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arXiv 2402.03002physics.flu-dyn
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基于人工神经网络的低耗散可压缩单相与多相流重构格式

A low-dissipation reconstruction scheme for compressible single- and multi-phase flows based on artificial neural networks

Minsheng Huang, Lidong Cheng, Wenjun Ying, Xi Deng, Feng Xiao

AI总结:

本文提出基于人工神经网络的deepMTBVD低耗散重构格式,通过ANN为每个单元预选最优重构方案,在保持MUSCL-THINC-BVD格式精度的同时将计算时间最多降低40%。

摘要:

求解同时包含光滑和不连续流动结构的可压缩流动仍然是有限体积法面临的重大挑战。Godunov型有限体积法常用于可压缩流动的数值模拟,获得高质量解的关键因素之一是高保真的空间重构。本文提出了一种构建可压缩流动高分辨率混合重构格式的新范式。该方法基于BVD格式生成监督学习所需的训练数据,并利用人工神经网络(ANN)构建一个指示器,为每个单元预先选择最合适的重构格式,从而实现全局最低的数值耗散。该范式下的数值格式比BVD框架内的类似格式计算效率更高,因为每个单元只需构造一个插值函数。遵循该范式,本文基于MUSCL-THINC-BVD格式提出了一种新型低耗散重构格式,命名为deepMTBVD格式,用于可压缩单相和多相流动。所提格式的性能已通过单相和多相可压缩流动的基准测试得到广泛验证,在这些测试中,激波、接触间断和物质界面等不连续流动结构与不同尺度的涡旋和剪切不稳定性同时共存。数值结果表明,新的deepMTBVD格式在可压缩流动数值模拟中表现与原始MUSCL-THINC-BVD格式相当,同时计算时间最多可减少40%。

英文摘要:

Solving compressible flows containing both smooth and discontinuous flow structures remains a significant challenge for finite volume methods. Godunov-type finite volume methods are commonly used for numerical simulations of compressible flows. One of the key factors in obtaining high-quality solutions is high-fidelity spatial reconstruction. In this work, we introduce a new paradigm for constructing high-resolution hybrid reconstruction schemes for compressible flows. This approach generates training data based on BVD schemes for supervised learning and employs ANN to create an indicator that pre-selects the most suitable reconstruction scheme for each cell, achieving the lowest global numerical dissipation. The numerical schemes under this paradigm are more computationally efficient than similar schemes within the BVD framework, as each cell only requires constructing a single interpolation function. Following this paradigm, a novel low-dissipation reconstruction scheme based on the MUSCL-THINC-BVD scheme, named the deepMTBVD scheme, is proposed for compressible single- and multi-phase flows. The performance of the proposed scheme has been extensively verified through benchmark tests of single- and multi-phase compressible flows, where discontinuous and vortical flow structures, like shock waves, contact discontinuities, and material interfaces, as well as vortices and shear instabilities of different scales, coexist simultaneously. Numerical results indicate that the new deepMTBVD scheme performs as well as the original MUSCL-THINC-BVD scheme for numerical simulations of compressible flows while reducing computational time by up to 40%.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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