Semi-Analytical Model for Design and Analysis of On-Orbit Servicing Architecture

Ho, Koki; Hai, Wang; DeTrempe, Paul A.; Jonchay, Tristan Sarton du; Tomita, Kento

doi:10.2514/1.a34663

Cited by 14 publications

(12 citation statements)

References 22 publications

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“…There are three OOS CONOPs that have been proposed [7]. The first method is the most traditional, in which an OOS is launched with all servicing resources it needs carried on board, throughout all orbital maneuvers, rendezvouses, and servicing events.…”

Section: Concept Of Operation Of Oosmentioning

confidence: 99%

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Valuation of On-Orbit Servicing in Proliferated Low-Earth Orbit Constellations

Luu

Hastings

2020

Ascend 2020

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On-orbit servicing (OOS) presents new opportunities for refueling, inspection, repair, maintenance, and upgrade of spacecraft (s/c). OOS is a significant area of need for future space growth, enabled by the maturation of technology and the economic prospects. This congestion is leading s/c operators to explore how they can leverage OOS. OOS missions for s/c in geostationary orbit (GEO) are currently underway. This is being driven by the closure of the business case for refueling long lived monolithic chemically propelled GEO assets. However, there are currently no plans for OOS of low-earth orbit (LEO) s/c, aside from technology demonstrations, because of their shorter design life and lower cost. It will become particularly important to enable the servicing of LEO s/c as the industry shifts its focus towards LEO. Designing OOS systems for LEO constellations differs from that of GEO based systems, this difference is attributed to LEO's proliferation of satellites, environmental effects (J2 nodal precession, drag), and different constellation patterns. Satellite constellations in LEO are becoming more distributed due to increased access, distributed risk, flexibility, and cost. OOS of s/c may enable the reduction of requirements on subsystems such as safety and the need for redundancy. These requirement reductions will enable lower risks, lower costs, and increased system resilience. This paper analyzes the benefits of OOS in proliferated LEO constellations. Several OOS system architectures are modeled; in each system architecture the model will vary qualities such as number of servicers, altitudes, and orbital maneuvers. The objective of the model will be to optimize for cost, time, and utility to generate a tradespace for an OOS system architecture.

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Section: Concept Of Operation Of Oosmentioning

confidence: 99%

“…The second CONOP utilizing depots has many considerations as it becomes a "traveling salesman" problem and is bespoke to individual cases, it will not be reviewed in this paper. Other researchers are reviewing the depot servicing model [7,8].…”

Section: Concept Of Operation Of Oosmentioning

confidence: 99%

Valuation of On-Orbit Servicing in Proliferated Low-Earth Orbit Constellations

Luu

Hastings

2020

Ascend 2020

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“…Previous projects specifically focused on the rigorous and accurate analysis and design of high-thrust [11] and lowthrust [12] servicer trajectories using high-fidelity force models. Others analyzed the operations of OOS infrastructures from a scheduling [13] and/or design standpoint [14][15][16] through simulations [15,[17][18][19][20], optimization [13,16,21],…”

Section: Introductionmentioning

confidence: 99%

On-Orbit Servicing Optimization Framework with High- and Low-Thrust Propulsion Tradeoff

Jonchay

Chen

Isaji

et al. 2022

Journal of Spacecraft and Rockets

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This paper proposes an on-orbit servicing logistics optimization framework that is 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-theart 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, while 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. Nomenclature 𝐵 𝑣𝑠𝑡 = Servicer dispatch variables 𝒞 = Index set of Customer Nodes

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“…(2) One-to-many architectures in which one servicer provides services to a fleet of several client satellites [6][7][8][9][10][11]. An orbital spare depot or refueling station may be considered to further support the OOS operations;…”

Section: Introductionmentioning

confidence: 99%

“…b. The analyses of the operational strategy of a particular OOS infrastructure from a scheduling [7] and/or design standpoint [5,8,9]. Simulation [8,10,13], optimization [7,9,11] or a mixture of both [5] may be used to explore the value of the investigated OOS architecture by varying its operational scheduling and/or design.…”

Section: Introductionmentioning

confidence: 99%

Framework for Modeling and Optimization of On-Orbit Servicing Operations Under Demand Uncertainties

Jonchay

Chen

Gunasekara

et al. 2021

Journal of Spacecraft and Rockets

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This paper develops a framework that models and optimizes the operations of complex onorbit 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 integrate 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.

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Semi-Analytical Model for Design and Analysis of On-Orbit Servicing Architecture

Cited by 14 publications

References 22 publications

Valuation of On-Orbit Servicing in Proliferated Low-Earth Orbit Constellations

Valuation of On-Orbit Servicing in Proliferated Low-Earth Orbit Constellations

On-Orbit Servicing Optimization Framework with High- and Low-Thrust Propulsion Tradeoff

Framework for Modeling and Optimization of On-Orbit Servicing Operations Under Demand Uncertainties

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