This notebook was created by Sergey Tomin (sergey.tomin@desy.de) and Igor Zagorodnov for Workshop: Designing future X-ray FELs. Source and license info is on GitHub. Updated January 2018.
Influence of corrugated structure on the electron beam. This example based on the work: I. Zagorodnov, G. Feng, T. Limberg. Corrugated structure insertion for extending the SASE bandwidth up to 3% at the European XFEL.
Gerometry of the corrugated structure. The blue ellipse represents an electron beam propagating along the z axis.
In order to take into account the impact of the wake field on the beam the longitudinal wake function of point charge through the second order Taylor expansion is used. In general case it uses 13 one-dimensional functions to represent the longitudinal component of the wake function for arbitrary sets of the source and the wittness particles near to the reference axis. The wake field impact on the beam is included as series of kicks.
The implementation of the wakefields follows closely the approach described in:
We use the same format of the wakes as implemented in ASTRA and the description of the format can be found in M. Dohlus, K. Floettmann, C. Henning, Fast particle tracking with wake fields, Report No. DESY 12-012, 2012.
Second order Taylor expansion of the longitudinal wake ($w_z$) in the transverse coordinates
$$ w_z(x_s, y_s, x_o, y_o, s) = \begin{bmatrix} 1 \\ x_s\\ y_s\\ x_o \\ y_o \end{bmatrix}^T \begin{bmatrix} h_{00}(s) & h_{01}(s) & h_{02}(s) & h_{03}(s) & h_{04}(s) \\ 0 & h_{11}(s) & h_{12}(s) & h_{13}(s) & h_{14}(s)\\ 0 & h_{12}(s) & -h_{11}(s) & h_{23}(s) & h_{24}(s) \\ 0 & h_{13}(s) & h_{23}(s) & h_{33}(s) & h_{34}(s)\\ 0 & h_{14}(s) & h_{24}(s) & h_{34}(s) & -h_{33}(s) \end{bmatrix} \begin{bmatrix} 1 \\ x_s\\ y_s\\ x_o \\ y_o \end{bmatrix} ; $$where $x_s$ and $y_s$ transverse coordinates of the source particle and $x_o$ and $y_o$ are transverse coordinates of the observer, $s$ is distance between source and observer. Thus to describe longitudinal wake we need 13 functions $h_{\alpha \beta}$.
The transverse components are uniquely related to the longitudinal wake by causality and Panofsky-Wenzel-Theorem.
For each of these coefficients, we use the representation in O. Zagorodnova, T. Limberg, Impedance budget database for the European XFEL \begin{equation} h(s) = w_0(s) + \frac{1}{C} + R c\delta(s) + c\frac{\partial}{\partial s}\left[L c \delta(s) + w_1 (s) \right] \end{equation} where $w_0(s)$, $w_1(s)$ are nonsingular functions, which can be tabulated easily, and constants $R$, $L$, and $C$ have the meaning of resistivity, inductance, and capacitance, correspondingly. The functions $w_0(s)$, $w_1(s)$ can be represented by table, e.g. [$s_i$, $w_0^i$].
Now we can describe whole table how it is saved in a file.
| $N_h$ | $0$ |
|---|---|
| $N_{w_0}$ | $N_{w_1}$ |
| $R,\: [Us]$ | $L,\: [Us^2]$ |
| $C,\: [1/Us]$ | $10\alpha + \beta$ |
| $s_1,\: [m]$ | $w_0(s_1),\: [U]$ |
| $s_2,\: [m]$ | $w_0(s_2),\: [U]$ |
| ... | ... |
| $s_{N_{w_0}},\: [m]$ | $w_0(s_{N_{w_0}}),\: [U]$ |
| $s_1,\: [m]$ | $w_1(s_1),\: [U]$ |
| $s_2,\: [m]$ | $w_1(s_2),\: [U]$ |
| ... | ... |
| $s_{N_{w_1}},\: [m]$ | $w_1(s_{N_{w_1}}),\: [U]$ |
| $N_{w_0}$ | $N_{w_1}$ |
| $R,\: [Us]$ | $L,\: [Us^2]$ |
| $C,\: [1/Us]$ | $10\alpha + \beta$ |
| $s_1,\: [m]$ | $w_0(s_1),\: [U]$ |
| ... | ... |
| $N_{w_0}$ | $N_{w_1}$ |
| $R,\: [Us]$ | $L,\: [Us^2]$ |
| $C,\: [1/Us]$ | $10\alpha + \beta$ |
| $s_1,\: [m]$ | $w_0(s_1),\: [U]$ |
| ... | ... |
In the very first line, $N_h$ is number of $h_{\alpha\beta}(s)$ functions in the table. After that, a typical table repeated $N_h$ times describing every $h_{\alpha\beta}(s)$ function.
