Spacecraft design using a genetic algorithm optimization approach

Mosher, Todd

doi:10.1109/aero.1998.685783

Cited by 20 publications

(17 citation statements)

References 4 publications

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“…The genetic algorithm successfully found INTRODUCTION spacecraft bus designs -the decision variables were the solar cell type, solar array type, battery type, structure type, propulsion type, and the required launch vehicle -with a lower system (i.e. bus + launch vehicle) cost than the actual NEAR design [Mosher, 1998]. While the NEAR mission came within three percent of its initial projected cost and was a technical success, Dr. Mosher's work illustrated that even greater cost savings were possible.…”

Section: Mdo Literature Review (Aerospace Applications)mentioning

confidence: 99%

“…In doing so, the methodology adds structure to the conceptual design process and advances the state-of-the-art in complex distributed satellite network design by providing a means to explore the trade space before converging on a single point design. Historically, the conceptual design of space systems has been unstructured, with designers often pursuing a single concept or modifying an existing idea rather than generating new alternatives [Mosher, 1998]. With this traditional aerospace design approach, there is no guarantee that a system-level focus will be taken, and often the final system architecture chosen achieves only feasibility instead of optimality [Mosher, 1996].…”

Section: Thesis Summarymentioning

confidence: 99%

“…[Skinner, 1992] SCOUT The Aerospace Corporation Single spacecraft mission bus component and launch vehicle selection. [Mosher, 1998] SMALLSAT…”

Section: Meritmentioning

confidence: 99%

“…- [Mosher, 1998] With the traditional aerospace point design approach, there is no guarantee that a systemlevel focus will be taken, and often the final design architecture chosen achieves only feasibility instead of optimality [Mosher, 1996]. System-level trades are often delayed until after a point design has been selected because of the perceived time and effort required to conduct a credible analysis [Riddle, 1998].…”

Section: Motivationmentioning

confidence: 99%

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A Multiobjective, Multidisciplinary Design Optimization Methodology for the Conceptual Design of Distributed Satellite Systems

Jilla¹

2002

9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization

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A multiobjective, multidisciplinary design optimization methodology for mathematically modeling the distributed satellite system (DSS) conceptual design problem as an optimization problem has been developed to advance the state-of-the-art in complex distributed satellite network design. An increasing number of space missions are utilizing DSS architectures in which multiple satellites work in a coordinated fashion to improve system performance, cost, and survivability. The trade space for distributed satellite systems can be enormous -too large to enumerate, analyze, and compare all possible architectures. The seven-step methodology enables an efficient search of the trade space for the best families of architectures, and explores architectures that might not otherwise be considered during the conceptual design phase, the phase of a DSS program in which the majority of lifecycle cost gets locked in.Four classes of multidisciplinary design optimization (MDO) techniques are investigated -Taguchi, heuristic, gradient, and univariate methods. The heuristic simulated annealing (SA) algorithm found the best DSS architectures with the greatest consistency due to its ability to escape local optima within a nonconvex trade space. Accordingly, this SA algorithm forms the core single objective MDO algorithm in the methodology. The DSS conceptual design problem scope is then broadened by expanding from single objective to multiobjective optimization problems, and two variant multiobjective SA algorithms are developed. The utility in knowing the global Pareto boundary of a DSS trade space is presented, and several methods are explored for approximating the true global Pareto boundary with only a limited knowledge of the full DSS trade space. Finally, methods for improving the performance of the SA algorithm are tested, and it was found that the 2-DOF variant of the SA algorithm is most effective at both single objective and multiobjective searches of a DSS trade space. The versatility of the methodology is demonstrated through its application to the conceptual design of three separate distributed satellite systems -the civil NASA Origins Terrestrial Planet Finder mission, the military TechSat 21 GMTI space-based radar mission, and the commercial broadband satellite communications mission. In each case, the methodology identifies more cost-effective system architectures than those previously considered for the single objective optimization problem, and a Pareto optimal set of architectures for the multiobjective optimization problem. In this manner, the methodology serves as a powerful, versatile systems engineering tool for the conceptual design of distributed satellite systems.

