latom.analyzer.analyzer_2d

@authors: Alberto FOSSA’ Giuliana Elena MICELI

Classes

Analyzer(body, sc)

Analyzer class defines the methods to analyze the results of a simulation.

HohmannTransfer(gm, dep, arr)

HohmannTransfer class implements a two-dimensional Hohmann transfer between two coplanar, circular orbits in the Keplerian two-body approximation.

ImpulsiveBurn(sc, dv)

ImpulsiveBurn class describes an impulsive burn.

TwoDimAnalyzer(body, sc)

TwoDimAnalyzer class defines the methods to analyze the results of a two dimensional simulation.

TwoDimAscAnalyzer(body, sc, alt)

TwoDimAscAnalyzer class defines the methods to analyze the results of a two dimensional ascent simulation from the Moon surface to a given Low Lunar Orbit.

TwoDimAscConstAnalyzer(body, sc, alt, theta, …)

TwoDimAscConstAnalyzer class defines the methods to analyze the results of a two dimensional ascent simulation from the Moon surface to a given Low Lunar Orbit performed at constant thrust.

TwoDimAscConstNLP(body, sc, alt, theta, …)

TwoDimAscConstNLP class transcribes a two-dimensional, continuous-time optimal control problem in trajectory optimization into a Non Linear Programming Problem (NLP) using the OpenMDAO and dymos libraries.

TwoDimAscVToffAnalyzer(body, sc, alt, …[, …])

TwoDimAscVToffAnalyzer class defines the methods to analyze the results of a two dimensional ascent simulation from the Moon surface to a given Low Lunar Orbit performed at variable thrust with vertical take-off.

TwoDimAscVToffNLP(body, sc, alt, alt_safe, …)

TwoDimAscVToffNLP class transcribes a two-dimensional, continuous-time optimal control problem in trajectory optimization into a Non Linear Programming Problem (NLP) using the OpenMDAO and dymos libraries.

TwoDimAscVarAnalyzer(body, sc, alt, …[, …])

TwoDimAscVarAnalyzer class defines the methods to analyze the results of a two dimensional ascent simulation from the Moon surface to a given Low Lunar Orbit performed at variable thrust.

TwoDimAscVarNLP(body, sc, alt, alpha_bounds, …)

TwoDimAscVarNLP class transcribes a two-dimensional, continuous-time optimal control problem in trajectory optimization into a Non Linear Programming Problem (NLP) using the OpenMDAO and dymos libraries.

TwoDimDescAnalyzer(body, sc, alt)

TwoDimDescAnalyzer class defines the methods to analyze the results of a two dimensional descent simulation from a given Low Lunar Orbit to the Moon surface.

TwoDimDescConstAnalyzer(body, sc, alt, …)

TwoDimDescConstAnalyzer class defines the methods to analyze the results of a two dimensional descent simulation from a given Low Lunar Orbit to the Moon surface performed at constant thrust.

TwoDimDescConstNLP(body, sc, alt, vp, theta, …)

TwoDimDescConstNLP class transcribes a two-dimensional, continuous-time optimal control problem in trajectory optimization into a Non Linear Programming Problem (NLP) using the OpenMDAO and dymos libraries.

TwoDimDescTwoPhasesAnalyzer(body, sc, alt, …)

TwoDimDescTwoPhasesAnalyzer class defines the methods to analyze the results of a two dimensional descent simulation from a given Low Lunar Orbit to the Moon surface composed by two subsequent phases.

TwoDimDescTwoPhasesNLP(body, sc, alt, …[, …])

TwoDimDescTwoPhasesNLP transcribes a continuous-time optimal control problem for a two-dimensional descent trajectory into a Non Linear Programming Problem (NLP) using the OpenMDAO and dymos libraries.

TwoDimDescTwoPhasesSolPlot(r, time, states, …)

Plot the two-dimensional two-phases descent simulation’s states and controls in time and in the xy plane.

TwoDimDescVLandAnalyzer(body, sc, alt, …)

TwoDimDescVarAnalyzer class defines the methods to analyze the results of a two dimensional descent simulation from a given Low Lunar Orbit to the Moon surface performed at variable thrust with vertical landing.

TwoDimDescVLandNLP(body, sc, alt, alt_safe, …)

TwoDimAscVToffNLP class transcribes a two-dimensional, continuous-time optimal control problem in trajectory optimization into a Non Linear Programming Problem (NLP) using the OpenMDAO and dymos libraries.

TwoDimDescVarAnalyzer(body, sc, alt, …[, …])

TwoDimDescVarAnalyzer class defines the methods to analyze the results of a two dimensional descent simulation from a given Low Lunar Orbit to the Moon surface performed at variable thrust.

