{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Optical forces\n",
"\n",
"Calculating optical forces involves integrating the Maxwell stress\n",
"tensor (MST) over a closed surface surrounding the region where the\n",
"force is to be evaluated.\n",
"\n",
"In the frequency domain, the **time-averaged Maxwell stress tensor** is\n",
"defined as\n",
"\n",
"$$ \\langle T_{ij} \\rangle = \\frac{1}{2}\\operatorname{Re}\\!\\left[\\epsilon E_i E_j^* + \\mu H_i H_j^* - \\frac{1}{2}\\left(\\epsilon |\\mathbf{E}|^2 + \\mu |\\mathbf{H}|^2\\right)\\delta_{ij}\\right] $$\n",
"\n",
"where $\\mathbf{E}$ and $\\mathbf{H}$ are the complex electric and\n",
"magnetic fields, $\\epsilon$ and $\\mu$ are the permittivity and\n",
"permeability of the medium, and $\\delta_{ij}$ is the Kronecker delta.\n",
"\n",
"The total time-averaged force acting on the enclosed object is obtained\n",
"by integrating the MST over a closed surface $S$,\n",
"\n",
"$$\\mathbf{F}\n",
"=\n",
"\\oint_S\n",
"\\langle \\mathbf{T} \\rangle \\cdot \\hat{\\mathbf{n}} \\, dS,$$\n",
"\n",
"where $\\hat{\\mathbf{n}}$ is the outward-pointing surface normal.\n",
"\n",
"In Tidy3D, one can define eight\n",
"[`FluxMonitor`s](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html#tidy3d.FluxMonitor)\n",
"surrounding the region of interest to form this closed surface and\n",
"evaluate the surface integrals numerically.\n",
"\n",
"In this notebook, we demonstrate the complete workflow:\n",
"\n",
"[1)](#1) Define auxiliary functions to generate the surface monitors\n",
"around the region of interest, compute the MST, and evaluate the\n",
"resulting force vector.\n",
"\n",
"[2)](#2) Validate the force calculation using a plane wave incident on a\n",
"dielectric interface.\n",
"\n",
"[3)](#3) Compute the optical forces associated with the symmetric and\n",
"antisymmetric modes of coupled waveguides, reproducing the results\n",
"presented in \n",
"Michelle L. Povinelli, Marko Lončar, Mihai Ibanescu, Elizabeth J.\n",
"Smythe, Steven G. Johnson, Federico Capasso, and John D. Joannopoulos,\n",
"*“Evanescent-wave bonding between optical waveguides”*, Optics Letters\n",
"**30**(22), 2005. [DOI:\n",
"https://doi.org/10.1364/OL.30.003042](https://opg.optica.org/ol/abstract.cfm?uri=ol-30-22-3042)\n",
"\n",
"![\"Schematic\"](\"img/opticalForces.png\")
"
],
"id": "3ecc0022-8d23-4178-a055-c995130a96fb"
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import tidy3d as td\n",
"import tidy3d.web as web\n",
"from tidy3d.plugins.mode import ModeSolver"
],
"id": "7280d9a5"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Auxiliary Functions \n",
"\n",
"### Creating Surface Monitors\n",
"\n",
"Now, we will define a function to create eight\n",
"[FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html)\n",
"objects around the region of interest."
],
"id": "f93e99ef-2106-492b-ade4-75eb63f0d6bd"
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def surface_mon(center, size, freqs):\n",
" \"\"\"Create paired field and permittivity monitors on the six faces of a box.\"\"\"\n",
"\n",
" mons = []\n",
"\n",
" size = np.array(size, dtype=float)\n",
" center = np.array(center, dtype=float)\n",
"\n",
" # Map axis labels to array indices for easy access.\n",
" axis = {\"x\": 0, \"y\": 1, \"z\": 2}\n",
"\n",
" # Unit vectors along each Cartesian axis, used to translate monitor centers.\n",
" normal = {\n",
" \"x\": np.array([1, 0, 0]),\n",
" \"y\": np.array([0, 1, 0]),\n",
" \"z\": np.array([0, 0, 1]),\n",
" }\n",
"\n",
" # Mask describing which dimensions should span each face monitor.\n",
" mask = {\n",
" \"x\": np.array([0, 1, 1]),\n",
" \"y\": np.array([1, 0, 1]),\n",
" \"z\": np.array([1, 1, 0]),\n",
" }\n",
"\n",
" for c in [\"x\", \"y\", \"z\"]:\n",
" i = axis[c]\n",
"\n",
" for d in [1, -1]:\n",
" # Position the monitor on the +/- face along axis c.\n",
" center_mon = tuple(center + d * normal[c] * size[i] / 2)\n",
" size_mon = tuple(size * mask[c])\n",
"\n",
" name = f\"{c}{'+' if d == 1 else '-'}\"\n",
"\n",
" mons.append(\n",
" td.FieldMonitor(\n",
" center=center_mon,\n",
" size=size_mon,\n",
" name=name,\n",
" freqs=freqs,\n",
" colocate=False,\n",
" )\n",
" )\n",
"\n",
" # Include matching permittivity monitor so material data is available\n",
" # when evaluating the Maxwell stress tensor.\n",
" mons.append(\n",
" td.PermittivityMonitor(\n",
" center=center_mon,\n",
" size=size_mon,\n",
" name=f\"e_{name}\",\n",
" freqs=freqs,\n",
" )\n",
" )\n",
"\n",
" return mons"
],
"id": "f1f9c29c"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Maxwell Stress Tensor Calculation\n",
"\n",
"Now, we will define a function to compute the Maxwell Stress Tensor from\n",
"a Tidy3D\n",
"[SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.components.data.sim_data.SimulationData.html)\n",
"object, using the monitors defined by the `surface_mon` function."
