rizer.io.thermo_transport_data_reader#

Attributes#

CH4_lte_data_minplascalc

Classes#

ThermoTransportDataReader

Load and process the thermodynamic and transport data for a gas, assuming LTE.

Module Contents#

class rizer.io.thermo_transport_data_reader.ThermoTransportDataReader(gas_name: str, pressure_atm: int, source: str, skip_missing_values: bool = False)#

Load and process the thermodynamic and transport data for a gas, assuming LTE.

LTE (Local Thermodynamic Equilibrium) means that thermal and chemical equilibrium are established. The data is assumed to be given at a fixed pressure.

Methods are provided to interpolate the data and plot it. The interpolations are linear, and out-of-bounds values take the value at the boundary.

Parameters:

gas_name (str) – Gas used for the thermo/transport data.
pressure_atm (int) – Pressure (in atm) for the thermo/transport data.
source (str) – Source used for the thermo/transport data.
skip_missing_values (bool, optional) – Whether to skip missing values in the data file (default is False).

Examples

Plot thermo data vs. temperature for a plasma of H2, O2 or N2 in LTE.

Plot mass enthalpy vs. temperature for methane and hydrogen.

Plot thermodynamic properties of H₂ vs. temperatures.

Plot thermodynamic properties of CH₄ vs. temperatures.

Plot thermodynamic and transport data vs. temperature for a plasma of Air, O2 or N2 in LTE.

Plot thermodynamic and transport data vs. temperature for a plasma of H2, O2, N2, Ar, He in LTE.

Compare thermal conductivity vs. temperature for a plasma of methane.

Plot electrical conductivity vs. temperature for a plasma of methane.

Plot transport data vs. temperature for a plasma of hydrogen.

Plot transport data vs. temperature for a plasma of methane.

skip_missing_values = False#

temperature#

density#

enthalpy#

heat_capacity_constant_pressure#

dynamic_viscosity#

thermal_conductivity#

electrical_conductivity#

thermal_diffusivity#

theta#

load_data() → tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]#

Load the data from the files.

Files are expected to be in ./data/LTE_thermo_transport.

The thermo/transport data file is expected to be a CSV file with the following columns:

Temperature [K]
Density [kg/m3]
Enthalpy [J/kg]
Specific heat [J/kg.K]
Viscosity [kg/m.s]
Thermal conductivity [W/m.K]
Electrical conductivity [S/m]

It is assumed that the data is sorted by increasing temperature. It is also assumed that the header is 3 lines long.

Returns:

Tuple containing the following arrays:

temperature: Temperature [K]
density: Density [kg/m3]
enthalpy: Enthalpy [J/kg]
cp: Specific heat at constant pressure [J/kg.K]
dynamic_viscosity: Viscosity [kg/m.s]
thermal_conductivity: Thermal conductivity [W/m.K]
electrical_conductivity: Electrical conductivity [S/m]

Return type:

tuple of numpy.ndarray

compute_theta() → numpy.ndarray#

Compute the integrated thermal conductivity.

Notes

The integrated thermal conductivity is defined as:

\[\theta = \int_0^{T} \kappa(T') dT'\]

It is computed using the trapezoidal rule.

rho(T: float) → float#

Get the density at a given temperature.

Parameters:: T (float) – Temperature [K].
Returns:: Density [kg/m^3] at the given temperature.
Return type:: float

h(T: float) → float#

Get the enthalpy at a given temperature.

Parameters:: T (float) – Temperature [K].
Returns:: Enthalpy [J/kg] at the given temperature.
Return type:: float

T_from_h(h: float) → float#

Get the temperature at a given enthalpy.

Parameters:: h (float) – Enthalpy [J/kg].
Returns:: Temperature [K] at the given enthalpy.
Return type:: float

cp(T: float) → float#

Get the heat capacity at constant pressure at a given temperature.