TwoDimDescVarNLP(body, sc, alt, …[, …])

TwoDimDescVarNLP class transcribes a two-dimensional, continuous-time optimal control problem in trajectory optimization into a Non Linear Programming Problem (NLP) using the OpenMDAO and dymos libraries.

TwoDimOrb(gm, **kwargs)

Defines a two-dimensional orbit from its keplerian parameters.

TwoDimSinglePhaseAnalyzer(body, sc, alt)

TwoDimSinglePhaseAnalyzer class defines the methods to analyze the results of a two dimensional single phase simulation.

TwoDimSolPlot(r, time, states, controls[, …])

Plot the two-dimensional simulation’s states and controls in time and in the xy plane.
class latom.analyzer.analyzer_2d.TwoDimAnalyzer(body, sc)[source]

Bases: latom.analyzer.analyzer.Analyzer

TwoDimAnalyzer class defines the methods to analyze the results of a two dimensional simulation.

Parameters

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

Variables

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • phase_name (str) – Describes the phase name in case of multi-phase trajectories

  • nlp (NLP) – Instance of NLP object describing the type of Non Linear Problem solver used

  • tof (float) – Value of the time of flight resulting by the simulation [s]

  • tof_exp (float) – Value of the time of flight of the explicit simulation [s]

  • err (float) – Value of the error between the optimized simulation results and the explicit simulation results

  • rm_res (float) – Value of the central body radius [-] or [m]

  • states_scalers (ndarray) – Reference values of the states with which perform the scaling

  • controls_scalers (ndarray) – Reference values of the controls with which perform the scaling

get_time_series_phase(p, phase_name, scaled=False)[source]

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [-] or [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

get_discretization_phase(p, phase_name, scaled=False)[source]

Access the time of flight, the states on the states discretization nodes and the controls on the control discretization nodes for a given Problem and Phase.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • states (ndarray) – States values on the state discretization nodes for the optimized simulation phase

  • controls (ndarray) – Controls values on the controls discretization nodes for the optimized simulation phase

get_solution_dictionary(p, scaled=False)
Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

sol – Dictionary containing the NLP solution

Return type

dict

get_solutions(explicit=True, scaled=False)

Access the simulation solution.

Parameters

  • explicit (bool) – Computes also the explicit simulation. Default is True

  • scaled (bool) – Scales the simulation results. Default is False

get_time_series(p, scaled=False)

Access the time series of the simulation.

Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

run_driver()

Runs the optimization.

Returns

failed – Returns the result of the optimization 0 or 1

Return type

int

save(rec_file)

Pickle the whole Analyzer class.

Parameters

rec_file (str) – Directory path to the file where the Analyzer is serialized

class latom.analyzer.analyzer_2d.TwoDimSinglePhaseAnalyzer(body, sc, alt)[source]

Bases: latom.analyzer.analyzer_2d.TwoDimAnalyzer

TwoDimSinglePhaseAnalyzer class defines the methods to analyze the results of a two dimensional single phase simulation.

Parameters

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • alt (float) – Value of the final orbit altitude [m]

Variables

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • phase_name (str) – Describes the phase name in case of multi-phase trajectories

  • nlp (NLP) – Instance of NLP object describing the type of Non Linear Problem solver used

  • tof (float) – Value of the time of flight resulting by the simulation [s]

  • tof_exp (float) – Value of the time of flight of the explicit simulation [s]

  • err (float) – Value of the error between the optimized simulation results and the explicit simulation results

  • rm_res (float) – Value of the central body radius [- or m]

  • states_scalers (ndarray) – Reference values of the states with which perform the scaling

  • controls_scalers (ndarray) – Reference values of the controls with which perform the scaling

  • alt (float) – Value of the final orbit altitude [m]

  • phase_name – Name assigned to the problem phase

get_time_series(p, scaled=False)[source]

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

get_discretization_phase(p, phase_name, scaled=False)

Access the time of flight, the states on the states discretization nodes and the controls on the control discretization nodes for a given Problem and Phase.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • states (ndarray) – States values on the state discretization nodes for the optimized simulation phase

  • controls (ndarray) – Controls values on the controls discretization nodes for the optimized simulation phase

get_solution_dictionary(p, scaled=False)
Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

sol – Dictionary containing the NLP solution

Return type

dict

get_solutions(explicit=True, scaled=False)

Access the simulation solution.

Parameters

  • explicit (bool) – Computes also the explicit simulation. Default is True

  • scaled (bool) – Scales the simulation results. Default is False

get_time_series_phase(p, phase_name, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [-] or [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

run_driver()

Runs the optimization.

Returns

failed – Returns the result of the optimization 0 or 1

Return type

int

save(rec_file)

Pickle the whole Analyzer class.

