{ "cells": [ { "cell_type": "markdown", "id": "e9308895-4c1c-4b70-8db4-65e3450c3aef", "metadata": {}, "source": [ "# Impact Manifolds of Lagrange Point Orbits in CR3BP(-Polyhedron)\n", "\n", "## Objectives\n", "\n", "This example demonstrates the setup and propagation of orbits and their invariant manifolds in the circular restricted three body problem (CR3BP) with polyhedral secondary. Additionally, it demonstrates the propagation of trajectories up to a surface impact (with the polyhedron), using hybrid termination conditions.\n", "\n", "The invariant manifolds of a planar Lyapunov orbit around the L2 point of the Mars-Phobos system are computed. The system is modeled using the CR3BP with Mars' gravity being described by a point mass and Phobos' gravity by a polyhedron (since Phobos is a small, irregular moon). Phobos is considered to be tidally locked. The invariant manifolds are propagated until a maximum time, maximum distance to Phobos, or impact with Phobos (whatever happens first). The trajectories are propagated using a dimensionless state (with the typical CR3BP units of length and time).\n", "\n", "The different trajectories are propagated with respect to a Mars-centered inertially-oriented frame, but analyzed with respect to a Phobos-centered rotating (synodic) frame." ] }, { "cell_type": "markdown", "id": "b67e5b80-e75e-40f0-a143-0f2f0dd9c011", "metadata": {}, "source": [ "## Import statements\n", "The required import statements are made here.\n", "\n", "Some standard modules are first loaded, these include `copy`, `numpy`, `matplotlib.pyplot`, and `os`.\n", "\n", "Then, the different modules of `tudatpy` that will be used are imported." ] }, { "cell_type": "code", "execution_count": 1, "id": "bc94174f-c062-4e0e-8490-e16fb470c2f1", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:41.838768Z", "iopub.status.busy": "2025-09-16T07:27:41.838659Z", "iopub.status.idle": "2025-09-16T07:27:42.183230Z", "shell.execute_reply": "2025-09-16T07:27:42.182550Z" } }, "outputs": [], "source": [ "# General imports\n", "import copy\n", "import numpy as np\n", "import os\n", "from matplotlib import pyplot as plt\n", "\n", "# Tudatpy imports\n", "import tudatpy\n", "from tudatpy.util import result2array\n", "from tudatpy import dynamics\n", "from tudatpy.dynamics import propagation_setup, environment_setup, parameters, parameters_setup, simulator\n", "from tudatpy.astro import polyhedron_utilities\n", "from tudatpy.math import interpolators, root_finders\n", "from tudatpy.astro.time_representation import DateTime" ] }, { "cell_type": "markdown", "id": "944d0b6f-21f0-4fda-bd13-45d065b31620", "metadata": {}, "source": [ "## Auxiliary Functions" ] }, { "cell_type": "markdown", "id": "befcfe5b-2d19-4aec-b96b-39dce97c0001", "metadata": {}, "source": [ "Since the CR3BP is being used, functions to compute the units of length and time used to make the CR3BP dimensionless are first defined." ] }, { "cell_type": "code", "execution_count": 2, "id": "0fd999c4-6b2b-427d-ae2d-34046c6fa1d7", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:42.185160Z", "iopub.status.busy": "2025-09-16T07:27:42.185021Z", "iopub.status.idle": "2025-09-16T07:27:42.187859Z", "shell.execute_reply": "2025-09-16T07:27:42.187342Z" } }, "outputs": [], "source": [ "########################################################################################################################\n", "# Compute unit of length of the CR3BP\n", "def cr3bp_unit_of_length (distance_between_primaries: float) -> float:\n", " return distance_between_primaries\n", "\n", "########################################################################################################################\n", "# Compute unit of time of the CR3BP\n", "def cr3bp_unit_of_time (gravitational_parameter_primary: float,\n", " gravitational_parameter_secondary: float,\n", " distance_between_primaries: float) -> float:\n", "\n", " mean_motion = np.sqrt((gravitational_parameter_primary + gravitational_parameter_secondary) / \\\n", " distance_between_primaries ** 3)\n", " unit = 1/mean_motion\n", "\n", " return unit" ] }, { "cell_type": "markdown", "id": "91578678-e4d5-491c-9f95-372bf9949299", "metadata": {}, "source": [ "In the CR3BP, trajectories are usually analyzed with respect to a synodic frame, i.e. a frame centered on the barycenter that rotates with the primaries (Mars and Phobos in this case). Instead of the usual barycentric synodic frame, here the synodic frame is considered to be Phobos centered (since orbits in the proximity of Phobos are being analyzed); therefore, the synodic frame coincides with Phobos' body-fixed frame. \n", "\n", "Since Tudat propagates the trajectories with respect to a frame with inertial origin and orientation, it is necessary to define functions to convert the state and state transition matrix (STM) to/from the body-fixed frame. The functions implemented here are valid for the conversion between an inertial frame and any **uniformly-rotating** (i.e. constant angular velocity) body-fixed frame." ] }, { "cell_type": "code", "execution_count": 3, "id": "559dbb15-84ab-4a9a-8065-34e6fe7caf9e", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:42.188967Z", "iopub.status.busy": "2025-09-16T07:27:42.188877Z", "iopub.status.idle": "2025-09-16T07:27:42.194806Z", "shell.execute_reply": "2025-09-16T07:27:42.194480Z" } }, "outputs": [], "source": [ "########################################################################################################################\n", "# Get full-state rotation matrix from inertial frame to body-fixed frame\n", "def get_inertial_to_body_fixed_full_matrix(bodies: tudatpy.dynamics.environment.SystemOfBodies,\n", " body_name: str,\n", " time: float) -> np.ndarray:\n", "\n", " inertial_to_body_fixed_matrix = bodies.get(body_name).rotation_model.inertial_to_body_fixed_rotation(time)\n", " inertial_to_body_fixed_matrix_derivative = bodies.get(body_name).rotation_model.time_derivative_inertial_to_body_fixed_rotation(time)\n", "\n", " inertial_to_body_fixed_full_matrix = np.zeros((6, 6))\n", " inertial_to_body_fixed_full_matrix[0:3,0:3] = inertial_to_body_fixed_matrix\n", " inertial_to_body_fixed_full_matrix[3:6,0:3] = inertial_to_body_fixed_matrix_derivative\n", " inertial_to_body_fixed_full_matrix[3:6,3:6] = inertial_to_body_fixed_matrix\n", "\n", " return inertial_to_body_fixed_full_matrix\n", "\n", "########################################################################################################################\n", "# Get full-state rotation matrix from body-fixed frame to inertial frame\n", "def get_body_fixed_to_inertial_full_matrix(bodies: tudatpy.dynamics.environment.SystemOfBodies,\n", " body_name: str,\n", " time: float) -> np.ndarray:\n", "\n", " body_fixed_to_inertial_matrix = bodies.get(body_name).rotation_model.body_fixed_to_inertial_rotation(time)\n", " body_fixed_to_inertial_matrix_derivative = bodies.get(body_name).rotation_model.time_derivative_body_fixed_to_inertial_rotation(time)\n", "\n", " body_fixed_to_inertial_full_matrix = np.zeros((6, 6))\n", " body_fixed_to_inertial_full_matrix[0:3,0:3] = body_fixed_to_inertial_matrix\n", " body_fixed_to_inertial_full_matrix[3:6,0:3] = body_fixed_to_inertial_matrix_derivative\n", " body_fixed_to_inertial_full_matrix[3:6,3:6] = body_fixed_to_inertial_matrix\n", "\n", " return body_fixed_to_inertial_full_matrix\n", "\n", "########################################################################################################################\n", "# Conversion of state from inertial to body-fixed frame\n", "def convert_state_history_inertial_to_body_fixed(\n", " bodies: tudatpy.dynamics.environment.SystemOfBodies,\n", " body_name: str,\n", " state_history_inertial: dict) -> dict:\n", "\n", " state_history_body_fixed = copy.deepcopy(state_history_inertial)\n", "\n", " for t in state_history_body_fixed.keys():\n", " body_state_inertial = bodies.get_body(body_name).state_in_base_frame_from_ephemeris(t)\n", " rotation_matrix = get_inertial_to_body_fixed_full_matrix(bodies, body_name, t)\n", " state_history_body_fixed[t] = rotation_matrix @ (state_history_inertial[t] - body_state_inertial)\n", "\n", " return state_history_body_fixed\n", "\n", "########################################################################################################################\n", "# Conversion of state from body-fixed to inertial frame\n", "def convert_state_history_body_fixed_to_inertial(\n", " bodies: tudatpy.dynamics.environment.SystemOfBodies,\n", " body_name: str,\n", " state_history_body_fixed: dict) -> dict:\n", "\n", " state_history_inertial = copy.deepcopy(state_history_body_fixed)\n", "\n", " for t in state_history_body_fixed.keys():\n", " body_state_inertial = bodies.get_body(body_name).state_in_base_frame_from_ephemeris(t)\n", " rotation_matrix = get_body_fixed_to_inertial_full_matrix(bodies, body_name, t)\n", " state_history_inertial[t] = rotation_matrix @ state_history_body_fixed[t] + body_state_inertial\n", "\n", " return state_history_inertial\n", "\n", "########################################################################################################################\n", "# Conversion state transition matrix from inertial to synodic frame\n", "def convert_stm_history_inertial_to_body_fixed(\n", " bodies: tudatpy.dynamics.environment.SystemOfBodies,\n", " body_name: str,\n", " stm_history_inertial: dict) -> dict:\n", "\n", " stm_history_body_fixed = copy.deepcopy(stm_history_inertial)\n", "\n", " body_fixed_to_inertial_matrix_initial = get_body_fixed_to_inertial_full_matrix(\n", " bodies, body_name, min(stm_history_inertial.keys()))\n", "\n", " for t in stm_history_inertial.keys():\n", " inertial_to_body_fixed_matrix_final = get_inertial_to_body_fixed_full_matrix(bodies, body_name, t)\n", " stm_history_body_fixed[t] = inertial_to_body_fixed_matrix_final @ stm_history_inertial[t] @ body_fixed_to_inertial_matrix_initial\n", "\n", " return stm_history_body_fixed\n", "\n", "########################################################################################################################\n", "# Conversion state transition matrix from inertial to synodic frame\n", "def convert_stm_inertial_to_body_fixed(\n", " bodies: tudatpy.dynamics.environment.SystemOfBodies,\n", " body_name: str,\n", " stm_inertial: np.ndarray,\n", " time_initial: float,\n", " time_final: float) -> np.ndarray:\n", "\n", " inertial_to_body_fixed_matrix_final = get_inertial_to_body_fixed_full_matrix(bodies, body_name, time_final)\n", " body_fixed_to_inertial_matrix_initial = get_body_fixed_to_inertial_full_matrix(bodies, body_name, time_initial)\n", "\n", " stm_synodic = inertial_to_body_fixed_matrix_final @ stm_inertial @ body_fixed_to_inertial_matrix_initial\n", "\n", " return stm_synodic" ] }, { "cell_type": "markdown", "id": "d4b899bf-5080-4595-a7ef-9e9f24db6662", "metadata": {}, "source": [ "Finally, two functions are defined to create the propagator settings. \n", "\n", "The `create_time_termination_propagator_settings` function creates the settings for an orbit propagation that terminates at an exact time.