This project aims to explore what the brand Land Rover could stand for in the future. The brands rich history of exploring unconquered terrain earned it admiration and desirability all around the world. Further extending it's reach onto new worlds is within reach. In the 2030s the first manned missions to Mars are planned. The first arrivers will have exploration vehicles, that are limited in range and capability. To really explore the planet, vehicles with greater off-road capability and range will be needed. The vehicles also need to allow the expedition crews to stay in the vehicle for longer periods comfortably and also offer extended life support on multi-week long journeys.With this project I am exploring possible answers to face the harsh conditions on Mars. Furthermore, the vehicle and it's features project a vision of what a future off-road driving experience could be.

Evolution is a key distinguishing trait of Systems-of-Systems (SoS) that introduces a layer of complexity in analysis that is not present when considering static systems. Some SoS analysis tools exist to determine and evaluate the evolution of an SoS, while other tools are better suited for studying individual instances of an SoS. System Operational Dependency Analysis (SODA) is one such method that has been used previously to study static SoS networks. SODA that has been proven effective in investigating the impacts of partial system disruptions and would benefit from a framework to apply SODA to evolving SoS. This thesis provides an approach to modeling evolving SoS in SODA and presents new data visualization methods to highlight the effects of changing network configurations across evolutionary phases. These visualization enhancements include Failure Impact Range sequence plots to show effects of deterministic system disruptions on capabilities of interest across evolutionary phases, as well as Stochastic Impact plots to quantify the impact of disruptions in particular systems in the context of the probabilistic operating statuses assigned to each system. Integration of SODA and the related method of System Developmental Dependency Analysis (SDDA) is explored to model how operational disruptions and developmental delays might interact and compound during the evolution of an SoS. The SODA enhancements provide decision makers with new information that can be used to explore design and implementation tradeoffs in an evolving SoS under budget and scheduling constraints. These ideas are demonstrated through a case study based on NASA's Artemis program to return humans to the Moon in commercially-built Human Landing Systems (HLS). The HLS concepts proposed to NASA consist of multiple elements that provide distinct capabilities in different phases of the lunar mission, and therefore can be considered an evolving SoS architecture. The operational dependencies of two HLS concepts are modeled across a four-phase lunar landing mission and results are generated using the new visualization methods to highlight the impacts of changing SoS configuration on the performance of key mission capabilities. The development timeline of the first three planned Artemis lunar landing missions is analyzed with SDDA and integrated with SODA results from one HLS concept to explore how developmental delays impact the likelihood of HLS mission completion and how operational failures requiring system redesign impact the program schedule. Connections between SDDA and Integrated Master Schedules (IMS) are discussed to show how SDDA results can be useful in a context more familiar to program managers.

Beyond the protective confines of Earth's atmosphere and magnetosphere, spacecraft are subject to constant bombardment by high-energy charged particles originating from our Sun in the form of Solar Particle Events (SPEs), and from outside the solar system in the form of Galactic Cosmic Rays (GCRs). The harm these particles do can be reduced or mitigated outright through radiation shielding. Because protons and other charged particles comprise most of these radiation particles, strong magnetic fields could be generated around spacecraft to deflect incoming charged radiation particles. This thesis investigates the performance of specific configurations of toroidal superconducting solenoids to generate magnetic fields that deflect incoming energetic protons via the Lorentz force. Bulk material shielding configurations using various thicknesses of liquid water are similarly investigated, as are combination shielding configurations combining the best-performing toroidal shielding configurations with a small bulk material shield surrounding the spacecraft. The water shielding configurations tested included shields of uniform thicknesses from 1 cm to 10 cm surrounding an Apollo CSM-sized cylindrical candidate spacecraft. Water shielding was found to be very effective at reducing the SPE dose, from a 86\% reduction at 1 cm of water to a 94\% reduction at 10 cm. However water shielding was found to be minimally effective against the much higher energy Galactic Cosmic Ray protons, with no dose reduction at 1 cm and a paltry 1\% reduction at 10 cm. The toroidal shielding geometric configurations tested consisted of either 5 or 10 primary toroidal shields surrounding the candidate spacecraft, as was the addition of smaller nested toroidal shields inside the primary toroids and of toroids on the spacecraft's endcaps. The magnetic field strengths tested were 1.7 Tesla, 8.5 Tesla, and 17 Tesla. The best geometric configurations of electrodynamic shielding consisted of 5 primary toroidal shields, 5 total nested shields placed inside the primary toroids, and 2 total shields on the spacecraft's endcaps. The second best geometric configuration consisted of 10 primary toroidal shields plus two total endcap shields. These configurations at 1.7 Tesla reduced the SPE dose by 87\% and 87\%, and reduced the GCR dose by 11\% and 10\%. At 17 Tesla, these configurations both reduced the SPE dose by 90\%, and reduced the GCR dose by 76\% and 61\%. Combining these two configurations with a 1 cm-thick shield of water improved performance against SPE protons to 95\% and 93\% at 1.7 Tesla, and a 97\% and 96\% reduction at 17 Tesla. GCR dose reductions decreased slightly. Passive material shielding was found capable of providing substantial protection against SPE protons, but was minimally effective against GCR protons without very thick shielding. Electrodynamic shielding, at magnetic field strengths of 1.7 Tesla, was found to be similarly effective against SPE protons, and marginally more effective against GCR protons. Combining the best toroidal shielding configurations, at magnetic field strengths of 1.7 Tesla, with water shielding yielded high protection against SPE protons, but still marginal protection against GCR protons. Increasing the magnetic field strength to 17 Tesla was found to provide very high protection against SPE protons, and to significantly reduce the radiation dose from GCR protons. Of all shielding configurations tested, only those electrodynamic configurations with magnetic fields of 17 Tesla were able to reduce the GCR dose by more than half.

