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Daniel Guggenheim School of Aerospace Engineering

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Now showing 1 - 10 of 94
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Georgia Tech Mars Exploration UAV Collaborative System: A Conceptual Design and Systems Engineering Study

2017-12-14 , Lacerda, Michel , Park, Dongjin , Beard, Robert , Schrage, Daniel

Exploration of Mars has been one of the focus of the aerospace community for its similarity with the Earth. The characteristics of Mars make possible an aerial flight even with the low pressure in its atmosphere. In an age where the ground rover exploration is dominant, the addition of a rotary-wing unmanned autonomous vehicle (UAV) would expand the exploration possibilities, increasing the range and the amount of scientific data collected. In this work, it is shown a conceptual systems design that combines a rotary-wing UAV and a ground rover in collaboration, for the exploration of the terrain. The work is being developed in the Systems Engineering level of conceptualization and Design phase. Secondary goal is the application of the IPPD method to compare different concepts. A subsequent CAD, FEA and Rapid prototyping, using Additive Manufacturing, of the initial concept is executed in this study. This study shows the steps through the design process, figures and results are show in an order of evolution of work.

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Flyby Trajectory Analysis and Thermal Simulation of a Venus Atmospheric Probe

2017-12-01 , Selvaratnam, Roshan

Cupid’s Arrow is a proposed interplanetary Venus mission aimed at sampling the noble gases in its atmosphere. These inert elements can provide an insight into the history of the planet’s formation and provide a reference for comparison with the Earth. The mission is comprised of a mothership and an atmospheric sample collection probe. This study is focused on the latter which will be deployed into Venus’ atmosphere and descend to an altitude of 120 km. The thermal environment of the Venusian exosphere is the primary driver of the probe design both in terms of its structure and material composition. The mission architecture being considered for this study takes advantage of a gravity assist flyby trajectory. The probe will be dropped off as a secondary payload en route to the spacecraft’s primary destination. The entry conditions at Venus and the trajectory of the probe relative to the mothership were determined using 2-body orbital mechanics. Using planar equations of motion, the probe’s entry into Venus’ atmosphere was simulated to predict the thermal environment that it would encounter. Initial results show a peak heat rate of approximately 220.3297 W/cm2 , a peak deceleration of 2.7654 Earth g’s and a total heat load of 15535 J/cm2 . The results of the thermal environment model and relative trajectory analysis were used to validate the baseline communications and TPS design. In addition to Venus, this mission concept could be used to explore other planetary atmospheres, especially those frequented by interplanetary flybys.

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Ambush games in discrete and continuous environments

2017-11-22 , Boidot, Emmanuel

We consider an autonomous navigation problem, whereby a traveler aims at traversing an environment in which an adversary sets an ambush. A two players zero- sum game is introduced, describing the initial strategy of the traveler and the ambusher based on a description of the environment and the traveler initial location and desired goal. The process is single-step in the sense that agents do not reevaluate their strategy after the traveler has started moving. Players’ strategies are computed as probabilistic path distributions, a realization of which is the path chosen by the traveler and the ambush location chosen by the ambusher. A parallel is drawn between the discrete problem, where the traveler moves on a network, and the continuous problem, where the traveler moves in a compact subset of R2. Analytical optimal policies are derived. Assumptions from the Minimal Cut - Maximal Flow literature for continuous domains are used. The optimal value of the game is shown to be related to the maximum flow on the environment for sub-classes of games where the reward function for the ambusher is uniform. This proof is detailed in the discrete and continuous setups. In order to relax the assumptions for the computation of the players’ optimal strategies, a sampling-based approach is proposed, inspired by re- cent sampling-based motion planning techniques. Given a uniform reward function for the ambusher, optimal strategies of the sampled ambush game are proven to converge to the optimal strategy of the continuous ambush game under some sampling and connectivity constraints. A linear program is introduced that allows for the computation of optimal policies. The sampling-based approach is more general in the sense that it is compatible with constrained motion primitives for the traveler and non-uniform reward functions for the ambusher. The sampling-based game is used to create example applications for situ- ations where no analytic solution of the Continuous Ambush Game have been identified.This leads to more interesting games, applicable to real-world robots using modern motion planning algorithms. Examples of such games are setups where the traveler’s motion satis- fies Dubins’ kinematic constraints and setups where the reach of the ambusher is dependent on the speed of the traveler.

