############################################################################### # This file is part of SWIFT. # Copyright (c) 2025 Thomas Sandnes (thomas.d.sandnes@durham.ac.uk) # 2016 Matthieu Schaller (matthieu.schaller@durham.ac.uk) # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published # by the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . # ############################################################################## import h5py import numpy as np # Generates a swift IC file for the Kelvin-Helmholtz test in a periodic box # Constants R_earth = 6371000 # Earth radius R_jupiter = 11.2089 * R_earth # Jupiter radius # Parameters N2_l = 128 # Particles along one edge in the low-density region N2_depth = 18 # Particles in z direction in low-density region matID1 = 304 # Central region material ID: AQUA matID2 = 307 # Outskirts material ID: CD21 H--He P1 = 3.2e12 # Central region pressure P2 = 3.2e12 # Outskirts pressure u1 = 283591514 # Central region specific internal energy u2 = 804943158 # Outskirts specific internal energy rho1_approx = 9000 # Central region density. Readjusted later rho2 = 3500 # Outskirts density boxsize_l = R_jupiter # size of simulation box in x and y dimension v1 = boxsize_l / 10000 # Central region velocity v2 = -boxsize_l / 10000 # Outskirts velocity boxsize_depth = boxsize_l * N2_depth / N2_l # size of simulation box in z dimension mass = rho2 * (boxsize_l * boxsize_l * boxsize_depth) / (N2_l * N2_l * N2_depth) fileOutputName = "kelvin_helmholtz.hdf5" # --------------------------------------------------- # Start by calculating N1_l and rho1 numPart2 = N2_l * N2_l * N2_depth numPart1_approx = int(numPart2 / rho2 * rho1_approx) # Consider numPart1 = N1_l * N1_l * N1_depth # Substituting boxsize_depth / boxsize_l = N1_depth / N1_l gives, # numPart1 = N1_l * N1_l * (boxsize_depth / boxsize_l) * N1_l, which ranges to: N1_l = int(np.cbrt(numPart1_approx * boxsize_l / boxsize_depth)) # Make sure this is a multiple of 4 since this is the number of KH vortices N1_l -= N1_l % 4 N1_depth = int(boxsize_depth * N1_l / boxsize_l) numPart1 = int(N1_l * N1_l * N1_depth) # The density of the central region can then be calculated rho1 = mass * (N1_l * N1_l * N1_depth) / (boxsize_l * boxsize_l * boxsize_depth) # Now construct two lattices of particles in the two regions A2_coords1 = np.empty((numPart1, 3)) A2_coords2 = np.empty((numPart2, 3)) A2_vel1 = np.zeros((numPart1, 3)) A2_vel2 = np.zeros((numPart2, 3)) A2_vel1[:, 0] = v1 A2_vel2[:, 0] = v2 A1_mat1 = np.full(numPart1, matID1) A1_mat2 = np.full(numPart2, matID2) A1_m1 = np.full(numPart1, mass) A1_m2 = np.full(numPart2, mass) A1_rho1 = np.full(numPart1, rho1) A1_rho2 = np.full(numPart2, rho2) A1_u1 = np.full(numPart1, u1) A1_u2 = np.full(numPart2, u2) A1_h1 = np.full(numPart1, boxsize_l / N1_l) A1_h2 = np.full(numPart2, boxsize_l / N2_l) # Particles in the central region for i in range(N1_depth): for j in range(N1_l): for k in range(N1_l): index = i * N1_l ** 2 + j * N1_l + k A2_coords1[index, 0] = (j / float(N1_l) + 1.0 / (2.0 * N1_l)) * boxsize_l A2_coords1[index, 1] = (k / float(N1_l) + 1.0 / (2.0 * N1_l)) * boxsize_l A2_coords1[index, 2] = ( i / float(N1_depth) + 1.0 / (2.0 * N1_depth) ) * boxsize_depth # Particles in the outskirts for i in range(N2_depth): for j in range(N2_l): for k in range(N2_l): index = i * N2_l ** 2 + j * N2_l + k A2_coords2[index, 0] = (j / float(N2_l) + 1.0 / (2.0 * N2_l)) * boxsize_l A2_coords2[index, 1] = (k / float(N2_l) + 1.0 / (2.0 * N2_l)) * boxsize_l A2_coords2[index, 2] = ( i / float(N2_depth) + 1.0 / (2.0 * N2_depth) ) * boxsize_depth # Masks for the particles to be selected for the outer and inner regions mask1 = abs(A2_coords1[:, 1] - 0.5 * boxsize_l) < 0.25 * boxsize_l mask2 = abs(A2_coords2[:, 1] - 0.5 * boxsize_l) > 0.25 * boxsize_l # The positions of the particles are now selected # and the placement of the lattices are adjusted to give appropriate interfaces A2_coords_inside = A2_coords1[mask1, :] A2_coords_outside = A2_coords2[mask2, :] # Calculate the separation of particles across the density discontinuity pcl_separation_1 = np.cbrt(mass / rho1) pcl_separation_2 = np.cbrt(mass / rho2) boundary_separation = 0.5 * (pcl_separation_1 + pcl_separation_2) # Shift all the "inside" particles to get boundary_separation across the bottom interface min_y_inside = np.min(A2_coords_inside[:, 1]) max_y_outside_bot = np.max( A2_coords_outside[A2_coords_outside[:, 1] - 0.5 * boxsize_l < -0.25 * boxsize_l, 1] ) shift_distance_bot = boundary_separation - (min_y_inside - max_y_outside_bot) A2_coords_inside[:, 1] += shift_distance_bot # Shift the top section of the "outside" particles to get boundary_separation across the top interface max_y_inside = np.max(A2_coords_inside[:, 1]) min_y_outside_top = np.min( A2_coords_outside[A2_coords_outside[:, 1] - 0.5 * boxsize_l > 0.25 * boxsize_l, 1] ) shift_distance_top = boundary_separation - (min_y_outside_top - max_y_inside) A2_coords_outside[ A2_coords_outside[:, 1] - 0.5 * boxsize_l > 0.25 * boxsize_l, 1 ] += shift_distance_top # Adjust box size in y direction based on the shifting of the lattices. new_box_y = boxsize_l + shift_distance_top # Now the two lattices can be combined A2_coords = np.append(A2_coords_inside, A2_coords_outside, axis=0) A2_vel = np.append(A2_vel1[mask1], A2_vel2[mask2], axis=0) A1_mat = np.append(A1_mat1[mask1], A1_mat2[mask2], axis=0) A1_m = np.append(A1_m1[mask1], A1_m2[mask2], axis=0) A1_rho = np.append(A1_rho1[mask1], A1_rho2[mask2], axis=0) A1_u = np.append(A1_u1[mask1], A1_u2[mask2], axis=0) A1_h = np.append(A1_h1[mask1], A1_h2[mask2], axis=0) numPart = np.size(A1_m) A1_ids = np.linspace(1, numPart, numPart) # Finally add the velocity perturbation vel_perturb_factor = 0.01 * (v1 - v2) A2_vel[:, 1] = vel_perturb_factor * np.sin( 2 * np.pi * A2_coords[:, 0] / (0.5 * boxsize_l) ) # Write ICs to file with h5py.File(fileOutputName, "w") as f: # Header grp = f.create_group("/Header") grp.attrs["BoxSize"] = [boxsize_l, new_box_y, boxsize_depth] grp.attrs["NumPart_Total"] = [numPart, 0, 0, 0, 0, 0] grp.attrs["NumPart_Total_HighWord"] = [0, 0, 0, 0, 0, 0] grp.attrs["NumPart_ThisFile"] = [numPart, 0, 0, 0, 0, 0] grp.attrs["Time"] = 0.0 grp.attrs["NumFileOutputsPerSnapshot"] = 1 grp.attrs["MassTable"] = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] grp.attrs["Flag_Entropy_ICs"] = [0, 0, 0, 0, 0, 0] grp.attrs["Dimension"] = 3 # Units grp = f.create_group("/Units") grp.attrs["Unit length in cgs (U_L)"] = 100.0 grp.attrs["Unit mass in cgs (U_M)"] = 1000.0 grp.attrs["Unit time in cgs (U_t)"] = 1.0 grp.attrs["Unit current in cgs (U_I)"] = 1.0 grp.attrs["Unit temperature in cgs (U_T)"] = 1.0 # Particle group grp = f.create_group("/PartType0") ds = grp.create_dataset("Coordinates", (numPart, 3), "d") ds[()] = A2_coords ds = grp.create_dataset("Velocities", (numPart, 3), "f") ds[()] = A2_vel ds = grp.create_dataset("Masses", (numPart, 1), "f") ds[()] = A1_m.reshape((numPart, 1)) ds = grp.create_dataset("Density", (numPart, 1), "f") ds[()] = A1_rho.reshape((numPart, 1)) ds = grp.create_dataset("SmoothingLength", (numPart, 1), "f") ds[()] = A1_h.reshape((numPart, 1)) ds = grp.create_dataset("InternalEnergy", (numPart, 1), "f") ds[()] = A1_u.reshape((numPart, 1)) ds = grp.create_dataset("ParticleIDs", (numPart, 1), "L") ds[()] = A1_ids.reshape((numPart, 1)) ds = grp.create_dataset("MaterialIDs", (numPart, 1), "i") ds[()] = A1_mat.reshape((numPart, 1))