This notebook estimates several parameters of the plasma in the context of tokamak fusion physics. These parameters include but are not limited to the safety factor, the electron temperature, electron pressure, plasma volume and electron thermal energy and electron energy confinement time. Other more general plasma parameters are calculated as well.
The formulas and explanations are mostly based on the book [1] WESSON, John. Tokamaks. 3. ed. Oxford: Clarendon press, 2004. ISBN 9780198509226. and the reader is encouraged to consult it for details.
The accuracy of these parameters stronly depends on the availability of the plasma position and size reconstruction.
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import integrate, signal, interpolate, constants
import holoviews as hv
hv.extension('bokeh')
import hvplot.pandas
import requests
# 180221 Dirigent
destination='Results/'
os.makedirs(destination, exist_ok=True)
def print_and_save(phys_quant, value, format_str='%.3f'):
print(phys_quant+" = %.5f" % value)
with open(destination+phys_quant, 'w') as f:
f.write(format_str % value)
#update_db_current_shot(phys_quant,value)
def update_db_current_shot(field_name, value):
#os.system('psql -c "UPDATE shots SET '+field_name+'='+str(value)+' WHERE shot_no IN(SELECT max(shot_no) FROM shots)" -q -U golem golem_database')
subprocess.call(["psql -q -U golem golem_database --command='UPDATE shots SET \""+field_name+"\"="+str(value)+" WHERE shot_no IN(SELECT max(shot_no) FROM shots)'"],shell=True)
shot_no = 46461 # 33516 is a good test case
The following analysis makes sense only if a plasma was present in the discharge
def plasma_scalar(name):
r = requests.get(f'http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/PlasmaDetection/Results/{name}')
return float(r.content)
t_plasma_start = plasma_scalar('t_plasma_start')
t_plasma_end = plasma_scalar('t_plasma_end')
if t_plasma_start == t_plasma_end:
raise RuntimeError('no plasma in this discharge, analysis cannot continue')
basic_diagn_signals = ['U_loop', 'Bt', 'Ip']
df = pd.concat([pd.read_csv(f'http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/BasicDiagnostics/Results/{sig}.csv',
names=['time', sig], index_col=0) for sig in basic_diagn_signals], axis='columns')
df = df.loc[t_plasma_start:t_plasma_end] # time slice with plasma
#try:
# df_position = pd.read_csv(f'http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/LimiterMirnovCoils/plasma_position.csv',
# index_col=0)
#except HTTPError:
df_position = None
using_plasma_position = df_position is not None
# TODO load from SQL
R0 = 0.4 # chamber center Major plasma radius [m]
a0 = 0.085 # maximum Minor radius - limiter [m]
If plasma position and size reconstruction is not available, the parameters of the chamber geometry are used for the minor and major plasma radii $a$ and $R$, respectivelly.
def interp_position2basic(name):
interp = interpolate.interp1d(df_position.index, df_position[name], bounds_error=False)
return interp(df.index) * 1e-3 # mm to m
if using_plasma_position:
df['R'] = R0 + interp_position2basic('r')
df['a'] = interp_position2basic('a')
else:
df['R'] = R0
df['a'] = a0
On any given closed flux surface in the plasma in the tokamak the magnetic field line performs $q$ transits in the toroidal angle $\phi$ per 1 one transit in the poloidal angle $\theta$. The stronger the toroidal magnetic field is, the more stable the plasma becomes against various instabilities, especially against the kink instability which can occur for $q<1$. For this reason $q$ is referred to as the safety factor.
In a simple tokamak with a circular cross-section (such as GOLEM) the poloidal magnetic field can be estimated at least at the very edge of the plasma from the total plasma current $I_p$ enclosed by the plasma column of minor radius $a$ and major radius $R$ as $$B_{\theta a} = \mu_0\frac{I_p}{2\pi a}$$
Typically, in a tokamak the toroidal magnetic field $B_\phi$ is several times stronger than the poloidal magnetic field $B_{\theta a}$ at the egde.
