While human presence in cislunar space continues to expand, so too does the demand for `lightweight' automated on-board processes. In nonlinear dynamical environments, computationally efficient guidance strategies are challenging. Many traditional approaches rely on either simplifying assumptions in the dynamical model or on abundant computational resources. This research employs reinforcement learning, a subset of machine learning, to produce a controller that is suitable for on-board low-thrust guidance in challenging dynamical regions of space. The proposed controller functions without knowledge of the simplifications and assumptions of the dynamical model, and direct interaction with the nonlinear equations of motion creates a flexible learning scheme that is not limited to a single force model. The learning process leverages high-performance computing to train a closed-loop neural network controller. This controller may be employed on-board, and autonomously generates low-thrust control profiles in real-time without imposing a heavy workload on a flight computer. Control feasibility is demonstrated through sample transfers between Lyapunov orbits in the Earth-Moon system. The sample low-thrust controller exhibits remarkable robustness to perturbations and generalizes effectively to nearby motion. Effective guidance in sample scenarios suggests extendibility of the learning framework to higher-fidelity domains.

With the growth of human interest in the Lunar region, methods of enabling Lunar access including surface and Low Lunar Orbit (LLO) from periodic orbit in the Lunar region is becoming more important. The current investigation explores the Lunar access capabilities of these periodic orbits. Impact trajectories originating from the 9:2 Lunar Synodic Resonant (LSR) Near Rectilinear Halo Orbit (NRHO) are determined through explicit propagation and mapping of initial conditions formed by applying small maneuvers at locations across the orbit. These trajectories yielding desirable Lunar impact final conditions are then used to converge impacting transfers from the NRHO to Shackleton crater near the Lunar south pole. The stability of periodic orbits in the Lunar region is analyzed through application of a stability index and time constant. The Lunar access capabilities of the Lunar region periodic orbits found to be sufficiently unstable are then analyzed through impact and periapse maps. Using the impact data, candidate periodic orbits are incorporated in the the NRHO to Shackleton crater mission design to control mission geometry. Finally, the periapse map data is used to determine periodic orbits with desirable apse conditions that are then used to design NRHO to LLO transfer trajectories.

Today, Mars is one of the most interesting and important destinations for humankind and copious methods have been proposed to accomplish these future missions. One of the more fascinating methods is the Earth-Mars cycler trajectory which is a trajectory that accomplishes repeat access to Earth and Mars with little to no fuel-burning maneuvers. This would allow fast travel to and from Mars, as well as grant the possibility of multiple missions using the same main vehicle. Insertion from Earth-orbit onto the cycler trajectory has not been thoroughly ex- plored and the only existing method so far is a Hohmann-esque transfer via direct burn. The use of manifolds from gravitational equilibrium points has not been con- sidered for low energy transfer to the cycler trajectory. This work is primarily focused on closing this gap and analyzing the feasibility of this maneuver. To accomplish this, a study of the cycler trajectory – and the S1L1-B class specif- ically – was completed. The required gravity assist maneuvers at each planet was analyzed through V∞ matching and the entire trajectory was generated over the re- quired inertial period. This method allowed for the generation of 2 cycler trajectories of the inbound and outbound classes, which combine to allow for a reduction in the amount of time the astronauts spend in space. The Earth-Sun L2 point is analyzed as a potential hub for the maneuver and a halo orbit about this libration point is optimized for low energy transfer from and Earth parking orbit. The associated invariant manifold is then optimized for launch date and distance to the first trajectory on the cycler in order to burn from a trajectory on the manifold to the cycler trajectory. iv The comparisons of this work lie in the required ∆V to perform each maneuver compared to a direct burn onto the cycler trajectory. These values are compared and the practicality of this maneuver is drawn from these comparisons. It was found that the total required ∆V for the manifold method is larger than a direct burn from Earth orbit. However, this considers the trajectory from Earth to the halo orbit and if this is removed from consideration the ∆V is significantly reduced. It was shown that the feasibility of this method relies heavily on the starting position of the cycler vehicle. If the vehicle begins in Earth-orbit, a direct burn is preferred, however, if the vehicle began in a halo orbit (say it was assembled there) the manifold maneuver is largely preferable.