],
"id": "08d3eedc-8528-4831-83b2-cb68afbb0054"
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def stressTensor(sim_data, monitor_name, eps_mon):\n",
" \"\"\"Compute the Maxwell stress tensor on the plane defined by `monitor_name`, following the naming convention of the `surface_mon` function.\"\"\"\n",
"\n",
" if monitor_name[0] == \"x\":\n",
" i, j = (\"y\", \"z\")\n",
"\n",
" Ex = sim_data[monitor_name].Ex[:, 1:-1, 1:-1, :]\n",
" Ey = sim_data[monitor_name].Ey[:, 1:-1, 1:-1, :]\n",
" Ez = sim_data[monitor_name].Ez[:, 1:-1, 1:-1, :]\n",
"\n",
" Hx = sim_data[monitor_name].Hx[:, 1:-1, 1:-1, :]\n",
" Hy = sim_data[monitor_name].Hy[:, 1:-1, 1:-1, :]\n",
" Hz = sim_data[monitor_name].Hz[:, 1:-1, 1:-1, :]\n",
"\n",
" # Interpolate permittivity onto the field grid covering this face.\n",
" eps_xx = sim_data[eps_mon].eps_xx.interp(y=Ex.coords[i], z=Ex.coords[j])\n",
" eps_yy = sim_data[eps_mon].eps_yy.interp(y=Ey.coords[i], z=Ey.coords[j])\n",
" eps_zz = sim_data[eps_mon].eps_zz.interp(y=Ez.coords[i], z=Ez.coords[j])\n",
"\n",
" elif monitor_name[0] == \"y\":\n",
" i, j = (\"x\", \"z\")\n",
"\n",
" Ex = sim_data[monitor_name].Ex[1:-1, :, 1:-1, :]\n",
" Ey = sim_data[monitor_name].Ey[1:-1, :, 1:-1, :]\n",
" Ez = sim_data[monitor_name].Ez[1:-1, :, 1:-1, :]\n",
"\n",
" Hx = sim_data[monitor_name].Hx[1:-1, :, 1:-1, :]\n",
" Hy = sim_data[monitor_name].Hy[1:-1, :, 1:-1, :]\n",
" Hz = sim_data[monitor_name].Hz[1:-1, :, 1:-1, :]\n",
"\n",
" eps_xx = sim_data[eps_mon].eps_xx.interp(x=Ex.coords[i], z=Ex.coords[j])\n",
" eps_yy = sim_data[eps_mon].eps_yy.interp(x=Ey.coords[i], z=Ey.coords[j])\n",
" eps_zz = sim_data[eps_mon].eps_zz.interp(x=Ez.coords[i], z=Ez.coords[j])\n",
"\n",
" elif monitor_name[0] == \"z\":\n",
" i, j = (\"x\", \"y\")\n",
"\n",
" Ex = sim_data[monitor_name].Ex[1:-1, 1:-1, :, :]\n",
" Ey = sim_data[monitor_name].Ey[1:-1, 1:-1, :, :]\n",
" Ez = sim_data[monitor_name].Ez[1:-1, 1:-1, :, :]\n",
"\n",
" Hx = sim_data[monitor_name].Hx[1:-1, 1:-1, :, :]\n",
" Hy = sim_data[monitor_name].Hy[1:-1, 1:-1, :, :]\n",
" Hz = sim_data[monitor_name].Hz[1:-1, 1:-1, :, :]\n",
"\n",
" eps_xx = sim_data[eps_mon].eps_xx.interp(x=Ex.coords[i], y=Ex.coords[j])\n",
" eps_yy = sim_data[eps_mon].eps_yy.interp(x=Ey.coords[i], y=Ey.coords[j])\n",
" eps_zz = sim_data[eps_mon].eps_zz.interp(x=Ez.coords[i], y=Ez.coords[j])\n",
"\n",
" coords = [Ex.x.squeeze(), Ex.y.squeeze(), Ex.z.squeeze()]\n",
"\n",
" # Convert xarray data to numpy arrays.\n",
" Ey = Ey.values\n",
" Ez = Ez.values\n",
" Hx = Hx.values\n",
" Hy = Hy.values\n",
" Hz = Hz.values\n",
" eps_xx = eps_xx.values\n",
" eps_yy = eps_yy.values\n",
" eps_zz = eps_zz.values\n",
"\n",
" # Displacement (D) and magnetic flux (B) densities.\n",
" Dx = Ex * eps_xx * td.EPSILON_0\n",
" Dy = Ey * eps_yy * td.EPSILON_0\n",
" Dz = Ez * eps_zz * td.EPSILON_0\n",
"\n",
" Bx = Hx * td.MU_0\n",
" By = Hy * td.MU_0\n",
" Bz = Hz * td.MU_0\n",
"\n",
" # Scalar energy-density terms appearing in the stress tensor formula.\n",
" E_mod = Ex * np.conj(Dx) + Ey * np.conj(Dy) + Ez * np.conj(Dz)\n",
" H_mod = Hx * np.conj(Bx) + Hy * np.conj(By) + Hz * np.conj(Bz)\n",
"\n",
" # Maxwell stress tensor components prior to taking the real (time-averaged) part.\n",
" Txx = (Ex * np.conj(Dx) - 0.5 * E_mod) + (Hx * np.conj(Bx) - 0.5 * H_mod)\n",
" Txy = (Ex * np.conj(Dy)) + (Hx * np.conj(By))\n",
" Txz = (Ex * np.conj(Dz)) + (Hx * np.conj(Bz))\n",
"\n",
" Tyy = (Ey * np.conj(Dy) - 0.5 * E_mod) + (Hy * np.conj(By) - 0.5 * H_mod)\n",
" Tyx = (Ey * np.conj(Dx)) + (Hy * np.conj(Bx))\n",
" Tyz = (Ey * np.conj(Dz)) + (Hy * np.conj(Bz))\n",
"\n",
" Tzz = (Ez * np.conj(Dz) - 0.5 * E_mod) + (Hz * np.conj(Bz) - 0.5 * H_mod)\n",
" Tzx = (Ez * np.conj(Dx)) + (Hz * np.conj(Bx))\n",
" Tzy = (Ez * np.conj(Dy)) + (Hz * np.conj(By))\n",