Parameters

rec_file (str) – Directory path to the file where the Analyzer is serialized

class latom.analyzer.analyzer_2d.TwoDimAscAnalyzer(body, sc, alt)[source]

Bases: latom.analyzer.analyzer_2d.TwoDimSinglePhaseAnalyzer

TwoDimAscAnalyzer class defines the methods to analyze the results of a two dimensional ascent simulation from the Moon surface to a given Low Lunar Orbit.

Parameters

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • alt (float) – Value of the final orbit altitude [m]

Variables

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • phase_name (str) – Describes the phase name in case of multi-phase trajectories

  • nlp (NLP) – Instance of NLP object describing the type of Non Linear Problem solver used

  • tof (float) – Value of the time of flight resulting by the simulation [s]

  • tof_exp (float) – Value of the time of flight of the explicit simulation [s]

  • err (float) – Value of the error between the optimized simulation results and the explicit simulation results

  • rm_res (float) – Value of the central body radius [- or m]

  • states_scalers (ndarray) – Reference values of the states with which perform the scaling

  • controls_scalers (ndarray) – Reference values of the controls with which perform the scaling

  • alt (float) – Value of the final orbit altitude [m]

  • phase_name – Name assigned to the problem phase

get_discretization_phase(p, phase_name, scaled=False)

Access the time of flight, the states on the states discretization nodes and the controls on the control discretization nodes for a given Problem and Phase.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • states (ndarray) – States values on the state discretization nodes for the optimized simulation phase

  • controls (ndarray) – Controls values on the controls discretization nodes for the optimized simulation phase

get_solution_dictionary(p, scaled=False)
Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

sol – Dictionary containing the NLP solution

Return type

dict

get_solutions(explicit=True, scaled=False)

Access the simulation solution.

Parameters

  • explicit (bool) – Computes also the explicit simulation. Default is True

  • scaled (bool) – Scales the simulation results. Default is False

get_time_series(p, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

get_time_series_phase(p, phase_name, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [-] or [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

run_driver()

Runs the optimization.

Returns

failed – Returns the result of the optimization 0 or 1

Return type

int

save(rec_file)

Pickle the whole Analyzer class.

Parameters

rec_file (str) – Directory path to the file where the Analyzer is serialized

class latom.analyzer.analyzer_2d.TwoDimAscConstAnalyzer(body, sc, alt, theta, tof, t_bounds, method, nb_seg, order, solver, snopt_opts=None, rec_file=None, check_partials=False, u_bound='lower')[source]

Bases: latom.analyzer.analyzer_2d.TwoDimAscAnalyzer

TwoDimAscConstAnalyzer class defines the methods to analyze the results of a two dimensional ascent simulation from the Moon surface to a given Low Lunar Orbit performed at constant thrust.

Parameters

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • alt (float) – Value of the final orbit altitude [m]

  • theta (float) – Value for the guessed angle spawn during the trajectory [rad]

  • tof (float) – Value for the guessed trajectory time of flight [s]

  • t_bounds (float) – Value for the time of flight bounds [-]

  • method (str) – NLP transcription method

  • nb_seg (int) – Number of segments for the transcription

  • order (int) – Transcription order

  • solver (str) – NLP solver

  • snopt_opts (dict) – Sets some SNOPT’s options. Default is None

  • rec_file (str) – Directory path for the solution recording file. Default is None

  • check_partials (bool) – Checking of partial derivatives. Default is False

  • u_bound (str) – Sets the bound of the radial velocity. Can be lower, upper or None. Default is lower

Variables

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • phase_name (str) – Describes the phase name in case of multi-phase trajectories

  • nlp (NLP) – Instance of NLP object describing the type of Non Linear Problem solver used

  • tof (float) – Value of the time of flight resulting by the simulation [s]

  • tof_exp (float) – Value of the time of flight of the explicit simulation [s]

  • err (float) – Value of the error between the optimized simulation results and the explicit simulation results

  • rm_res (float) – Value of the central body radius [- or m]

  • states_scalers (ndarray) – Reference values of the states with which perform the scaling

  • controls_scalers (ndarray) – Reference values of the controls with which perform the scaling

  • alt (float) – Value of the final orbit altitude [m]

  • phase_name – Name assigned to the problem phase

plot()[source]

Plots the states and controls resulting from the simulation and the ones from the explicit computation in time.

get_discretization_phase(p, phase_name, scaled=False)

Access the time of flight, the states on the states discretization nodes and the controls on the control discretization nodes for a given Problem and Phase.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • states (ndarray) – States values on the state discretization nodes for the optimized simulation phase

  • controls (ndarray) – Controls values on the controls discretization nodes for the optimized simulation phase

get_solution_dictionary(p, scaled=False)
Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

sol – Dictionary containing the NLP solution

Return type

dict

get_solutions(explicit=True, scaled=False)

Access the simulation solution.

Parameters

  • explicit (bool) – Computes also the explicit simulation. Default is True

  • scaled (bool) – Scales the simulation results. Default is False

get_time_series(p, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

get_time_series_phase(p, phase_name, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [-] or [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

run_driver()

Runs the optimization.

