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Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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https://finding-aids.library.gatech.edu/agents/corporate_entities/106

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Publication Search Results

Now showing 1 - 10 of 17

Gravity-Assist Low-Thrust Inter-System Trajectory Design with Manifold Captures

( 2022-04) Shimane, Yuri ; Ho, Koki

When traveling across multiple planetary or moon systems, invariant manifold structures may be leveraged to allow for efficient transfer within multi-body dynamics. However, the sole use of these structures is restrictive in many cases, as the manifold may not reach a desired departure or arrival location in the phase space. In particular, the use of low-thrust propulsion together with manifold dynamics requires an optimization framework that captures these mechanisms into a single problem. This work proposes an approach to design gravity-assist low-thrust transfers that leverage manifold structures at departure or arrival. The approach involves a modification to the Sims-Flanagan transcription by incorporating parametrization of arrival to a manifold Poincaré section instead of a celestial body. A key advantage is its use of two-body dynamics for the propagation of the majority of the transfer. This enables large-scale and realistic assessment of possible solutions through a combination of ODE-based propagation of the manifold, and Lagrange coefficients-based propagation of the inter-system portion of the transfer. Leveraging the proposed method, a low-thrust transfer from Earth to the Sun-Venus system, also incorporating an Earth fly-by in between, is studied.
Multidisciplinary Design Optimization Approach to Integrated Space Mission Planning and Spacecraft Design

(AIAA, 2022) Isaji, Masafumi ; Takubo, Yuji ; Ho, Koki

Space mission planning and spacecraft design are tightly coupled and need to be considered together for optimal performance; however, this integrated optimization problem results in a large-scale mixed-integer nonlinear programming (MINLP) problem, which is challenging to solve. In response to this challenge, this paper proposes a new solution approach to this problem based on decomposition-based optimization via augmented Lagrangian coordination. The proposed approach leverages the unique structure of the problem that enables its decomposition into a set of coupled subproblems of different types: a mixed-integer quadratic programming (MIQP) subproblem for mission planning, and one or more nonlinear programming (NLP) subproblem(s) for spacecraft design. Because specialized MIQP or NLP solvers can be applied to each subproblem, the proposed approach can efficiently solve the otherwise intractable integrated MINLP problem. An automatic and effective method to find an initial solution for this iterative approach is also proposed so that the optimization can be performed without a user-defined initial guess. The demonstration case study shows that, compared to the state-of-the-art method, the proposed formulation converges substantially faster and the converged solution is at least the same or better given the same computational time limit.
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design

( 2021-12) Takubo, Yuji ; Chen, Hao ; Ho, Koki

This paper develops a hierarchical reinforcement learning architecture for multimission spaceflight campaign design under uncertainty, including vehicle design, infrastructure deployment planning, and space transportation scheduling. This problem involves a high-dimensional design space and is challenging especially with uncertainty present. To tackle this challenge, the developed framework has a hierarchical structure with reinforcement learning and network-based mixed-integer linear programming (MILP), where the former optimizes campaign-level decisions (e.g., design of the vehicle used throughout the campaign, destination demand assigned to each mission in the campaign), whereas the latter optimizes the detailed mission-level decisions (e.g., when to launch what from where to where). The framework is applied to a set of human lunar exploration campaign scenarios with uncertain in situ resource utilization performance as a case study. The main value of this work is its integration of the rapidly growing reinforcement learning research and the existing MILP-based space logistics methods through a hierarchical framework to handle the otherwise intractable complexity of space mission design under uncertainty. This unique framework is expected to be a critical steppingstone for the emerging research direction of artificial intelligence for space mission design.
Space Exploration Architecture and Design Framework for Commercialization

( 2021-10) Chen, Hao ; Ornik, Melkior ; Ho, Koki

The trend of space commercialization is changing the decision-making process for future space exploration architectures, and there is a growing need for a new decision-making framework that explicitly considers the interactions between the mission coordinator (i.e., government) and the commercial players. In response to this challenge, this paper develops a framework for space exploration and logistics decision making that considers the incentive mechanism to stimulate commercial participation in future space infrastructure development and deployment. By extending the state-of-the-art space logistics design formulations from the game-theoretic perspective, the relationship between the mission coordinator and commercial players is first analyzed, and then the formulation for the optimal architecture design and incentive mechanism in three different scenarios is derived. To demonstrate and evaluate the effectiveness of the proposed framework, a case study on lunar habitat infrastructure design and deployment is conducted. Results show how total mission demands and in-situ resource utilization system performances after deployment may impact the cooperation among stakeholders. As an outcome of this study, an incentive-based decision-making framework that can benefit both the mission coordinator and the commercial players from commercialization is derived, leading to a mutually beneficial space exploration between the government and the industry.
Spare Strategy Analysis for Life Support Systems for Human Space Exploration

( 2021-07) Maxwell, Andrew J. ; Wilhite, Alan ; Ho, Koki

Systems enabling long-duration crewed missions are expected to have high probabilities of success through sparing of components. To enable such systems, the tradeoff between the reliability and the mass needs to be incorporated early in the design phase to ensure that the requirements are optimally captured. Additionally, effective operational sparing policies need to be devised in the early design phase as a means to mitigate the negative impact in case the delivered system does not meet all of the requirements. In response to this background, this paper presents a new approach for analyzing the probability of sufficiency and spares mass with consideration of sparing policies. The developed method improves the current state-of-the-art tool from two perspectives. First, it develops a new method based on the modified knapsack problem to generate the spares allocations that maximize the probability of sufficiency given a mass capacity. Additionally, it develops a simulation model with a failure queue to enhance the flexibility of the state-of-the-art model to evaluate different sparing policies. With the developed method, comparative studies using two allocation approaches and two different policies are presented.
On-Orbit Servicing Optimization Framework with High- and Low-Thrust Propulsion Tradeoff

( 2021-07) Sarton Du Jonchay, Tristan ; Chen, Hao ; Isaji, Masafumi ; Shimane, Yuri ; Ho, Koki

This paper proposes an on-orbit servicing logistics optimization framework capable of performing the short-term operational scheduling and long-term strategic planning of sustainable servicing infrastructures that involve high-thrust, low-thrust, and/or multimodal servicers supported by orbital depots. The proposed framework generalizes the state-of-the-art on-orbit servicing logistics optimization method by incorporating user-defined trajectory models and optimizing the logistics operations with the propulsion technology and trajectory tradeoff in consideration. Mixed-integer linear programming is leveraged to find the optimal operations of the servicers over a given period, whereas the rolling horizon approach is used to consider a long time horizon accounting for the uncertainties in service demand. Several analyses are carried out to demonstrate the value of the proposed framework in automatically trading off the high- and low-thrust propulsion systems for both short-term operational scheduling and long-term strategic planning of on-orbit servicing infrastructures.
Framework for Modeling and Optimization of On-Orbit Servicing Operations under Demand Uncertainties

( 2021-06) Sarton Du Jonchay, Tristan ; Chen, Hao ; Gunasekara, Onalli ; Ho, Koki

This paper develops a framework that models and optimizes the operations of complex on-orbit servicing infrastructures involving one or more servicers and orbital depots to provide multiple types of services to a fleet of geostationary satellites. The proposed method extends the state-of-the-art space logistics technique by addressing the unique challenges in on-orbit servicing applications and integrates it with the Rolling Horizon decision-making approach. The space logistics technique enables modeling of the on-orbit servicing logistical operations as a Mixed-Integer Linear Program whose optimal solutions can efficiently be found. The Rolling Horizon approach enables the assessment of the long-term value of an on-orbit servicing infrastructure by accounting for the uncertain service needs that arise over time among the geostationary satellites. Two case studies successfully demonstrate the effectiveness of the framework for 1) short-term operational scheduling and 2) long-term strategic decision making for on-orbit servicing architectures under diverse market conditions.
Flexibility Management for Space Logistics via Decision Rules

( 2021-04) Chen, Hao ; Gardner, Brian M. ; Grogan, Paul T. ; Ho, Koki

This paper develops a flexibility management framework for space logistics mission planning under uncertainty through decision rules and multistage stochastic programming. It aims to add built-in flexibility to space architectures in the phase of early-stage mission planning. The proposed framework integrates the decision rule formulation into a network-based space logistics optimization formulation model. It can output a series of decision rules and generate a Pareto front between the expected mission cost (i.e., initial mass in low Earth orbit) and the expected mission performance (i.e., effective crew operating time), considering the uncertainty in the environment and mission demands. The generated decision rules and the Pareto front plot can help decision makers create implementable policies immediately when uncertainty events occur during space missions. An example mission case study about space station resupply under rocket launch delay uncertainty is established to demonstrate the value of the proposed framework.
Multifidelity Space Mission Planning and Infrastructure Design Framework for Space Resource Logistics

( 2021-03) Chen, Hao ; Sarton Du Jonchay, Tristan ; Hou, Linyi ; Ho, Koki

To build a sustainable space transportation system for human space exploration, the design and deployment of space infrastructure, such as in-situ resource utilization plants, in-orbit propellant depots, and on-orbit servicing platforms, are critical. The design analysis and trade studies for these space infrastructure systems require the consideration of not only the design of the infrastructure elements themselves, but also their supporting systems (e.g., storage, power) and logistics transportation while exploring various architecture options (e.g., location, technology). This paper proposes a system-level space infrastructure and logistics design optimization framework to perform architecture trade studies. A new space-infrastructure logistics optimization problem formulation is proposed that considers the internal interactions of infrastructure subsystems and their external synergistic effects with space logistics simultaneously. Because the full-size version of this proposed problem formulation can be computationally prohibitive, a new multifidelity optimization formulation is developed by varying the granularity of the commodity-type definition over the space logistics network; this multifidelity formulation can find an approximate solution to the full-size problem computationally efficiently with little sacrifice in the solution quality. The proposed problem formulation and method are applied to the design of in situ resource utilization systems in a multimission lunar exploration campaign to demonstrate their values.
Analysis of Information-Theoretic Initial Sensor Search Method for Space Situation Awareness

( 2020-12) Ho, Koki ; Beeson, Ryne ; Tomita, Kento ; Gunasekara, Onalli ; Sinclair, Andrew J.