\n", "\n", "The `create_hybrid_termination_propagator_settings` function creates the settings for an orbit propagation with hybrid termination. This hybrid termination includes three possible termination conditions: maximum time, maximum distance to the origin of the secondary (Phobos), and impact with Phobos. The impact termination condition is defined using the Laplacian of the gravitational potential of the polyhedron. A given orbit propagation ends when one of these three conditions is met." ] }, { "cell_type": "code", "execution_count": 4, "id": "ca752d77-add6-4133-b836-7f1d97fa56c7", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:42.195888Z", "iopub.status.busy": "2025-09-16T07:27:42.195791Z", "iopub.status.idle": "2025-09-16T07:27:42.200371Z", "shell.execute_reply": "2025-09-16T07:27:42.200026Z" } }, "outputs": [], "source": [ "########################################################################################################################\n", "# Create propagator settings for time termination\n", "def create_time_termination_propagator_settings(central_bodies,\n", " acceleration_models,\n", " bodies_to_propagate,\n", " initial_state: np.ndarray,\n", " simulation_start_epoch,\n", " integrator_settings,\n", " simulation_end_epoch: float,\n", " dependent_variables_to_save: list):\n", "\n", " # Create propagation settings.\n", " termination_settings_time = propagation_setup.propagator.time_termination(\n", " simulation_end_epoch,\n", " terminate_exactly_on_final_condition=True)\n", " current_propagator = propagation_setup.propagator.encke\n", "\n", " propagator_settings = propagation_setup.propagator.translational(\n", " central_bodies,\n", " acceleration_models,\n", " bodies_to_propagate,\n", " initial_state,\n", " simulation_start_epoch,\n", " integrator_settings,\n", " termination_settings_time,\n", " propagator=current_propagator,\n", " output_variables=dependent_variables_to_save)\n", "\n", " return propagator_settings\n", "\n", "########################################################################################################################\n", "def create_hybrid_termination_propagator_settings(central_bodies,\n", " acceleration_models,\n", " bodies_to_propagate,\n", " initial_state: np.ndarray,\n", " simulation_start_epoch,\n", " integrator_settings,\n", " dependent_variables_to_save: list,\n", " name_spacecraft: str,\n", " name_secondary: str,\n", " gravitational_parameter_secondary: float,\n", " volume_secondary: float,\n", " hybrid_termination_max_distance: float,\n", " hybrid_termination_max_time: float):\n", "\n", " # Select target value of laplacian\n", " value = 2 * np.pi\n", " lower_bound_laplacian = - value * gravitational_parameter_secondary / volume_secondary\n", "\n", " # Create termination condition to detect impact (laplacian of polyhedron)\n", " termination_variable = propagation_setup.dependent_variable.gravity_field_laplacian_of_potential(\n", " name_spacecraft, name_secondary)\n", " root_finder_settings = root_finders.bisection(\n", " maximum_iteration=10,\n", " maximum_iteration_handling=root_finders.MaximumIterationHandling.accept_result)\n", " termination_settings_laplacian = propagation_setup.propagator.dependent_variable_termination(\n", " dependent_variable_settings=termination_variable,\n", " limit_value=lower_bound_laplacian,\n", " use_as_lower_limit=True,\n", " terminate_exactly_on_final_condition=True,\n", " termination_root_finder_settings=root_finder_settings)\n", "\n", " # Create termination condition based on maximum distance to secondary\n", " upper_bound_distance = hybrid_termination_max_distance\n", " termination_variable = propagation_setup.dependent_variable.relative_distance(name_spacecraft, name_secondary)\n", " root_finder_settings = root_finders.bisection(\n", " maximum_iteration=1,\n", " maximum_iteration_handling = root_finders.MaximumIterationHandling.accept_result)\n", " termination_settings_distance = propagation_setup.propagator.dependent_variable_termination(\n", " dependent_variable_settings = termination_variable,\n", " limit_value=upper_bound_distance,\n", " use_as_lower_limit=False,\n", " terminate_exactly_on_final_condition=True,\n", " termination_root_finder_settings=root_finder_settings)\n", "\n", " # Create termination condition based on propagation time\n", " termination_settings_time = propagation_setup.propagator.time_termination(\n", " hybrid_termination_max_time, terminate_exactly_on_final_condition=True)\n", "\n", " termination_conditions_list = [termination_settings_laplacian, termination_settings_distance, termination_settings_time]\n", "\n", " # Create hybrid termination condition\n", " termination_settings_hybrid = propagation_setup.propagator.hybrid_termination(\n", " termination_conditions_list, fulfill_single_condition=True)\n", "\n", " # Select propagator\n", " current_propagator = propagation_setup.propagator.cowell\n", "\n", " hybrid_termination_propagator_settings = propagation_setup.propagator.translational(\n", " central_bodies,\n", " acceleration_models,\n", " bodies_to_propagate,\n", " initial_state,\n", " simulation_start_epoch,\n", " integrator_settings,\n", " termination_settings_hybrid,\n", " propagator=current_propagator,\n", " output_variables=dependent_variables_to_save)\n", "\n", " return hybrid_termination_propagator_settings" ] }, { "cell_type": "markdown", "id": "3e338746-0688-43a3-b477-11de0e95b3ef", "metadata": {}, "source": [ "## Model and Propagation Setup" ] }, { "cell_type": "markdown", "id": "507dcf08-46c0-4531-9b94-ed93c1e1c829", "metadata": {}, "source": [ "To setup the used model (CR3BP with polyhedral secondary), it is first necessary to define a series of parameters. These include the gravitational parameters of Mars and Phobos, the semi-major axis of Phobos, the polyhedron of Phobos (coordinates of the vertices and vertices defining each facet), and the initial state and period of the used Lagrange point orbit. This periodic orbit was determined via continuation, which is currently not available via Tudat. This and other functionalities for the computation of periodic orbits will be added to Tudat in (near-ish) future.\n", "\n", "Since all the trajectories are here propagated in dimensionless coordinates, all the dimensional parameters are made dimensionless using the units of time and length of the CR3BP." ] }, { "cell_type": "code", "execution_count": 5, "id": "40c17d75-d351-4d40-80ca-e46b95cc3dcc", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:42.201489Z", "iopub.status.busy": "2025-09-16T07:27:42.201400Z", "iopub.status.idle": "2025-09-16T07:27:42.205989Z", "shell.execute_reply": "2025-09-16T07:27:42.205564Z" } }, "outputs": [], "source": [ "####################################################################################################################\n", "# Define dimensional model parameters and then make them dimensionless\n", "\n", "name_primary = \"Mars\"\n", "name_secondary = \"Phobos\"\n", "name_spacecraft = \"Spacecraft\"\n", "\n", "# Gravitational parameter of Mars\n", "gravitational_parameter_primary = 4.2828372854e4 * 1e9 # m^3/s^2\n", "# Gravitational parameter of Phobos\n", "gravitational_parameter_secondary = 7.127e-4 * 1e9 # m^3/s^2\n", "# Semi-major axis of Phobos\n", "distance_between_primaries = 9375e3 # m\n", "\n", "# Simplified polyhedron model\n", "# Get coordinates of vertices, converting them from kilometers to meters\n", "vertices_coordinates = np.loadtxt(\"input/phobos_500facets_vertices.txt\") * 1e3\n", "vertices_defining_each_facet = np.loadtxt(\"input/phobos_500facets_facets.txt\", dtype=int)\n", "# Rotate polyhedron model so that the x axis points in the anti-Mars direction, and the y-axis in the direction of orbital motion\n", "vertices_coordinates[:,0] = -vertices_coordinates[:,0]\n", "vertices_coordinates[:,1] = -vertices_coordinates[:,1]\n", "\n", "# Correct vertices coordinates: center of mass coincident with origin\n", "vertices_coordinates = polyhedron_utilities.modify_centroid(\n", " vertices_coordinates, vertices_defining_each_facet, desired_centroid=np.zeros(3) )\n", "\n", "# Get CR3BP units\n", "tu_cr3bp = cr3bp_unit_of_time(gravitational_parameter_primary, gravitational_parameter_secondary,\n", " distance_between_primaries)\n", "lu_cr3bp = cr3bp_unit_of_length(distance_between_primaries)\n", "\n", "# Select hybrid termination conditions\n", "hybrid_termination_max_time = 2 * np.pi * tu_cr3bp # 1 revolutions of the secondary\n", "hybrid_termination_max_distance = 50e3 # km\n", "\n", "# Make values dimensionless\n", "gravitational_parameter_primary = gravitational_parameter_primary / lu_cr3bp**3 * tu_cr3bp**2\n", "gravitational_parameter_secondary = gravitational_parameter_secondary / lu_cr3bp**3 * tu_cr3bp**2\n", "distance_between_primaries = distance_between_primaries / lu_cr3bp\n", "vertices_coordinates = vertices_coordinates / lu_cr3bp\n", "hybrid_termination_max_time = hybrid_termination_max_time / tu_cr3bp\n", "hybrid_termination_max_distance = hybrid_termination_max_distance / lu_cr3bp\n", "\n", "####################################################################################################################\n", "# Define dimensionless model parameters\n", "\n", "# State and period of a planar Lyapunov orbit around the L2 point\n", "# Specified in dimensionless coordinates wrt the secondary's body-fixed frame\n", "initial_state_lpo_body_fixed = np.array(\n", " [2.023218488555800221e-03, -6.805660450040498197e-28, -2.809659056979557109e-05,\n", " -4.813620400827587031e-05, -1.486990610364946619e-03, 3.598307742062465862e-04])\n", "period_lpo = 3.032280964384842736e+00\n", "\n", "simulation_start_epoch = DateTime(2000, 1, 1).epoch()\n", "\n", "manifolds_position_perturbation = 1e-6\n", "\n", "# Define settings for manifold propagation\n", "no_manifold_nodes = 50\n", "\n", "# Compute dimensionless volume\n", "volume_secondary = polyhedron_utilities.volume(vertices_coordinates, vertices_defining_each_facet)" ] }, { "cell_type": "markdown", "id": "cfb0b5a4-e2b1-469d-8e35-294a7a86eb0b", "metadata": {}, "source": [ "Next, the used system of bodies is created according to the assumptions of the CR3BP with polyhedral secondary:\n", "\n", "* Mars: located at origin of reference frame and having point mass gravity \n", "* Phobos: moving in circular orbit around the Mars, and having uniformly-rotating polyhedron gravity. Therefore, the polyhedron rotates with the same angular velocity of Phobos' orbit around Mars.