df['B_theta'] = constants.mu_0 * df['Ip'] * 1e3 / (2*np.pi*df['a']) # Ip is in [kA], B_theta will be in [T]
df[['Bt','B_theta']].hvplot(ylabel='B [T]', xlabel='time [ms]', grid=True, logy=True, ylim=(1e-4,None))
For a large aspect ratio tokamak (i.e. the inverse aspect ratio is small $\epsilon = \frac{a}{R} <<1$) such as GOLEM the safety factor at the edge on the last closed flux surface (LCFS) delimited by the limiter ring can be estimated as $$q_a=\frac{a B_\phi}{RB_\theta}$$
df['q_a'] = df['a'] * df['Bt'] / (df['R'] * df['B_theta'])
df['q_a'].hvplot(logy=True, grid=True)
WARNING:param.CurvePlot02008: Logarithmic axis range encountered value less than or equal to zero, please supply explicit lower-bound to override default of 426.423.
To obtain information on $q$ and $B_\theta$ deeper inside the plasma torus one must have knowledge of or assume a specific profile for the toroidal current density $j_\phi$. A common approximation for a tokamak such as GOLEM is a poloidally symmetric radial profile $$j_\phi(r) = j_0\left(1-\left(\frac{r}{a}\right)\right)^\nu$$ where $r$ is the radius with respect to the plasma center and $\nu$ a so called "peaking factor". A common choice is $\nu=1$ for a "parabolic" profile or $\nu=2$ for a more peaked profile (likely more realistic). With the average current density defined as $\langle j \rangle_a=\frac{I_p}{\pi a^2}$ the maximum current density $j_0$ can be estimated from the relation $\frac{j_0}{\langle j \rangle_a}=\nu+1$
nu = 2 # probably more realistic than parabolic
df['j_avg_a'] = df['Ip'] *1e3 / (np.pi*df['a']**2)
df['j_0'] = df['j_avg_a'] * (nu+1)
Under this assumption the safety factor in the plasma core ($r=0$) is reduced according to the relation $\frac{q_a}{q_0}=\nu+1$. which could result in the following profiles for the time when $q_a$ is the lowest (i.e. closest to an instability).
t_q_a_min = df['q_a'].idxmin()
df_q_a_min = df.loc[t_q_a_min]
print(f'min(q_a)={df_q_a_min["q_a"]:.2f} at t={t_q_a_min:.3f} ms')
min(q_a)=-50772.74 at t=2.805 ms
r = np.linspace(0, df_q_a_min['a'])
df_r = pd.DataFrame({
'j': df_q_a_min['j_0'] * (1-(r/df_q_a_min['a'])**2)**nu,
'B_theta': constants.mu_0 * df_q_a_min['j_0'] * df_q_a_min['a']**2 / (2*(nu+1)) * (1-(1-(r/df_q_a_min['a'])**2)**(nu+1))/r,
'q': 2*(nu+1)/(constants.mu_0 * df_q_a_min['j_0']) * (df_q_a_min['Bt']/df_q_a_min['R']) * (r/df_q_a_min['a'])**2 / (1-(1-(r/df_q_a_min['a'])**2)**(nu+1))
}, index=pd.Index(r, name='r')
)
<ipython-input-1-ec0bca19fa99>:4: RuntimeWarning: invalid value encountered in divide 'B_theta': constants.mu_0 * df_q_a_min['j_0'] * df_q_a_min['a']**2 / (2*(nu+1)) * (1-(1-(r/df_q_a_min['a'])**2)**(nu+1))/r, <ipython-input-1-ec0bca19fa99>:5: RuntimeWarning: invalid value encountered in divide 'q': 2*(nu+1)/(constants.mu_0 * df_q_a_min['j_0']) * (df_q_a_min['Bt']/df_q_a_min['R']) * (r/df_q_a_min['a'])**2 / (1-(1-(r/df_q_a_min['a'])**2)**(nu+1))
df_r.hvplot.line(subplots=True, shared_axes=False, width=300, grid=True)
The plasma is typically as conductive as copper, i.e. is a good conductor with a relatively low resitivity. However, whereas the resitivity of metals increases with temperature, the resitivity of a plasma decreases, because at higher timperatures collisions between particles become less frequent, leading to less resistance to their movement. While with higher particle density the number of collisions increases, the number of charge cariers also increases, so in the end the resistivity does not depend on density.