Returns

failed – Returns the result of the optimization 0 or 1

Return type

int

save(rec_file)

Pickle the whole Analyzer class.

Parameters

rec_file (str) – Directory path to the file where the Analyzer is serialized

class latom.analyzer.analyzer_2d.TwoDimAscVarAnalyzer(body, sc, alt, t_bounds, method, nb_seg, order, solver, snopt_opts=None, rec_file=None, check_partials=False, u_bound='lower')[source]

Bases: latom.analyzer.analyzer_2d.TwoDimAscAnalyzer

TwoDimAscVarAnalyzer class defines the methods to analyze the results of a two dimensional ascent simulation from the Moon surface to a given Low Lunar Orbit performed at variable thrust.

Parameters

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • alt (float) – Value of the final orbit altitude [m]

  • theta (float) – Value for the guessed angle spawn during the trajectory [rad]

  • tof (float) – Value for the guessed trajectory time of flight [s]

  • t_bounds (float) – Value for the time of flight bounds [-]

  • method (str) – NLP transcription method

  • nb_seg (int) – Number of segments for the transcription

  • order (int) – Transcription order

  • solver (str) – NLP solver

  • snopt_opts (dict) – Sets some SNOPT’s options. Default is None

  • rec_file (str) – Directory path for the solution recording file. Default is None

  • check_partials (bool) – Checking of partial derivatives. Default is False

  • u_bound (str) – Sets the bound of the radial velocity. Can be lower, upper or None. Default is lower

Variables

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • phase_name (str) – Describes the phase name in case of multi-phase trajectories

  • nlp (NLP) –

  • of NLP object describing the type of Non Linear Problem solver used (Instance) –

  • tof (float) – Value of the time of flight resulting by the simulation [s]

  • tof_exp (float) – Value of the time of flight of the explicit simulation [s]

  • err (float) – Value of the error between the optimized simulation results and the explicit simulation results

  • rm_res (float) – Value of the central body radius [- or m]

  • states_scalers (ndarray) – Reference values of the states with which perform the scaling

  • controls_scalers (ndarray) – Reference values of the controls with which perform the scaling

  • alt (float) – Value of the final orbit altitude [m]

  • phase_name – Name assigned to the problem phase

plot()[source]

Plots the states and controls resulting from the simulation and the ones from the explicit computation in time.

get_discretization_phase(p, phase_name, scaled=False)

Access the time of flight, the states on the states discretization nodes and the controls on the control discretization nodes for a given Problem and Phase.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • states (ndarray) – States values on the state discretization nodes for the optimized simulation phase

  • controls (ndarray) – Controls values on the controls discretization nodes for the optimized simulation phase

get_solution_dictionary(p, scaled=False)
Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

sol – Dictionary containing the NLP solution

Return type

dict

get_solutions(explicit=True, scaled=False)

Access the simulation solution.

Parameters

  • explicit (bool) – Computes also the explicit simulation. Default is True

  • scaled (bool) – Scales the simulation results. Default is False

get_time_series(p, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

get_time_series_phase(p, phase_name, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [-] or [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

run_driver()

Runs the optimization.

Returns

failed – Returns the result of the optimization 0 or 1

Return type

int

save(rec_file)

Pickle the whole Analyzer class.

Parameters

rec_file (str) – Directory path to the file where the Analyzer is serialized

class latom.analyzer.analyzer_2d.TwoDimAscVToffAnalyzer(body, sc, alt, alt_safe, slope, t_bounds, method, nb_seg, order, solver, snopt_opts=None, rec_file=None, check_partials=False, u_bound='lower')[source]

Bases: latom.analyzer.analyzer_2d.TwoDimAscVarAnalyzer

TwoDimAscVToffAnalyzer class defines the methods to analyze the results of a two dimensional ascent simulation from the Moon surface to a given Low Lunar Orbit performed at variable thrust with vertical take-off.

Parameters

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • alt (float) – Value of the final orbit altitude [m]

  • alt_safe (float) – Value of the minimun safe altitude to avoid geographical constraints [m]

  • slope (float) – Value of the slope of the constraint on minimum safe altitude [-]

  • theta (float) – Value for the guessed angle spawn during the trajectory [rad]

  • tof (float) – Value for the guessed trajectory time of flight [-]

  • t_bounds (float) – Value for the time of flight bounds [s]

  • method (str) – NLP transcription method

  • nb_seg (int) – Number of segments for the transcription

  • order (int) – Transcription order

  • solver (str) – NLP solver

  • snopt_opts (dict) – Sets some SNOPT’s options. Default is None

  • rec_file (str) – Directory path for the solution recording file. Default is None