\n", "* Spacecraft: body with zero mass" ] }, { "cell_type": "code", "execution_count": 6, "id": "71f446cb-5f66-4f64-9c2e-917b443d2f90", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:42.207428Z", "iopub.status.busy": "2025-09-16T07:27:42.207321Z", "iopub.status.idle": "2025-09-16T07:27:42.211938Z", "shell.execute_reply": "2025-09-16T07:27:42.211629Z" } }, "outputs": [], "source": [ "####################################################################################################################\n", "# Create system of bodies \n", "\n", "# Initial state of primary in CR3BP, with primary as propagation/ephemeris origin\n", "initial_state_keplerian_primary = np.zeros(6)\n", "# Initial state of secondary in CR3BP, with primary as propagation/ephemeris origin\n", "initial_state_keplerian_secondary = np.zeros(6)\n", "initial_state_keplerian_secondary[0] = distance_between_primaries\n", "\n", "# Frame origin and orientation\n", "global_frame_origin = name_primary\n", "global_frame_orientation = \"ECLIPJ2000\"\n", "\n", "# Define body settings\n", "body_settings = environment_setup.BodyListSettings(global_frame_origin, global_frame_orientation)\n", "\n", "# Primary\n", "body_settings.add_empty_settings(name_primary)\n", "# Gravity: point mass\n", "body_settings.get(name_primary).gravity_field_settings = environment_setup.gravity_field.central(\n", " gravitational_parameter_primary)\n", "# Ephemeris: primary at origin -> constant ephemeris\n", "body_settings.get(name_primary).ephemeris_settings = environment_setup.ephemeris.constant(\n", " initial_state_keplerian_primary,\n", " \"SSB\",\n", " global_frame_orientation)\n", "\n", "# Secondary\n", "body_settings.add_empty_settings(name_secondary)\n", "body_frame_secondary = name_secondary + \"_Fixed\"\n", "# Gravity: polyhedron\n", "body_settings.get(name_secondary).gravity_field_settings = environment_setup.gravity_field.polyhedron_from_mu(\n", " gravitational_parameter=gravitational_parameter_secondary,\n", " vertices_coordinates=vertices_coordinates,\n", " vertices_defining_each_facet=vertices_defining_each_facet,\n", " associated_reference_frame=body_frame_secondary)\n", "# Ephemeris: secondary has circular orbit around primary\n", "body_settings.get(name_secondary).ephemeris_settings = environment_setup.ephemeris.keplerian(\n", " initial_state_keplerian_secondary,\n", " simulation_start_epoch,\n", " gravitational_parameter_primary + gravitational_parameter_secondary)\n", "# Rotation model: tidally locked\n", "# Using \"simple\" instead of \"synchronous\" model because the latter doesn't have the derivatives of the rotation\n", "# matrix implemented tudat\n", "rotation_rate = 1 / cr3bp_unit_of_time(\n", " gravitational_parameter_primary, gravitational_parameter_secondary, distance_between_primaries)\n", "body_settings.get(name_secondary).rotation_model_settings = environment_setup.rotation_model.simple(\n", " base_frame=\"ECLIPJ2000\",\n", " target_frame=body_frame_secondary,\n", " initial_orientation=np.eye(3),\n", " initial_time=simulation_start_epoch,\n", " rotation_rate=rotation_rate)\n", "\n", "# Spacecraft\n", "body_settings.add_empty_settings(name_spacecraft)\n", "body_settings.get(name_spacecraft).constant_mass = 0.0\n", "\n", "# Create system of selected celestial bodies\n", "bodies = environment_setup.create_system_of_bodies(body_settings)" ] }, { "cell_type": "markdown", "id": "9743cb92-80d2-4787-89a7-f9f57569b87f", "metadata": {}, "source": [ "The acceleration models are now created. As mentioned, the primary (Mars) has point mass gravity and the secondary (Phobos) has polyhedral gravity." ] }, { "cell_type": "code", "execution_count": 7, "id": "ea188d2a-0581-47e0-9502-d5d6885d83e3", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:42.213028Z", "iopub.status.busy": "2025-09-16T07:27:42.212934Z", "iopub.status.idle": "2025-09-16T07:27:42.215024Z", "shell.execute_reply": "2025-09-16T07:27:42.214779Z" } }, "outputs": [], "source": [ "####################################################################################################################\n", "# Create acceleration models\n", "\n", "# Define bodies that are propagated.\n", "bodies_to_propagate = [name_spacecraft]\n", "# Define central bodies.\n", "central_bodies = [name_primary]\n", "\n", "# Define accelerations acting on spacecraft\n", "acceleration_settings_on_spacecraft = {\n", " name_primary: [propagation_setup.acceleration.point_mass_gravity()],\n", " name_secondary: [propagation_setup.acceleration.polyhedron_gravity()]\n", "}\n", "\n", "# Create global accelerations settings dictionary.