Every table starts with $N_{w_0}$ and $N_{w_1}$ which are number of points of $w_0(s_i)$ and $w_1(s_i)$ functions. Next two lines are included $R$, $L$, $C$ and entry $10\alpha + \beta$ which describes the subscript of the auxiliary function $h_{\alpha\beta}(s)$. Next $N_{w_0}$ lines described function $w_0(s)$, and after that next $N_{w_1}$ lines described function $w_1(s)$.
And to describe next $h_{\alpha\beta}(s)$ we repeat procedure.
The unit $U$ is $V/(A\cdot s)$ for $\alpha\beta = 00$, $V/(A\cdot s \cdot m)$ for $\alpha\beta = 01, ... 04$ and $V/(A\cdot s \cdot m^2)$ for all other coefficients.
In [1]:
# the output of plotting commands is displayed inline within frontends,
# directly below the code cell that produced it
%matplotlib inline
# this python library provides generic shallow (copy) and deep copy (deepcopy) operations
from copy import deepcopy
import time
# import from Ocelot main modules and functions
from ocelot import *
# import from Ocelot graphical modules
from ocelot.gui.accelerator import *
# load beam distribution
# this function convert Astra beam distribution to Ocelot format
# - ParticleArray. ParticleArray is designed for tracking.
# in order to work with converters we have to import
# specific module from ocelot.adaptors
from ocelot.adaptors.astra2ocelot import *
In [2]:
D00m25 = Drift(l = 0.25)
D01m = Drift(l = 1)
D02m = Drift(l = 2)
# Create markers for defining places of the wakes applying
w1_start = Marker()
w1_stop = Marker()
w2_start = Marker()
w2_stop = Marker()
w3_start = Marker()
w3_stop = Marker()
w4_start = Marker()
w4_stop = Marker()
w5_start = Marker()
w5_stop = Marker()
w6_start = Marker()
w6_stop = Marker()
# quadrupoles
Q1 = Quadrupole(l = 0.5, k1 = 0.215)
# lattice
lattice = (D01m, w1_start, D02m, w1_stop, w2_start, D02m, w2_stop,
w3_start, D02m, w3_stop, D00m25, Q1, D00m25,
w4_start, D02m, w4_stop, w5_start, D02m, w5_stop,
w6_start, D02m, w6_stop, D01m)
# creation MagneticLattice
method = MethodTM()
method.global_method = SecondTM
lat = MagneticLattice(lattice, method=method)
# calculate twiss functions with initial twiss parameters
tws0 = Twiss()
tws0.E = 14 # in GeV
tws0.beta_x = 22.5995
tws0.beta_y = 22.5995
tws0.alpha_x = -1.4285
tws0.alpha_y = 1.4285
tws = twiss(lat, tws0, nPoints=None)
# ploting twiss paramentrs.
plot_opt_func(lat, tws, top_plot=["Dx"], fig_name="i1", legend=False)
plt.show()
In [3]:
# load and convert ASTRA file to OCELOT beam distribution
# p_array_init = astraBeam2particleArray(filename='beam_chirper.ast')
# save ParticleArray to compresssed numpy array
# save_particle_array("chirper_beam.npz", p_array_init)
p_array_init = load_particle_array("chirper_beam.npz")
plt.plot(-p_array_init.tau()*1000, p_array_init.p(), "r.")
plt.grid(True)
plt.xlabel(r"$\tau$, mm")
plt.ylabel(r"$\frac{\Delta E}{E}$")
plt.show()
In [4]:
from ocelot.cpbd.wake3D import *
# load wake tables of corrugated structures
wk_vert = WakeTable('wake_vert_1m.txt')
wk_hor = WakeTable('wake_hor_1m.txt')
# creation of wake object with parameters
wake_v1 = Wake()
# w_sampling - defines the number of the equidistant sampling points for the one-dimensional
# wake coefficients in the Taylor expansion of the 3D wake function.
wake_v1.w_sampling = 500
wake_v1.wake_table = wk_vert
wake_v1.step = 1 # step in Navigator.unit_step, dz = Navigator.unit_step * wake.step [m]
wake_h1 = Wake()
wake_h1.w_sampling = 500
wake_h1.wake_table = wk_hor
wake_h1.step = 1
wake_v2 = deepcopy(wake_v1)
wake_h2 = deepcopy(wake_h1)
wake_v3 = deepcopy(wake_v1)
wake_h3 = deepcopy(wake_h1)
Navigator defines step (dz) of tracking and which, if it exists, physical process will be applied on each step. In order to add collective effects (Space charge, CSR or wake) method add_physics_proc() must be run.