"\n",
" T = np.array([[Txx, Txy, Txz], [Tyx, Tyy, Tyz], [Tzx, Tzy, Tzz]])\n",
"\n",
" # Time-average via the real part and include the 1/2 prefactor.\n",
" return 0.5 * T.real, coords"
],
"id": "b5cfc1a4"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Force Calculation\n",
"\n",
"Finally, we will define a function to integrate all monitors from a\n",
"[SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.components.data.sim_data.SimulationData.html)\n",
"object and compute the force vector."
],
"id": "63724e14-bc30-4e6d-a8cd-af32fa5a2ba0"
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def integrateStressTensor(sim_data):\n",
" \"\"\"Integrate the Maxwell stress tensor over a closed box of monitors.\"\"\"\n",
"\n",
" Fx_x = Fy_x = Fz_x = 0.0\n",
" Fx_y = Fy_y = Fz_y = 0.0\n",
" Fx_z = Fy_z = Fz_z = 0.0\n",
"\n",
" for c in [\"x\", \"y\", \"z\"]:\n",
" for d in [1, -1]:\n",
" name = c + (\"+\" if d == 1 else \"-\")\n",
" T, coords = stressTensor(sim_data, name, \"e_\" + name)\n",
"\n",
" if c == \"x\":\n",
" # Extract tensor components T_{ix} on the x± faces.\n",
" Txx = T[0, 0].squeeze()\n",
" Tyx = T[1, 0].squeeze()\n",
" Tzx = T[2, 0].squeeze()\n",
"\n",
" iy = np.argsort(coords[1])\n",
" iz = np.argsort(coords[2])\n",
"\n",
" y_sorted = np.asarray(coords[1])[iy]\n",
" z_sorted = np.asarray(coords[2])[iz]\n",
"\n",
" Txx = Txx[iy][:, iz]\n",
" Tyx = Tyx[iy][:, iz]\n",
" Tzx = Tzx[iy][:, iz]\n",
"\n",
" Fx_x += d * np.trapezoid(np.trapezoid(Txx, y_sorted, axis=0), z_sorted, axis=0)\n",
" Fy_x += d * np.trapezoid(np.trapezoid(Tyx, y_sorted, axis=0), z_sorted, axis=0)\n",
" Fz_x += d * np.trapezoid(np.trapezoid(Tzx, y_sorted, axis=0), z_sorted, axis=0)\n",
"\n",
" elif c == \"y\":\n",
" # Integrate T_{iy} over the y± faces.\n",
" Txy = T[0, 1].squeeze()\n",
" Tyy = T[1, 1].squeeze()\n",
" Tzy = T[2, 1].squeeze()\n",
"\n",
" ix = np.argsort(coords[0])\n",
" iz = np.argsort(coords[2])\n",
"\n",
" x_sorted = np.asarray(coords[0])[ix]\n",
" z_sorted = np.asarray(coords[2])[iz]\n",
"\n",
" Txy = Txy[ix][:, iz]\n",
" Tyy = Tyy[ix][:, iz]\n",
" Tzy = Tzy[ix][:, iz]\n",
"\n",
" Fx_y += d * np.trapezoid(np.trapezoid(Txy, x_sorted, axis=0), z_sorted, axis=0)\n",
" Fy_y += d * np.trapezoid(np.trapezoid(Tyy, x_sorted, axis=0), z_sorted, axis=0)\n",
" Fz_y += d * np.trapezoid(np.trapezoid(Tzy, x_sorted, axis=0), z_sorted, axis=0)\n",
"\n",
" elif c == \"z\":\n",
" # Integrate T_{iz} over the z± faces.\n",
" Txz = T[0, 2].squeeze()\n",
" Tyz = T[1, 2].squeeze()\n",
" Tzz = T[2, 2].squeeze()\n",
"\n",
" ix = np.argsort(coords[0])\n",
" iy = np.argsort(coords[1])\n",
"\n",
" x_sorted = np.asarray(coords[0])[ix]\n",
" y_sorted = np.asarray(coords[1])[iy]\n",
"\n",
" Txz = Txz[ix][:, iy]\n",
" Tyz = Tyz[ix][:, iy]\n",
" Tzz = Tzz[ix][:, iy]\n",
"\n",
" Fx_z += d * np.trapezoid(np.trapezoid(Txz, x_sorted, axis=0), y_sorted, axis=0)\n",
" Fy_z += d * np.trapezoid(np.trapezoid(Tyz, x_sorted, axis=0), y_sorted, axis=0)\n",
" Fz_z += d * np.trapezoid(np.trapezoid(Tzz, x_sorted, axis=0), y_sorted, axis=0)\n",
"\n",
" # Sum contributions from all faces and apply the outward normal convention.\n",
" Fx = Fx_x + Fx_y + Fx_z\n",
" Fy = Fy_x + Fy_y + Fy_z\n",
" Fz = Fz_x + Fz_y + Fz_z\n",
"\n",
" return Fx, Fy, Fz"
],
"id": "bab09d7c"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Force at a Planar Interface With Oblique Incidence \n",
"\n",
"Consider an incident plane wave in medium 1 (index $n_1$) impinging on a\n",
"planar interface with medium 2 (index $n_2$), with incidence angle\n",
"$\\theta_i$ measured from the surface normal $\\hat{\\mathbf{n}}$ pointing\n",
"from medium 1 to medium 2. With no absorption, the net time-averaged\n",
"**normal** force comes from the change in normal momentum flux of the\n",
