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A generalised and low-dissipative multi-directional characteristics-based scheme with inclusion of the local Riemann problem investigating incompressible flows without free-surfaces

机译:基于广义和低耗散的多向特征的方案,包括局部riemann问题研究没有自由表面的不可压缩流动

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In the present study, we develop a generalised Godunov-type multi-directional characteristics-based (MCB) scheme which is applicable to any hyperbolic system for modelling incompressible flows. We further extend the MCB scheme to include the solution of the local Riemann problem which leads to a hybrid mathematical treatment of the system of equations. We employ the proposed scheme to hyperbolic-type incompressible flow solvers and apply it to the Artificial Compressibility (AC) and Fractional-Step, Artificial Compressibility with Pressure Projection (FSAC-PP) method. In this work, we show that the MCB scheme may improve the accuracy and convergence properties over the classical single-directional characteristics-based (SCB) and non-characteristic treatments. The inclusion of a Riemann solver in conjunction with the MCB scheme is capable of reducing the number of iterations up to a factor of 4.7 times compared to a solution when a Riemann solver is not included. Furthermore, we found that both the AC and FSAC-PP method showed similar levels of accuracy while the FSAC-PP method converged up to 5.8 times faster than the AC method for steady state flows. Independent of the characteristics and Riemann solver-based treatment of all primitive variables, we found that the FSAC-PP method is 7-200 times faster than the AC method per pseudo-time step for unsteady flows. We investigate low- and high-Reynolds number problems for well-established validation benchmark test cases focusing on a flow inside of a lid driven cavity, evolution of the Taylor-Green vortex and forced separated flow over a backward-facing step. In addition to this, comparisons between a central difference scheme with artificial dissipation and a low-dissipative interpolation scheme have been performed. The results show that the latter approach may not provide enough numerical dissipation to develop the flow at high-Reynolds numbers. We found that the inclusion of a Riemann solver is able to overcome this s
机译:在本研究中,我们开发了一种基于Godunov型多向特征的(MCB)方案,适用于任何用于建模不可压缩流动的双曲线系统。我们进一步扩展了MCB方案,包括本地riemann问题的解决方案,导致方程式的混合数学处理。我们采用所提出的方案来双曲型不可压缩的流量溶剂,并将其应用于人工压缩性(AC)和分数步骤,具有压力投影(FSAC-PP)方法的人工压缩性。在这项工作中,我们表明MCB方案可以通过基于经典的单向特征(SCB)和非特征处理来提高精度和收敛性。与MCB方案结合使用Riemann求解器的包含能够将迭代的数量减少到与Riemann求解器不包括的解决方案相比的迭代次数为4.7倍。此外,我们发现,AC和FSAC-PP方法都显示出类似的精度水平,而FSAC-PP方法比稳态流动的AC方法达到高达5.8倍。独立于所有原始变量的特征和基于Riemann求解器的处理,我们发现FSAC-PP方法比不稳定流量的每个伪时间步骤的AC方法快7-200倍。我们研究了良好建立的验证基准测试用例的低雷诺数问题,其专注于盖子驱动腔内的流动,泰勒 - 绿色涡流的演变,并在后面的步骤中强制分离流动。除此之外,已经进行了具有人工耗散和低耗散插值方案的中心差分方案之间的比较。结果表明,后一种方法可能无法提供足够的数值耗散,以在高雷诺数的情况下开发流量。我们发现包含riemann求解器能够克服这一点

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