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非均匀矩形网格的局部网格细化LBM算法研究

RESEARCH ON THE LATTICE BOLTZMANN ALGORITHM FOR GRID REFINEMENT BASED ON NON-UNIFORM RECTANGULAR GRID

  • 摘要: 传统的格子玻尔兹曼方法(LBM), 特别是基于均匀正方形网格的经典单松弛计算模型(SLBM), 其算法鲁棒性和数值稳定性较差, 限制了LBM的发展和应用. 而网格细化策略可以有效缓解这一窘境, 但是传统LBM中网格细化必然会导致计算效率骤降, 计算设备要求攀高. 为了解决这一问题, 文章基于非均匀矩形网格结构, 结合插值LBM算法的思路, 在保证物面处和流动变化剧烈区域的局部网格细化以及计算精度的前提下, 提出了25点拉格朗日插值LBM算法. 以经典顶盖驱动方腔内流为算例, 开展了包括不同网格分辨率和插值格式的对比分析研究. 验证算例既包括了定常流动的数值模拟, 也涉及了非定常周期性流动的求解. 计算结果表明, 相较于其他插值格式, 拉格朗日插值格式表现优异; 文章局部网格细化工作可以确保物面处及流动变化剧烈区域流动细节的捕捉; 数值模拟算法可以为数值仿真提供可信的计算结果; 同时大幅降低了总网格数量. 因此很大程度上提升了计算效率; 数值模拟方法鲁棒性较好, 适用于包括定常和非定常流动的数值模拟.

     

    Abstract: The traditional lattice Boltzmann method (LBM), especially the classic single-relaxation model (SLBM) based on the uniform square grid, has poor robustness and numerical stability, which limits the development and applications of LBM. Grid refinement strategy can effectively alleviate this dilemma, however for the traditional LBM, the grid refinement will inevitably lead to a sudden drop in computational efficiency and a rise in equipment requirements. Therefore, in order to solve this problem, based on the non-uniform rectangular grid, combined with the idea of interpolation LBM, the 25-bit Lagrangian interpolation LBM is proposed on the premise of ensuring the local grid refinement for the surfaces and area with severe flow changes, and the computational accuracy as well. Taking the classic lid-driven cavity flow for instance, a comparative analysis including different grid resolutions and interpolation schemes is performed. The verification includes both the numerical simulations of steady states and unsteady periodic solutions. The results show that the Lagrangian interpolation scheme performs better than other interpolation schemes. In this paper, the local grid refinement is able to ensure the capture of the flow details adjacent to surfaces and in the area of intense flow changes. The numerical algorithm can provide reliable results for numerical simulations. Meanwhile, the total grid number is greatly reduced, as a result the computational efficiency is greatly improved; The numerical simulation method has good robustness and is suitable for numerical simulations for both steady states and unsteady solutions.

     

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