<p>  </p> <p>In the last years, lithium-ion batteries (LIBs) have become the most important energy storage system for consumer electronics, electric vehicles, and smart grids. The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030--this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with the increasing energy demand. To that end, a design methodology to accelerate the LIB development is needed. This can be achieved through the integration of electro-chemical numerical simulations and machine learning algorithms.</p> <p><br></p> <p>To help this cause, this study develops a design methodology and framework using Simcenter Battery Design Studio® (BDS) and Bayesian optimization for design and optimization of cylindrical cell type 18650. The materials of the cathode are Nickel-Cobalt-Aluminum (NCA)/Nickel-Manganese-Cobalt-Aluminum (NMCA), anode is graphite, and electrolyte is Lithium hexafluorophosphate (LiPF6). Bayesian optimization has emerged as a powerful gradient-free optimization methodology to solve optimization problems that involve the evaluation of expensive black-box functions. The black-box functions are simulations of the cyclic performance test in Simcenter Battery Design Studio. </p> <p>The physics model used for this study is based on full system model described by Fuller and Newman. It uses Butler-Volmer Equation for ion-transportation across an interface and solvent diffusion model (Ploehn Model) for Aging of Lithium-Ion Battery Cells. The BDS model considers effects of SEI, cell electrode and microstructure dimensions, and charge-discharge rates to simulate battery degradation. Two objectives are optimized: maximization of the specific energy and minimization of the capacity fade. We perform global sensitivity analysis and see that thickness and porosity of the coating of the LIB electrodes that affect the objective functions the most. As such the design variables selected for this study are thickness and porosity of the electrodes. The thickness is restricted to vary from 22 micron to 240 microns and the porosity varies from 0.22 to 0.54. </p> <p>Two case studies are carried out using the above-mentioned objective functions and parameters. In the first study, cycling tests of 18650 NCA cathode Li-ion cells are simulated. The cells are charged and discharged using a constant 0.2C rate for 500 cycles. In the second case study a cathode active material more relevant to the electric vehicle industry, Nickel-Manganese-Cobalt-Aluminum (NMCA), is used. Here, the cells are cycled for 5 different charge-discharge scenarios to replicate charge-discharge scenario that an EVs battery module experiences. The results show that the design and optimization methodology can identify cells to satisfy the design objective that extend and improve the pareto front outside the original sampling plan for several practical charge-discharge scenarios which maximize energy density and minimize capacity fade. </p>

Artificial Neural Network (ANN) ensemble and Response Surface Method (RSM) surrogate models were generated from Finite Element (FE) simulations to predict the overpressure load and vehicle impact response of a novel rapidly deployable protective structure. A Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used in conjunction with the surrogate models to determine structure topology input variable configurations which were suited to produce the optimal balance of minimum mass, minimum rotation angle, minimum displacement, and maximum total length of the deployable structure. The structure was designed to retract into a container, be lightweight to facilitate transportation, and be able to adapt to varying terrain slopes. This research demonstrates that, in comparison to the RSM, ANN ensembles can more accurately and efficiently be used for identifying optimal design solutions for multi-objective design problems when two surrogate models from the same method corresponding to separate FE models are used simultaneously in a NSGA-II.

<p>The research effort addressed important gaps in the modeling to simulate Urban Air Mobility (UAM) operations and couple optimization analyses for vehicle design, fleet allocations, and operational choices for next generation urban travel. Urban Air Mobility is expected to be a \$1 trillion dollar industry by 2040, but operators and designers have limited models and tools to estimate fleet performance, cost metrics, emissions performance, and profit for a given concept under future concepts of operations. A review of the literature reveals 14 modeling gaps related to infrastructure, operations, airspace, vehicles, and customers. In addition, the UAM industry requires better understanding of how operational choices may impact vehicle design and fleet allocations in a market with significant economic barriers and infrastructure needs. To address those needs, this effort proposed alternatives to address modeling challenges and develop studies to evaluate UAM vehicle concepts and concepts of operations in ways once not possible using the enhanced modeling tools. The research findings revealed that modeling coupled design/fleet and operational choices can affect daily profitability potential by 2-4\times\, for piloted and autonomous operations and affect the fleet size from between 12-50 vehicles across small, medium, and large metropolitan areas. The modeling capability provided by the improvements in UAM operations simulations and accessing vehicle and fleet metrics enables future studies to address UAM in a holistic manner. The increased capability could benefit the UAM community and inform future operations and concepts of operations in preparation for ubiquitous operations.</p>

