Clicking on the 'Solver->Parameters' menu, the next panel is opened.
Solver parameters panel
Options: Allows the user to enable/disable the different terms of the thermal equation.
- Absorption (abs): Heat transferred by the sun to the illuminated facets.
- Impressed (impr): Thermal power transferred by Cooling/Heating sources with variable temperature. The user can delete the variable sources to disable it.
- Transmission (trans): Transmission of power because of material conduction. It is only taken into account for objects/surfaces with duplicated normal vectors.
- Convection (conv): Determines the temperature variation because of the wind and the geometry movement.
- Conduction (cond): Determines the transference of thermal power between neighbouring facets that share an edge.
- Radiation (rad): Determines the transference of thermal power due to radiation of surfaces set as sources or with duplicated normals. If this box selected all the facets whose normals are facing surfaces sources or duplicated surfaces willl receive radiated power from them.
Iterative process: The iterative process finish when the facets reach a steady state temperature, or the maximum number of iterations is reached.
- Min. Iterations: The simulation process makes use of an iterative process. This setting sets the minimum iterations required before checking if convergence error is satisfied.
- Max. Iterations: This setting can limit the number of iterations performed by the calculation in order to prevent simulations that don't converge simulating infinititely.
- Convergence Error: The Convergence error parameter indicates the maximum error allowed in the iterative method in order to reach a solution. This means that a smaller gap or equal to the value of this parameter in the temperature of the facet on two consecutive iterations are considered stable. The value used for this parameter is 0.0025 Kelvin as negligible difference.
- Thermal Inertia Scaling: Multiplies the thermal inertia in the equation. This makes the temperature variations are greater in each iteration if the factor is between 0 and 1, this can speed up the convergence. If the factor is very aggressive you can get erroneous results.