"incident, reflected, and transmitted waves.\n",
"\n",
"If $I$ is the incident intensity in medium 1, and $R,T$ are the\n",
"**power** reflectance/transmittance (so $R+T=1$), then the normal force\n",
"on an illuminated area $A$ can be written as\n",
"\n",
"$$F_{normal} \\;=\\; \\frac{I\\,A}{c_0}\\Big[\\cos(\\theta_i)\\,(1+R)\\;-\\;n_2\\,\\cos(\\theta_t)\\,T\\Big].$$\n",
"\n",
"To implement this in Tidy3D, we will create a function that generates\n",
"the simulation object as a function of the permittivity of medium 2 and\n",
"the incident angle. We will define a simple simulation with\n",
"[PlaneWave](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PlaneWave.html)\n",
"excitation and Bloch boundary conditions. To create the monitors, we\n",
"will use the predefined"
],
"id": "c8a69397-2716-4466-9401-e0f6b40cbc69"
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"image/png": 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4NcGzYMEC7d69W5LMAbCBQrgDCKnKykrdcccd5uczZswIYWsQDHFxcbrzzjsl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\ngMUQ7gAAWAzhDgCAxfw/Sg+Z3vaDcs0AAAAASUVORK5CYII=\n"
}
}
],
"source": [
"wl = 1\n",
"freq0 = td.C_0 / 1\n",
"fwidth = 0.1 * freq0\n",
"\n",
"\n",
"def getSim(theta, permittivity=3.5**2, direction=\"z\", min_steps_per_wvl=20):\n",
" \"\"\"Build a planar-scattering simulation for a slab and surrounding monitors.\"\"\"\n",
"\n",
" plane_size = 1.3\n",
"\n",
" medium = td.Medium(permittivity=permittivity)\n",
"\n",
" # Frequencies to sample (single point for steady-state response).\n",
" freqs = [freq0]\n",
"\n",
" # Convert incidence and polarization angles to radians.\n",
" theta_rad = np.deg2rad(theta)\n",
"\n",
" # Select injection plane and Bloch axes depending on propagation direction.\n",
" if direction == \"z\":\n",
" center = (0, 0, 1)\n",
" size = [td.inf, td.inf, 0]\n",
" axis_1 = 0\n",
" axis_2 = 1\n",
"\n",
" elif direction == \"y\":\n",
" center = (0, 1, 0)\n",
" size = [td.inf, 0, td.inf]\n",
" axis_1 = 0\n",
" axis_2 = 2\n",
"\n",
" elif direction == \"x\":\n",
" center = (1, 0, 0)\n",
" size = [0, td.inf, td.inf]\n",
" axis_1 = 1\n",
" axis_2 = 2\n",
"\n",
" # Define the incident plane wave illumination.\n",
" planewave_0 = td.PlaneWave(\n",
" size=size,\n",
" center=center,\n",
" source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
" direction=\"-\",\n",
" angle_theta=theta_rad,\n",
" )\n",
"\n",
" # Bloch boundaries.\n",
" bloch_1 = td.Boundary.bloch_from_source(\n",
" source=planewave_0, domain_size=plane_size, axis=axis_1, medium=td.Medium(permittivity=1)\n",
" )\n",
"\n",
" bloch_2 = td.Boundary.bloch_from_source(\n",
" source=planewave_0, domain_size=plane_size, axis=axis_2, medium=td.Medium(permittivity=1)\n",
" )\n",
"\n",
" # Create closed surface monitors used later for force calculations.\n",
" surface_mons = surface_mon(center=(0, 0, 0), size=(1, 1, 1), freqs=freqs)\n",
"\n",
" if direction == \"x\":\n",
" bspec = td.BoundarySpec(y=bloch_1, z=bloch_2, x=td.Boundary.absorber(num_layers=80))\n",
" center_box = [-55.5, 500, 500]\n",
" size_box = [111, 1222, 1222]\n",
" sim_size = [4, plane_size, plane_size]\n",
" elif direction == \"y\":\n",
" bspec = td.BoundarySpec(x=bloch_1, z=bloch_2, y=td.Boundary.absorber(num_layers=80))\n",
" center_box = [500, -55.5, 500]\n",
" size_box = [1222, 111, 1222]\n",
" sim_size = [plane_size, 4, plane_size]\n",
" elif direction == \"z\":\n",
" bspec = td.BoundarySpec(x=bloch_1, y=bloch_2, z=td.Boundary.absorber(num_layers=80))\n",
" center_box = [500, 500, -55.5]\n",
" size_box = [1222, 1222, 111]\n",
" sim_size = [plane_size, plane_size, 4]\n",
"\n",
" # Slab structure occupying the high-index region.\n",
" box_0 = td.Structure(\n",
" geometry=td.Box(center=center_box, size=size_box), name=\"box_0\", medium=medium\n",
