales, while a ligament has one and a sheet has two significantly larger length scales. The subsequent breakup of ligaments and droplets depends on the atomizer geometry, operating conditions, and fluid physical properties. While it's straightforward to apply DNS and estimate this breakup, it is proven to be computationally expensive. The second part of the thesis deals with developing a sphere model that would essentially reduce this computational cost. After identifying a liquid structure, the sphere model utilizes the level set data in the domain to quantify the structure using spheres. By using the evolution information of these spheres as they separate from each other, the subsequent droplet size distribution can be evaluated.
ContributorsKashetty, Sindhuja (Author) / Herrmann, Marcus (Thesis advisor) / Wells, Valana (Committee member) / Kim, Jeonglae (Committee member) / Arizona State University (Publisher)
Created2021