  • check_partials (bool) – Checking of partial derivatives. Default is False

  • u_bound (str) – Sets the bound of the radial velocity. Can be lower, upper or None. Default is lower

Variables

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • phase_name (str) – Describes the phase name in case of multi-phase trajectories

  • nlp (NLP) – Instance of NLP object describing the type of Non Linear Problem solver used

  • tof (float) – Value of the time of flight resulting by the simulation [s]

  • tof_exp (float) – Value of the time of flight of the explicit simulation [s]

  • err (float) – Value of the error between the optimized simulation results and the explicit simulation results

  • rm_res (float) – Value of the central body radius [- or m]

  • states_scalers (ndarray) – Reference values of the states with which perform the scaling

  • controls_scalers (ndarray) – Reference values of the controls with which perform the scaling

  • alt (float) – Value of the final orbit altitude [m]

  • phase_name – Name assigned to the problem phase

  • alt_safe (float) – Value of the minimum safe altitude to avoid geographical constraints [m]

  • slope (float) – Value of the slope of the constraint on minimum safe altitude [-]

  • r_safe (float) – Value of the minimum orbit radius to be compliant with the constraints [m]

get_solutions(explicit=True, scaled=False)[source]

Access the simulation solution.

Parameters

  • explicit (bool) – Computes also the explicit simulation. Default is True

  • scaled (bool) – Scales the simulation results. Default is False

plot()[source]

Plots the states and controls resulting from the simulation and the ones from the explicit computation in time.

get_discretization_phase(p, phase_name, scaled=False)

Access the time of flight, the states on the states discretization nodes and the controls on the control discretization nodes for a given Problem and Phase.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • states (ndarray) – States values on the state discretization nodes for the optimized simulation phase

  • controls (ndarray) – Controls values on the controls discretization nodes for the optimized simulation phase

get_solution_dictionary(p, scaled=False)
Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

sol – Dictionary containing the NLP solution

Return type

dict

get_time_series(p, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

get_time_series_phase(p, phase_name, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [-] or [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

run_driver()

Runs the optimization.

Returns

failed – Returns the result of the optimization 0 or 1

Return type

int

save(rec_file)

Pickle the whole Analyzer class.

Parameters

rec_file (str) – Directory path to the file where the Analyzer is serialized

class latom.analyzer.analyzer_2d.TwoDimDescAnalyzer(body, sc, alt)[source]

Bases: latom.analyzer.analyzer_2d.TwoDimSinglePhaseAnalyzer

TwoDimDescAnalyzer class defines the methods to analyze the results of a two dimensional descent simulation from a given Low Lunar Orbit to the Moon surface.

Parameters

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • alt (float) – Value of the final orbit altitude [m]

Variables

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • phase_name (str) – Describes the phase name in case of multi-phase trajectories

  • nlp (NLP) – Instance of NLP object describing the type of Non Linear Problem solver used

  • tof (float) – Value of the time of flight resulting by the simulation [s]

  • tof_exp (float) – Value of the time of flight of the explicit simulation [s]

  • err (float) – Value of the error between the optimized simulation results and the explicit simulation results

  • rm_res (float) – Value of the central body radius [- or m]

  • = ndarray (states_scalers) – Reference values of the states with which perform the scaling

  • controls_scalers (ndarray) – Reference values of the controls with which perform the scaling

  • alt (float) – Value of the final orbit altitude [m]

  • phase_name – Name assigned to the problem phase

get_discretization_phase(p, phase_name, scaled=False)

Access the time of flight, the states on the states discretization nodes and the controls on the control discretization nodes for a given Problem and Phase.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • states (ndarray) – States values on the state discretization nodes for the optimized simulation phase

  • controls (ndarray) – Controls values on the controls discretization nodes for the optimized simulation phase

get_solution_dictionary(p, scaled=False)
Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

sol – Dictionary containing the NLP solution

Return type

dict

get_solutions(explicit=True, scaled=False)

Access the simulation solution.

Parameters

  • explicit (bool) – Computes also the explicit simulation. Default is True

  • scaled (bool) – Scales the simulation results. Default is False

get_time_series(p, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

get_time_series_phase(p, phase_name, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [-] or [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

run_driver()

Runs the optimization.

Returns

failed – Returns the result of the optimization 0 or 1

Return type

int

save(rec_file)

Pickle the whole Analyzer class.

Parameters

rec_file (str) – Directory path to the file where the Analyzer is serialized

class latom.analyzer.analyzer_2d.TwoDimDescConstAnalyzer(body, sc, alt, alt_p, theta, tof, t_bounds, method, nb_seg, order, solver, snopt_opts=None, rec_file=None, check_partials=False, u_bound='upper')[source]

Bases: latom.analyzer.analyzer_2d.TwoDimDescAnalyzer

TwoDimDescConstAnalyzer class defines the methods to analyze the results of a two dimensional descent simulation from a given Low Lunar Orbit to the Moon surface performed at constant thrust.

