.. _NICFD:

SU2 DataMiner Configuration for NICFD
=====================================

.. contents:: :depth: 2

    
The following section describes the most important SU2 DataMiner configuration class functions for NICFD applications. 
*SU2 DataMiner* uses *CoolProp* abstract state to calculate thermodynamic states. The equation of state used by *CoolProp* can be set to 
the Helmholtz equation of state ("HEOS") or RefProp ("REFPROP") if a local installation of RefProp is detected. A warning is raised if the equation of state model 
is not recognized by *CoolProp*.

Equation of State and Fluid 
---------------------------


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.SetEquationOfState 


The fluid for which thermodynamic states are generated is specified with the following function. The available list of fluids supported by *CoolProp* can be 
retrieved by running the following code snippet:

::

    from CoolProp.CoolProp import get_global_param_string
    fluids = get_global_param_string("FluidsList").split(',')
    print(fluids)


.. note::

    Support for mixtures of fluids has not yet been validated. 


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.SetFluid


Range of the Thermodynamic State Data 
-------------------------------------


The following functions are used for setting the limits within which thermodynamic states are generated. 
By default, the limits of the fluid data are automatically determined based on the temperature and pressure limits supported by *CoolProp*.

.. autofunction:: Common.DataDrivenConfig.Config_NICFD.UseAutoRange 


The fluid data can be generated on a pressure-termperature grid or a density-static energy grid.


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.UsePTGrid


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.GetPTGrid


The limits of the fluid data grid are set with the following functions 


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.SetTemperatureBounds 


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.SetPressureBounds 


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.SetEnergyBounds 


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.SetDensityBounds



Resolution 
----------


Fluid data are generated on a 2D grid (density-energy or pressure-temperature), for which the number of samples along each direction is set with the following functions 
A linear distribution is used for the temperature and static energy dimension of the data grid, while a cosine distribution is used for the density or pressure dimension to increase the density of the data sets in dilute conditions.
The following distribution was used 


.. math::

    x_i = (x_{\mathrm{min}} - x_{\mathrm{max}}) * \cos(\theta_i) + x_{\mathrm{max}}\quad 0\leq \theta_i \leq \frac{1}{2}\pi,


where :math:`x_{\mathrm{min}}` and :math:`x_{\mathrm{max}}` are the lower and upper bound values.


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.SetNpTemp 


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.SetNpPressure 


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.SetNpEnergy 


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.SetNpDensity



Machine Learning 
----------------

*SU2 DataMiner* can be used to train networks according to the EEoS-PINN method documented in :ref:`Bunschoten et al. (2026) <eeos_article>`. The training loss function is the fitting loss of a set of thermodynamic variables.
By default, these are entropy, pressure, temperature, and the speed of sound, but a custom set can be accessed with the following function.


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.SetStateVars 


.. autofunction:: Common.DataDrivenConfig.Config_NICFD.GetStateVars 




   

References
----------

.. _MLPCpp : https://github.com/EvertBunschoten/MLPCpp.git 

.. _eeos_article:

E.Bunschoten, A.Cappiello, M.Pini "Data-driven regression of thermodynamic models in entropic form using physics-informed machine learning". In: Computers and Fluids 306 (2026), DOI:https://doi.org/10.1016/j.compfluid.2025.106932