Basic checks#

Class Check#

class multipy.check.Check#

Supports performing basic checks of the computed quantities.

Check.sum_of_species_fractions#

multipy.check.Check.sum_of_species_fractions(self, species_fractions, tolerance=1e-12, verbose=False)#

Checks if all species mole/mass/volume fractions sum to 1.0 for every observation within a specified tolerance.

For mole fractions:

\[\sum_{i=1}^{n} X_i = 1.0\]

For mass fractions:

\[\sum_{i=1}^{n} Y_i = 1.0\]

For volume fractions:

\[\sum_{i=1}^{n} V_i = 1.0\]

where \(n\) is the number of species.

Parameters

  • species_fractions – scalar numpy.ndarray specifying all species mole/mass/volume fractions in \([-]\). It should be of size (n_species, n_observations) where n_species is at least 2.

  • tolerance – (optional) float specifying the tolerance. It should be larger than 0.0 and smaller than 1.0.

  • verbose – (optional) bool for printing verbose information.

Returns

  • idx - indices of observations where species mole/mass/volume fractions do not sum to 1.0 within a specified tolerance.

Check.range_of_species_fractions#

multipy.check.Check.range_of_species_fractions(self, species_fractions, tolerance=1e-12, verbose=False)#

Checks if all species mole/mass/volume fraction values are bounded between 0 and 1.

For mole fractions:

\[X_i \in \langle 0, 1 \rangle\]

For mass fractions:

\[Y_i \in \langle 0, 1 \rangle\]

For volume fractions:

\[V_i \in \langle 0, 1 \rangle\]
Parameters

  • species_fractions – scalar numpy.ndarray specifying all species mole/mass/volume fractions in \([-]\). It should be of size (n_observations,n_species) where n_species is at least 2.

  • verbose – (optional) bool for printing verbose information.

Returns

  • idx_below_zero - indices of observations where species mole/mass/volume fractions are less than 0.0 within a specified tolerance.

  • idx_above_one - indices of observations where species mole/mass/volume fractions are larger than 1.0 within a specified tolerance.

Check.sum_of_species_gradients#

multipy.check.Check.sum_of_species_gradients(self, species_gradients, tolerance=1e-12, verbose=False)#

Checks if all species mole/mass/volume fraction gradients sum to 0.0 for every observation within a specified tolerance.

For mole fractions:

\[\sum_{i=1}^{n} \nabla X_i = 0.0\]

For mass fractions:

\[\sum_{i=1}^{n} \nabla Y_i = 0.0\]

For volume fractions:

\[\sum_{i=1}^{n} \nabla V_i = 0.0\]

where \(n\) is the number of species.

Parameters

  • species_gradients – scalar numpy.ndarray specifying all species mole/mass/volume fraction gradients in \([-]\). It should be of size (n_species, n_observations) where n_species is at least 2.

  • tolerance – (optional) float specifying the tolerance. It should be larger than 0.0 and smaller than 1.0.

  • verbose – (optional) bool for printing verbose information.

Returns

  • idx - indices of observations where species mole/mass/volume fraction gradients do not sum to 0.0 within a specified tolerance.

Check.sum_of_species_production_rates#

multipy.check.Check.sum_of_species_production_rates(self, species_production_rates, tolerance=1e-12, verbose=False)#

Checks if all species production rates sum to 0.0 for every observation within a specified tolerance:

For net molar production rates:

\[\sum_{i=1}^{n} s_i = 0.0\]

For net mass production rates:

\[\sum_{i=1}^{n} \omega_i = 0.0\]

where \(n\) is the number of species.

Parameters

  • species_production_rates – scalar numpy.ndarray specifying all species production rates, \(s_i\) in \(mole/(m^3s)\) or \(\omega_i\) in \(kg/(m^3s)\). It should be of size (n_species,n_observations) where n_species is at least 2.

  • tolerance – (optional) float specifying the tolerance. It should be larger than 0.0 and smaller than 1.0.

  • verbose – (optional) bool for printing verbose information.

Returns

  • idx - indices of observations where species source terms do not sum to 0.0 within a specified tolerance.