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A methodology for risk-informed launch vehicle architecture selection

2017-11-13 , Edwards, Stephen James

Modern society in the 21st century has become inseparably dependent on human mastery of the near-Earth regions of space. Billions of dollars in on-orbit assets provide a set of fundamental, requisite services to such diverse domains as telecom, military, banking, and transportation. While orbiting satellites provide these services, launch vehicles (LVs) are unquestionably the most critical piece of infrastructure in the space economy value chain. The past decade has seen a significant level of activity in LV development, including some fundamental changes to the industry landscape. Every space-faring nation is engaged in new program developments; most notable, however, is the surge in commercial investments and development efforts, which has been spurred by a combination of private investments by wealthy individuals, new government policies and acquisition strategies, and the increased competition that has resulted from both. In all the LV programs of today, affordability is acknowledged as the single biggest objective. Governments seek assured access to space that can be realized within constrained budgets, and commercial entities vie for survival, profitability, and market-share. From literature, it is clear that the biggest opportunity for affecting affordability resides in improving decision-making early on in the design process. However, a review of historical LV architecture studies shows that very little has changed over the past 50 years in how early architecting decisions are analyzed. In particular, architecture analyses of alternatives are still conducted deterministically, despite uncertainty being at its highest in the very early stages of design. This thesis argues that the ``design freedom'' that exists early on manifests itself as volitional uncertainty during the LV architect's deliberation, motivating the objective statement ``to develop a methodology for enabling risk-informed decision making during the architecture selection phase of LV programs.'' NASA's Risk-Informed Decision Making process is analyzed with respect to the particulars of the LV architecture selection problem. The most significant challenge is found to be LV performance modeling via trajectory optimization, which is not well suited to probabilistic analysis. To overcome this challenge, an empirical modeling approach is proposed. However, this in turn introduces the challenge of generalizing the empirical model, as creating distinct performance models for every architecture concept under consideration is considered infeasible. A review of the main drivers in LV trajectory performance observes T/W not only to be one of the parameters with most sensitivity, but also reveals it to be a functional in its true form. Based on the performance-driving nature of the T/W profile, and the fact that in its infinite-dimensional form it offers a common basis for representing diverse architectures, functional regression techniques are proposed as a potential means of constructing an architecture-spanning empirical performance model. A number of techniques are formulated and tested, and prove capable of supporting the LV performance modeling in support of risk-informed architecture selection.

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Experimental investigation of fast plasma production for the VAIPER antenna

2017-12-11 , Chan, Cheong Yu

For this Master’s thesis, I will conduct the preliminary experimental study of fast plasma ignition times under varying conditions. The timing of the plasma needs to be characterized before a small scale plasma antenna can be completed. The experimental part of this project is a collaboration between Prof. Mitchell Walker’s High Power Electric Propulsion Lab (HPEPL) in Aerospace Engineering and Prof. Morris Cohen’s group in Electrical and Computer Engineering at the Georgia Institute of Technology.

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Solar Activity Investigation (SAI): A 6U CubeSat Mission Concept

2017-12 , Murphy, Neil , Jefferies, Stuart , Fleck, Bernhard , Berrilli, Francesco , Velli, Marco , Lightsey, E. Glenn , Gizon, Laurent , Braun, Doug

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A time accurate fluid-structure interaction framework using a Cartesian grid CFD solver

2017-11-15 , Bopp, Matthew Scott

The landing of the Mars Science Laboratory (MSL) in 2012 demonstrated the limits of supersonic planetary entry technology through the use of a disk-gap-band parachute deployed from behind the aeroshell capsule. With the eventual goal of sending humans to Mars, the payload requirements are estimated to increase by a factor of 40, far outside the current technological envelope. With a density of less than 1% of Earth's, the Martian atmosphere makes the task of generating aerodynamic drag very challenging. Larger aeroshells produce more drag, but the vehicle is then too large to fit as payload inside a rocket. By utilizing inflatable aerodynamic decelerators, the drag area can be significantly increased, while the pre-deployed configuration has high packing efficiency. New technologies bring with them the requirement to study their behavior, and characterize their flight limits. Wind tunnel tests are difficult due scaling concerns, and flight tests are costly and time consuming. Thus, accurate computational modeling of the fluid-structure interactions (FSI) is critical in the development of aerodynamic decelerators. Much of the current research in FSI focuses on high fidelity analysis, which is often very computationally expensive, and requires significant user intervention. The current work fills a niche where the analysis time and human interaction is reduced, by utilizing an adaptive, Cartesian grid framework for solving the computational fluid dynamics (CFD). A time accurate, partitioned coupling strategy is employed to study FSI applied to flexible materials under high dynamic pressure loads. The structural dynamics is solved using LS-DYNA, and care must be taken at the interface boundary conditions to reduce numerical errors. The development of this tool has relied on a complete re-write of the in-house CFD code, NASCART-GT, where significant improvements have been made in computational efficiency and scalability. CFD simulations with prescribed motions are studied in order to validate the fluid dynamics of high speed flows with non-stationary boundary conditions, and to study the effects of solution-based grid adaption for these simulations. The interaction with rigid body dynamics is presented in simulations of the free flight dynamics of the MSL capsule. FSI simulations are then presented for a series of test cases, where the physics is validated for the unsteady, time accurate coupling of 1-D piston motion and 2-D beam deformation. Finally, steady state and time accurate simulations of an inflatable aerodynamic decelerator demonstrate the effectiveness of the current methodology in furthering the development of decelerator technologies.

