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Title:Space logistics network optimization with embedded propulsion technology selection
Author(s):Jagannatha, Bindu Bhargavi
Director of Research:Ho, Koki
Doctoral Committee Chair(s):Ho, Koki
Doctoral Committee Member(s):Coverstone, Victoria L.; Conway, Bruce A.; Kim, Harrison H. M.
Department / Program:Aerospace Engineering
Discipline:Aerospace Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Logistics optimization
Space mission design
Trajectory design
Linear programming
Abstract:Establishing long-term human presence beyond low Earth-orbit will require cooperative use of emerging technologies, such as low-thrust solar electric propulsion, along with existing space exploration technologies. Trajectory analysis plays a key role in deciding the costs of using different propulsion technologies. Traditional trajectory design methods are usually confined to analyzing individual missions using high-thrust or low-thrust propulsion options --- these techniques also do not usually consider the architectural aspects of the in-space network. On the other hand, determination of network architecture and mission sequence often relies on the expertise of mission designers. Thus, formulating and optimizing the problem as a multi-mission campaign can guide top-level decisions through rigorous mathematical modeling. However, designing a multi-mission campaign with both propulsion options is generally computationally challenging due to the coupling of logistics network design and space transportation costs. Specifically, conventional space logistics planning methods are unable to account for the use of low-thrust vehicles for transportation due to the inherent nonlinear nature of associated costs. The aim of this work is to develop ways of handling low-thrust trajectory models within space logistics frameworks, so that propulsion technology trade-offs can be conducted internally. This work develops two new frameworks for optimizing the combined use of low-thrust and high-thrust propulsion options within campaign-level space logistics planning tools. The first framework uses a chromosomal representation of network arc parameters to drive a multiobjective genetic algorithm that explores the tradespace. The second framework combines the generalized multicommodity network flow model with novel event-based time steps for dynamic space logistics optimization in the presence of nonlinear flight times associated with low-thrust transportation. These methodologies are applied to the case study of Apollo-style crew missions to the lunar surface supported by in-space refueling via predeployed tanks delivered by cargo tugs. Although the costs for high-thrust trajectories are a part of the inputs to the formulation, the costs for low-thrust trajectories are determined internally because of their dependence on the thrust-to--mass ratio. For an efficient evaluation of low-energy, low-thrust transfers in the Earth–moon system, an approximation method is implemented based on a Lyapunov feedback control law called Q-law, and dynamical systems theory. This preliminary trajectory design technique is validated against literature sources and is then used to closely estimate the costs associated with cargo deliveries using low-thrust tugs. Numerical results from the new space logistics frameworks reveal campaign profiles where high-thrust and low-thrust propulsion options can be used cooperatively to achieve substantial improvement over the baseline no-refuel cases. These results also present multiple options that involve in-space propellant storage and transfer, deep-space rendezvous, and solar electric propulsion tugs for cargo delivery. By trading mission costs with campaign duration, these methods help quantify the impact of low-thrust solar electric propulsion in logistics supply planning.
Issue Date:2018-12-06
Rights Information:Copyright 2018 by Bindu Bhargavi Jagannatha
Date Available in IDEALS:2019-02-07
Date Deposited:2018-12

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