New Horizons, Voyager 1 & 2, and Pioneer 10 & 11 are the only spacecraft to ever venture past Pluto and provide information about space at those large distances. These spacecraft were very expensive and primarily designed to study planets during gravitational assist maneuvers. They were not designed to explore space past Pluto and their study of this environment is at best a secondary mission. These spacecraft rely on radioisotope thermoelectric generators (RTGs) to provide power, an expensive yet necessary approach to generating sufficient power. With Cubesats graduating to interplanetary capabilities, such as the Mars-bound MarCO spacecraft, matching the modest payload requirements to study the outer Solar System (OSS) with the capabilities of low-power nano-satellites may enable much more affordable access to deep space. This paper explores a design concept for a low-cost, small spacecraft, designed to study the OSS and satisfy mission requirements with solar power. The general spacecraft design incorporates a parabolic reflector that acts as both a solar concentrator and a high gain antenna. This paper explores a working design concept for a small spacecraft to operate up to 100 astronomical units (AU) from the sun. Deployable reflector designs, thermal and radiation environments, communications and power requirements, solar system escape trajectory options, and scientific payload requirements are detailed, and a working system is proposed that can fulfill mission requirements with expected near-future innovations in a few key technologies.

In this thesis, the development of a structure for use in an educational CubeSat kit is explored. The potential uses of this kit include augmenting existing curricula with aspects of hands on learning, developing new ways of training students on proper space systems engineering practices, and overall contributing to academic capacity building at Cal Poly and its collaborators. The design improves on existing CubeSat kit structures by increasing accessibility to internal components by implementing a modular backplane system, as well as adding the ability to be environmentally tested. Manufacturing of the structure is completed with both additive (Fused Deposition Modeling with ABS polymer and Selective Laser Melting with AlSi10Mg metal) and subtractive (milling with Al-6061) technologies. Modal, harmonic, and random vibration analyses and tests are done to ensure the structure passes vibration testing qualification loads, as outlined by the National Aeronautics and Space Administration’s General Environmental Standards. Successful testing of the structure, defined as deforming less than 0.5 millimeters and maintaining a factor of safety above 2, is achieved with all materials of interest. Thus, the structure becomes the first publicly available CubeSat kit designed to survive environmental testing. Achieving this goal with a structure made of the cheap, widely available material ABS showcases the potential usability of 3D-printed polymers in CubeSat structures.

The CalPoly Spacecraft Attitude Dynamics Simulator mimics the rotational dynamics of a spacecraft in orbit and acts as a testbed for spacecraft attitude control system development and demonstration. Prior to this thesis, the simulator platform and several subsystems had been designed and manufactured, but the total simulator system was not yet capable of closed-loop attitude control. Previous attempts to make the system controllable were primarily mired by data transport performance. Rather than exporting data to an external command computer, the strategy implemented in this thesis relies on a compact computer onboard the simulator platform to handle both attitude control processing and data acquisition responsibilities. Software drivers were created to interface the computer’s data acquisition boards with Matlab, and a Simulink library was developed to handle hardware interface functions and simplify the composition of attitude control schemes. To improve the usability of the system, a variety of actuator control, hardware testing, and data visualization utilities were also created. A closedloop attitude control strategy was adapted to facilitate future sensor installations, and was tested in numerical simulation. The control model was then updated to interface with the simulator hardware, and for the first time in the project history, attitude control was performed onboard the CalPoly spacecraft attitude dynamics simulator. The demonstration served to validate the numerical model and to verify the functionality of the entire simulator system.

