def test_SPS_RL(self):

        PL_gain = 1000      # gain of phase loop
        rtol = 1e-4         # relative tolerance
        atol = 0              # absolute tolerance
        # Phase loop setup

        phase_loop = BeamFeedback(self.ring, self.rf_station, self.profile,
                                  {'machine': 'SPS_RL', 'PL_gain': PL_gain})

        # Tracker setup
        section_tracker = RingAndRFTracker(
            self.rf_station, self.beam, Profile=self.profile,
            BeamFeedback=phase_loop, interpolation=False)
        tracker = FullRingAndRF([section_tracker])

        # average beam position
        beamAvgPos = np.zeros(self.ring.n_turns)

        n_turns = self.ring.n_turns
        for turn in range(n_turns):
            beamAvgPos[turn] = np.mean(self.beam.dt)
            self.profile.track()
            tracker.track()

        # difference between beam position and synchronuous position
        # (assuming no beam loading)
        delta_tau = beamAvgPos - (np.pi - self.rf_station.phi_rf[0, :-1])\
            / self.rf_station.omega_rf[0, :-1]

        # initial position for analytic solution
        init_pos = self.time_offset

        omega_eff = cmath.sqrt(-PL_gain**2 + 4*self.rf_station.omega_s0[0]**2)
        time = np.arange(n_turns) * self.ring.t_rev[0]
        # initial derivative for analytic solution;
        # defined such that analytical solution at turn 1 agrees with numerical
        # solution
        init_slope = 0.5 * (delta_tau[1] * omega_eff * np.exp(0.5*PL_gain*time[1])
                            / np.sin(0.5*omega_eff*time[1]) - delta_tau[0]
                            * (PL_gain+omega_eff/np.tan(0.5*omega_eff*time[1]))).real

        delta_tau_analytic = init_pos * np.exp(-0.5*PL_gain*time)
        delta_tau_analytic *= np.cos(0.5*time*omega_eff).real\
            + (PL_gain+2*init_slope/init_pos)\
            * (np.sin(0.5*time*omega_eff)/omega_eff).real

