The Genetic Algorithm as an Automated Methodology for Helicopter Conceptual Design

Crossley, Wiilliam A.; Laananen, David H.

doi:10.1080/09544829708907963

Cited by 14 publications

(4 citation statements)

References 8 publications

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“…In addition, because the condenser is a component used only by a PEM electrolysis choice, the value assigned to x 6 is only relevant when PEM Electrolysis is chosen. This approach to variable decoding mimics the concept of sex-limited inheritance in genetics where a particular gene is only expressed if another gene (indicating sex) is present 11 . Male-pattern baldness is an example of a gene that women can carry, but is only expressed in men.…”

Section: Genetic Algorithm Parameters and Variable Codingmentioning

confidence: 99%

A Modeling Framework for Applying Discrete Optimization to System Architecture Selection and Application to In-Situ Resource Utilization

Chepko

Weck

Crossley

et al. 2008

12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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This paper presents an approach to apply optimization to the selection of a system architecture. Early-stage design decisions that define the subsystem and component technologies that comprise a system architecture are often made with information generated from a limited number of trade studies and a historical background. These decisions can lock in a design too early, resulting in a sub-optimal or even infeasible design. Decisions for systems that have little or no historical background, such as lunar in-situ resource utilization (ISRU) systems, have only analyses and tests to be based on. To enable a wider search of the design space, a modeling framework is discussed that decomposes a system in a hierarchical fashion to describe primary subsystem functions and their technology implementation alternatives. This framework allows a system model to be built that captures all of the subsystem technology alternatives in one reconfigurable model for which the categorical, discrete technology decisions can be treated as design variables in an optimization routine. The NASA ISRU System Model follows this framework and is used as an application of system architecture optimization. A genetic algorithm is used to explore both the discrete and continuous design space of the model. Because the current ISRU System Model has a small set of discrete variables, the performance and computational cost of the genetic algorithm search are compared to a previously developed ISRU optimization scheme that involves full enumeration of the discrete variables followed by gradient-based optimization with the continuous variables. The initial tests of the genetic algorithm approach provide comparable results to the previously used approach, requiring 5100 function evaluations compared to a range of 4800-10,000 function evaluations with the enumeration methods.

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Section: Genetic Algorithm Parameters and Variable Codingmentioning

confidence: 99%

A Modeling Framework for Applying Discrete Optimization to System Architecture Selection and Application to In-Situ Resource Utilization

Chepko

Weck

Crossley

et al. 2008

12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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“…Researchers have tried many different crossover strategies. One of the conclusions is that the operation should yield as different a chromosome as possible so as to search the design space more thoroughly [3]. Hence, the uniform crossover is preferred by some researchers [4].…”

Section: Crossover For Sizing Optimizationmentioning

confidence: 99%

Improving the Efficiency of Genetic Algorithms for Frame Designs

Chen

Rajan²

1998

Engineering Optimization

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The focus of this paper is on the development of a design software system that has enough flexibility and capability to search for the most economical steel roof truss design in a reasonable amount of time. This objective is achieved by improving the efficiency and robustness of the genetic algorithm (GA) methodology developed earlier. The effects of schema representation, schema survival, type of crossover, problem definition, the size of the population, and the number of design iterations on the computational expense and the value of the objective function are studied. The research results show that while the final AISl (American Iron and Steel Institute) code-conforming 'best' designs are very close to each other when different starting designs (ground structures) are used, the use of some GA strategies can lead to either highly non-optimal designs or design processes that are computationally expensive. The results also show some other interestink conclusions. The size of the population and the maximum number of design iterations (or generations) need to be at least the size of the chromosome. The schema representation is perhaps one of the most important factors. Depending on the complexity of the initial design (density of the ground structure) and the size of the chromosome, a newly developed Association String strategy has led to a computationally effective GA process when combined with the elitist, one-point and uniform crossover strategies.

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“…This problem is particularly evident during evaluation of design concepts involving complex nonlinear load-bearing structures that are integrated within or inherently connected to complex mechanical engineering systems. In case of such systems, the prevailing evaluation approach is based on personal intuition and historical experience of the design team or individual experts [Knosala and Pedrycz (1992); Cleland and King (1993); Crossley and Laananen (1997); Qiu et al (2002); Yeo et al (2004); Ayag and Ozdemir (2009)]. Therefore, the conventional approach is very sensitive to the individual capabilities of the participants, which makes it ineffective in absence of prior relevant experience.…”

Section: Introductionmentioning

confidence: 99%

An Extended Substructuring Technique for Efficient Evaluation of Nonlinear Load-Bearing Structures in the Conceptual Design Stage

Kajtaz

Subic

Takla

2014

Int. J. Comput. Methods

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The research presented in this paper has extended the substructuring technique into the nonlinear domain in order to apply the finite element analysis (FEA) method to complex nonlinear structural design problems in the conceptual design stage. As conventional FE models based on substructures allow only linear analysis, it was necessary in this research to introduce a new algorithm capable of linearizing nonlinear structural problems with sufficient accuracy in order to enable evaluation of engineering design concepts in a more objective and rigorous manner in the early stages of design. The developed method was implemented within a commercial FE solver, and validated using a select number of case studies. The results obtained for the two sample solutions indicate that the new method has achieved an improvement in accuracy of 90% and 98% respectively compared to the conventional FE-based approach applied to the same class of design problems.

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The Genetic Algorithm as an Automated Methodology for Helicopter Conceptual Design

Cited by 14 publications

References 8 publications

A Modeling Framework for Applying Discrete Optimization to System Architecture Selection and Application to In-Situ Resource Utilization

A Modeling Framework for Applying Discrete Optimization to System Architecture Selection and Application to In-Situ Resource Utilization

Improving the Efficiency of Genetic Algorithms for Frame Designs

An Extended Substructuring Technique for Efficient Evaluation of Nonlinear Load-Bearing Structures in the Conceptual Design Stage

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