The conceptual design phase of an aerospace system development program is typically characterized by short duration and relatively limited resources, yet design decisions are made that have critical implications on program risk. To address the more aggressive requirements, one of these decisions is the selection of advanced technologies. System developers need to assess advanced technologies early on, but are faced with uncertainties surrounding the potential net benefits. The concept introduced in this study is uncertainty characterization as a way to better understand the associated risk. A framework was developed to guide the interaction between the technology developer and the system developer. The objective is to gain a more comprehensive landscape of the technology options by explicitly considering the effects of uncertainty in the decision making process. This can ultimately facilitate prioritization and resource management during conceptual design. An example case of advanced wing technology was applied to the design of a high-altitude long-endurance unmanned aerial vehicle to demonstrate the implementation of the framework.

Synthetic Aperture Radar (SAR) has come into widespread use in several civilian and military applications. The focus of this paper is the military application of imaging point targets captured by an airborne SAR platform. Using the traditional SAR method of determining target motion by analyzing the difference between subsequent images takes a relatively large amount of processing resources. Using methods in this thesis, target motion can be estimated before even a single image is obtained, reducing the amount of time and power used by a significantly large amount. This thesis builds on work done by Brain Zaharri and David So. Brain Zaharri successfully created a SAR simulation that accurately models the airborne SAR system capturing data of a target space using the Range Doppler Algorithm (RDA). David So extended this work by adding functionality to determine target velocity in the range and azimuth directions by processing the intermittent data created by the several steps of Brian Zaharri’s simulation. This thesis shows further extensions of processing the intermittent data using unique methods. The methods in this thesis successfully demonstrate the ability to quickly and accurately estimate target position, velocity, and acceleration without the need for using multiple SAR images. Target motion in the range direction is detected without using any part of the RDA, while the azimuth direction cuts out several steps, including the range compression phase and the range cell migration correction. Removing these unneeded processing steps dramatically decreases target motion data acquisition time. Both Brian Zaharri’s and David So’s work, along with this thesis, are part of the Cal Poly SAR Automatic Target Recognition (ATR) group of projects, which is sponsored by Raytheon Space & Airborne Systems Division. Because U.S. military SAR data remains classified, the Cal Poly SAR ATR projects addresses the need to educate researchers on the processing of SAR data.

System design is an inherently expensive and time consuming process. Engineers are constantly tasked to investigate new solutions for various programs. Model-based systems engineering (MBSE) is an up and coming successful method used to reduce the time spent during the design process. By utilizing simulations, model-based systems engineering can verify high-level system requirements quickly and at low cost early in the design process. The Horizon Simulation Framework, or HSF, provides the capability of simulating a system and verifying the system performance. This paper outlines an improvement to the Horizon Simulation Framework by providing information to the user regarding schedule failures due to subsystem failures and constraint violations. Using the C# language, constraint violation rates and subsystem failure rates are organized by magnitude and written to .csv files. Also, proper subsystem failure and constraint violation checking orders were stored for HSF to use as new evaluation sequences. The functionalities of the systemEval framework were verified by five test cases. The output information can be used for the user to improve their system and possibly reduce the total run-time of the Horizon Simulation Framework.

The paper describes the background and concepts behind a master’s thesis platform known as COMET (Constrained Optimization of Multiple-dimensions for Efficient Trajectories) created for mission designers to determine and evaluate suitable interplanetary trajectories. This includes an examination of the improvements to the global optimization algorithm, Differential Evolution, through a cascading search space pruning method and decomposition of optimization parameters. Results are compared to those produced by the European Space Agency’s Advanced Concept Team’s Multiple Gravity Assist Program. It was found that while discrepancies in the calculation of ΔV’s for flyby maneuvers exist between the two programs, COMET showed a noticeable improvement in its ability to avoid premature convergence and find highly isolated solutions.