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.

Lockheed Martin Space Systems Company, Sunnyvale, California is in the business of designing and engineering satellites for commercial and government customers. This task is by no means a trivial endeavor but Lockheed MartinSunnyvale has many decades of experience in Systems Engineering. Training in systems engineering is a long process whose training tools must be kept up-to-date and appropriate as new advances in technology evolve along-side with the processes that develop these ever-evolving solutions. For many years new systems engineers have been introduced to the Bicycle Model as a paradigm for designing and developing complex systems that are composed of diverse subsystems that interact and function as a whole system. The Bicycle Model originally utilized the idea that a bicycle integrates electrical and mechanical subsystems into a system that provides basic transportation that functions on the input of the operator. This model was relevant back when satellites were merely remote-controlled systems that performed based on input from earth-based operators. Over the decades, much of satellites’ routine operations have migrated from the earth-based operators to onboard computers where routine tasks are encoded in their flight software. With more and more of the satellites’ functionality now being controlled by software, the present Bicycle Model becomes less and less relevant because the model doesn’t address a major component of the satellites’ design; the onboard flight software. It is time to bring the Bicycle Model up-to-date to address the use of software and its implication and considerations in the design of Lockheed Martin’s products. This is the focus of this project; to update the Bicycle Model training exercise to incorporate Electrical, Mechanical and Software interfaces in a complex system.

In recent decades, complexity in aerospace programs has been increasing, leading to large budget and schedule overruns. Many of the risks of complex system development can be attributed to the inadequacy of linear methods when applied to nonlinear domains, i.e., oversimplification in a program amplifies the amount of risk produced when a system behaves unexpectedly. Effectively managing complexity involves responding to the various sources of complexity, whether it appears in the objective behavior of the system itself or in the subjective behavior of the people developing it. Thus, the engineering of complex systems requires nonlinear modeling methods of the system as well as nonlinear processes for developing the system. Much effort tends to be focused on addressing the objective sources of complexity and less is given to understanding and responding to the subjective sources of complexity. This present study examines how facilitating creativity in aerospace system development can serve as a potential strategy for managing complexity. Creativity is a kind of psychological process that integrates linear and nonlinear modes of thinking, and therefore systems engineering processes that reflect the creative process could reduce the risks of complexity. There are three primary results of this work: a novel application of creativity research to aerospace engineering processes; the most comprehensive published review of existing research on creativity in aerospace known to-date; and the proposal of two new systems engineering methods for facilitating creativity to manage complexity. These two new methods designed to improve the Waterfall methodology are as follows: the formation of a Parallel Systems Engineering group that functions analogously to how linear and nonlinear information are coordinated in creativity; and a conceptual model wherein aerospace programs are treated as a series of interdependent creative processes, which can be used to trace the propagation of complexity through various phases of system development.

The research presented provides an overview of a 1U+ form factor propulsion system design developed for the Cal Poly CubeSat Laboratory (CPCL). This design utilizes a Radiofrequency Electrothermal Thruster (RFET) called Pocket Rocket that can generate 9.30 m/s of delta-V with argon, and 20.2 ± 3 m/s of delta-V with xenon. Due to the demand for advanced mission capabilities in the CubeSat form factor, a need for micro-propulsion systems that can generate between 1 – 1500 m/s of delta-V are necessary. By 2019, Pocket Rocket had been developed to a Technology Readiness Level (TRL) of 5 and ground tested in a 1U CubeSat form factor that incorporated propellant storage, pressure regulation, RF power and thruster control, as well as two Pocket Rocket thrusters under vacuum, and showcased a thrust of 2.4 mN at a required 10 Wdc of power with Argon propellant. The design focused on ground testing of the thruster and did not incorporate all necessary components for operation of the thruster. Therefore in 2020, a 1U+ Propulsion Module that incorporates Pocket Rocket, the RF amplification PCB, a propellant tank, propellant regulation and delivery, as well as a DC-RF conversion with a PIB, that are all attached to a 2U customer CubeSat for a 3U+ overall form factor. This design was created to increase the TRL level of Pocket Rocket from 5 to 8 by demonstrating drag compensation in a 400 km orbit with a delta-V of 20 ± 3 m/s in the flight configuration. The 1U+ Propulsion Module design included interface and requirements definition, assembly instructions, Concept of Operations (ConOps), as well as structural and thermal analysis of the system. The 1U+ design enhances the capabilities of Pocket Rocket in a 1U+ form factor propulsion system and increases future mission capabilities as well as propulsion system heritage for the CPCL.

This thesis examines the feasibility of substituting the system input script of Cal Poly’s Horizon Simulation Framework (HSF) with a Model Based Systems Engineering (MBSE) model designed with the Systems Modeling Language (SysML). A concurrent student project, SysML Output Interface Creation for the Horizon Simulation Framework, focused on design of the HSF Translator Plugin which converts SysML models to an HSF specific XML format. A SysML model of the HSF test case, Aeolus, was designed. The original Aeolus HSF input script and the translated SysML input script retained the format and dependency structure required by HSF. Both input scripts returned identical results and thus validated the feasibility of linking SysML with HSF through the HSF Translator Plugin. A second SysML model of the Cal Poly CubeSat mission, ExoCube, was also designed and converted into an HSF input script. The ExoCube input script also retained the format and dependency structure required by HSF. This demonstrated that future SysML models can be used in conjunction with the HSF Translator Plugin to create a functional HSF system input script.

With the rise of CubeSats and the demonstration of their many space applications, there is interest in interplanetary CubeSats to act for example as scientific investigations or communications relays. In line with the increasing demand for this class of small satellites, the Cal Poly CubeSat Lab (CPCL) seeks to develop a bus that could support an interplanetary science payload. To facilitate this, a mission concept to conduct science of the moons of Mars, Phobos and Deimos, is investigated by determining the mission needs for a CubeSat in a Phobos-Deimos cycler orbit through the development of a baseline design to meet mission objectives. This baseline design is then compared by subsystem to CPCL’s current capabilities to identify technology, facility, and knowledge gaps and recommend a path forward to close them. The resulting baseline design is a 16U bus capable of transferring from an initial low Mars orbit to a Phobos-Deimos cycler orbit using a combined chemical and electric propulsion system. The bus is designed for a 3.5 year mission lifetime collecting radiation data and images, utilizing a relay architecture to downlink payload data. Estimates for mass, volume, and power available for an additional payload are up to 2.3 kg in ~4U with power consumption up to 13 to 38 W. This baseline requires further iteration due to non-closure of the thermal protection subsystem and improvement of other subsystems but serves as a starting point for exploration into CPCL’s next steps in becoming an interplanetary bus provider. Major subsystem areas identified for hardware performance improvement within CPCL are propulsion, communications, power, and mechanisms.