BRADFORD SCHOLARS

    • Sign in
    View Item 
    •   Bradford Scholars
    • Engineering and Informatics
    • Engineering and Informatics Publications
    • View Item
    •   Bradford Scholars
    • Engineering and Informatics
    • Engineering and Informatics Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Bradford ScholarsCommunitiesAuthorsTitlesSubjectsPublication DateThis CollectionAuthorsTitlesSubjectsPublication Date

    My Account

    Sign in

    HELP

    Bradford Scholars FAQsCopyright Fact SheetPolicies Fact SheetDeposit Terms and ConditionsDigital Preservation Policy

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Application of Permutation Genetic Algorithm for Sequential Model Building–Model Validation Design of Experiments

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    View/Open
    kianifar_et_al_2016.pdf (1.675Mb)
    Download
    Publication date
    2016-08
    Author
    Kianifar, Mohammed R.
    Campean, I. Felician
    Wood, Alastair S.
    Keyword
    Design of experiments; Optimal Latin hypercube; Permutation genetic algorithm; Model based engine calibration
    Rights
    © 2016 The Authors. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    The work presented in this paper is motivated by a complex multivariate engineering problem associated with engine mapping experiments, which require efficient Design of Experiment (DoE) strategies to minimise expensive testing. The paper describes the development and evaluation of a Permutation Genetic Algorithm (PermGA) to support an exploration-based sequential DoE strategy for complex real-life engineering problems. A known PermGA was implemented to generate uniform OLH DoEs, and substantially extended to support generation of Model Building–Model Validation (MB-MV) sequences, by generating optimal infill sets of test points as OLH DoEs, that preserve good space filling and projection properties for the merged MB + MV test plan. The algorithm was further extended to address issues with non-orthogonal design spaces, which is a common problem in engineering applications. The effectiveness of the PermGA algorithm for the MB-MV OLH DoE sequence was evaluated through a theoretical benchmark problem based on the Six-Hump-Camel-Back (SHCB) function, as well as the Gasoline Direct Injection (GDI) engine steady state engine mapping problem that motivated this research. The case studies show that the algorithm is effective at delivering quasi-orthogonal space-filling DoEs with good properties even after several MB-MV iterations, while the improvement in model adequacy and accuracy can be monitored by the engineering analyst. The practical importance of this work, demonstrated through the engine case study, also is that significant reduction in the effort and cost of testing can be achieved.
    URI
    http://hdl.handle.net/10454/7701
    Version
    Accepted Manuscript
    Citation
    Kianifar MR, Campean IF and Wood AS (2016) Application of Permutation Genetic Algorithm for Sequential Model Building–Model Validation Design of Experiments. Soft Computing. 10(8): 3023-3044.
    Link to publisher’s version
    http://dx.doi.org/10.1007/s00500-015-1929-5
    Type
    Article
    Collections
    Engineering and Digital Technology Publications

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.