Parameters

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • alt (float) – Value of the final orbit altitude [m]

  • alt_p (float) – Value of the orbit periselene altitude [m]

  • theta (float) – Value for the guessed angle spawn during the trajectory [rad]

  • tof (float) – Value for the guessed trajectory time of flight [s]

  • t_bounds (float) – Value for the time of flight bounds [-]

  • method (str) – NLP transcription method

  • nb_seg (int) – Number of segments for the transcription

  • order (int) – Transcription order

  • solver (str) – NLP solver

  • snopt_opts (dict) – Sets some SNOPT’s options. Default is None

  • rec_file (str) – Directory path for the solution recording file. Default is None

  • check_partials (bool) – Checking of partial derivatives. Default is False

  • u_bound (str) – Sets the bound of the radial velocity. Can be lower, upper or None. Default is upper

Variables

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • phase_name (str) – Describes the phase name in case of multi-phase trajectories

  • nlp (NLP) – Instance of NLP object describing the type of Non Linear Problem solver used

  • tof (float) – Value of the time of flight resulting by the simulation [s]

  • tof_exp (float) – Value of the time of flight of the explicit simulation [s]

  • err (float) – Value of the error between the optimized simulation results and the explicit simulation results

  • rm_res (float) – Value of the central body radius [- or m]

  • states_scalers (ndarray) – Reference values of the states with which perform the scaling

  • controls_scalers (ndarray) – Reference values of the controls with which perform the scaling

  • alt (float) – Value of the final orbit altitude [m]

  • phase_name – Name assigned to the problem phase

  • alt_p (float) – Value of the final orbit altitude [m]

plot()[source]

Plots the states and controls resulting from the simulation and the ones from the explicit computation in time.

get_discretization_phase(p, phase_name, scaled=False)

Access the time of flight, the states on the states discretization nodes and the controls on the control discretization nodes for a given Problem and Phase.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • states (ndarray) – States values on the state discretization nodes for the optimized simulation phase

  • controls (ndarray) – Controls values on the controls discretization nodes for the optimized simulation phase

get_solution_dictionary(p, scaled=False)
Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

sol – Dictionary containing the NLP solution

Return type

dict

get_solutions(explicit=True, scaled=False)

Access the simulation solution.

Parameters

  • explicit (bool) – Computes also the explicit simulation. Default is True

  • scaled (bool) – Scales the simulation results. Default is False

get_time_series(p, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

get_time_series_phase(p, phase_name, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [-] or [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

run_driver()

Runs the optimization.

Returns

failed – Returns the result of the optimization 0 or 1

Return type

int

save(rec_file)

Pickle the whole Analyzer class.

Parameters

rec_file (str) – Directory path to the file where the Analyzer is serialized

class latom.analyzer.analyzer_2d.TwoDimDescVarAnalyzer(body, sc, alt, t_bounds, method, nb_seg, order, solver, snopt_opts=None, rec_file=None, check_partials=False, u_bound='upper')[source]

Bases: latom.analyzer.analyzer_2d.TwoDimDescAnalyzer

TwoDimDescVarAnalyzer class defines the methods to analyze the results of a two dimensional descent simulation from a given Low Lunar Orbit to the Moon surface performed at variable thrust.

Parameters

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • alt (float) – Value of the final orbit altitude [m]

  • t_bounds (float) – Value for the time of flight bounds [-]

  • method (str) – NLP transcription method

  • nb_seg (int) – Number of segments for the transcription

  • order (int) – Transcription order

  • solver (str) – NLP solver

  • snopt_opts (dict) – Sets some SNOPT’s options. Default is None

  • rec_file (str) – Directory path for the solution recording file. Default is None

  • check_partials (bool) – Checking of partial derivatives. Default is False

  • u_bound (str) – Sets the bound of the radial velocity. Can be lower, upper or None. Default is upper

Variables

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • phase_name (str) – Describes the phase name in case of multi-phase trajectories

  • nlp (NLP) – Instance of NLP object describing the type of Non Linear Problem solver used

  • tof (float) – Value of the time of flight resulting by the simulation [s]

  • tof_exp (float) – Value of the time of flight of the explicit simulation [s]

  • err (float) – Value of the error between the optimized simulation results and the explicit simulation results

  • rm_res (float) – Value of the central body radius [- or m]

  • states_scalers (ndarray) – Reference values of the states with which perform the scaling

  • controls_scalers (ndarray) – Reference values of the controls with which perform the scaling

  • alt (float) – Value of the final orbit altitude [m]

  • phase_name – Name assigned to the problem phase

plot()[source]

Plots the states and controls resulting from the simulation and the ones from the explicit computation in time.

get_discretization_phase(p, phase_name, scaled=False)

Access the time of flight, the states on the states discretization nodes and the controls on the control discretization nodes for a given Problem and Phase.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • states (ndarray) – States values on the state discretization nodes for the optimized simulation phase

  • controls (ndarray) – Controls values on the controls discretization nodes for the optimized simulation phase

get_solution_dictionary(p, scaled=False)
Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

sol – Dictionary containing the NLP solution

Return type

dict

get_solutions(explicit=True, scaled=False)

Access the simulation solution.

