rizer.models.nrp.engineering_model.VT_relaxation

Classes

VibrationalTranslationalRelaxationInputs
VibrationalTranslationalRelaxationModel	Compute temperature and pressure during the vibrational-translational relaxation phase.

Module Contents

class rizer.models.nrp.engineering_model.VT_relaxation.VibrationalTranslationalRelaxationInputs

theta_uh: float

Fraction of the deposited energy that goes to trans-rotational ultrafast heating.

theta_sh: float

Fraction of the deposited energy that goes to vibrational heating.

Theta_vib: float

Characteristic vibrational temperature [K].

cv_vib_max: float

Maximum vibrational heat capacity [J/(kg.K)].

cv_tr_rot: float

Specific trans-rotational heat capacity [J/(kg.K)].

ΔE_pulse_per_mass_J_kg: float

Energy deposited per unit mass during the spark pulse [J/kg].

T_0: float

Initial temperature of the discharge [K].

T_rarefaction_K: float

Temperature of the rarefaction wave [K]. Temperature after the abrupt fall due to the rarefaction wave.

T_TE_K: float

Temperature after thermal equilibration between vibrational and trans-rotational modes [K].

P_rarefaction_Pa: float

Pressure after the abrupt fall due to the rarefaction wave [Pa].

δ_rarefaction_s: float

Duration of the abrupt fall due to the rarefaction wave [s].

τ_rarefaction_s: float

Characteristic time scale for the rarefaction wave to propagate across the kernel [s].

τ_VT_s: float

Characteristic time scale for vibrational-translational relaxation [s].

get_T_TE() → float

class rizer.models.nrp.engineering_model.VT_relaxation.VibrationalTranslationalRelaxationModel(inputs: VibrationalTranslationalRelaxationInputs)

Compute temperature and pressure during the vibrational-translational relaxation phase.

Main assumptions of the vibrational-translational relaxation model are:

• (a1) Isobaric VT energy exchange.

• (a2) Constant τ_VT.

Parameters:

inputs (VibrationalTranslationalRelaxationInputs) – Inputs for the vibrational-translational relaxation model.

inputs

F_TE(t: float) → float

T(t: float) → float

T2(t: float, T1: Callable[[float], float]) → float

P(t: float) → float

P2(t: float, P1: Callable[[float], float]) → float

solve(t_eval: numpy.ndarray, T1: Callable[[float], float], P1: Callable[[float], float]) → rizer.models.nrp.engineering_model.base.TimeSeriesState