" )\n",
"\n",
" # Assemble the full simulation with sources, monitors, and structure.\n",
" sim = td.Simulation(\n",
" size=sim_size,\n",
" boundary_spec=bspec,\n",
" grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl),\n",
" run_time=5e-12,\n",
" sources=[planewave_0],\n",
" monitors=surface_mons,\n",
" structures=[box_0],\n",
" )\n",
"\n",
" return sim\n",
"\n",
"\n",
"sim = getSim(45)\n",
"_ = sim.plot(y=0)\n",
"\n",
"plt.show()"
],
"id": "fd86e5d6"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will vary the incidence angle and compare the results with the\n",
"analytical solution. To test all three directions, we will create three\n",
"batches, corresponding to plane-wave incidence along the x, y, and z\n",
"directions."
],
"id": "6a067787-0b8f-40f7-8298-b3eb4466a56b"
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:53:15 UTC Started working on Batch containing 6 tasks. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:53:25 UTC Maximum FlexCredit cost: 1.961 for the whole batch. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" Use 'Batch.real_cost()' to get the billed FlexCredit cost after \n",
" completion. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:53:45 UTC Batch complete. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:53:49 UTC Started working on Batch containing 6 tasks. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:53:54 UTC Maximum FlexCredit cost: 1.961 for the whole batch. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" Use 'Batch.real_cost()' to get the billed FlexCredit cost after \n",
" completion. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:54:25 UTC Batch complete. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:54:29 UTC Started working on Batch containing 6 tasks. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:54:34 UTC Maximum FlexCredit cost: 1.961 for the whole batch. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" Use 'Batch.real_cost()' to get the billed FlexCredit cost after \n",
" completion. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:55:07 UTC Batch complete. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
}
],
"source": [
"sims_x = {}\n",
"sims_y = {}\n",
"sims_z = {}\n",
"thetas = [0, 10, 20, 30, 40, 50]\n",
"for theta in thetas:\n",
" sims_x[f\"{theta}\"] = getSim(theta, direction=\"x\")\n",
" sims_y[f\"{theta}\"] = getSim(theta, direction=\"y\")\n",
" sims_z[f\"{theta}\"] = getSim(theta, direction=\"z\")\n",
"\n",
"batch_x = web.Batch(simulations=sims_x, folder_name=\"OpticalForces_x\")\n",
"batch_data_thetas_x = batch_x.run(path_dir=\"batchThetas_x\")\n",
"\n",
"batch_y = web.Batch(simulations=sims_y, folder_name=\"OpticalForces_y\")\n",
"batch_data_thetas_y = batch_y.run(path_dir=\"batchThetas_y\")\n",
"\n",
"batch_z = web.Batch(simulations=sims_z, folder_name=\"OpticalForces_z\")\n",
"batch_data_thetas_z = batch_z.run(path_dir=\"batchThetas_z\")"
],
"id": "da1c6ddd"
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"0\n",
"10\n",
"20\n",
"30\n",
"40\n",
"50"
]
}
],
"source": [
"axes = [\"x\", \"y\", \"z\"]\n",
"components = [\"Fx\", \"Fy\", \"Fz\"]\n",
"\n",
"# Pre-allocate containers for the force components keyed by face axis.\n",
"forces = {axis: {comp: [] for comp in components} for axis in axes}\n",
"\n",
"for theta_key in batch_data_thetas_x.keys():\n",
" print(theta_key)\n",
"\n",
" # Evaluate the stress tensor on each pair of monitors.\n",
" evaluations = {\n",
" \"x\": integrateStressTensor(batch_data_thetas_x[theta_key]),\n",
" \"y\": integrateStressTensor(batch_data_thetas_y[theta_key]),\n",
" \"z\": integrateStressTensor(batch_data_thetas_z[theta_key]),\n",
" }\n",
"\n",
" # Append the results to the appropriate component lists.\n",
