Variables | |
char * | algo |
char * | est_error_pre_2 |
char * | est_error_der_2 |
char * | est_error_cor_2 |
char * | est_error_tot_2 |
algo |
Fields used to check algorithm behavior.
Fields of the form "algo:<type>_<variable_name>" are reserved by the code to allow visualization of some intermediate computational field values.
If the user creates such a field, and the code calls the matching operator, this field will be updated automatically.
Current reserved fields are:
algo:predicted_velocity
Velocity field after the prediction step, cell-based field of dimension 3. algo:predicted_vel_divergence
Divergence of the velocity field after the prediction step, cell-based field of dimension 1. algo:gradient_pressure
Pressure gradient, cell-based field of dimension 3. algo:gradient_
supplemented with a transported field name Field gradient, cell-based field of dimension 3x the dimension of the corresponding field. algo:gradient_velocity
Velocity gradient, cell-based field of dimension 3x3. algo:gradient_pressure_increment
Gradient of the pressure increment solved in the correction step, cell-based field of dimension 3. algo:rij_divergence
Divergence of the Reynolds stress in the momentum equation for Reynolds stress RANS models, cell-based field of dimension 3. algo:rij_production
So called production term in Reynolds stress RANS models, cell-based field of dimension 6. algo:rij_pressure_strain_correlation
So called pressure strain correlation term in Reynolds stress RANS models, cell-based field of dimension 6. algo:rij_buoyancy
So called buoyancy term in Reynolds stress RANS models, cell-based field of dimension 6. algo:tke_production
So called production term in k-epsilon RANS models, cell-based field of dimension 1. algo:tke_buoyancy
So called buoyancy term in k-epsilon RANS models, cell-based field of dimension 1. algo:turbulent_flux_divergence
Divergence of the turbulent flux in the energy equation, cell-based field of dimension 1. algo:grad_clip_factor_<variable_name>
Least-squares gradient clip factor if gradient clipping is activated (scalar field on cells). algo:grad_b_iter_<variable_name>
Number of iterations for convergence of fixed-point algorithm for least-squares boundary gradient of vector and tensor fields. (scalar field on boundary faces). est_error_cor_2 |
Error estimator for Navier-Stokes: correction.
The estimator \( \eta^{\,corr}_{\,i,k}(\vect{u}^{\,n+1})\) comes directly from the mass flow calculated with the updated velocity field:
\begin{eqnarray*} \eta^{\,corr}_{\,i,k}(\vect{u}^{\,n+1})= |\Omega_i|^{\,\delta_{\,2,k}}\ |div (\rho^n \vect{u}^{n+1}) - \Gamma| \end{eqnarray*}
est_error_der_2 |
Error estimator for Navier-Stokes: drift.
The estimator \(\eta^{\,der}_{\,i,k}(\vect{u}^{\,n+1})\) is based on the following quantity (intrinsic to the code):
\begin{eqnarray*} \eta^{\,der}_{\,i,k}(\vect{u}^{\,n+1}) &=& {|\Omega_i|}^{(k-2)/2} || \divs (\text{corrected mass flow after the pressure step}) - \Gamma||_{{L}^{2}(\Omega_i)} \\ &=& {|\Omega_i|}^{(1-k)/2} | \divs (\text{corrected mass flow after the pressure step})- \Gamma| \end{eqnarray*}
est_error_pre_2 |
Error estimator for Navier-Stokes: prediction.
After the velocity prediction step (yielding \(\vect{u}^*\)), the estimator \(\eta^{\,pred}_{\,i,k}(\vect{u}^*)\), local variable calculated at every cell \( \Omega_i \), is created from \(\vect{\mathcal R}^{\,pred}(\vect{u}^*)\), which represents the residual of the equation solved during this step: \(\vect{u}\) and \( P \):
\begin{eqnarray*} \vect{\mathcal R}^{\,pred}(\vect{u}^*) & = & \rho^n \dfrac{\vect{u}^*-\vect{u}^n}{\Delta t} + \rho^n \vect{u}^n \cdot \gradt (\vect{u}^*) - \divv \left((\mu+\mu_t)^n \gradt (\vect{u}^*) \right) + \grad(P^n) \\ & - & \text{rest of the right-hand member } (\vect{u}^n, P^n, \text{other variables}^n) \end{eqnarray*}
est_error_tot_2 |
Error estimator for Navier-Stokes: total.
The estimator \( \eta^{\,tot}_{\,i,k}(\vect{u}^{\,n+1})\), local variable calculated at every cell \(\Omega_i\), is based on the quantity \(\vect{\mathcal R}^{\,tot}(\vect{u}^{\,n+1})\), which represents the residual of the equation using the updated values of \(\vect{u}\) and \(P\):
\begin{eqnarray*} \vect{\mathcal R}^{\,pred}(\vect{u}^*) & = & \rho^n \dfrac{\vect{u}^*-\vect{u}^n}{\Delta t} + \rho^n \vect{u}^n \cdot \gradt (\vect{u}^*) - \divv \left((\mu+\mu_t)^n \gradt (\vect{u}^*) \right) + \grad(P^n) \\ & - & \text{rest of the right-hand member } (\vect{u}^n, P^n, \text{other variables}^n) \end{eqnarray*}