Derivation of Deterministic Design Data from Stochastic Analysis in the Aircraft Design Process

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Armani_2012,
  author =       "U. Armani and S. Coggon and V. V. Toropov",
  title =        "Derivation of Deterministic Design Data from
                 Stochastic Analysis in the Aircraft Design Process",
  booktitle =    "Proceedings of the Eleventh International Conference
                 on Computational Structures Technology (CST2012)",
  year =         "2012",
  editor =       "B. H. V. Topping",
  pages =        "Paper 216",
  address =      "Dubrovnik, Croatia",
  publisher_address = "Stirlingshire, UK",
  month =        "4-7 " # sep,
  publisher =    "Civil-Comp Press",
  keywords =     "genetic algorithms, genetic programming, industrial
                 optimisation, metamodel, polynomial chaos expansion,
                 sensitivity analysis, particle swarm optimisation,
                 dimensionality reduction",
  URL =          "http://webapp.tudelft.nl/proceedings/cst2012/html/summary/armani.htm",
  URL =          "http://webapp.tudelft.nl/proceedings/cst2012/pdf/armani.pdf",
  DOI =          "doi:10.4203/ccp.99.216",
  size =         "16 pages",
  abstract =     "The application of uncertainty management techniques
                 to the aircraft design process is currently a high
                 profile research area and of key strategic interest
                 within aerospace industry. Within the aircraft design
                 process there is always a difficult balance between non
                 specific and specific design steps for configuration
                 and design maturity versus the overall project lead
                 time. This leads to either an immature design that
                 causes delays of the entry into service or significant
                 re-design loops within the aircraft development project
                 again resulting in a significant cost penalty. The
                 ability to quantify uncertainties in the design enables
                 the application of more robust optimisation approaches
                 to balance the quantitative risks of design evolution
                 against the aircraft performance implications (e.g.
                 aircraft weight) and specific design lead
                 time.

                 Although the application of stochastic analysis is a
                 powerful way of making informed design decisions, its
                 integration into the standard design process requires
                 the generation of deterministic design data which
                 achieve the design targets from an uncertainty
                 approach.

                 In this paper the problem of retrieving deterministic
                 design data from a collection of responses provided by
                 aircraft structural computer models is addressed.
                 Firstly, a framework that enables metamodel generation
                 and dimensionality reduction is presented. The
                 framework relies on polynomial chaos expansion (PCE)
                 for metamodel generation [1]. The technique was chosen
                 for its ability to ease the sensitivity analysis
                 process, as sensitivity information in the form of
                 Sobol indices can be extracted analytically from the
                 PCE metamodels. Secondly, a search algorithm that can
                 be used to explore the metamodels generated by PCE is
                 presented. The algorithm, based on the particle swarm
                 optimisation (PSO) paradigm [2], was developed
                 specifically to be used in constrained search problems:
                 it performs a search of the design configurations that
                 produces a specified target response level. Constraints
                 can also be defined using additional metamodels.

                 The framework and the search algorithm have been
                 validated on an aircraft structural analysis problem.
                 The accuracy of the results and the reduced
                 computational cost of the entire process make the
                 presented methodology a valuable tool for uncertainty
                 and sensitivity analysis in the aerospace industry.",
  notes =        "PSO rather than
                 GP?

                 http://webapp.tudelft.nl/proceedings/cst2012/html/home.htm",
}

Genetic Programming entries for Umberto Armani S Coggon Vassili V Toropov

Citations