" for axis, (fx_val, fy_val, fz_val) in evaluations.items():\n",
" for comp, value in zip(components, (fx_val, fy_val, fz_val)):\n",
" forces[axis][comp].append(np.array(value))\n",
"\n",
"# Unpack back into the original variable names for downstream cells.\n",
"FxT_x, FyT_x, FzT_x = forces[\"x\"][\"Fx\"], forces[\"x\"][\"Fy\"], forces[\"x\"][\"Fz\"]\n",
"FxT_y, FyT_y, FzT_y = forces[\"y\"][\"Fx\"], forces[\"y\"][\"Fy\"], forces[\"y\"][\"Fz\"]\n",
"FxT_z, FyT_z, FzT_z = forces[\"z\"][\"Fx\"], forces[\"z\"][\"Fy\"], forces[\"z\"][\"Fz\"]"
],
"id": "131ef11c"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will define the analytical function. Note that the\n",
"[PlaneWave](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PlaneWave.html)\n",
"source is normalized to inject 1 W of power. Therefore, the total power\n",
"crossing the monitor area must be corrected by the simulation plane\n",
"size. More information on source normalization can be found\n",
"[here](https://docs.flexcompute.com/projects/tidy3d/en/latest/faq/docs/faq/How-are-results-normalized.html)."
],
"id": "5499bc21-fabc-4cc9-bdec-bbed83402fac"
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def interface_force_eps(eps, I=1, A=1, theta_i=0):\n",
" import numpy as np\n",
"\n",
" theta_i = np.deg2rad(theta_i)\n",
" n1 = 1.0\n",
" n2 = np.sqrt(eps)\n",
"\n",
" theta_t = np.arcsin(np.sin(theta_i) / n2)\n",
"\n",
" # Fresnel power coefficients (TE and TM)\n",
" R = (\n",
" (n2 * np.cos(theta_i) - n1 * np.cos(theta_t))\n",
" / (n2 * np.cos(theta_i) + n1 * np.cos(theta_t))\n",
" ) ** 2\n",
"\n",
" T = 1 - R # lossless\n",
"\n",
" Fz = (I * A / td.C_0) * (np.cos(theta_i) * (1 + R) - n2 * np.cos(theta_t) * T)\n",
"\n",
" return np.abs(Fz)\n",
"\n",
"\n",
"# Adjusting the incident intensity to match the simulation results\n",
"I0 = (1 / 1.3) ** 2\n",
"Fz_a_theta = interface_force_eps(eps=3.5**2, theta_i=np.array(thetas), I=I0)"
],
"id": "20a528a2"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can visualize the results. The force normal to the incident plane\n",
"follows the analytical expression for all three directions, and the\n",
"other components are close to zero, as expected."
],
"id": "65839818-e7a4-47d1-a354-a8e750aa42bb"
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
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mV+sWDeXl5aH4o6dzdT/BgBIg6d57772rxxUtWlRFixbN0Lm+vr6q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P04HA5VVVVJ+vX+HoCmJyMzRwsXr1JxSZnTWM/uHTViWH9ZrfwZB+qLBqUmICgoSKWlpTIMQ4WF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\nJEufrvy7AgAAgFtQT7Qs1BMAAABwF2qJlod6AgAAnCqztwPAPWwLV7ppn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QoDj00EMrrV+2bZeWlla4dF82Zf2rb+uHTp06Ze7h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\nRERERERERERERERERERERERkNP8PoHNdSlKuShsAAAAASUVORK5CYII=\n"
}
}
],
"source": [
"fig, axes = plt.subplots(1, 3, figsize=(15, 5), sharey=False)\n",
"axes[0].plot(thetas, np.array([np.sum(i) for i in FxT_x]), label=\"Fx\")\n",
"axes[0].plot(thetas, np.array([np.sum(i) for i in FyT_x]), label=\"Fy\")\n",
"axes[0].plot(thetas, np.array([np.sum(i) for i in FzT_x]), label=\"Fz\")\n",
"axes[0].plot(thetas, np.array(Fz_a_theta), \"o\", label=\"Analytic\")\n",
"axes[0].set_title(\"x\")\n",
"axes[0].legend()\n",
"\n",
"axes[1].plot(thetas, np.array([np.sum(i) for i in FxT_y]), label=\"Fx\")\n",
"axes[1].plot(thetas, np.array([np.sum(i) for i in FyT_y]), label=\"Fy\")\n",
"axes[1].plot(thetas, np.array([np.sum(i) for i in FzT_y]), label=\"Fz\")\n",
"axes[1].plot(thetas, np.array(Fz_a_theta), \"o\", label=\"Analytic\")\n",
"axes[1].set_title(\"y\")\n",
"axes[1].legend()\n",
"\n",
"axes[2].plot(thetas, np.array([np.sum(i) for i in FxT_z]), label=\"Fx\")\n",
"axes[2].plot(thetas, np.array([np.sum(i) for i in FyT_z]), label=\"Fy\")\n",