Parameters

  • explicit (bool) – Computes also the explicit simulation. Default is True

  • scaled (bool) – Scales the simulation results. Default is False

get_time_series(p, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

get_time_series_phase(p, phase_name, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [-] or [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

run_driver()

Runs the optimization.

Returns

failed – Returns the result of the optimization 0 or 1

Return type

int

save(rec_file)

Pickle the whole Analyzer class.

Parameters

rec_file (str) – Directory path to the file where the Analyzer is serialized

class latom.analyzer.analyzer_2d.TwoDimDescVLandAnalyzer(body, sc, alt, alt_safe, slope, t_bounds, method, nb_seg, order, solver, snopt_opts=None, rec_file=None, check_partials=False, u_bound='upper')[source]

Bases: latom.analyzer.analyzer_2d.TwoDimDescVarAnalyzer

TwoDimDescVarAnalyzer class defines the methods to analyze the results of a two dimensional descent simulation from a given Low Lunar Orbit to the Moon surface performed at variable thrust with vertical landing.

Parameters

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • alt (float) – Value of the final orbit altitude [m]

  • alt_safe (float) – Value of the minimum safe altitude to avoid geographical constraints [m]

  • slope (float) – Value of the slope of the constraint on minimum safe altitude [-]

  • t_bounds (float) – Value for the time of flight bounds [-]

  • method (str) – NLP transcription method

  • nb_seg (int) – Number of segments for the transcription

  • order (int) – Transcription order

  • solver (str) – NLP solver

  • snopt_opts (dict) – Sets some SNOPT’s options. Default is None

  • rec_file (str) – Directory path for the solution recording file. Default is None

  • check_partials (bool) – Checking of partial derivatives. Default is False

  • u_bound (str) – Sets the bound of the radial velocity. Can be lower, upper or None. Default is upper

Variables

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • phase_name (str) – Describes the phase name in case of multi-phase trajectories

  • nlp (NLP) – Instance of NLP object describing the type of Non Linear Problem solver used

  • tof (float) – Value of the time of flight resulting by the simulation [s]

  • tof_exp (float) – Value of the time of flight of the explicit simulation [s]

  • err (float) – Value of the error between the optimized simulation results and the explicit simulation results

  • rm_res (float) – Value of the central body radius [- or m]

  • states_scalers (ndarray) – Reference values of the states with which perform the scaling

  • controls_scalers (ndarray) – Reference values of the controls with which perform the scaling

  • alt (float) – Value of the final orbit altitude [m]

  • phase_name – Name assigned to the problem phase

  • alt_safe (float) – Value of the minimum safe altitude to avoid geographical constraints [m]

  • slope (float) – Value of the slope of the constraint on minimum safe altitude [-]

  • r_safe (float) – Value of the minimum orbit radius to be compliant with the constraints [m]

get_time_series(p, scaled=False)[source]

Access the time series of the simulation.

Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

  • tof (float) – Time of flight resulting from the optimized simulation [s]

  • t (ndarray) – Time of flight time series for the optimized simulation [s]

  • states (ndarray) – States time series for the optimized simulation

  • controls (ndarray) – Controls time series for the optimized simulation

get_solutions(explicit=True, scaled=False)[source]

Access the simulation solution.

Parameters

  • explicit (bool) – Computes also the explicit simulation. Default is True

  • scaled (bool) – Scales the simulation results. Default is False

plot()[source]

Plots the states and controls resulting from the simulation and the ones from the explicit computation in time.

get_discretization_phase(p, phase_name, scaled=False)

Access the time of flight, the states on the states discretization nodes and the controls on the control discretization nodes for a given Problem and Phase.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • states (ndarray) – States values on the state discretization nodes for the optimized simulation phase

  • controls (ndarray) – Controls values on the controls discretization nodes for the optimized simulation phase

get_solution_dictionary(p, scaled=False)
Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

sol – Dictionary containing the NLP solution

Return type

dict

get_time_series_phase(p, phase_name, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [-] or [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

run_driver()

Runs the optimization.

Returns

failed – Returns the result of the optimization 0 or 1

Return type

int

save(rec_file)

Pickle the whole Analyzer class.