<p>Many opportunities for frequent transit between the lunar vicinity and the heliocentric region will arise in the near future, including servicing missions to space telescopes and proposed missions to various asteroids and other destinations in the solar system. The overarching goal of this investigation is the development a framework for periodic and transit options in the Earth-Moon-Sun system. Rather than overlapping different dynamical models to capture the dynamics of the cislunar and heliocentric region, this analysis leverages a four-body dynamical model, the Bicircular Restricted Four-Body Problem (BCR4BP), that includes the dynamical structures that exist due to the combined influences of the Earth, the Moon, and the Sun. The BCR4BP is an intermediate step in fidelity between the CR3BP and the higher-fidelity ephemeris model. The results demonstrate that dynamical structures from the Earth-Moon-Sun BCR4BP provide valuable information on the flow between cislunar and heliocentric spaces. </p> <p><br></p> <p>Dynamical structures associated with periodic and bounded motion within the BCR4BP are successfully employed to construct transfers between the 9:2 NRHO and locations of interest in heliocentric space. The framework developed in this analysis is effective for transit between any cislunar orbit and the Sun-Earth libration point regions; a current important use case for this capability involves departures from the NRHOs, orbits that possess complex dynamics and near-stable properties. Leveraging this methodology, one-way trajectories from the lunar vicinity to a destination orbit in heliocentric space are constructed, as well as round-trip trajectories that returns to the NRHO after completion of the objectives in heliocentric space. The challenges of such trajectory design include the phasing of the trajectory with respect to the Earth, the Moon, the Sun, on both the outbound and inbound legs of the trajectory. Applications for this trajectory include servicing missions to a space telescope in heliocentric space, where the initial and final locations of the mission is the Gateway near the Moon. Lastly, the results of this analysis demonstrate that the properties and geometry of the periodic orbits, bounded motion, and transfers that are delivered from the BCR4BP are maintained when the trajectories are transitioned to the higher-fidelity ephemeris model. </p>

Recent interest surrounding large scale satellite constellations has increased analysis efforts to create the most efficient designs. Multiple studies have successfully optimized constellation patterns using equations of motion propagation methods and genetic algorithms to arrive at optimal solutions. However, these approaches are computationally expensive for large scale constellations, making them impractical for quick iterative design analysis. Therefore, a minimalist algorithm and efficient computational method could be used to improve solution times. This thesis will provide a tool for single target constellation optimization using spherical trigonometry propagation, and an evolutionary genetic algorithm based on a multi-objective optimization function. Each constellation will be evaluated on a normalized fitness scale to determine optimization. The performance objective functions are based on average coverage time, average revisits, and a minimized number of satellites. To adhere to a wider audience, this design tool was written using traditional Matlab, and does not require any additional toolboxes. To create an efficient design tool, spherical trigonometry propagation will be utilized to evaluate constellations for both coverage time and revisits over a single target. This approach was chosen to avoid solving complex ordinary differential equations for each satellite over a long period of time. By converting the satellite and planetary target into vectors of latitude and longitude in a common celestial sphere (i.e. ECI), the angle can be calculated between each set of vectors in three-dimensional space. A comparison of angle against a maximum view angle, , controlled by the elevation angle of the target and the satellite’s altitude, will determine coverage time and number of revisits during a single orbital period. Traditional constellations are defined by an altitude (a), inclination (I), and Walker Delta Pattern notation: T/P/F. Where T represents the number of satellites, P is the number of orbital planes, and F indirectly defines the number of adjacent planes with satellite offsets. Assuming circular orbits, these five parameters outline any possible constellation design. The optimization algorithm will use these parameters as evolutionary traits to iterate through the solutions space. This process will pass down the best traits from one generation to the next, slowly evolving and converging the population towards an optimal solution. Utilizing tournament style selection, multi-parent recombination, and mutation techniques, each generation of children will improve on the last by evaluating the three performance objectives listed. The evolutionary algorithm will iterate through 100 generations (G) with a population (n) of 100. The results of this study explore optimal constellation designs for seven targets evenly spaced from 0° to 90° latitude on Earth, Mars and Jupiter. Each test case reports the top ten constellations found based on optimal fitness. Scatterplots of the constellation design solution space and the multi-objective fitness function breakdown are provided to showcase convergence of the evolutionary genetic algorithm. The results highlight the ratio between constellation altitude and planetary radius as the most influential aspects for achieving optimal constellations due to the increased field of view ratio achievable on smaller planetary bodies. The multi-objective fitness function however, influences constellation design the most because it is the main optimization driver. All future constellation optimization problems should critically determine the best multi-objective fitness function needed for a specific study or mission.

<p>Increasingly, space flight missions are planned to traverse regions of space with complex dynamical environments influenced by multiple gravitational bodies. The nature of these systems produces motion and regions of sensitivity that are, at times, unintuitive,</p> <p>and the accumulation of trajectory dispersions from a variety of sources guarantees that spacecraft will deviate from their pre-planned trajectories in this complex environment, necessitating the use of a targeting process to generate a new feasible reference path. To ensure mission success and a robust path planning process, trajectory designers require insight into the interaction between the targeting process, the baseline trajectory, and the dynamical environment. In this investigation, the convergence behavior of these targeting processes is examined. This work summarizes a framework for characterizing and predicting the convergence behavior of perturbed targeting problems, consisting of a set of constraints, design variables, perturbation variables, and a reference solution within a dynamical system. First, this work identifies the typical features of a convergence basin and identifies a measure of worst-case performance. In the absence of an analytical method, efficient numerical discretization procedures are proposed based on the evaluation of partial derivatives at the reference solution to the perturbed targeting problem. A method is also proposed for approximating the tradespace of position and velocity perturbations that achieve reliable</p> <p>convergence toward the baseline solution. Additionally, evaluated scalar quantities are introduced to serve as predictors of the simulation-measured worst-case convergence behavior based on the local rate of growth in the constraints as well as the local relative change in the targeting-employed partial derivatives with respect to perturbations.</p> <p><br></p> <p>A variety of applications in different dynamical regions and force models are introduced to evaluate the improved discretization techniques and their correlation to the predictive metrics of convergence behavior. Segments of periodic orbits and transfer trajectories from past and planned missions are employed to evaluate the relative convergence performance across sets of candidate solutions. In the circular restricted three-body problem (CRTBP), perturbed targeting problems are formulated along a distant retrograde orbit and a near-rectilinear halo orbit (NRHO) in the Earth-Moon system. To investigate the persistence of results from the CRTBP in an ephemeris force model, a targeting problem applied to an NRHO is analyzed in both force models. Next, an L1 -to-L2 transit trajectory in the Sun-Earth system is studied to explore the effect of moving a maneuver downstream along</p> <p>a trajectory and altering the orientations of the gravitational bodies. Finally, a trans-lunar return trajectory is explored, and the convergence behavior is analyzed as the final maneuver time is varied.</p>

As development of CubeSat based architectures increase, methods of deploying constellations of CubeSats are required to increase functionality of future systems. Given their low cost and quickly increasing launch opportunities, large numbers of CubeSats can easily be developed and deployed in orbit. However, as secondary payloads, CubeSats are severely limited in their options for deployment into appropriate constellation geometries. This thesis examines the current methods for deploying cubes and proposes new and efficient geometries using secondary launch opportunities. Due to the current deployment hardware architecture, only the use of different launch opportunities, deployment direction, and deployment timing for individual cubes in a single launch are explored. The deployed constellations are examined for equal separation of Cubes in a single plane and effectiveness of ground coverage of two regions. The regions examined are a large near-equatorial zone and a medium sized high latitude, high population density zone. Results indicate that simple deployment strategies can be utilized to provide significant CubeSat dispersion to create efficient constellation geometries. The same deployment strategies can be used to develop a multitude of differently dispersed constellations. Different launch opportunities can be utilized to tailor a constellation for a specific region or mission objective. Constellations can also be augmented using multiple launch opportunities to optimize a constellation towards a specific mission or region. The tools developed to obtain these results can also be used to perform specific analysis on any region in order to optimize future constellations for other applications.