\n", "acceleration_settings = {name_spacecraft: acceleration_settings_on_spacecraft}\n", "\n", "# Create acceleration models\n", "acceleration_models = propagation_setup.create_acceleration_models(\n", " bodies, acceleration_settings, bodies_to_propagate, central_bodies)" ] }, { "cell_type": "markdown", "id": "88068b0b-0c39-401e-8bb2-bfe284e42175", "metadata": {}, "source": [ "Next, the settings of the integrator are defined. A variable-step RKDP8(7) integrator is used. " ] }, { "cell_type": "code", "execution_count": 8, "id": "e65c38c5-8dc2-4013-913f-3c412644633a", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:42.215874Z", "iopub.status.busy": "2025-09-16T07:27:42.215762Z", "iopub.status.idle": "2025-09-16T07:27:42.218292Z", "shell.execute_reply": "2025-09-16T07:27:42.217918Z" } }, "outputs": [], "source": [ "####################################################################################################################\n", "# Create integrator settings\n", "\n", "current_coefficient_set = propagation_setup.integrator.CoefficientSets.rkdp_87\n", "# Define absolute and relative tolerance\n", "current_tolerance = 1e-12\n", "initial_time_step = 1e-6\n", "# Maximum step size: inf; minimum step size: eps\n", "integrator_settings = propagation_setup.integrator.runge_kutta_variable_step_size(\n", " initial_time_step, current_coefficient_set, np.finfo(float).eps, np.inf,\n", " current_tolerance, current_tolerance)" ] }, { "cell_type": "markdown", "id": "f003c482-246c-4477-977c-5eb9e5aaa8df", "metadata": {}, "source": [ "Finally, the dependent variables to save during the propagation are selected. Here, no dependent variable is saved, though the code can be modified to do so if desired." ] }, { "cell_type": "code", "execution_count": 9, "id": "f9294abd-26f0-4615-a36c-7a1ff151fd37", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:42.219451Z", "iopub.status.busy": "2025-09-16T07:27:42.219352Z", "iopub.status.idle": "2025-09-16T07:27:42.220815Z", "shell.execute_reply": "2025-09-16T07:27:42.220582Z" } }, "outputs": [], "source": [ "####################################################################################################################\n", "# Select dependent variables\n", "\n", "dependent_variables_to_save = []" ] }, { "cell_type": "markdown", "id": "7cfc8725-9aa7-43e6-ac2a-1ca637121636", "metadata": {}, "source": [ "## Propagation of the Lagrange point orbit\n", "\n", "Having defined the model to use, it is finally possible to propagate the Lagrange point orbit from which the invariant manifolds will depart. Recall that the initial state and period of this orbit was defined above. Since this initial state was defined with respect to Phobos' body-fixed frame, it is first necessary to convert it to the inertial frame. \n", "\n", "Next, the propagator settings are created using the `create_time_termination_propagator_settings` function: these settings define the propagation of the orbit to an exact final time (the period of the orbit). \n", "\n", "Having the propagator settings and the initial state in the inertial frame, it is now possible to propagate the orbit. The `create_variational_equations_solver` is used, to allow the propagation of the STM (necessary for computing the invariant manifolds).\n", "\n", "After the propagation is finished, the state and STM histories with respect to the inertial frame are retrieved and converted to the body-fixed frame." ] }, { "cell_type": "code", "execution_count": 10, "id": "f35def95-45f8-4d38-885e-b952f020e592", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:42.221865Z", "iopub.status.busy": "2025-09-16T07:27:42.221732Z", "iopub.status.idle": "2025-09-16T07:27:43.357908Z", "shell.execute_reply": "2025-09-16T07:27:43.357478Z" } }, "outputs": [], "source": [ "####################################################################################################################\n", "# Propagate lagrange point orbit with variational equations\n", "\n", "# Get initial state in the inertial frame\n", "state_history_lpo_inertial = convert_state_history_body_fixed_to_inertial(\n", " bodies, name_secondary, {simulation_start_epoch: initial_state_lpo_body_fixed})\n", "\n", "# Create propagator settings\n", "time_propagator_settings = create_time_termination_propagator_settings(\n", " central_bodies, acceleration_models, bodies_to_propagate, state_history_lpo_inertial[simulation_start_epoch],\n", " simulation_start_epoch, integrator_settings, period_lpo, dependent_variables_to_save)\n", "\n", "# Propagate variational equations, propagating just the STM\n", "parameter_settings = parameters_setup.initial_states(time_propagator_settings, bodies)\n", "lpo_single_arc_solver = simulator.create_variational_equations_solver(\n", " bodies, time_propagator_settings,\n", " parameters_setup.create_parameter_set(parameter_settings, bodies),\n", " simulate_dynamics_on_creation=True)\n", "\n", "# Retrieve state and STM history and convert them to body-fixed frame\n", "state_history_lpo_inertial = lpo_single_arc_solver.state_history\n", "stm_history_lpo_inertial = lpo_single_arc_solver.state_transition_matrix_history\n", "\n", "state_history_lpo_body_fixed = convert_state_history_inertial_to_body_fixed(\n", " bodies, name_secondary, state_history_lpo_inertial)\n", "stm_history_lpo_body_fixed = convert_stm_history_inertial_to_body_fixed(\n", " bodies, name_secondary, stm_history_lpo_inertial)" ] }, { "cell_type": "markdown", "id": "544a63ee-f926-4888-9fdd-568f1f4dcd5a", "metadata": {}, "source": [ "## Propagation of the invariant manifolds" ] }, { "cell_type": "markdown", "id": "b7bf5fde-0784-47be-8afa-2c3969bc2fd4", "metadata": {}, "source": [ "Having propagated an unstable Lagrange point orbit, its invariant manifolds are now computed. Only the unstable invariant manifolds are propagated; the stable ones can be obtained in a similar way, but using a negative time step instead (backward propagation). \n", "\n", "The initial state of each manifold branch can be obtained by perturbing the state at a node of the orbit with the most unstable eigenvector of the monodromy matrix associated with that node, which corresponds to the eigenvector associated with the eigenvalue with the largest norm.