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Section: Mdo Literature Review (Aerospace Applications)mentioning

confidence: 99%

Section: Thesis Summarymentioning

confidence: 99%

“…[Skinner, 1992] SCOUT The Aerospace Corporation Single spacecraft mission bus component and launch vehicle selection. [Mosher, 1998] SMALLSAT…”

Section: Meritmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

See 2 more Smart Citations

A Multiobjective, Multidisciplinary Design Optimization Methodology for the Conceptual Design of Distributed Satellite Systems

Jilla¹

2002

9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization

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“…The related literature offers no similar tool; on the contrary, a discrete number of papers devoted to the algorithm description for tools implemented to support the detailed design process can be found [4], [5], [6], [7], [8]. The paper proposes an algorithmic architecture, to perform a high-level configuration analysis to be exploited -as a powerful input -by the engineers' team who works on the Pre-phase A process.…”

Section: Td1-03mentioning

confidence: 99%

Game theory and possibilistic logic to face the space mission preliminary design optimization: a coevolutive architecture with a semicooperative protocol

Lavagna

Mafficini

Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

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The paper presents a possible approach for supporting the preliminary space system design in the very early phases. The proposed method generates the inputs for the so-called pre-phase A feasibility study, the engineers' team works on to define a preliminary space system solution. Space missions, particularly focusing on interplanetary exploration, are fast growing in complexity: several modules can be involved in the same mission such as multiple orbiters, entry modules, landers/rovers, ascending vehicles, and, eventually, human transportation units; missions can be split into more different phases, according to particular operative modes of each module; mission objectives and requirements can be definitely ambitious and demanding. Such various scenario identifies a hyperspace in which a rough mission configuration must be selected to begin the quantitative space system design; however, at the very beginning of the mission design, the qualitative mission objectives and related possible requirements only are available as inputs. The proposed tool, starting from those generic and qualitative mission objectives, not only generates all possible high level architectures, but, thanks to a co-evolutive multi-objective optimization algorithm, sorts the final Pareto solutions, according to a predefined metric. No system sizing is accomplished; that is why the criteria vector is, here, represented by a set of indexes specifically and robustly modeled thanks to the Possibilistic Logic Theory, the statistical approach and the Fuzzy Logic Theory. A co-evolutive approach has been selected because of the extended dimension of the criteria vector. In particular, the Game Theory is applied with a two-player architecture with a semi-cooperative protocol to manage the systematic information exchange and the population selection to be carried on in the optimization process. Comparisons with the classical non-cooperative and fully cooperative protocols show the validity of the proposed negotiation strategy to converge to the Pareto front for the whole set of variables and cost functions. The co-evolutive algorithm showed very good performance according to the classical test functions available in literature.

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Increased System Consistency through Incorporation of Coupling in Value‐Based Systems Engineering

2017

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The design of large-scale complex engineered systems involves hundreds to thousands of designers making decisions across different organizations and at different levels of organizational hierarchy. These systems are designed within a systems engineering framework, where requirements are used as proxies for stakeholder preference. Requirements drive the development process and are flowed across organizations and down through the organizational hierarchies. Value-driven design offers a new perspective where the preferences of the stakeholder are communicated directly through a decomposable value function, rather than decomposable requirements, thereby enabling improved consistency in system preference. This paper investigates two key aspects of achieving improved system consistency through a valuebased systems engineering approach, using a commercial satellite system as a testbed. The paper first contrasts the diverse systems that result from traditional requirements-based versus preference-based formulations, demonstrating how a value-based approach aids in capturing the true preferences of the stakeholder in problem formulation. The paper demonstrates the importance of using system couplings to enable an improved accuracy for value function decomposition. The paper demonstrates that ensuring system analysis consistency through use of system sensitivities can overcome issues pertaining to: dependencies of attributes; inadequately capturing system interactions; and direct modification of attributes to determine value impact. C⃝ 2017 Wiley Periodicals, Inc. Syst Eng 20: 21-44, 2017

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Spacecraft design using a genetic algorithm optimization approach

Cited by 20 publications

References 4 publications

A Multiobjective, Multidisciplinary Design Optimization Methodology for the Conceptual Design of Distributed Satellite Systems

A Multiobjective, Multidisciplinary Design Optimization Methodology for the Conceptual Design of Distributed Satellite Systems

Game theory and possibilistic logic to face the space mission preliminary design optimization: a coevolutive architecture with a semicooperative protocol

Increased System Consistency through Incorporation of Coupling in Value‐Based Systems Engineering

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