Parameters

rec_file (str) – Directory path to the file where the Analyzer is serialized

class latom.analyzer.analyzer_2d.TwoDimDescTwoPhasesAnalyzer(body, sc, alt, alt_p, alt_switch, theta, tof, t_bounds, method, nb_seg, order, solver, snopt_opts=None, rec_file=None, check_partials=False, fix='alt')[source]

Bases: latom.analyzer.analyzer_2d.TwoDimAnalyzer

TwoDimDescTwoPhasesAnalyzer class defines the methods to analyze the results of a two dimensional descent simulation from a given Low Lunar Orbit to the Moon surface composed by two subsequent phases.

Firstly, the spacecraft is injected in a ballistic arc (Hohmann transfer) to move from the initial Low Lunar Orbit to a lower altitude. Starting from the periselene of the Hohmann transfer, the spacecraft performs a powered descent at constant thrust and free attitude (first phase) until the final vertical descent (second phase) is triggered at a given altitude or time-to-go.

Parameters

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • alt (float) – Value of the final orbit altitude [m]

  • alt_switch (float) – Value of the minimum safe altitude to avoid geographical constraints [m]

  • theta (float) – Value for the guessed angle spawn during the trajectory [rad]

  • tof (float) – Value for the guessed trajectory time of flight [s]

  • t_bounds (float) – Value for the time of flight bounds [-]

  • method (str) – NLP transcription method

  • nb_seg (int) – Number of segments for the transcription

  • order (int) – Transcription order

  • solver (str) – NLP solver

  • snopt_opts (dict) – Sets some SNOPT’s options. Default is None

  • rec_file (str) – Directory path for the solution recording file. Default is None

  • check_partials (bool) – Checking of partial derivatives. Default is False

  • fix (str) – Chooses to switch from the optimized phase to the vertical one at a specific altitude or time. Can be alt or time. Default is alt

Variables

  • body (Primary) – Instance of Primary class describing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class describing the spacecraft characteristics

  • phase_name (str) – Describes the phase name in case of multi-phase trajectories

  • nlp (NLP) – Instance of NLP object describing the type of Non Linear Problem solver used

  • tof (float) – Value of the time of flight resulting by the simulation [s]

  • tof_exp (float) – Value of the time of flight of the explicit simulation [s]

  • err (float) – Value of the error between the optimized simulation results and the explicit simulation results

  • rm_res (float) – Value of the central body radius [- or m]

  • states_scalers (ndarray) – Reference values of the states with which perform the scaling

  • controls_scalers (ndarray) – Reference values of the controls with which perform the scaling

  • alt (float) – Value of the final orbit altitude [m]

  • alt_p (float) – Value of the minimum safe altitude to avoid geographical constraints [m]

  • alt_switch (float) – Value of the minimum safe altitude to avoid geographical constraints [m]

  • ht (Guess_2d) – Instance of Guess_2d class to define an Hohmann transfer trajectory

  • deorbit_burn (Guess_2d) – Instance of Guess_2d class to define an an impulsive burn

get_time_series(p, scaled=False)[source]

Access the time series of the simulation.

Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

  • tof (float) – Time of flight resulting from the optimized simulation [s]

  • t (ndarray) – Time of flight time series for the optimized simulation [s]

  • states (ndarray) – States time series for the optimized simulation

  • controls (ndarray) – Controls time series for the optimized simulation

plot()[source]

Plots the states and controls resulting from the simulation and the ones from the explicit computation in time.

get_discretization_phase(p, phase_name, scaled=False)

Access the time of flight, the states on the states discretization nodes and the controls on the control discretization nodes for a given Problem and Phase.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • states (ndarray) – States values on the state discretization nodes for the optimized simulation phase

  • controls (ndarray) – Controls values on the controls discretization nodes for the optimized simulation phase

get_solution_dictionary(p, scaled=False)
Parameters

  • p (Problem) – Instance of Problem class

  • scaled (bool) – Scales the simulation results

Returns

sol – Dictionary containing the NLP solution

Return type

dict

get_solutions(explicit=True, scaled=False)

Access the simulation solution.

Parameters

  • explicit (bool) – Computes also the explicit simulation. Default is True

  • scaled (bool) – Scales the simulation results. Default is False

get_time_series_phase(p, phase_name, scaled=False)

Access the time series of one of the problem phases.

Parameters

  • p (Problem) – Instance of Problem class

  • phase_name (str) – Name defined for the problem phase

  • scaled (bool, optional) – If True scales the simulation results. Default is False

Returns

  • tof (float) – Time of flight resulting from the optimized simulation phase [-] or [s]

  • t (ndarray) – Time of flight time series for the optimized simulation phase [-] or [s]

  • states (ndarray) – States time series for the optimized simulation phase

  • controls (ndarray) – Controls time series for the optimized simulation phase

run_driver()

Runs the optimization.

Returns

failed – Returns the result of the optimization 0 or 1

Return type

int

save(rec_file)

Pickle the whole Analyzer class.

Parameters

rec_file (str) – Directory path to the file where the Analyzer is serialized