Method:
Also must be define unit_step in [m] (by default 1 m). unit_step is minimal step of tracking for any collective effect. For each collective effect must be define number of unit_steps so step of applying physics process will be
dz = unit_step*step [m]
In [5]:
navi = Navigator(lat)
# add physics proccesses
navi.add_physics_proc(wake_v1, w1_start, w1_stop)
navi.add_physics_proc(wake_h1, w2_start, w2_stop)
navi.add_physics_proc(wake_v2, w3_start, w3_stop)
navi.add_physics_proc(wake_h2, w4_start, w4_stop)
navi.add_physics_proc(wake_v3, w5_start, w5_stop)
navi.add_physics_proc(wake_h3, w6_start, w6_stop)
# definiing unit step in [m]
navi.unit_step = 0.2
# deep copy of the initial beam distribution
p_array = deepcopy(p_array_init)
print("tracking with Wakes .... ")
start = time.time()
tws_track, p_array = track(lat, p_array, navi)
print("\n time exec:", time.time() - start, "sec")
In [6]:
tau0 = p_array_init.tau()
p0 = p_array_init.p()
tau1 = p_array.tau()
p1 = p_array.p()
print(len(p1))
plt.figure(1)
plt.plot(-tau0*1000, p0, "r.", -tau1*1000, p1, "b.")
plt.legend(["before", "after"], loc=4)
plt.grid(True)
plt.xlabel(r"$\tau$, mm")
plt.ylabel(r"$\frac{\Delta E}{E}$")
plt.show()
In [7]:
# by default the beam head on the left side
show_e_beam(p_array, figsize=(8,6))
plt.show()
In [8]:
# plotting twiss parameters.
plot_opt_func(lat, tws_track, top_plot=["Dx"], fig_name="i1", legend=False)
plt.show()
For some FEL applications, e.g. a two-color scheme, only one flat corrugated structure can be used to get a correlated transverse kick along the electron bunch. In that case, we can use analytical approach from I. Zagorodnov, G. Feng, T. Limberg. Corrugated structure insertion for extending the SASE bandwidth up to 3% at the European XFEL and K. Bane, G. Stupakov, and I. Zagorodnov, Wakefields of a Beam near a Single Plate in a Flat Dechirper to calculate described above the wakefield tables.
In [9]:
# create a simple lattice MagneticLattice
m1 = Marker()
m2 = Marker()
# quadrupoles
Q1 = Quadrupole(l = 0.5, k1 = 0.215)
lattice = (Drift(l=1), m1, Drift(l=1), m2, Drift(l=2), Q1, Drift(l=2))
method = MethodTM()
method.global_method = SecondTM
lat = MagneticLattice(lattice, method=method)
In [10]:
# description of args can be also be shown with Shift+Tab
wk_tv_kick = WakeTableDechirperOffAxis(b=500*1e-6, # distance from the plate in [m]
a=0.01, # half gap between plates in [m]
width=0.02, # width of the corrugated structure in [m]
t=0.25*1e-3, # longitudinal gap in [m]
p=0.5*1e-3, # period of corrugation in [m]
length=1, # length of the corrugated structure in [m]
sigma=30e-6, # characteristic (rms) longitudinal beam size in [m]
orient="horz") # "horz" or "vert" plate orientation
# creation of wake object with parameters
wake = Wake()
# w_sampling - defines the number of the equidistant sampling points for the one-dimensional
# wake coefficients in the Taylor expansion of the 3D wake function.
wake.w_sampling = 500
wake.wake_table = wk_tv_kick
wake.step = 1 # step in Navigator.unit_step, dz = Navigator.unit_step * wake.step [m]
navi = Navigator(lat)
# add physics proccesses
navi.add_physics_proc(wake, m1, m2)
In [11]:
# deep copy of the initial beam distribution
p_array = deepcopy(p_array_init)
print("tracking with Wakes .... ")
start = time.time()
tws_track, p_array = track(lat, p_array, navi)
print("\n time exec:", time.time() - start, "sec")
In [12]:
# by default the beam head on the left side
show_e_beam(p_array, figsize=(8,6))
plt.show()
In [ ]: