Model-Based Systems Engineering in industry has been constantly increasing its presence within the aerospace industry. SysML is one such MBSE tool that shows complex system organization and relationships. The Horizon Simulation Framework is another MBSE tool, created by Cal Poly students, that gives users the ability to run “day-in-the-life” simulations of systems. Finding a way to link these two tools could allow systems engineers to reap the benefits of both. This thesis investigates the background and design process involved with developing the code that can convert an output file generated in SysML, into a format specifically made for the Horizon Simulation Framework. The goal was to create an interface that can allow users to model a system in SysML, and analyze the model and verify system requirements using HSF. Another goal was to expand the capabilities of the Horizon Simulation Framework by designing and develop a module that would allow users to define and analyze system-level requirements. To evaluate the effectiveness of both codes, the Aeolus example case was used. A SysML model of the system was created as the product of another thesis; SysML based CubeSat Model Design and Integration with the Horizon Simulation Framework. The Aeolus SysML model was converted and used as input in an HSF simulation. The SysML model simulation data was compared against those of the original test case. To test the requirement module, system level requirements were formulated within the Aeolus system and run in simulation, providing an analysis of the results. The results of the analysis confirmed a successful conversion of the SysML model into an equivalent HSF model and a successful analysis of system-level requirements.

Interplanetary space exploration has thus far consisted of single, expensive spacecraft missions. Mission costs are particularly high on missions to the outer planets and while invaluable, finite budgets limit our ability to perform extensive and frequent investigations of the planets. Planetary systems such as Jupiter and Saturn provide extremely complex exploration environments with numerous targets of interest. Exploring these targets in addition to the main planet requires multiple fly-bys and long mission timelines. In LEO, CubeSats have changed the exploration paradigm, offering a fast and low cost alternative to traditional space vehicles. This new mission development philosophy has the potential to significantly change the economics of interplanetary exploration and a number of missions are being developed to utilize CubeSat class spacecraft beyond earth orbit (e.g., NEAScout, Lunar Ice Cube, Marco and BioSentinel). This paper takes the CubeSat philosophical approach one step further by investigating the potential for small satellite swarms to provide extensive studies of the Saturn system. To do this, an architecture was developed to best replicate the Cassini Primary Mission science objectives using swarms of CubeSats. Cassini was chosen because of its complexity and it defines a well-understood baseline to compare against. The paper outlines the overall mission architecture developed and provides a feasible initial design for the spacecraft in the architecture. The number of swarms needed, number of CubeSats per swarm, size of the CubeSats, overall science output and estimated mission cost are all presented. Additional science objectives beyond Cassini's capabilities are also proposed. Significant scientific returns can be achieved by the swarm based architecture and the risk tolerance afforded by the utilization of large numbers of low-cost sensor carriers. This study found a potential architecture that could reduce the cost of replicating Cassini by as much as 63%. The results of this investigation are not constrained to Saturn and can be easily translated to other targets such as Uranus, Neptune or the asteroid belt.

Powering spacecraft with electric propulsion is becoming more common, especially in CubeSat-class satellites. On account of the risk of spacecraft interactions, it is important to have robust analysis and modeling tools of electric propulsion engines, particularly of the plasma plume. The Navier-Stokes equations used in classic continuum computational fluid dynamics do not apply to the rarefied plasma, and therefore another method must be used to model the flow. A good solution is to use the DSMC method, which uses a combination of particle modeling and statistical methods for modeling the simulated molecules. A DSMC simulation known as SINATRA has been developed with the goal to model electric propulsion plumes. SINATRA uses an octree mesh, is written in C++, and is designed to be expanded by further research. SINATRA has been initially validated through several tests and comparisons to theoretical data and other DSMC models. This thesis examines expanding the functionality of SINATRA to simulate charged particles and make SINATRA a DSMC-PIC hybrid. The electric potential is calculated through a 7-point 3D stencil on the mesh nodes and solved with a Gauss-Seidel solver. It is validated through test cases of charged particles to demonstrate the accuracy and capabilities of the model. An ambipolar diffusion test case is compared to a neutral diffusion case and the electric field is shown to stabilize the diffusion rate. A steady state flow test case shows the simulation is able to stabilize and solve the electric potential for a plume-like scenario. It includes additional features to simplify further research including a comprehensive user manual, industry-standard version control, text file inputs, GUI control, and simple parallelism of the simulation. Compilation and execution are standardized to be simple and platform independent to allow longevity of the code base. Finally, the execution bottlenecks of linking particles to cells and particle moving were removed to reduce the simulation time by 95%.

The CubeSat Astronomy Network is a proposed system of multiple CubeSat spacecraft capable of performing follow-up observations of astronomical targets of interest. The system is intended to serve as a space-borne platform that can complement existing systems utilized for astronomical research by undergraduate and high school students. Much research and development work has been performed to develop model-based system engineering methodologies and products for CubeSat missions, including the Horizon Simulation Framework. The Horizon Simulation Framework enables the development of system models using the Extended Markup Language (XML), and its simulation program can generate system simulations over model-specified timespans. System requirements and constraints, as well as subsystem dependencies and functions, can also be directly specified in these models. Previous work using the framework has been performed to characterize “day-in-the-life” operations for Earth-observing spacecraft. A similar goal is intended for modeling the CubeSat Astronomy Network: simulating mission operations during nominal conditions to validate system and subsystem requirements. By developing this model, system and subsystem requirements derived in the course of preliminary design for the Network can be analyzed, modelled, and evaluated for feasibility. These results can then be used to inform design decisions related to system architecture and concept of operations at the early stages of design, while the models themselves can grow and mature alongside project development and be re-used for future design work.

Space debris has become an increasingly hazardous obstacle to continued spaceflight operations. In an effort to mitigate this problem an investigation of the feasibility of a microsatellite active debris removal system was conducted. Through proposing a novel concept of operation, utilizing a grapple-and-tug system architecture, and by analyzing each resultant mission phase in the frame of a representative example, it was found that microsatellite scale systems are capable of fulfilling the active debris removal mission. Analysis of rendezvous, docking, control and deorbit mission requirements determined that the design of a grapple-and-tug system will be driven by sizing of the propellant required to deorbit the target vehicle. Further sensitivity analysis determined that target altitude and mass are critical factors in determining the capabilities of a microsatellite mission. Preliminary sizing demonstrated that hardware considerations for both satellite core and mission related activities do not impede microsatellite feasibility. Further investigation of microsatellite debris removal missions including detailed design analysis and engineering is suggested.

With the More Electric Aircraft paradigm, commercial commuter aircraft are increasing the size and complexity of electrical power systems by increasing the number of electrical loads. With this increase in complexity comes a need to analyze electrical power systems using new tools. The Hybrid Power System Optimizer (HyPSO) developed by Airbus SAS is a simulator designed to analyze new aircraft power systems. This thesis project will first provide a method to assess the reliability of complex aircraft electrical power systems before and after failure and reconfiguration events. Next, an add-on to HyPSO is developed to integrate the previously developed reliability calculations. Proof-of-concepts including new data visualizations are performed and provided.

This work proposes a new method for using reduced order models in lieu of high fidelity analysis during the sensitivity analysis step of gradient based design optimization. The method offers a reduction in the computational cost of finite difference based sensitivity analysis in that context. The method relies on interpolating reduced order models which are based on proper orthogonal decomposition. The interpolation process is performed using radial basis functions and Grassmann manifold projection. It does not require additional high fidelity analyses to interpolate a reduced order model for new points in the design space. The interpolated models are used specifically for points in the finite difference stencil during sensitivity analysis. The proposed method is applied to an airfoil shape optimization (ASO) problem and a transport wing optimization (TWO) problem. The errors associated with the reduced order models themselves as well as the gradients calculated from them are evaluated. The effects of the method on the overall optimization path, computation times, and function counts are also examined. The ASO results indicate that the proposed scheme is a viable method for reducing the computational cost of these optimizations. They also indicate that the adaptive step is an effective method of improving interpolated gradient accuracy. The TWO results indicate that the interpolation accuracy can have a strong impact on optimization search direction.

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.

A significant challenge in the design of multidisciplinary systems (e.g., airplanes, robots, cell phones) is to predict the effects of design decisions at the time these decisions are being made early in the design process. These predictions are used to choose among design options and to validate design decisions. System behavioral models, which predict a system's response to stimulus, provide an analytical method for evaluating a system's behavior. Because multidisciplinary systems contain many different types of components that have diverse interactions, system behavioral models are difficult to develop early in system design and are challenging to maintain as designs are refined. This research develops methods to create, verify, and maintain multidisciplinary system models developed from models that are already part of system design. First, this research introduces a system model formulation that enables virtually any existing engineering model to become part of a large, trusted population of component models from which system behavioral models can be developed. Second, it creates a new algorithm to efficiently quantify the feasible domain over which the system model can be used. Finally, it quantifies system model accuracy early in system design before system measurements are available so that system models can be used to validate system design decisions. The results of this research are enabling system designers to evaluate the effects of design decisions early in system design, improving the predictability of the system design process, and enabling exploration of system designs that differ greatly from existing solutions.

To achieve a high level of performance and efficiency, contemporary aerospace systems must become increasingly complex. While complexity management traditionally focuses on a product’s components and their interconnectedness, organizational representation in complexity analysis is just as essential. This thesis addresses this organizational aspect of complexity through an Organizational Complexity Metric (OCM) to aid complexity management. The OCM augments Sinha’s structural complexity metric for product architectures into a metric that can be applied to organizations. Utilizing nested numerical design structure matrices (DSMs), a compact visual representation of organizational complexity was developed. Within the nested numerical DSM are existing organizational datasets used to quantify the complexity of both organizational system components and their interfaces. The OCM was applied to a hypothetical system example, as well as an existing aerospace organizational architecture. Through the development of the OCM, this thesis assumed that each dataset was collected in a statistically sufficient manner and has a reasonable correlation to system complexity. This thesis recognizes the lack of complete human representation and aims to provide a platform for expansion. Before a true organizational complexity metric can be applied to real systems, additional human considerations should be considered. These limitations differ from organization to organization and should be taken into consideration before implementation into a working system. The visualization of organizational complexity uses a color gradient to show the relative complexity density of different parts of the organization.