Satellite Constellation Design Tradeoffs Using Multiple-Objective Evolutionary Computation

Ferringer, Matthew P.; Spencer, David B.

doi:10.2514/1.18788

Cited by 66 publications

(29 citation statements)

References 9 publications

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“…The two terms found on the right-hand-side of Equation (18) refer to the friction induced by the pipe inner wall, and the heat convected to the gas from the pipe. The friction factor is computed in the pipe flow calculations using the Colebrook equation shown below:…”

Section: Pipe Flow Equationsmentioning

confidence: 99%

“…The above ODE is solved as part of a marching method scheme, where initial conditions from the pressurized cylinder are used in conjunction with the mass flowrate demanded by the thrusters to calculate the thermodynamic state of the gas as it travels through the feed system. Each pipe is discretized into multiple nodes where gas pressures and temperatures are computed using Equation (18).…”

Section: Pipe Flow Equationsmentioning

confidence: 99%

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Optimization of a small satellite tridyne propulsion system

Cohen

Legge

2014

2014 IEEE Aerospace Conference

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This paper presents the integrated modeling and optimization of a Tridyne™-based small satellite propulsion system. A detailed integrated model is developed to compute propellant thermodynamic properties throughout the entire propulsion system, from the propellant tank through the valves, regulator and thrusters. The simulation uses a compressible flow model incorporating both friction and heat transfer to predict these properties. The detailed model was validated against, and showed good agreement with two existing professional software tools. While the detailed model proved to be accurate, it was computationally expensive, necessitating the development of a fast screening model for use in optimization. The screening model utilizes polynomial curve fits for NIST thermodynamic data, lumped pipe flow calculations and larger time steps. The NSGA II multi-objective optimization routine, employing the screening model, found a set of non-dominated designs that showed the tradeoff between the two objectives of maximizing IJ.V and minimizing tank pressure. These non dominated designs are classified into five design families and three point designs were chosen for further detailed analysis and comparison. This approach shows great promise for preliminary designs of highly integrated small satellite propulsion systems.

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Section: Pipe Flow Equationsmentioning

confidence: 99%

Section: Pipe Flow Equationsmentioning

confidence: 99%

Optimization of a small satellite tridyne propulsion system

Cohen

Legge

2014

2014 IEEE Aerospace Conference

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“…Asvial et al [8] addressed the design of constellation configurations taking into account the total number of satellites and their altitudes, the angle shift between satellites, the angle between planes, and the inclination angle. Multi-objective evolutionary algorithms, dealing with the discrete and nonlinear characteristics of the metrics, have been developed for the design of satellite constellations for regional coverage by Ferringer and Spencer [9] and by Wang et al [10]. Ferringer et al [11] proposed two parallel multi-objective evolutionary algorithmic frameworks (master-slave and island approach) for the same problem, which approximate the true Pareto frontier.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Satellite Constellations for the Continuous Coverage of Ground Regions

Dai¹,

Chen²,

Wang³

et al. 2017

Journal of Spacecraft and Rockets

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This paper studies the problem of analyzing multisatellite constellations with respect to their coverage capacity of areas on Earth's surface. The geometric configuration of constellation projection points on Earth's surface is investigated. A geometric subdivision approach is described, and the coverage target area belonging to each satellite and its maximum circle radius are defined and calculated. Accordingly, the target area can be decomposed into subregions, and thus the multisatellite coverage problem is decomposed into a one-satellite coverage problem. An accurate and effective solution method is proposed that solves both continuous and discontinuous coverage problems for any type of ground area. In addition, a procedure for calculating satellite orbital parameters is also proposed. The performance of our approach is analyzed using the Globalstar system as an example, and it is shown that it compares favorably with the classical grid-point technique and the longitude method.

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“…For example, Ely used two-branch tournament GA for a constellation design with eccentric orbits, aiming to minimize the maximum constellation's orbit altitude and the total number of satellites [16]. Several works from Ferringer are also focused on multiobjective optimization of satellite constellation designs including the trade-off analysis [17][18][19][20]. Related to MOGA, Mason et al introduced a type of MOGA approach, called the Modified Illinois Nondominated Sorting Genetic Algorithm (MINSGA), combined with Satellite Tool Kit (STK) software to produce several constellation designs that provides a continuous global coverage [21].…”

Section: Introductionmentioning

confidence: 99%

Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach

Savitri

Kim

et al. 2017

International Journal of Aerospace Engineering

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This paper focuses on maximizing the percent coverage and minimizing the revisit time for a small satellite constellation with limited coverage. A target area represented by a polygon defined by grid points is chosen instead of using a target point only. The constellation consists of nonsymmetric and circular Low Earth Orbit (LEO) satellites. A global optimization method, Genetic Algorithm (GA), is chosen due to its ability to locate a global optimum solution for nonlinear multiobjective problems. From six orbital elements, five elements (semimajor axis, inclination, argument of perigee, longitude of ascending node, and mean anomaly) are varied as optimization design variables. A multiobjective optimization study is conducted in this study with percent coverage and revisit time as the two main parameters to analyze the performance of the constellation. Some efforts are made to improve the objective function and to minimize the computational load. A semianalytical approach is implemented to speed up the guessing of initial orbital elements. To determine the best parametric operator combinations, the fitness value and the computational time from each study cases are compared.

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Satellite Constellation Design Tradeoffs Using Multiple-Objective Evolutionary Computation

Cited by 66 publications

References 9 publications

Optimization of a small satellite tridyne propulsion system

Optimization of a small satellite tridyne propulsion system

Analysis of Satellite Constellations for the Continuous Coverage of Ground Regions

Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach

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