Stochastic Optimization of Parameters and Control Laws of the Aircraft Gas-Turbine Engines – A Step to a Robust Design

Egorov, Igor; Kretinin, Gennady V.; Leshchenko, I. A.

doi:10.1016/b978-008043951-8/50042-4

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Marczyk (2000) presented stochastic simulation using the Monte Carlo technique as an alternative to traditional optimisation. In recent years probabilistic design analysis and optimisation methods have also been developed (Tong 2000;Koch et al 2000;Egorov et al 2002) to account for uncertainty and randomness through stochastic simulation and probabilistic analysis. Much work has been proposed to achieve highfidelity design optimisation at reduced computational cost.…”

Section: Introductionmentioning

confidence: 99%

Progressive design methodology for complex engineering systems based on multiobjective genetic algorithms and linguistic decision making

Kumar

Bauer

2008

Soft Comput

View full text Add to dashboard Cite

This work focuses on a design methodology that aids in design and development of complex engineering systems. This design methodology consists of simulation, optimization and decision making. Within this work a framework is presented in which modelling, multiobjective optimization and multi criteria decision making techniques are used to design an engineering system. Due to the complexity of the designed system a three-step design process is suggested. In the first step multi-objective optimization using genetic algorithm is used. In the second step a multi attribute decision making process based on linguistic variables is suggested in order to facilitate the designer to express the preferences. In the last step the fine tuning of selected few variants are performed. This methodology is named as progressive design methodology. The method is applied as a case study to design a permanent magnet brushless DC motor drive and the results are compared with experimental values.

show abstract

Section: Introductionmentioning

confidence: 99%

Progressive design methodology for complex engineering systems based on multiobjective genetic algorithms and linguistic decision making

Kumar

Bauer

2008

Soft Comput

View full text Add to dashboard Cite

show abstract

“…In recent years, probabilistic design analysis and optimization methods have been developed 3,5,6,[8][9][10] to account for uncertainty and randomness through stochastic simulation and probabilistic analysis. Totality of such methods can be treated as the new scientific direction, n amed "Robust Design Optimization" (RDO).…”

Section: Introductionmentioning

confidence: 99%

How to Execute Robust Design Optimization

Egorov¹,

Kretinin²,

Leshchenko³

2002

9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization

View full text Add to dashboard Cite

The problem of robust design optimization consists in the search for technical solutions that can be implemented in practice with high probability. From mathematical point of view the problem comes to optimization of design parameters of the system under investigation according to probabilistic criteria. This paper presents essential stages of robust design optimization solution: identifying uncertainties and choosing mathematical model; forming of probabilistic criteria; optimization problem statement; choosing the procedure for calculation of probabilistic criteria and choosing the optimization technique. Options for robust design optimization solution are considered with the use of multicriteria optimization techniques as well as with procedures of multilevel optimizations. Examples of real-life problems solution are also given.

show abstract