The simple, unmagnetized plasma resistivity derived by Spitzer $$\eta_s = 0.51 \frac{\sqrt{m_e} e^2 \ln \Lambda}{3 \epsilon_0^2 (2\pi k_B T_e)^\frac{3}{2}}$$ with the constants electron mass $m_e$, elementary charge $e$, vacuum permitivity $\epsilon_0$ and $k_B$ the Boltzmann constant. $\ln \Lambda$ is the so called Coulomb logarithm which has a weak dependence on density and temperature and for typical GOLEM plasmas can be held at $\ln \Lambda\sim 14$. The factor 0.51 comes from more precise calculations which show that the parallel resitivity $\eta_\|=\eta_s$ (along the magnetic field-line the resistivity is not affected by the field) is halved compared to the classical (analytical) perpendicular resitivity $\eta_\perp = 1.96 \eta_\|$ though in reality the perpendicular resitivity can be higher due to anomalous transport (turbelence, etc.). If one is interested in the electron temperature $T_e$ in the units of electron-volts (typically used in the field), the relation is $T_e \mathrm{[eV]}=\frac{k_B}{e}T_e\mathrm{[K]}$.
Additional corrections:
This results in $\eta_{measured}=\eta_s Z_{eff} (1-\sqrt{\epsilon})^{-2}$.
These considerations lead to the relation $$T_e \mathrm{[eV]}=\frac{1}{e2\pi}\left( \frac{1.96}{Z_{eff}} (1-\sqrt{\epsilon})^2 \eta_{measured}\frac{3 \epsilon_0^2}{\sqrt{m_e} e^2 \ln \Lambda} \right)^{-\frac{2}{3}}$$
def electron_temperature_Spitzer_eV(eta_measured, Z_eff=3, eps=0, coulomb_logarithm=14):
eta_s = eta_measured / Z_eff * (1-np.sqrt(eps))**2
term = 1.96 * eta_s * (3 * constants.epsilon_0**2 /
(np.sqrt(constants.m_e) * constants.elementary_charge**2 * coulomb_logarithm))
return term**(-2./3) / (constants.elementary_charge * 2*np.pi)
To estimate $\eta_{measured}$ one can use Ohm's law in the form $j_\phi = \sigma E_\phi$ with the plasma conductivity $\sigma=\frac{1}{\eta_{measured}}$. The toroidal electric field can be estimated from the loop voltage, but one must take into account inductive effects as well. Neglecting mutual inductances between e.g. the plasma and the chamber, the loop voltage induced in the plasma by the primary winding is "consumed" by the electric field and current inductance as $$U_{loop}= 2\pi R E_\phi + (L_i + L_e) \frac{dI_p}{dt}$$ where $L_i$ and $L_e$ are the internal and external plasma inductances, respectively. The external inductance of a closed toroidal current (assuming a uniform current density) is $L_e=\mu_0 R\ln\left(\frac{8R}{a}-\frac{7}{4}\right)$. The internal plasma inductance is usually parametrized as $L_i=\mu_0 R \frac{l_i}{2}$ where $l_i$ is the so called normalized internal inductance which depends on the $B_\theta$ (or rather current) profile. For the assumed current profile an accurate estimate is $l_i \approx \ln(1.65+0.89\nu)$.
l_i = np.log(1.65+0.89*nu)
df['L_p'] = constants.mu_0 * df['R'] * (np.log(8*df['R']/df['a']) - 7/4. + l_i/2)
dt = np.diff(df.index.values[:2]).item()
n_win = int(0.5 / dt) # use a window of 0.5 ms
if n_win % 2 == 0:
n_win += 1 # window must be odd
# needs SI units: convert current in kA -> A, time step in ms->s
df['dIp_dt'] = signal.savgol_filter(df['Ip']*1e3, n_win, 3, 1, delta=dt*1e-3) # 1. derivative of an order 3 polynomial lsq SG-filter
df['E_phi_naive'] = df['U_loop'] / (2*np.pi*df['R']) # V/m
df['E_phi'] = (df['U_loop'] - df['L_p']*df['dIp_dt']) / (2*np.pi*df['R'])
df[['E_phi_naive', 'E_phi']].hvplot(ylabel='E_phi [V/m]', grid=True)