Enhancing Fluid Modeling with Turbulence and Acceleration

Chen Fan
Kent State University, College of Arts and Sciences, Department of Computer Science
Kent State University, 2015


   author={Chen, Fan},


   school={Kent State University}


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In this dissertation, we have proposed our solutions to four important and challenging topics in enhancing fluid modeling with turbulence and acceleration: distance field representation of obstacles in fluid, adaptive and controllable turbulence enhancement, Langevin Particles and GPU acceleration in fluid modeling. All these fields aims at creating realistic and fast fluid field which are significant in Computer Graphics. In summary, our main contributions of this dissertation can be generalized as follows: We proposed a novel distance field transform method based on an iterative method adaptively performed on an evolving active band; We introduced a new scheme for enhancing fluid fluctuated by turbulent variation mod- eled as a random process of forcing; Langevin particles we introduced imposes agitation forces in a self-adaptive manner to inject turbulence energy into flow simulations; To achieve fast fluid modeling, we accelerated LBM solver, FTLE field computation and fluid decompression.
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