Highly Efficient Exploration of Large Design Spaces: Fractionated Satellites as an Example of Adaptable Systems

Kichkaylo, Tatiana; Hoag, Lucy; Lennon, Elizabeth B.; Roesler, Gordon

doi:10.1016/j.procs.2012.01.082

Cited by 9 publications

(9 citation statements)

References 16 publications

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“…ETAM can be seen as a particular implementation of these approaches in the context of tradespace exploration. Some methods suggest using a combination of model fidelities, a combination of artificial intelligence-based search and optimization engines, or even the combination of DOE and optimization 2,3,4 The Architecture Enumeration and Evaluation (AEE) process developed by the United Technologies Research Center uses a set of design rules to identify the sparse set of valid architectures in a combinatorially large architecture space. The AEE builds up to complete architecture choices by gradually defining more and more of the architecture.…”

Section: Existing Methodsmentioning

confidence: 99%

Exploiting Multidimensional Design of Experiments and Kriging Methods: An Application to a Satellite Radar System Tradespace and Orbital Transfer Vehicle Tradespace

Fulcoly

Shah

Ross

et al. 2012

AIAA SPACE 2012 Conference &Amp; Exposition

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A major benefit to performing tradespace exploration as early as possible and on as broad of a scale as possible is avoiding premature fixation on potentially non-optimal point designs. As the size of a tradespace grows in both the number of variables and the levels for each variable, the number of alternatives, or allowed combinations of various levels of design variables, grows at a combinatorial rate. The Expedited Tradespace Approximation Method (ETAM) was developed in response to this challenge. ETAM leverages intelligent subsampling and interpolation methods, including design of experiments and Kriging Methods, to generate acceptable data for a large tradespace, using fewer computational resources than applying a performance model to every design point. ETAM is applied to two case studies to demonstrate its accuracy and explore its potential for computational savings. NomenclatureN = Number of variables L i = Number of levels enumerated for the i th variable S = Krigable subset T = Training set K = Kriging set H = Number of single attribute utility functions A = Vector of H attributes U(A) = Multi-Attribute Utility function u h (A h ) = h th Single-Attribute Utility function j h = h th "small j" weight J = Normalization constant for a set of small js M p = Propulsion system mass (kg) m p0 = Propulsion system base mass (kg) m pf = Propulsion system mass fraction M f = Fuel mass (kg) M b = Vehicle bus mass (kg) M m = Manipulator mass (kg) m bf = Bus mass fraction DfE = Design for Evolvability mass penalty (%) M d = Vehicle dry mass (kg) M w = Vehicle wet mass (kg) c d = Dry mass cost ($/kg) c w = Wet mass cost ($/kg) C = Cost ($) Δv = Delta V (m/s) I sp = Specific impulse (s) D = Determinant TDF = Time Dilation Factor n = Number of design-epoch pairs in the full tradespace t = Execution time of the performance model for a single design-epoch pair k = Ratio of training set size to Krigable subset size E = Upfront time needed to integrate the tradespace into the ETAM model

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Section: Existing Methodsmentioning

confidence: 99%

Exploiting Multidimensional Design of Experiments and Kriging Methods: An Application to a Satellite Radar System Tradespace and Orbital Transfer Vehicle Tradespace

Fulcoly

Shah

Ross

et al. 2012

AIAA SPACE 2012 Conference &Amp; Exposition

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show abstract

“…In equation (14), g i : ð Þ ¼ 0 is the limit state function, g i : ð Þ 5 0 describes the failure region and g i : ð Þ 4 0 is related to the safe region. The statistical formulation of the failure of probabilistic constraints g i : ð Þ is defined by the cumulative distribution function (CDF) as…”

Section: Generic Rbdo Formulationmentioning

confidence: 99%

“…One of the robust approaches for solving the problem defined in equation (14) could be MCS. In these techniques, the probability of failure (as shown in equation (16)) is estimated through generating a number of samples randomly based on some density functions.…”

Section: Monte Carlo Simulation Techniquementioning

confidence: 99%

“…11 More information in this field could be found in the previous studies which are the major sources in satellite design. 9,12–17…”

Section: Introductionmentioning

confidence: 99%

“…11 More information in this field could be found in the previous studies which are the major sources in satellite design. 9,[12][13][14][15][16][17] There are many studies in the literature that propose systematic design optimization methodologies for solving a space system design problem. [18][19][20][21][22] For instance, in 1998, Mosher 23 developed spacecraft concept optimization and utility tool (SCOUT), while Taylor (2000) studied the application of traditional and nontraditional optimization techniques for remote sensing satellite preliminary design problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spacecraft mission design optimization under uncertainty

Jafarsalehi

Fazeley

Mirshams

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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The design of space systems is a complex and multidisciplinary process. In this study, two deterministic and nondeterministic approaches are applied to the system design optimization of a spacecraft which is actually a small satellite in low Earth orbit with remote sensing mission. These approaches were then evaluated and compared. Different disciplines such as mission analysis, payload, electrical power supply, mass model, and launch manifest were properly combined for further use. Furthermore, genetic algorithm and sequential quadratic programming were employed as the system-level and local-level optimizers. The main optimization objective of this study is to minimize the resolution of the satellite imaging payload while there are several constraints. A probabilistic analysis was performed to compare the results of the deterministic and nondeterministic approaches. Analysis of the results showed that the deterministic approaches may lead to an unreliable design (because of leaving little or no room for uncertainties), while using the reliability-based multidisciplinary design optimization architecture, all probabilistic constraints were satisfied.

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Multiple-payload fractionated spacecraft for earth observation

AlandiHallaj

Emami

2022

Acta Astronautica

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Highly Efficient Exploration of Large Design Spaces: Fractionated Satellites as an Example of Adaptable Systems

Cited by 9 publications

References 16 publications

Exploiting Multidimensional Design of Experiments and Kriging Methods: An Application to a Satellite Radar System Tradespace and Orbital Transfer Vehicle Tradespace

Exploiting Multidimensional Design of Experiments and Kriging Methods: An Application to a Satellite Radar System Tradespace and Orbital Transfer Vehicle Tradespace

Spacecraft mission design optimization under uncertainty

Multiple-payload fractionated spacecraft for earth observation

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