With the pursuit of more advanced and environmentally-friendly technologies of today’s society, the airline industry has been pushed further to investigate solutions that will reduce airport noise and congestion, cut down on emissions, and improve the overall performance of aircraft. These items directly influence airport size (runway length), flight patterns in the community surrounding the airport, cruise speed, and many other aircraft design considerations which are setting the requirements for next generation aircraft. Leading the research in this movement is NASA, which has set specific goals for the next generation regional airliners and has categorized the designs that meet the criteria as Cruise Efficient Short Takeoff and Land (CESTOL) aircraft. With circulation control (CC) technology addressing most of the next generation requirements listed above, it has recently been gaining more interest, thus the basis of this research. CC is an active flow control method that uses a thin sheet of high momentum jet flow ejected over a curved trailing edge surface and in turn utilizes Coanda effect to increase the airfoil’s circulation, augmenting lift, drag, and pitching moment. The technology has been around for more than 75 years, but is now gaining more momentum for further development due to its significant payoffs in both performance and system complexity. The goal of this research was to explore the design of the CC flap shape and how it influences the local flow field of the system, in attempt to improve the performance of existing CC flap configurations and provide insight into the aerodynamic characteristics of the geometric parameters that make up the CC flap. Multiple dual radius flaps and alternative flap geometry, prescribed radius, flaps were developed by varying specific flap parameters from a baseline dual radius flap configuration that had been previously developed and researched. The aerodynamics of the various flap geometries were analyzed at three different flight conditions using two-dimensional CFD. The flight conditions examined include two low airspeed cases with blown flaps at 60° and 90° of deflection, and a transonic cruise case with no blowing and 0° of flap deflection. Results showed that the shorter flaps of both flap configurations augmented greater lift for the low airspeed cases, with the dual radius flaps producing more lift than the corresponding length prescribed radius. The large lift generation of these flaps was accompanied by significant drag and negative pitching moments. The incremental lift per drag and moment produced was best achieved by the longer flap lengths, with the prescribed radius flaps out-performing each corresponding dual radius. Longer flap configurations also upheld the better cruise performance with the least amount of low airspeed flow, drag, and required angle of attack for a given cruise lift coefficient. The prescribed radius flaps also presented a favorable trait of keeping a more continuous skin friction distribution over the flap when the flaps were deflected, where all dual radius configurations experienced a distinct fluctuation at the location where the surface curvature changes between its two radii. The prescribed radius flaps displayed a similar behavior when the flaps were not deflected, during the cruise conditions analyzed. Performance trends for the different flap configurations, at all three flight conditions, are presented at the end of each respective section to provide guidance into the design of CC geometry. The results of the presented research show promise in modifying geometric surface parameters to yield improved aerodynamics and performance.

The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA’s Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can be an abstract task and relies on an orbit analyst to structure a mission based off an established mission design methodology associated to the performance of Orion and NASA's Space Launch System. Introducing a machine learning algorithm trained to construct mission scenarios within the feasible range of known trajectories reduces the required interaction of the orbit analyst by removing the needed step of optimizing the orbit to fit an expected translational response required of the spacecraft. In this study, k-Nearest Neighbor and Bayesian Linear Regression successfully predicted classical orbital elements for the launch windows observed. However both algorithms had limitations due to their approaches to model fitting. Training machine learning algorithms off of classical orbital elements introduced a repetitive approach to reconstructing mission segments for different arrival opportunities through the launch window and can prove to be a viable method of launch window scan generation for future missions.

Interplanetary multiple gravity assist (MGA) trajectory optimization has long been a field of interest to space scientists and engineers. Gravity assist maneuvers alter a spacecraft's velocity vector and potentially allow spacecraft to achieve changes in velocity which would otherwise be unfeasible given our current technological limitations. Unfortunately, designing MGA trajectories is difficult and in order to find good solutions, deep space maneuvers (DSM) are often required which further increase the complexity of the problem. In addition, despite the active research in the field over the last 50 years, software for MGA trajectory optimization is scarce. A few good commercial, and even fewer open-source, options exist, but a majority of quality software remains proprietary. The intent of this thesis is twofold. The first part of this work explores the realm of global optimization applied to multiple gravity assist trajectories with deep space maneuvers (MGA-DSM). With the constant influx of new global optimization algorithms and heuristics being developed in the global optimization community, this work aims to be a high level optimization approach which makes use of those algorithms instead of trying to be one itself. Central to this approach is PaGMO, which is the open-source Parallel Multiobjective Global Optimizer created by ESA's Advanced Concepts Team (ACT). PaGMO is an implementation of the Island Model Paradigm which allows the parallelization of different global optimizers. The second part of this work introduces the IGATO software which improves PaGMO by complementing it with dynamic restart capabilities, a pruning algorithm which learns over time, subdomain decomposition, and other techniques to create a powerful optimization tool. IGATO aims to be an open-source platform independent C++ application with a robust graphical user interface (GUI). The application is equipped with 2D plotting and simulations, real time Porkchop Plot generation, and other useful features for analyzing various problems. The optimizer is tested on several challenging MGA-DSM problems and performs well: consistently performing as well or better than PaGMO on its own.