论文标题
广播:使用算法分化的高阶可压缩CFD工具箱,用于稳定性和灵敏度
BROADCAST: A high-order compressible CFD toolbox for stability and sensitivity using Algorithmic Differentiation
论文作者
论文摘要
任何复杂的动力系统的演变都由其状态导数运算符描述。但是,精确的N级态衍生物运算符的提取通常不准确,需要近似值。称为广播的开源CFD代码可以离散可压缩的Navier-Stokes方程,然后通过算法分化(AD)提取线性化的新启动器,从而为层状流动动态分析提供工具箱。此外,通过线性化操作员的换位或通过AD工具的向后模式提取通过伴随推导的梯度提取。该软件包括通过线性化运算符的特征分类或通过分辨率运算符的单数值分解来分类的基本流量计算和线性全局稳定性分析。灵敏度工具以及弱非线性分析完成包装。方程空间离散的数值方法由在2D曲线结构网格上应用于有限体积框架中应用的有限差分高阶捕获方案。在两种情况下证明了稳定性和灵敏度工具:低马赫数的气缸流量和超音速边界层。
The evolution of any complex dynamical system is described by its state derivative operators. However, the extraction of the exact N-order state derivative operators is often inaccurate and requires approximations. The open-source CFD code called BROADCAST discretises the compressible Navier-Stokes equations and then extracts the linearised Nderivative operators through Algorithmic Differentiation (AD) providing a toolbox for laminar flow dynamic analyses. Furthermore, the gradients through adjoint derivation are extracted either by transposition of the linearised operator or through the backward mode of the AD tool. The software includes base-flow computation and linear global stability analysis via eigen-decomposition of the linearised operator or via singular value decomposition of the resolvent operator. Sensitivity tools as well as weakly nonlinear analysis complete the package. The numerical method for the spatial discretisation of the equations consists of a finite-difference high-order shock-capturing scheme applied within a finite volume framework on 2D curvilinear structured grids. The stability and sensitivity tools are demonstrated on two cases: a cylinder flow at low Mach number and a hypersonic boundary layer.