Visual Simulation of Heat Shimmering and Mirage

Zhao, Ye; Han, Yiping W.; Fan, Zhe; Qiu, Feng; Kuo, Yu-Chuan; Kaufman, Arie; Mueller, Klaus

doi:10.1109/tvcg.2007.24

Cited by 46 publications

(24 citation statements)

References 25 publications

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“…Zhao et al [30] have used the HTLBM to model the weakly compressible thermal flow for shimmering effects. We adopt it for smoke simulation in an urban environments.…”

Section: Modeling Of Smoke In Urban Environmentmentioning

confidence: 99%

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Efficient LBM Visual Simulation on Face-Centered Cubic Lattices

Petkov

Qiu

Fan

et al. 2009

IEEE Trans. Visual. Comput. Graphics

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Abstract-The Lattice Boltzmann method (LBM) for visual simulation of fluid flow generally employs cubic Cartesian (CC) lattices such as the D3Q13 and D3Q19 lattices for the particle transport. However, the CC lattices lead to suboptimal representation of the simulation space. We introduce the face-centered cubic (FCC) lattice, fD3Q13, for LBM simulations. Compared to the CC lattices, the fD3Q13 lattice creates a more isotropic sampling of the simulation domain and its single lattice speed (i.e., link length) simplifies the computations and data storage. Furthermore, the fD3Q13 lattice can be decomposed into two independent interleaved lattices, one of which can be discarded, which doubles the simulation speed. The resulting LBM simulation can be efficiently mapped to the GPU, further increasing the computational performance. We show the numerical advantages of the FCC lattice on channeled flow in 2D and the flow-past-a-sphere benchmark in 3D. In both cases, the comparison is against the corresponding CC lattices using the analytical solutions for the systems as well as velocity field visualizations. We also demonstrate the performance advantages of the fD3Q13 lattice for interactive simulation and rendering of hot smoke in an urban environment using thermal LBM.

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“…Zhao et al [30] have used the HTLBM to model the weakly compressible thermal flow for shimmering effects. We adopt it for smoke simulation in an urban environments.…”

Section: Modeling Of Smoke In Urban Environmentmentioning

confidence: 99%

“…The force is applied through the velocity moment of the equilibrium distribution, calculated in (23). More details are available in the paper on HTLBM [17] and in the work of Zhao et al [30].…”

Section: Modeling Of Smoke In Urban Environmentmentioning

confidence: 99%

Efficient LBM Visual Simulation on Face-Centered Cubic Lattices

Petkov

Qiu

Fan

et al. 2009

IEEE Trans. Visual. Comput. Graphics

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“…Clusters of GPUs have been used to implement other heavy workload tasks [38], mostly within the simulation and visualization fields. For example, numerical methods for finite element computations used in 3D interactive simulations [39], and nuclear, gas dispersion and heat shimmering simulations [40].…”

Section: Related Workmentioning

confidence: 99%

Non-rigid Registration for Large Sets of Microscopic Images on Graphics Processors

Ruiz

Ujaldón

Cooper

et al. 2008

J Sign Process Syst Sign Image Video Technol

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Microscopic imaging is an important tool for characterizing tissue morphology and pathology. 3D reconstruction and visualization of large sample tissue structure requires registration of large sets of high-resolution images. However, the scale of this problem presents a challenge for automatic registration methods. In this paper we present a novel method for efficient automatic registration using graphics processing units (GPUs) and parallel programming. Comparing a C++ CPU implementation with Compute Unified Device Architecture (CUDA) libraries and pthreads running on GPU we achieve a speed-up factor of up to 4.11× with a single GPU and 6.68× with a GPU pair. We present execution times for a benchmark composed of two sets of large-scale images: mouse placenta (16K × 16K pixels) and breast cancer tumors (23K × 62K pixels). It takes more than 12 hours for the genetic case in C++ to register a typical sample composed of 500 consecutive slides, which was reduced to less than 2 hours using two GPUs, in addition to a very promising scalability for extending those gains easily on a large number of GPUs in a distributed system.

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“…An index function similar to distance field was combined with the LBM to track the interface in multiphase flow dynamics [11]. In computer graphics and visualization, the LBM was used to visually simulate fluid flow phenomena with complex boundary conditions, such as wind flow and fire [37,27], ink dispersion [4], free surface liquids [35,36], floating objects [38], melting and flowing [45], fluid control [34], heat shimmering and mirage [44]. A level-of-detail scheme was adopted to simulate 3D fluid dynamics based on the locally-refined LBM on multiple grids, which improves the simulation accuracy and performance.…”

Section: Related Workmentioning

confidence: 99%

GPU-accelerated surface denoising and morphing with lattice Boltzmann scheme

Zhao

2008

2008 IEEE International Conference on Shape Modeling and Applications

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In this paper, we introduce a parallel numerical scheme, the lattice Boltzmann method, to shape modeling applications. The motivation of using this originally-designed fluid dynamics solver in surface modeling is its simplicity, locality, parallelism from the cellular-automata-originated updating rules, which can directly be mapped onto modern graphics hardware. A surface is implicitly represented by the signed distance field. The distances are then used in a modified LBM scheme as its computing primitive, instead of the densities in traditional LBM. The scheme can simulate curvature motions to smooth the surface with a diffusion process. Furthermore, an initial value level set method can be implemented for surface morphing. The distance difference between a morphing surface and a target surface defines the speed function of the evolving level sets, and is used as the driving force in the LBM. Our GPUaccelerated LBM algorithm has achieved outstanding performance for the denoising and morphing examples. It has the great potential to be further applied as a general GPU computing framework to many other solid and shape modeling applications.

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Visual Simulation of Heat Shimmering and Mirage

Cited by 46 publications

References 25 publications

Efficient LBM Visual Simulation on Face-Centered Cubic Lattices

Efficient LBM Visual Simulation on Face-Centered Cubic Lattices

Non-rigid Registration for Large Sets of Microscopic Images on Graphics Processors

GPU-accelerated surface denoising and morphing with lattice Boltzmann scheme

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