Spacecraft Component Adaptive Layout Environment (SCALE): An efficient optimization tool

Fakoor, Mahdi; Ghoreishi, Seyed Mohammad Navid; Sabaghzadeh, Hossein

doi:10.1016/j.asr.2016.07.020

Cited by 20 publications

(12 citation statements)

References 21 publications

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“…Best component assignment S 1 [4,8,16,18,21,23,28,42, 43,47,53,56,60] [17,19,20,21,23,36,37,46, 47,58,59,60] S 2 [15,17,19,22,25,29,30,31,34,41,45,51,58,59] [2,4,6,8,25,27,28,31,33,34,35,39,40,42,52,53,54,56,57] S 3 [1,2,3,5,6,7,9,11,24,35,44,49,54,55,57] [1,3,5,7,9,10,11,…”

Section: Performance Index Rbs Dynamic_fsmentioning

confidence: 99%

“…Fakoor et al presented a spacecraft component adaptive layout environment (SCALE) based on the multiobjective algorithm. 11 The SCALE integrated several software for the optimization such as Excel, MATLAB, Solidwork, Ansys and ModeFronttier. The component number was 26 (N¼26).…”

Section: Introductionmentioning

confidence: 99%

“…It is worth noting that some research studies [10][11][12] considered component migration from one panel to another. However, the authors did not discuss how to implement the component assignment.…”

Section: Introductionmentioning

confidence: 99%

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A collaborative design method for satellite module component assignment and layout optimization

Cui

Zhong

Wang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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The collaborative design of the multi-module satellite component (equipment) assignment and layout is the key aspect of the overall satellite design, and the two parts are closely related. In the past, satellite module component layout optimization usually adopted fixed component assignments, which remained constant in the layout optimization stage. If the components were improperly distributed in these modules, it would seriously affect the layout optimization. To overcome this disadvantage, a collaborative design method for the component assignment and layout design is presented for the multi-module (or multi-bearing plate) satellite component layout problem, based on a multi-agent system. First, the component assignment agent adopted a multi-objective optimization method (the non-dominated sorting genetic algorithm II, NSGA-II) to obtain the approximate Pareto solution set of the satellite component assignment scheme. Second, it adopted a fuzzy multi-objective decision method to select a high-quality component assignment scheme from the approximate Pareto solution set. Third, the layout agent employed a dual system co-evolutionary method for the layout optimization design. In the process of the layout optimization, the layout result is fed back to the component assignment design, and the component assignment is adjusted according to the result of the layout optimization. Thus, the above process is continually iterated to achieve the optimal collaborative design of the component assignment and the layout. The proposed method is applied to a simplified multi-module satellite component assignment and layout optimization problem and aims to provide a reference and technical support for other similar multi-module equipment assignment and layout optimization problems.

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Section: Performance Index Rbs Dynamic_fsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A collaborative design method for satellite module component assignment and layout optimization

Cui

Zhong

Wang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

show abstract

“…Chaos theory, Honeybees, Semantic interoperability, Singular value decomposition, Lagrangian particle mode, Multi-stepahead prediction etc.). [55], [56], [57] 11 Unicast and Multicast Fuzzy logic with Multi-value [58], [59] CONCLUSION AND FUTURE SCOPE…”

Section: Difference Between Hard Computing and Soft Computing Techniquesmentioning

confidence: 99%

Application of Soft Computing Techniques Over Hard Computing Techniques: A Survey

Chakraborty

Sharma²,

Tewari³

2017

ijsrtm

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Soft computing is the fusion of different constituent elements. The main aim of this fusion to solve real-world problems, which are not solve by traditional approach that is hard computing. Actually, in our daily life maximum problem having uncertainty and vagueness information. So hard computing fail to solve this problems, because it give exact solution. To overcome this situation soft computing techniques plays a vital role, because it has capability to deal with uncertainty and vagueness and produce approximate result. This paper focuses on application of soft computing techniques over hard computing techniques.

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“…In addition, there is a class of software developed to analyze and design a particular subsystem, such as the Princeton satellite system control toolbox designing attitude determination and control subsystem (ADCS) 12,13 or SCALE, a combination of several structural analysis software, providing optimal placement for satellite components. 14 Although making use of such software will ameliorate the design accuracy of each subsystem, they slow down the process of achieving the results and need experts for each subsystem. Table 2 compares the design methods available in different references from computational effort and orbit points of view. The high launch times, and design cost and time in GEO satellites have brought the designers to change their production approach to the use of universal platform.…”

Section: Introductionmentioning

confidence: 99%

FADSat: A system engineering tool for the conceptual design of geostationary Earth orbit satellites platform

Mirshams

Zabihian

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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With regard to the increasing use of satellites platform in the geostationary orbit, a system engineering tool, FADSat, has been developed to reduce the prohibitive cost and time of their conceptual design phase. The proposed tool effectively conducts the design of the geostationary Earth orbit satellite platform lying in the mass range of 1–7000 kg. The main feature of the FADSat is to determine the conceptual design of the satellite platform with both high time performance and acceptable accuracy. Using this tool, one can readily extract the characteristics of the structure, attitude determination and control, command and data handling, electrical power, and other subsystems of a satellite. The FADSat exploits a statistical design model in the first instance to yield a rough estimation of the satellite design, i.e. a rapid extraction of the budgets for mass, power, and dimensions of the satellite subsystems as well as the cost of the satellite. Then, using a parametric design model approach, it performs subsystems design more accurately and ascertains their component specifications in terms of a catalog of products with corresponding manufacturers. A database of 462 geostationary Earth orbit communication satellites (with 30 different geostationary Earth orbit satellites platforms (launched from 2000 to 2017 has been used in this paper to implement the statistical design model approach. This tool developed in the LabVIEW software is capable of contributing to the satellite production phases as a connection to the hardware simulators of different subsystems. Herein, after describing the general concepts utilized in the satellite design, we have introduced various parts and relations of the FADSat tool. The tool’s accuracy was amply verified through flight prototypes, indicating an average error of 15% in the obtained results.

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Spacecraft Component Adaptive Layout Environment (SCALE): An efficient optimization tool

Cited by 20 publications

References 21 publications

A collaborative design method for satellite module component assignment and layout optimization

A collaborative design method for satellite module component assignment and layout optimization

Application of Soft Computing Techniques Over Hard Computing Techniques: A Survey

FADSat: A system engineering tool for the conceptual design of geostationary Earth orbit satellites platform

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