fWaveFlux¶

The fWaveFlux uses a Riemann decomposition to determine the upwind direction.

$\notag \begin{equation} \frac{\partial q}{\partial t}+\nabla\cdot f\left(q,v\right)=0 \end{equation}$

is linearized in 1d to produce

$\notag \begin{equation} \frac{\partial q}{\partial t}+\frac{\partial f}{\partial q}\frac{\partial q}{\partial x}=0 \end{equation}$

The Jacobian $\frac{\partial f}{\partial q}$ contains information about wave speeds and direction. Applying an eigenvalue decomposition to the Jacobian we have $\frac{\partial f}{\partial q}=R\,\Lambda\,L$ where $R$ is the matrix of right eigenvectors, $L$ is the matrix of left eigenvectors and $\Lambda$ is the diagonal matrix of eigenvalues denoted by $\lambda_{\alpha}$ , or wave speeds. The sign of these waves tells us which direction is upwind.

The left eigenvector matrix $L$ is applied to the fluxes at the face of a cell so that $\beta^{+}_{\alpha}=\left[L\,f^{+}_{i}\right]_{\alpha}$ , $\beta^{-}_{\alpha}=\left[L\,f^{-}_{i+1}\right]_{\alpha}$ , $\omega^{+}_{\alpha}=\left[L\,q^{+}_{i}\right]_{\alpha}$ and $\omega^{-}_{\alpha}=\left[L\,q^{-}_{i+1}\right]_{\alpha}$ . The fluxes are then chosen based on the sign of the eigenvalues, therefore

$\notag \begin{align} \beta_{\alpha} =\left( \begin{array}{cc} \beta^{-}_{\alpha} & \mbox{if } \lambda_{\alpha}<0 \\ \beta^{+}_{\alpha} & \mbox{if } \lambda_{\alpha}>0 \\ \frac{1}{2}\left(\beta^{+}_{\alpha}+\beta^{-}_{\alpha}\right)-\frac{1}{2}|\lambda_{max}|\left(\omega^{-}_{\alpha}-\omega^{+}_{\alpha}\right) & \mbox{otherwise} \\ \end{array}\right) \end{align}$

where $\lambda_{max}$ is the maximum eigenvalue of the system (in absolute value) at the interface.

The transformed flux is the rotated back to give

$\notag \begin{equation} \tilde{f}_{i+1/2}=R\beta\,. \end{equation}$

Riemann decompositions typically produce the least diffusive fluxes, however they do not preserve positivity of pressure and density so are typically not used in problems with vacuum regions.

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fWaveFlux¶