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12 changes: 12 additions & 0 deletions
12
Examples/Tests/effective_potential_electrostatic/CMakeLists.txt
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| # Add tests (alphabetical order) ############################################## | ||
| # | ||
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| add_warpx_test( | ||
| test_3d_effective_potential_electrostatic_picmi # name | ||
| 3 # dims | ||
| 2 # nprocs | ||
| inputs_test_3d_effective_potential_electrostatic_picmi.py # inputs | ||
| analysis.py # analysis | ||
| diags/field_diag/ # output | ||
| OFF # dependency | ||
| ) |
90 changes: 90 additions & 0 deletions
90
Examples/Tests/effective_potential_electrostatic/analysis.py
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| #!/usr/bin/env python3 | ||
| # | ||
| # --- Analysis script for the effective potential Poisson solver test. This test | ||
| # --- is based on the adiabatic plasma expansion benchmark from Connor et al. (2021) | ||
| # --- doi.org/10.1109/TPS.2021.3072353. | ||
| # --- The electron density distribution (as a function of radius) is compared | ||
| # --- with the analytically calculated density based on the input parameters | ||
| # --- of the test simulation at each output timestep. | ||
|
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| import os | ||
| import sys | ||
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| import dill | ||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
| from openpmd_viewer import OpenPMDTimeSeries | ||
| from scipy.interpolate import RegularGridInterpolator | ||
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| from pywarpx import picmi | ||
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| constants = picmi.constants | ||
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| # load simulation parameters | ||
| with open("sim_parameters.dpkl", "rb") as f: | ||
| sim = dill.load(f) | ||
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| # characteristic expansion time | ||
| tau = sim["sigma_0"] * np.sqrt(sim["M"] / (constants.kb * (sim["T_e"] + sim["T_i"]))) | ||
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| def get_analytic_density(r, t): | ||
| expansion_factor = 1.0 + t**2 / tau**2 | ||
| T = sim["T_e"] / expansion_factor | ||
| sigma = sim["sigma_0"] * np.sqrt(expansion_factor) | ||
| return ( | ||
| sim["n_plasma"] * (T / sim["T_e"]) ** 1.5 * np.exp(-(r**2) / (2.0 * sigma**2)) | ||
| ) | ||
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| def get_radial_function(field, info): | ||
| """Helper function to transform Cartesian data to polar data and average | ||
| over theta and phi.""" | ||
| r_grid = np.linspace(0, np.max(info.z) - 1e-3, 30) | ||
| theta_grid = np.linspace(0, 2 * np.pi, 40, endpoint=False) | ||
| phi_grid = np.linspace(0, np.pi, 20) | ||
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| r, t, p = np.meshgrid(r_grid, theta_grid, phi_grid, indexing="ij") | ||
| x_sp = r * np.sin(p) * np.cos(t) | ||
| y_sp = r * np.sin(p) * np.sin(t) | ||
| z_sp = r * np.cos(p) | ||
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| interp = RegularGridInterpolator((info.x, info.y, info.z), field) | ||
| field_sp = interp((x_sp, y_sp, z_sp)) | ||
| return r_grid, np.mean(field_sp, axis=(1, 2)) | ||
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| diag_dir = "diags/field_diag" | ||
| ts = OpenPMDTimeSeries(diag_dir, check_all_files=True) | ||
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| rms_errors = np.zeros(len(ts.iterations)) | ||
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| for ii, it in enumerate(ts.iterations): | ||
| rho_e, info = ts.get_field(field="rho_electrons", iteration=it) | ||
| r_grid, n_e = get_radial_function(-rho_e / constants.q_e, info) | ||
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| n_e_analytic = get_analytic_density(r_grid, ts.t[ii]) | ||
| rms_errors[ii] = ( | ||
| np.sqrt(np.mean(np.sum((n_e - n_e_analytic) ** 2))) / n_e_analytic[0] | ||
| ) | ||
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| plt.plot(r_grid, n_e_analytic, "k--", alpha=0.6) | ||
| plt.plot(r_grid, n_e, label=f"t = {ts.t[ii]*1e6:.2f} $\mu$s") | ||
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| print("RMS error (%) in density: ", rms_errors) | ||
| assert np.all(rms_errors < 0.05) | ||
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| plt.ylabel("$n_e$ (m$^{-3}$)") | ||
| plt.xlabel("r (m)") | ||
| plt.grid() | ||
| plt.legend() | ||
| plt.show() | ||
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| if len(sys.argv) > 1: | ||
| sys.path.insert(1, "../../../../warpx/Regression/Checksum/") | ||
| import checksumAPI | ||
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| filename = sys.argv[1] | ||
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| test_name = os.path.split(os.getcwd())[1] | ||
| checksumAPI.evaluate_checksum(test_name, filename, output_format="openpmd") |
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