Aircraft Family Design Using Decomposition-Based Methods

Allison, James T.; Roth, Brian; Kokkolaras, Michael; Kroo, Ilan; Papalambros, Panos Y.

doi:10.2514/6.2006-6950

Cited by 33 publications

(29 citation statements)

References 20 publications

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“…It has been most frequently applied to design problems in the field for which it was developed, the automotive industry [175,176,177,178,29,179,180,181]. However, it has also proved useful in aircraft design [182,150,54] and building design [23].…”

Section: F Analytical Target Cascading (Atc)mentioning

confidence: 99%

“…Examples include the design of launch vehicles [39], rocket engines [40], satellite constellations [147], flight trajectories [72,148], and flight control systems [149], as well as the preliminary design of complete aircraft [17,18] and aircraft family design [150]. Beyond aerospace engineering, CO has been applied to problems involving automobile engines [28], bridge design [22], and railway cars [26], and even the design of a scanning optical microscope [27].…”

Section: Collaborative Optimization (Co)mentioning

confidence: 99%

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Multidisciplinary Design Optimization: A Survey of Architectures

2013

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Multidisciplinary design optimization (MDO) is a field of research that studies the application of numerical optimization techniques to the design of engineering systems involving multiple disciplines or components. Since the inception of MDO, various methods (architectures) have been developed and applied to solve MDO problems. This paper provides a survey of all the architectures that have been presented in the literature so far. All architectures are explained in detail using a unified description that includes optimization problem statements, diagrams, and detailed algorithms. The diagrams show both data and process flow through the multidisciplinary system and computational elements, which facilitates the understanding of the various architectures, and how they relate to each other.A classification of the MDO architectures based on their problem formulations and decomposition strategies is also provided, and the benefits and drawbacks of the architectures are discussed from both a theoretical and experimental perspective. For each architecture, several applications to the solution of engineering design problems are cited. The result is a comprehensive but straightforward introduction to MDO for non-specialists, and a reference detailing all current MDO architectures for specialists.

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Section: F Analytical Target Cascading (Atc)mentioning

confidence: 99%

Section: Collaborative Optimization (Co)mentioning

confidence: 99%

Multidisciplinary Design Optimization: A Survey of Architectures

2013

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“…A Multi-Objective Particle Swarm Optimization (MOPSO) method was proposed by Seng Ki et al [8]. Moreover, James et al and Dongwook et al suggested an evolutionary method and data mining technique for a family of aircraft design [9,10]. They assumed values for the requirements needed for the market purposes, which often differed from actual market requirements.…”

Section: Jonathan Et Al Utilized the Generation Of A Paretomentioning

confidence: 99%

“…Many researchers studied techniques for derivative design. These research efforts considered the whole set of design variables and designed the baseline and derivatives simultaneously [1][2][3][4][5][6][7][8][9][10]. Richard et al, Robert et al, and Deepak et al identified the important parameters for the family design by applying the market requirement analysis [1, 2, and 3]…”

Section: Introductionmentioning

confidence: 99%

Aircraft derivative design optimization considering global sensitivity and uncertainty of analysis models

Park¹,

Chung²,

Lee³

2016

International Journal of Aeronautical and Space Sciences

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Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet new market demands while keeping the development time and cost to a minimum. Many researchers have studied the derivative design process, but these research efforts consider baseline and derivative designs together, while using the whole set of design variables. Therefore, an efficient process that can reduce cost and time for aircraft derivative design is needed. In this research, a more efficient design process is proposed which obtains global changes from local changes in aircraft design in order to develop aircraft derivatives efficiently. Sensitivity analysis was introduced to remove unnecessary design variables that have a low impact on the objective function. This prevented wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, uncertainty from the fidelity of analysis tools was considered in design optimization to increase the probability of optimization results. The Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were proposed to handle the uncertainty in aircraft conceptual design optimization. In this paper, Collaborative Optimization (CO) based framework with RBDO and PBDO was implemented to consider uncertainty. The proposed method was applied for civil jet aircraft derivative design that increases cruise range and the number of passengers. The proposed process provided deterministic design optimization, RBDO, and PBDO results for given requirements.

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“…The product platform and its corresponding family of products were simultaneously optimized using a genetic algorithm while considering levels of platform [13, 14, and 15]. Furthermore, James Allison et al (2006), and Dongwook Lim and Dimitri N. Mavris (2007) researched other methods in order to design an aircraft family [16,17]. The previous researchers considered the whole set of design variables simultaneously in order to design the baseline and its derivatives together.…”

Section: Derivative Designmentioning

confidence: 99%