"axes[2].plot(thetas, np.array([np.sum(i) for i in FzT_z]), label=\"Fz\")\n",
"axes[2].plot(thetas, np.array(Fz_a_theta), \"o\", label=\"Analytic\")\n",
"axes[2].set_title(\"z\")\n",
"axes[2].legend()\n",
"\n",
"\n",
"for ax in axes:\n",
" ax.set_xlabel(\"Angle (deg)\")\n",
" ax.set_ylabel(\"Force (N)\")\n",
"\n",
"\n",
"plt.show()"
],
"id": "d9125a2a"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Force in Coupled Waveguides \n",
"\n",
"Next, we calculate the attraction and repulsion forces in a system of\n",
"two coupled waveguides, as described in the [reference\n",
"paper](https://opg.optica.org/ol/abstract.cfm?uri=ol-30-22-3042).\n",
"\n",
"The system consists of two square waveguides with size $a = 0.31$ µm and\n",
"varying separation $d$. Two types of modes can be supported: symmetric\n",
"modes (same $E_z$ field sign in both waveguides) and antisymmetric modes\n",
"(opposite signs).\n",
"\n",
"We will define a function to create the setup based on the separation\n",
"$d$, add a surface monitor around one of the waveguides, and calculate\n",
"the net force in the direction perpendicular to the waveguides."
],
"id": "a8b3c9d4-9597-44a0-88cf-83a4078a4c40"
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"\n",
"\n",
"def build_double_waveguide_sim(\n",
" a: float = 0.310,\n",
" d: float = 0.25,\n",
" wavelength: float = 1.55,\n",
" wg_medium: td.Medium | None = td.Medium(permittivity=3.45**2),\n",
" x_span: float = 6.0,\n",
" buffer_y: float = 2,\n",
" buffer_z: float = 2,\n",
" source_offset: float = 0.2,\n",
" mode_index=1,\n",
") -> td.Simulation:\n",
" \"\"\"Create a Tidy3D simulation containing two identical square waveguides\"\"\"\n",
"\n",
" if a <= 0.0:\n",
" raise ValueError(\"Waveguide size 'a' must be positive.\")\n",
" if d < 0.0:\n",
" raise ValueError(\"Waveguide separation 'd' must be non-negative.\")\n",
" if source_offset >= x_span / 2:\n",
" raise ValueError(\"Source offset must be smaller than half of the simulation span.\")\n",
"\n",
" # Distance from simulation centre to each waveguide centre along y.\n",
" half_center_sep = 0.5 * (a + d)\n",
" waveguide_centers: List[float] = [-half_center_sep, half_center_sep]\n",
"\n",
" # Simulation extents along each dimension (µm).\n",
" y_half_span = a + d / 2 + buffer_y\n",
" z_half_span = a / 2 + buffer_z\n",
" sim_size = (x_span, 2 * y_half_span, 2 * z_half_span)\n",
"\n",
" structures = [\n",
" td.Structure(\n",
" geometry=td.Box(center=(0.0, yc, 0.0), size=(td.inf, a, a)),\n",
" medium=wg_medium,\n",
" )\n",
" for yc in waveguide_centers\n",
" ]\n",
"\n",
" freq0 = td.C_0 / wavelength\n",
" pulse = td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10)\n",
" source_x = -0.5 * x_span + source_offset\n",
"\n",
" sources = [\n",
" td.ModeSource(\n",
" center=(source_x, 0, 0.0),\n",
" size=(0.0, td.inf, td.inf),\n",
" source_time=pulse,\n",
" direction=\"+\",\n",
" mode_spec=td.ModeSpec(num_modes=4),\n",
" mode_index=mode_index,\n",
" )\n",
" ]\n",
"\n",
" sim = td.Simulation(\n",
" size=sim_size,\n",
" grid_spec=td.GridSpec.auto(min_steps_per_wvl=50),\n",
" structures=structures,\n",
" sources=sources,\n",
" run_time=5e-12,\n",
" monitors=surface_mon(\n",
" center=(0, (d / 2 + a / 2 + 0.5), 0), size=(1, a + d / 2 + 1, 1), freqs=[freq0]\n",
" ),\n",
" symmetry=(0, 0, -1),\n",
" )\n",
"\n",
" ms = ModeSolver(\n",
" simulation=sim,\n",
" plane=sources[0].bounding_box,\n",
" freqs=[sources[0].source_time.freq0],\n",
" mode_spec=sources[0].mode_spec,\n",
" )\n",
"\n",
" return sim, ms\n",
"\n",
"\n",
"sim, ms = build_double_waveguide_sim(d=0.1)"
],
"id": "f03e27c0"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we will run a mode solver simulation to identify the effective\n",
"indices of the symmetric and antisymmetric modes."
