论文标题
耦合流体流量的多相湍流闭合稀疏鉴定
Sparse identification of multiphase turbulence closures for coupled fluid--particle flows
论文作者
论文摘要
在这项工作中,开发了用于均质,完全发达的气体流量的多相雷诺平均纳维尔 - 斯托克斯(RANS)方程的模型封闭。迄今为止,大多数租金封闭是基于单相湍流模型的扩展,这些模型无法捕获跨稀释和致密状态的复杂的两相流动动力学,尤其是当相之间的双向耦合很重要时。在本研究中,颗粒在重力下在无界粘性液中定位。在足够的质量载荷下,相之间的相间动量交换导致粒子簇的自发产生,从而在流体中维持速度波动。从Eulerian-Lagrangian模拟产生的数据用于稀疏回归方法,用于确保形成不变性的模型闭合。特别注意对多相传播方程(阻力生产,阻力交换,压力应变和粘性耗散)独有的未挂起的术语进行建模。提出了一组最小的张量,以作为建模的基础。发现稀疏回归确定了紧凑的,代数模型,这些模型在流动条件下是准确的,并且稳健到稀疏的训练数据。
In this work, model closures of the multiphase Reynolds-Average Navier-Stokes (RANS) equations are developed for homogeneous, fully-developed gas--particle flows. To date, the majority of RANS closures are based on extensions of single-phase turbulence models, which fail to capture complex two-phase flow dynamics across dilute and dense regimes, especially when two-way coupling between the phases is important. In the present study, particles settle under gravity in an unbounded viscous fluid. At sufficient mass loadings, interphase momentum exchange between the phases results in the spontaneous generation of particle clusters that sustain velocity fluctuations in the fluid. Data generated from Eulerian--Lagrangian simulations are used in a sparse regression method for model closure that ensures form invariance. Particular attention is paid to modelling the unclosed terms unique to the multiphase RANS equations (drag production, drag exchange, pressure strain and viscous dissipation). A minimal set of tensors is presented that serve as the basis for modelling. It is found that sparse regression identifies compact, algebraic models that are accurate across flow conditions and robust to sparse training data.