        difference = delta_tau - delta_tau_analytic
        # normalize result
        difference = difference / np.max(difference)
        # expected difference
        difference_exp = np.array([
            -1.56306635e-05, -1.55605315e-05,  -2.10224435e-05, -3.18525050e-05,
            -4.74014489e-05, -6.70584402e-05, -9.01307422e-05, -1.15823959e-04,
            -1.43290487e-04, -1.71572162e-04,  -1.99820151e-04,  -2.27071730e-04,
            -2.52331681e-04,  -2.74668126e-04,  -2.93165304e-04, -3.06972913e-04,
            -3.15442474e-04,  -3.17857324e-04, -3.13794970e-04,  -3.02786089e-04,
            -2.84680298e-04, -2.59322215e-04,  -2.26874004e-04,  -1.87452375e-04,
            -1.41293604e-04,  -8.89863575e-05,  -3.08865701e-05, 3.22411495e-05,
            9.97408029e-05,   1.70914181e-04, 2.44766912e-04, 3.20596833e-04,
            3.97403451e-04, 4.74233283e-04,   5.50189125e-04, 6.24368453e-04,
            6.95836553e-04,   7.63737143e-04,   8.27069057e-04, 8.84995559e-04,
            9.36770723e-04,   9.81561780e-04, 1.01869959e-03,   1.04738842e-03,
            1.06711062e-03, 1.07736961e-03,   1.07778386e-03,   1.06805613e-03,
            1.04797776e-03,   1.01747638e-03,   9.76519221e-04, 9.25420191e-04,
            8.64415092e-04,   7.93844624e-04, 7.14396030e-04, 6.26549187e-04,
            5.31154439e-04, 4.28985322e-04,   3.21198916e-04,   2.08550190e-04,
            9.21607082e-05,  -2.68249728e-05,  -1.47278123e-04, -2.67890543e-04,
            -3.87642210e-04,  -5.05244473e-04, -6.19660328e-04,  -7.29670300e-04,
            -8.34272846e-04, -9.32388033e-04,  -1.02301036e-03,  -1.10520861e-03,
            -1.17824066e-03,  -1.24119243e-03,  -1.29350096e-03, -1.33458128e-03,
            -1.36388379e-03,  -1.38105465e-03, -1.38595634e-03,  -1.37832214e-03,
            -1.35829791e-03, -1.32588558e-03,  -1.28146000e-03,  -1.22518721e-03,
            -1.15769141e-03,  -1.07943574e-03,  -9.91143310e-04, -8.93671637e-04,
            -7.87961546e-04,  -6.74866999e-04, -5.55444011e-04,  -4.30919368e-04,
            -3.02270469e-04, -1.70824836e-04,  -3.77396109e-05,   9.56816273e-05,
            2.28299979e-04,   3.58842001e-04,   4.86074690e-04, 6.08875045e-04,
            7.26090501e-04,   8.36677390e-04, 9.39639556e-04,   1.03407702e-03,
            1.11906014e-03, 1.19386315e-03,   1.25779004e-03,   1.31037519e-03,
            1.35108872e-03,   1.37958003e-03,   1.39570542e-03, 1.39927441e-03,
            1.39033118e-03,   1.36892681e-03, 1.33533475e-03,   1.28987173e-03,
            1.23311389e-03, 1.16551418e-03,   1.08773037e-03,   1.00059786e-03,
            9.04879918e-04,   8.01551710e-04,   6.91575582e-04, 5.75952750e-04,
            4.55756793e-04,   3.32302985e-04, 2.06487043e-04,   7.95882588e-05,
            -4.72208138e-05, -1.72823958e-04,  -2.96101535e-04,  -4.15925168e-04,
            -5.31250383e-04,  -6.41017819e-04,  -7.44349685e-04, -8.40276057e-04,
            -9.28032591e-04,  -1.00688055e-03, -1.07610640e-03,  -1.13518206e-03,
            -1.18370702e-03, -1.22129557e-03,  -1.24764964e-03,  -1.26264035e-03,
            -1.26627364e-03,  -1.25857717e-03,  -1.23964021e-03,
            -1.20980891e-03,  -1.16944284e-03,  -1.11887385e-03,
            -1.05870668e-03,  -9.89617769e-04,  -9.12311681e-04,
            -8.27560752e-04,  -7.36170045e-04,  -6.39042153e-04,
            -5.37114997e-04,  -4.31247869e-04,  -3.22637131e-04,
            -2.12194968e-04,  -1.00869243e-04,   1.03136916e-05,
            1.20241207e-04,   2.27979265e-04,   3.32675130e-04,
            4.33323979e-04,   5.29105666e-04,   6.19142445e-04,
            7.02728753e-04,   7.79114404e-04,   8.47787258e-04,
            9.08216047e-04,   9.59821724e-04,   1.00228122e-03,
            1.03538392e-03,   1.05880009e-03,   1.07260841e-03,
            1.07662550e-03,   1.07093155e-03,   1.05577003e-03,
            1.03129797e-03,   9.97904596e-04,   9.55975595e-04,
            9.05955028e-04,   8.48396342e-04,   7.83925297e-04,
            7.13242537e-04,   6.36896396e-04,   5.55809454e-04,
            4.70697276e-04,   3.82464668e-04,   2.91766220e-04,
            1.99564879e-04,   1.06707654e-04,   1.40463177e-05,
            -7.76333806e-05,  -1.67470574e-04,  -2.54708122e-04,
            -3.38623857e-04,  -4.18484684e-04])
        difference_exp = difference_exp/np.max(difference_exp)
        np.testing.assert_allclose(difference_exp, difference,
                                   rtol=rtol, atol=atol,
                                   err_msg='In TestBeamFeedback test_SPS_RL: difference between simulated and analytic result different than expected')
Example #2
0
my_beam = Beam(general_params, n_macroparticles, n_particles)

cut_options = CutOptions(cut_left=0, cut_right=2.0 * 0.9e-6, n_slices=200)
slices_ring = Profile(my_beam, cut_options)