The initial phases of the design process -- including interactions with stakeholders, ideation of concept candidates, and the selection of the best candidates --- have a large impact on the success of a project as a whole. Much of the value generated during these phases comes from the designers' exploration of the design space as they create concepts for the final solution. Unfortunately, an entire dimension of the design space is often ignored during the initial phases of the design process -- the tactics dimension. Engineers tend to emphasize the design of technology in their work, while paying less attention to how that technology is used. By adding tactics to technology as two dimensions of the design space and creating the Tech/Tac plot as a means for visualizing those dimensions, the designer's ability to visualize, understand, and explore an expanded design space is improved. In this paper, we introduce a deliberate design-space structure that can help teams generate and evaluate integrated Tech/Tac concepts. The structure improves concept exploration during the early phases of the design process by harnessing the information provided by a two-dimensional, structured design space. This design space is represented here as a vector space with a basis of technology and tactics. Also presented are definitions and principles that facilitate the use of the technology-tactics framework to represent the design space in various useful ways. Six tests were carried out during this research to develop and evaluate the structure. The final instantiation of the concepts presented in this paper has been shown to be meaningful to design teams during ideation.

<p>The long-term sustainable human exploration of Mars is approached via the design and analysis of an orbital logistics architecture as part of a robust logistics infrastructure. In this investigation, we analyze the advantages of an orbital logistics node around Mars (which we call Mars Spacedock), which plays a crucial role to support the transport of vehicles and resupply of cargo to a base on the surface. The Mars Spacedock serves as one of the many logistics nodes at different locations between Earth and Mars that support the continuous movement of crew and cargo to and from Mars for the next several decades. The need of multiple nodes at strategic locations is supported by lessons learned from terrestrial analogs of complex missions such as military, Antarctic exploration, and the International Space Station. The Mars Spacedock is envisaged to have at least aggregation, refueling, resupply and refurbishing capabilities. The stationing orbit of the Spacedock is one of the primary design drivers in determining the associated propellant requirement and surface accessibility. The stationing orbit is selected from a range of Mars orbits such that it best accommodates (delta V  cost being a major determinant) arrival from a variety of interplanetary approaches, capture into Mars orbit, deorbit and entry into Mars atmosphere, surface accessibility, launch from surface to stationing orbit, and departure to Earth. A variety of mission types are evaluated over a 15-year cycle as follows: long-stay crewed missions, short-stay crewed missions, cargo transfer missions on low-thrust and ballistic trajectories. The perturbation of orbits due to aspherical gravity of Mars and timeline of missions are found to be crucial factors in selection of orbit. The Low Mars Orbits are found to be comparable to the Highly Elliptical Mars Orbits in total delta V requirement. The optimal stationing orbit is selected by minimizing a combination of mission propellant mass and transfer time for a given set of mission parameters. The sensitivity of the optimal solution to various mission parameters (landing site latitude, propellant, refueling capability in Mars orbit, deorbit method, mission type, and frequency of different mission types) is assessed. The analysis on orbit considerations aids mission designers in selecting suitable stationing orbit for a set of mission parameters and assessing the long term impacts of mission design choices on the logistics requirements. Finally, the viability of the Spacedock is analyzed in terms of landing site accessibility, station-keeping requirement, and initial mass in cislunar staging orbit. Here also Low Mars Orbits have accessibility over a wider range of landing sites compared to 1 sol orbit. The station-keeping requirement is found to be insignificant over the scale of the missions. The Spacedock refuel capability leads to lower mass in cislunar staging orbit, about 60 Mg lower per crewed MTV mission, and compensates for the higher capture and departure delta Vs.</p> <p>   </p> <p>   A logistics architecture stationed in a strategic orbit around Mars would enable long term sustainable operations for human exploration, reduce the logistics footprint of the exploration campaigns, and aid in transitioning to an eventual permanent presence on Mars. </p>