],
"id": "dab80ef4-ee36-4fa9-8b3e-a3de446fd1d1"
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:55:15 UTC Created task 'mode' with resource_id \n",
" 'mo-32163a0f-969e-4b77-bbfc-1dd797039bb6' and task_type \n",
" 'MODE_SOLVER'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" View task using web UI at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=mo-32163a0f-969e-\n",
" 4b77-bbfc-1dd797039bb6'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" Task folder: 'default'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:55:17 UTC Estimated FlexCredit cost: 0.004. For this solver type, the \n",
" estimate is the final billed cost. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:55:18 UTC status = queued \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" To cancel the simulation, use 'web.abort(task_id)' or \n",
" 'web.delete(task_id)' or abort/delete the task in the web UI. \n",
" Terminating the Python script will not stop the job running on the \n",
" cloud. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:55:37 UTC starting up solver \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" running solver \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:55:38 UTC status = success \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" View simulation result at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=mo-32163a0f-969e-\n",
" 4b77-bbfc-1dd797039bb6'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:55:40 UTC Loading results from simulation_data.hdf5 \n",
""
]
}
}
],
"source": [
"mode_data = web.run(ms, \"mode\")"
],
"id": "c8878979"
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
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fPNN8NaixUrViAMq5XXrLVYtWoV7rvvPvzP//wPVq1ahRtuuAHHHXfcqI99TBJIrr/+epx++ul4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\nDzzwAL70pS9BCIEvfvGLOOyww4a9z7x583D++ec3YJREREMbaYxlbZFtj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}
}
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(12, 4), constrained_layout=True)\n",
"\n",
"for ax, (mode_index, title) in zip(\n",
" axes,\n",
" [\n",
" (0, \"E_z (symmetric, mode 0)\"),\n",
" (1, \"E_z (antisymmetric, mode 1)\"),\n",
" ],\n",
"):\n",
" ms.plot_field(\"Ez\", \"real\", mode_index=mode_index, ax=ax)\n",
" ax.set_title(title)\n",
"\n",
"# 0 → symmetric fundamental mode\n",
"# 1 → antisymmetric higher-order mode\n",
"\n",
"plt.show()"
],
"id": "81bc1cdc"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we will sweep the values of $d$ and create two batch simulations\n",
"for each symmetry."
],
"id": "ac6a48c5-7771-4b7b-ae21-0f148f072e1c"
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:55:53 UTC Started working on Batch containing 21 tasks. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:56:15 UTC Maximum FlexCredit cost: 30.901 for the whole batch. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" Use 'Batch.real_cost()' to get the billed FlexCredit cost after \n",
" completion. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:57:12 UTC Batch complete. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:57:26 UTC Started working on Batch containing 21 tasks. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:57:47 UTC Maximum FlexCredit cost: 30.901 for the whole batch. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" Use 'Batch.real_cost()' to get the billed FlexCredit cost after \n",
" completion. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"07:59:36 UTC Batch complete. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
}
],
"source": [
"d_values = np.linspace(0.03, 0.31, 21)\n",
"sims_1 = {str(i): build_double_waveguide_sim(d=i, mode_index=0)[0] for i in d_values}\n",
"batch_1 = web.Batch(simulations=sims_1, folder_name=\"double_waveguide_1\")\n",
"batch_data_1 = batch_1.run(path_dir=\"double_waveguide_1\")\n",
"\n",
"sims_2 = {str(i): build_double_waveguide_sim(d=i, mode_index=1)[0] for i in d_values}\n",
"batch_2 = web.Batch(simulations=sims_2, folder_name=\"double_waveguide_2\")\n",
"batch_data_2 = batch_2.run(path_dir=\"double_waveguide_2\")"
],
"id": "1ec2dbbd"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we calculate the forces for each simulation and plot the y\n",
"component.\n",
"\n",
"As we can see, the results closely match Fig. 1(b) of the [reference\n",
"paper](https://opg.optica.org/ol/abstract.cfm?uri=ol-30-22-3042)."
],
"id": "fda2237e-3e10-4de8-ade2-77ff9917906c"
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"F_1 = [integrateStressTensor(batch_data_1[i]) for i in batch_data_1.keys()]\n",
"F_2 = [integrateStressTensor(batch_data_2[i]) for i in batch_data_2.keys()]"
],
"id": "018d665e"
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"image/png": 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0NFy7dk12rXnXxK74448/cPjwYRw9ehTp6enIysqSXTM1NcXcuXPxxhtvwNvb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\nAAAAgAqFIAtA6RiGtPgxad/Ptu0Ne0hjP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}
}
],
"source": [
"fig, ax = plt.subplots()\n",
"factor = 0.31 * td.C_0 / 1 # Source is normalized to 1W\n",
"ax.plot(d_values / 0.31, np.array([i[1] for i in F_2]) * factor, label=\"Antisymmetric\")\n",
"ax.plot(d_values / 0.31, np.array([i[1] for i in F_1]) * factor, label=\"Symmetric\")\n",
"ax.set_xlabel(\"d/a\")\n",
"ax.set_ylabel(\"Force (N/L)(ac/P)\")\n",
"\n",
"ax.legend()\n",
"plt.show()"
],
"id": "af9abd60"
}
],
"nbformat": 4,
"nbformat_minor": 5,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
}
}