\n", "\n", "Since the linearized dynamics are being considered via the STM, one can instead use the monodromy matrix just to determine the unstable eigenvector at the 1st node. The unstable eigenvectors at the remaining nodes of the orbit can then be obtained using the STM. However, to do that, one needs to know the state and STM at each node of the orbit, which here is done using an interpolator. This allow having nodes equally spaced in time along the orbit. A 4th order Lagrange interpolator is used." ] }, { "cell_type": "code", "execution_count": 11, "id": "c27765f8-d554-4b0e-ac1e-96ffa6e871ab", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:43.359124Z", "iopub.status.busy": "2025-09-16T07:27:43.359014Z", "iopub.status.idle": "2025-09-16T07:27:43.362997Z", "shell.execute_reply": "2025-09-16T07:27:43.362721Z" } }, "outputs": [], "source": [ "####################################################################################################################\n", "# Propagate the invariant manifolds\n", "\n", "lpo_initial_time = min(state_history_lpo_body_fixed.keys())\n", "lpo_final_time = max(state_history_lpo_body_fixed.keys())\n", "\n", "# Get monodromy matrix\n", "monodromy_matrix_body_fixed = stm_history_lpo_body_fixed[lpo_final_time]\n", "# Get unstable eigenvector of monodromy matrix\n", "eigenvalues, eigenvectors = np.linalg.eig(monodromy_matrix_body_fixed)\n", "unstable_eigenvector_id = np.argmax(np.abs(eigenvalues))\n", "unstable_eigenvector = eigenvectors[:, unstable_eigenvector_id]\n", "\n", "# Create STM interpolator\n", "interpolator_settings = interpolators.lagrange_interpolation(4)\n", "stm_history_lpo_body_fixed_interpolator = interpolators.create_one_dimensional_matrix_interpolator(\n", " stm_history_lpo_body_fixed, interpolator_settings)\n", "# Create state interpolator\n", "state_history_lpo_body_fixed_interpolator = interpolators.create_one_dimensional_vector_interpolator(\n", " state_history_lpo_body_fixed, interpolator_settings)" ] }, { "cell_type": "markdown", "id": "065e1040-6b8f-4844-a57e-c14ed288f745", "metadata": {}, "source": [ "Having defined the interpolators, it is now possible to loop over the nodes of the Lagrange point orbit and determine the initial state of the unstable invariant manifold at each of them. This initial state is defined with respect to Phobos' body-fixed frame, so it needs to be converted to the inertial frame before executing the propagation.\n", "\n", "Next, the propagator settings are created. Hybrid propagator settings are used, which terminate the propagation after a maximum time or maximum distance to Phobos is reached, or after the spacecraft impacts Phobos (whatever happens first). Finally, the `create_dynamics_simulator` function is called to propagate each manifold.\n", "\n", "The two manifold branches (i.e. initial state of the manifold obtained by a positive or negative perturbation) of the orbit are here propagated." ] }, { "cell_type": "code", "execution_count": 12, "id": "f2b4cee5-f7d4-44d8-b91b-34ecd5578e4c", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:27:43.363967Z", "iopub.status.busy": "2025-09-16T07:27:43.363875Z", "iopub.status.idle": "2025-09-16T07:28:04.981618Z", "shell.execute_reply": "2025-09-16T07:28:04.981015Z" } }, "outputs": [], "source": [ "# Loop over manifold nodes and propagate the manifold\n", "manifold_single_arc_solvers = [[],[]]\n", "for manifold_direction_to_propagate in [-1, 1]:\n", " for i in range(no_manifold_nodes):\n", " # Deal with i=0 differently to avoid cases with numerical errors in the initial time\n", " if i == 0:\n", " current_state = state_history_lpo_body_fixed[lpo_initial_time]\n", " current_unstable_eigenvector = unstable_eigenvector\n", " # Compute unstable eigenvector at current node\n", " else:\n", " time_since_arc_start = i * (lpo_final_time - lpo_initial_time) / no_manifold_nodes\n", " current_state = state_history_lpo_body_fixed_interpolator.interpolate(time_since_arc_start)\n", " current_stm = stm_history_lpo_body_fixed_interpolator.interpolate(time_since_arc_start)\n", " current_unstable_eigenvector = current_stm @ unstable_eigenvector\n", " current_unstable_eigenvector = current_unstable_eigenvector / np.linalg.norm(current_unstable_eigenvector)\n", "\n", " # Sanity check\n", " if not np.all(np.imag(current_unstable_eigenvector) == 0):\n", " raise RuntimeError(\"Error when creating manifold initial state: eigenvector has imaginary components\")\n", " else:\n", " current_unstable_eigenvector = np.real(current_unstable_eigenvector)\n", "\n", " # Compute initial state of manifold\n", " manifold_initial_state_body_fixed = current_state + manifold_direction_to_propagate * manifolds_position_perturbation / \\\n", " np.linalg.norm(current_unstable_eigenvector[0:3]) * current_unstable_eigenvector\n", "\n", " # Get initial state in the inertial frame\n", " state_history_manifold_inertial = convert_state_history_body_fixed_to_inertial(\n", " bodies, name_secondary,\n", " {simulation_start_epoch: manifold_initial_state_body_fixed})\n", "\n", " # Create propagator settings\n", " hybrid_propagator_settings = create_hybrid_termination_propagator_settings(\n", " central_bodies, acceleration_models, bodies_to_propagate, state_history_manifold_inertial[simulation_start_epoch],\n", " simulation_start_epoch, integrator_settings, dependent_variables_to_save, name_spacecraft, name_secondary, gravitational_parameter_secondary,\n", " volume_secondary, hybrid_termination_max_distance, hybrid_termination_max_time)\n", "\n", " # Propagate manifold\n", " manifold_single_arc_solver = simulator.create_dynamics_simulator(\n", " bodies, hybrid_propagator_settings)\n", "\n", " if manifold_direction_to_propagate == -1:\n", " manifold_single_arc_solvers[0].append(manifold_single_arc_solver)\n", " else:\n", " manifold_single_arc_solvers[1].append(manifold_single_arc_solver)" ] }, { "cell_type": "markdown", "id": "0e7385c4-7e3d-4518-8e7b-100e977ce727", "metadata": {}, "source": [ "## Plotting the results" ] }, { "cell_type": "markdown", "id": "e130a794-5181-4a85-993a-ad61a6c2546e", "metadata": {}, "source": [ "Finally, we can plot the computed orbit and its manifolds. Before plotting the manifolds, their state history is retrieved from the single arc solver and converted to the body-fixed frame. \n", "\n", "Phobos' shape is also plotted, using the `tricontourf` function." ] }, { "cell_type": "code", "execution_count": 13, "id": "7667c928-555e-4f83-8c28-85109a3a8be4", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:04.983256Z", "iopub.status.busy": "2025-09-16T07:28:04.983167Z", "iopub.status.idle": "2025-09-16T07:28:05.358445Z", "shell.execute_reply": "2025-09-16T07:28:05.358168Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'z [km]')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "####################################################################################################################\n", "# Make plot: x vs y, x vs z\n", "\n", "fig, ax = plt.subplots(1, 2, figsize=(12,6), constrained_layout=True)\n", "\n", "state_history_lpo_body_fixed_array = result2array(state_history_lpo_body_fixed)[:,1:]\n", "\n", "# Plot orbit\n", "ax[0].plot(state_history_lpo_body_fixed_array[:, 0] * lu_cr3bp/1e3,\n", " state_history_lpo_body_fixed_array[:, 1] * lu_cr3bp/1e3, lw=2, zorder=10, label=\"Orbit\")\n", "ax[1].plot(state_history_lpo_body_fixed_array[:, 0] * lu_cr3bp/1e3,\n", " state_history_lpo_body_fixed_array[:, 2] * lu_cr3bp/1e3, lw=2, zorder=10)\n", "\n", "for manifold_branch_id in [0,1]:\n", " for single_arc_solver in manifold_single_arc_solvers[manifold_branch_id]:\n", "\n", " # Extract manifold state history and convert it to body-fixed frame\n", " state_history_manifold_inertial = single_arc_solver.state_history\n", " state_history_manifold_body_fixed = convert_state_history_inertial_to_body_fixed(\n", " bodies, name_secondary, state_history_manifold_inertial)\n", " state_history_manifold_body_fixed_array = result2array(state_history_manifold_body_fixed)[:,1:]\n", "\n", " if manifold_branch_id == 0:\n", " c = \"m\"\n", " else:\n", " c = \"r\"\n", "\n", " if state_history_manifold_body_fixed_array[-1,1] > 0:\n", " zorder_y = 6\n", " else:\n", " zorder_y = 4\n", "\n", " if state_history_manifold_body_fixed_array[-1,2] > 0:\n", " zorder_z = 6\n", " else:\n", " zorder_z = 4\n", "\n", " # Plot manifold\n", " ax[0].plot(state_history_manifold_body_fixed_array[:, 0] * lu_cr3bp/1e3,\n", " state_history_manifold_body_fixed_array[:, 1] * lu_cr3bp/1e3, lw=0.2, c=c, zorder=zorder_z)\n", " ax[1].plot(state_history_manifold_body_fixed_array[:, 0] * lu_cr3bp/1e3,\n", " state_history_manifold_body_fixed_array[:, 2] * lu_cr3bp/1e3, lw=0.2, c=c, zorder=zorder_y)\n", "\n", "# Add label to manifolds\n", "ax[0].plot([np.nan], [np.nan], label=\"Manifold: -1 branch\", c=\"m\", lw=4)\n", "ax[0].plot([np.nan], [np.nan], label=\"Manifold: +1 branch\", c=\"r\", lw=4)\n", "\n", "# Plot the shape of the secondary\n", "ax[0].tricontourf(vertices_coordinates[:,0] * lu_cr3bp/1e3, vertices_coordinates[:,1] * lu_cr3bp/1e3,\n", " np.zeros(np.shape(vertices_coordinates[:,0])), colors=\"tab:grey\", zorder=5)\n", "ax[1].tricontourf(vertices_coordinates[:,0] * lu_cr3bp/1e3, vertices_coordinates[:,2] * lu_cr3bp/1e3,\n", " np.zeros(np.shape(vertices_coordinates[:,0])), colors=\"tab:grey\", zorder=5)\n", "\n", "for ax_ in ax:\n", " ax_.set_xlabel('x [km]')\n", " ax_.grid()\n", " ax_.set_axisbelow(True)\n", " ax_.set_aspect('equal')\n", "\n", " ax_.set_xlim(left=ax_.get_xlim()[0] - 1)\n", " ax_.set_ylim(bottom=ax_.get_ylim()[0] - 1)\n", " ax_.set_ylim(top=ax_.get_ylim()[1] + 1)\n", "\n", "ax[0].legend()\n", "ax[0].set_ylabel('y [km]')\n", "ax[1].set_ylabel('z [km]')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 5 }