<p>Computer Vision is a mathematical tool formulated to extend human vision to machines. This tool can perform various tasks such as object classification, object tracking, motion estimation, and image segmentation. These tasks find their use in many applications, namely robotics, self-driving cars, augmented reality, and mobile applications. However, opposed to the traditional technique of incorporating handcrafted features to understand images, convolution neural networks are being used to perform the same function. Computer vision applications widely use CNNs due to their stellar performance in interpreting images. Over the years, there have been numerous advancements in machine learning, particularly to CNNs. However, the need to improve their accuracy, model size and complexity increased, making their deployment in restricted environments a challenge. Many researchers proposed techniques to reduce the size of CNN while still retaining its accuracy. Few of these include network quantization, pruning, low rank, and sparse decomposition and knowledge distillation. Some methods developed efficient models from scratch. This thesis achieves a similar goal using design space exploration techniques on the latest variant of MobileNets, MobileNet V3. Using Depthwise Pointwise Depthwise (DPD) blocks, escalation in the number of expansion filters in some layers and mish activation function MobileNet V3 is reduced to 84.96% in size and made 0.2% more accurate. Furthermore, it is deployed in NXP i.MX RT1060 for image classification on CIFAR-10 dataset.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Computer Vision is a mathematical tool formulated to extend human vision to machines. This tool can perform various tasks such as object classification, object tracking, motion estimation, and image segmentation. These tasks find their use in many applications, namely robotics, self-driving cars, augmented reality, and mobile applications. However, opposed to the traditional technique of incorporating handcrafted features to understand images, convolution neural networks are being used to perform the same function. Computer vision applications widely use CNNs due to their stellar performance in interpreting images. Over the years, there have been numerous advancements in machine learning, particularly to CNNs.However, the need to improve their accuracy, model size and complexity increased, making their deployment in restricted environments a challenge. Many researchers proposed techniques to reduce the size of CNN while still retaining its accuracy. Few of these include network quantization, pruning, low rank, and sparse decomposition and knowledge distillation. Some methods developed efficient models from scratch. This thesis achieves a similar goal using design space exploration techniques on the latest variant of MobileNets, MobileNet V3. Using DPD blocks, escalation in the number of expansion filters in some layers and mish activation function MobileNet V3 is reduced to 84.96% in size and made 0.2% more accurate. Furthermore, it is deployed in NXP i.MX RT1060 for image classification on CIFAR-10 dataset.

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.

CPU cache hierarchies are the central solution in bridging the memory wall. A proper understanding of how to trade-off their high cost against performance can lead to cost-savings without sacrificing performance.Due to the combinatorial nature of the problem, there exist a large number of configurations to investigate, making design space exploration slow and cumbersome. To improve this process, this Thesis develops and evaluates a model for optimally trading-off cost and performance of CPU cache hierarchies, named the Optimal Cache Problem (OCP), in the form of a Non-linear Integer Problem. A second goal of this work is the development of an efficient solver for the OCP, which was found to be a branch &amp; bound algorithm and proven to function correctly. Experiments were conducted to empirically analyse and validate the model and to showcase possible use-cases. There, it was possible to ascribe the model outputs on measurable performance metrics. The model succeeded in formalising the inherent trade-off between cost and performance in a way that allows for an efficient and complete search of the configuration space of possible cache hierarchies. In future work, the model needs to be refined and extended to allow for the simultaneous analysis of multiple programs.

When designing an engineering part, better decisions are made by exploring the entire space of design variations. This design space exploration (DSE) may be accomplished manually or via optimization. In engineering, evaluating a design during DSE often consists of running expensive simulations, such as finite element analysis (FEA) in order to understand the structural response to design changes. The computational cost of these simulations can make thorough DSE infeasible, and only a relatively small subset of the designs are explored. Surrogate models have been used to make cheap predictions of certain simulation results. Commonly, these models only predict single values (SV) that are meant to represent an entire part's response, such as a maximum stress or average displacement. However, these single values cannot return a complete prediction of the detailed nodal results of these simulations. Recently, surrogate models have been developed that can predict the full field (FF) of nodal responses. These FF surrogate models have the potential to make thorough and detailed DSE much more feasible and introduce further design benefits. However, these FF surrogate models have not yet been applied to real engineering activities or been demonstrated in DSE contexts, nor have they been directly compared with SV surrogate models in terms of accuracy and benefits.This thesis seeks to build confidence in FF surrogate models for engineering work by applying FF surrogate models to real DSE and engineering activities and exploring their comparative benefits with SV surrogate models. A user experiment which explores the effects of FF surrogate models in simple DSE activities helps to validate previous claims that FF surrogate models can enable interactive DSE. FF surrogate models are used to create Goodman diagrams for fatigue analysis, and found to be more accurate than SV surrogate models in predicting fatigue risk. Mode shapes are predicted and the accuracy of mode comparison predictions are found to require a larger amount of training samples when the data is highly nonlinear than do SV surrogate models. Finally, FF surrogate models enable spatially-defined objectives and constraints in optimization routines that efficiently search a design space and improve designs.The studies in this work present many unique FF-enabled design benefits for real engineering work. These include predicting a complete (rather than a summary) response, enabling interactive DSE of complex simulations, new three-dimensional visualizations of analysis results, and increased accuracy.