Parameters:: T (float) – Temperature [K].
Returns:: Heat capacity at constant pressure [J/(kg.K)] at the given temperature.
Return type:: float

mu(T: float) → float#

Get the dynamic viscosity at a given temperature.

Parameters:: T (float) – Temperature [K].
Returns:: Dynamic viscosity [kg/(m.s)] at the given temperature.
Return type:: float

kappa(T: float) → float#

Get the thermal conductivity at a given temperature.

Parameters:: T (float) – Temperature [K].
Returns:: Thermal conductivity [W/(m.K)] at the given temperature.
Return type:: float

sigma(T: float) → float#

Get the electrical conductivity at a given temperature.

Parameters:: T (float) – Temperature [K].
Returns:: Electrical conductivity [S/m] at the given temperature.
Return type:: float

alpha(T: float) → float#

Get the thermal diffusivity at a given temperature.

Parameters:: T (float) – Temperature [K].
Returns:: Thermal diffusivity [m^2/s] at the given temperature.
Return type:: float

Notes

The thermal diffusivity is computed as:

\[\alpha = \frac{\kappa}{\rho c_p}\]

where:

\(\kappa\) is the thermal conductivity [W/(m.K)]
\(\rho\) is the density [kg/m^3]
\(c_p\) is the heat capacity at constant pressure [J/(kg.K)]

plot(x: str, y: str, show: bool = True, yscale: str = 'linear', fig_ax: tuple[matplotlib.figure.Figure, matplotlib.axes.Axes] | None = None, ax_title: str | None = None, **plot_options) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]#

Plot data.

Parameters:

x (str) –
The variable to plot on the x-axis. Options are:
- ”temperature” or “T”: Temperature in Kelvin.
- ”theta”: Integrated thermal conductivity in W/m.
y (str) –
The variable to plot on the y-axis. Options are:
- ”temperature” or “T”: Temperature in Kelvin,
- ”enthalpy” or “h”: Enthalpy in J/kg,
- ”density” or “rho”: Density in kg/m³,
- ”heat_capacity” or “cp” or “c_p”: Specific heat capacity at constant pressure in J/(kg·K),
- ”dynamic_viscosity” or “mu”: Dynamic viscosity in Pa·s,
- ”electrical_conductivity” or “sigma”: Electrical conductivity in S/m,
- ”thermal_conductivity” or “kappa”: Thermal conductivity in W/(m·K),
- ”theta”: Integrated thermal conductivity in W/m,
- ”thermal_diffusivity” or “alpha”: Thermal diffusivity in m²/s.
show (bool, optional) – Whether to display the plot immediately (default is True).
yscale (str, optional) – The scale of the y-axis. Options are “linear” or “log” (default is “linear”).
fig_ax (tuple of matplotlib.figure.Figure, matplotlib.axes.Axes or None, optional) – A tuple containing a Matplotlib Figure and Axes to plot on. If None, a new figure and axes are created (default is None).
**plot_options – Additional keyword arguments to pass to the plotting function. E.g., label, color, linestyle, marker, etc.

Returns:

The figure and axes objects of the plot.

Return type:

tuple of matplotlib.figure.Figure, matplotlib.axes.Axes

Raises:

ValueError – If an invalid variable is specified for the x or y axis.

Examples

>>> from rizer.io.thermo_transport_data_reader import ThermoTransportDataReader
>>> # Define the hydrogen data.
>>> hydrogen_data = ThermoTransportDataReader(
...     gas_name="H2",
...     pressure_atm=1,
...     source="Boulos2023",
... )
>>> # Plot the data.
>>> fig, ax = hydrogen_data.plot(x="temperature", y="enthalpy", show=False)

plot_all(x: str = 'temperature', show: bool = True, yscale: str = 'linear', fig_ax: tuple[matplotlib.figure.Figure, matplotlib.axes.Axes] | None = None, **plot_options)#

Plot all data.

See plot() for parameters.

rizer.io.thermo_transport_data_reader.CH4_lte_data_minplascalc#