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A Categorical Model for Airport Capacity Estimation Using Hierarchical Clustering

2017-12 , Cinar, Gokcin , Jimenez, Hernando , Mavris, Dimitri N.

Motivated by the need for very inexpensive, easily updated, first-order-accurate estimates of airport capacity required in system-wide analyses, we propose a novel approach to generate a predictive categorical model. The underlying hypothesis tested in this work is that for the same weather conditions airports with a similar runway configuration and fleet mix will have similar capacities. Accordingly, if airport categories with known capacity are defined a-priori on the basis of similarity in fleet mix and runway configuration, then a membership function to the set of categories essentially constitutes a predictive model. We test this hypothesis by formulating and implementing such a model in order to examine its feasibility and discuss key practical considerations. Verification demonstrates model fit error within 4% with a categorical training set of 35 major United States airports. Validation against European airports for model representation error is limited by data availability but shown to be in the order of 7-10%. Results suggest that elemental runway configurations are the primary driver for categorical definition, and variations within each category can be associated to fleet mix variations. The implementation of the proposed method to generate other such models with different data sets is encouraged.

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Learning control via probabilistic trajectory optimization

2017-11-29 , Pan, Yunpeng

A central problem in the field of robotics is to develop real-time planning and control algorithms for autonomous systems to behave intelligently under uncertainty. While classical optimal control provides a general theoretical framework, it relies on strong assumption of full knowledge of the system dynamics and environments. Alternatively, modern reinforcement learning (RL) offers a computational framework for controlling autonomous systems with minimal prior knowledge and user intervention. However, typical RL approaches require many interactions with the physical systems, and suffer from slow convergence. Furthermore, both optimal control and RL have the difficulty of scaling to high-dimensional state and action spaces. In order to address these challenges, we present probabilistic trajectory optimization methods for solving optimal control problems for systems with unknown or partially known dynamics. Our methods share two key characteristics: (1) we incorporate explicit uncertainty into modeling, prediction and decision making using Gaussian processes; (2) our algorithms bypass the \textit{curse of dimensionality} via local approximation of the value function or linearization of the Hamilton-Jacobi-Bellman (HJB) equation. Compared to related approaches, our methods offer superior combination of data efficiency and scalability. We present experimental results and comparative analyses to demonstrate the strengths of the proposed methods. In addition, we develop fast Bayesian approximate inference methods which enable probabilistic trajectory optimizer to perform real-time receding horizon control. It can be used to train deep neural network controllers that map raw observations to actions directly. We show that our approach can be used to perform high-speed off-road autonomous driving with low-cost sensors, and without on-the-fly planning and optimization.

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Stochastic optimal control - a forward and backward sampling approach

2017-11-15 , Exarchos, Ioannis

Stochastic optimal control has seen significant recent development, motivated by its success in a plethora of engineering applications, such as autonomous systems, robotics, neuroscience, and financial engineering. Despite the many theoretical and algorithmic advancements that made such a success possible, several obstacles remain; most notable are (i) the mitigation of the curse of dimensionality inherent in optimal control problems, (ii) the design of efficient algorithms that allow for fast, online computation, and (iii) the expansion of the class of optimal control problems that can be addressed by algorithms in engineering practice. The aim of this dissertation is the development of a learning stochastic control framework which capitalizes on the innate relationship between certain nonlinear partial differential equations (PDEs) and forward and backward stochastic differential equations (FBSDEs), demonstrated by a nonlinear version of the Feynman-Kac lemma. By means of this lemma, we are able to obtain a probabilistic representation of the solution to the nonlinear Hamilton-Jacobi-Bellman PDE, expressed in form of a system of decoupled FBSDEs. This system of FBSDEs can then be simulated by employing linear regression techniques. We present a novel discretization scheme for FBSDEs, and enhance the resulting algorithm with importance sampling, thereby constructing an iterative scheme that is capable of learning the optimal control without an initial guess, even in systems with highly nonlinear, underactuated dynamics. The framework we develop within this dissertation addresses several classes of stochastic optimal control, such as L2, L1, risk sensitive control, as well as some classes of differential games, in both fixed-final-time as well as first-exit settings.

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