#Phase loop
#configuration = {'machine': 'PSB', 'PL_gain': 0., 'RL_gain': [34.8,16391],
#                 'PL_period': 10.e-6, 'RL_period': 7}
configuration = {
    'machine': 'PSB',
    'PL_gain': 0,
    'RL_gain': [1.e7, 1.e11],
    'period': 10.e-6
}
phase_loop = BeamFeedback(general_params, rf_params, slices_ring,
                          configuration)

#Long tracker
long_tracker = RingAndRFTracker(rf_params, my_beam, BeamFeedback=phase_loop)

full_ring = FullRingAndRF([long_tracker])

distribution_type = 'gaussian'
bunch_length = 200.0e-9
distribution_variable = 'Action'

matched_from_distribution_function(my_beam,
                                   full_ring,
                                   bunch_length=bunch_length,
                                   distribution_type=distribution_type,
                                   distribution_variable=distribution_variable,
Example #3
0
profile = Profile(beam, CutOptions(n_slices=n_slices, cut_left=-0.5e-9,
                                   cut_right=(cutRange-0.5e-9)))
mpiprint("Beam generated, profile set...")
mpiprint("Using %d slices" % n_slices)

# Define emittance BUP feedback
noiseFB = LHCNoiseFB(rf, profile, bl_target)
mpiprint("Phase noise feedback set...")

# Define phase loop and frequency loop gain
PL_gain = 1./(5.*ring.t_rev[0])
SL_gain = PL_gain/10.

# Noise injected in the PL delayed by one turn and opposite sign
config = {'machine': 'LHC', 'PL_gain': PL_gain, 'SL_gain': SL_gain}
PL = BeamFeedback(ring, rf, profile, config, PhaseNoise=LHCnoise,
                  LHCNoiseFB=noiseFB)
mpiprint("   PL gain is %.4e 1/s for initial turn T0 = %.4e s" % (PL.gain,
                                                                  ring.t_rev[0]))
mpiprint("   SL gain is %.4e turns" % PL.gain2)
mpiprint("   Omega_s0 = %.4e s at flat bottom, %.4e s at flat top"
         % (rf.omega_s0[0], rf.omega_s0[n_turns]))
mpiprint("   SL a_i = %.4f a_f = %.4f" % (PL.lhc_a[0], PL.lhc_a[n_turns]))
mpiprint("   SL t_i = %.4f t_f = %.4f" % (PL.lhc_t[0], PL.lhc_t[n_turns]))

# Injecting noise in the cavity, PL on

# Define machine impedance from http://impedance.web.cern.ch/impedance/
ZTot = np.loadtxt(os.path.join(inputDir, 'Zlong_Allthemachine_450GeV_B1_LHC_inj_450GeV_B1.dat'),
                  skiprows=1)
ZTable = InputTable(ZTot[:, 0], ZTot[:, 1], ZTot[:, 2])
indVoltage = InducedVoltageFreq(
Example #4
0
        PLoffset = None
    PLdict = {
        'time_offset': PLoffset,
        'PL_gain': PLgain,
        'window_coefficient': PLalpha
    }
    PL_save_turns = 50
    if PL_2ndLoop == 'R_Loop':
        gain2nd = 5e9
        PLdict['machine'] = 'SPS_RL'
        PLdict['RL_gain'] = gain2nd
    elif PL_2ndLoop == 'F_Loop':
        gain2nd = 0.9e-1
        PLdict['machine'] = 'SPS_F'
        PLdict['FL_gain'] = gain2nd
    phaseLoop = BeamFeedback(ring, rf_station, profile, PLdict)
    beamPosPrev = t_batch_begin + 0.5 * t_rf

