Quantification of the responsiveness of on-orbit servicing infrastructure for modularized earth-orbiting platforms

Jonchay, Tristan Sarton du; Ho, Koki

doi:10.1016/j.actaastro.2016.12.021

Cited by 11 publications

(11 citation statements)

References 5 publications

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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%

“…(3) Many-to-many architectures in which several servicers provide services to a fleet of client satellites [12][13][14][15].…”

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%

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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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Section: Introductionmentioning

confidence: 99%

“…(3) Many-to-many architectures in which several servicers provide services to a fleet of client satellites [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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

Jonchay

Chen

Gunasekara

et al. 2021

Journal of Spacecraft and Rockets

Self Cite

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“…Alternatively, a more sustainable concept is to use a permanent reusable servicing infrastructure that responds to random failures [11,16]. One example of such a concept would be as follows: when a module fails, the OOS system dispatches a servicer to the satellite to replace the failed module with a new spare.…”

Section: Introductionmentioning

confidence: 99%

“…Conventionally, the analysis of space systems with random failures and repairs (e.g., OOS systems) has been performed using computationally costly simulations [16,19,20]. However, as space systems become complex, designing and evaluating their performance using only simulations becomes computationally challenging.…”

Section: Introductionmentioning

confidence: 99%

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

Ho,

Wang,

DeTrempe

et al. 2019

Preprint

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Robotic on-orbit servicing (OOS) is expected to be a key technology and concept for future sustainable space exploration. This paper develops a semi-analytical model for OOS systems analysis, responding to the growing needs and ongoing trend of robotic OOS. An OOS infrastructure system is considered whose goal is to provide responsive services to the random failures of a set of customer modular satellites distributed in space (e.g., at the geosynchronous equatorial orbit). The considered OOS architecture is comprised of a servicer that travels and provides module-replacement services to the customer satellites, an on-orbit depot to store the spares, and a series of launch vehicles to replenish the depot. The OOS system performance is analyzed by evaluating the mean waiting time before service completion for a given failure and its relationship with the depot capacity. Leveraging the queueing theory and inventory management methods, the developed semi-analytical model is capable of analyzing the OOS system performance without relying on computationally costly simulations. The effectiveness of the proposed model is demonstrated using a case study compared with simulation results. This paper is expected to provide a critical step to push the research frontier of analytical/semianalytical models development for complex space systems design. Nomenclature C= on-orbit depot spare capacity, in units of modules

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Maintenance Architecture Optimization of a Distributed CubeSat Network Based on Parametric Model

Zhang

2019

Smart Service Systems, Operations Management, and Analytics

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Quantification of the responsiveness of on-orbit servicing infrastructure for modularized earth-orbiting platforms

Cited by 11 publications

References 5 publications

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

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

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

Maintenance Architecture Optimization of a Distributed CubeSat Network Based on Parametric Model

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