# SPS --- Tracker Setup ----------------------------------------

mpiprint('Setting up tracker')
tracker = RingAndRFTracker(rf_station,
                           beam,
                           Profile=profile,
                           TotalInducedVoltage=inducedVoltage,
                           interpolation=True)
fulltracker = FullRingAndRF([tracker])

mpiprint('Creating SPS bunch from PS bunch')
# create 72 bunches from PS bunch
    def setUp(self, negativeEta=True, acceleration=True, singleRF=True):
        # Defining parameters -------------------------------------------------
        # Bunch parameters
        N_b = 1.e9  # Intensity
        N_p = 100000  # Macro-particles
        tau_0 = 50.e-9  # Initial bunch length, 4 sigma [s]

        # Machine parameters
        C = 1567.5  # Machine circumference [m]
        p_1i = 3.e9  # Synchronous momentum [eV/c]
        p_1f = 30.0e9  # Synchronous momentum, final
        p_2f = 40.e9  # Synchronous momentum [eV/c]
        p_2i = 60.e9  # Synchronous momentum, final
        gamma_t = 31.6  # Transition gamma
        alpha_1 = -1. / gamma_t / gamma_t  # First order mom. comp. factor
        alpha_2 = 1. / gamma_t / gamma_t  # First order mom. comp. factor

        # RF parameters
        h = [9, 18]  # Harmonic number
        V = [1800.e3, 110.e3]  # RF voltage [V]
        phi_1 = [np.pi + 1., np.pi / 6 + 2.]  # Phase modulation/offset
        phi_2 = [1., np.pi / 6 + 2.]  # Phase modulation/offset
        N_t = 43857

        # Defining classes ----------------------------------------------------
        # Define general parameters
        if (negativeEta == True):

            if acceleration == True:
                # eta < 0, acceleration
                general_params = Ring(C, alpha_1,
                                      np.linspace(p_1i, p_1f, N_t + 1),
                                      Proton(), N_t)
            elif acceleration == False:
                # eta < 0, deceleration
                general_params = Ring(C, alpha_1,
                                      np.linspace(p_1f, p_1i, N_t + 1),
                                      Proton(), N_t)

            if singleRF == True:
                rf_params = RFStation(general_params,
                                      9,
                                      1.8e6,
                                      np.pi + 1.,
                                      n_rf=1)
            elif singleRF == False:
                rf_params = RFStation(general_params, h, V, phi_1, n_rf=2)
                rf_params.phi_s = calculate_phi_s(
                    rf_params,
                    Particle=general_params.Particle,
                    accelerating_systems='all')

        elif (negativeEta == False):

            if acceleration == True:
                # eta > 0, acceleration
                general_params = Ring(C, alpha_2,
                                      np.linspace(p_2i, p_2f, N_t + 1),
                                      Proton(), N_t)
            elif acceleration == False:
                # eta > 0, deceleration
                general_params = Ring(C, alpha_2,
                                      np.linspace(p_2f, p_2i, N_t + 1),
                                      Proton(), N_t)

            if singleRF == True:
                rf_params = RFStation(general_params, 9, 1.8e6, 1., n_rf=1)
            elif singleRF == False:
                rf_params = RFStation(general_params, h, V, phi_2, n_rf=2)
                rf_params.phi_s = calculate_phi_s(
                    rf_params,
                    Particle=general_params.Particle,
                    accelerating_systems='all')

        # Define beam and distribution
        beam = Beam(general_params, N_p, N_b)
        bigaussian(general_params, rf_params, beam, tau_0 / 4, seed=1234)
        #print(np.mean(beam.dt))
        slices = Profile(
            beam, CutOptions(cut_left=0.e-9, cut_right=600.e-9, n_slices=1000))
        slices.track()
        configuration = {
            'machine': 'LHC',
            'PL_gain': 0.1 * general_params.t_rev[0]
        }
        PL = BeamFeedback(general_params, rf_params, slices, configuration)
        PL.beam_phase()

        # Quantities to be compared
        self.phi_s = rf_params.phi_s[0]
        self.phi_b = PL.phi_beam
        self.phi_rf = rf_params.phi_rf[0, 0]
        self.dE_sep = separatrix(general_params, rf_params,
                                 [-5.e-7, -3.e-7, 1.e-7, 3.e-7, 7.e-7, 9.e-7])