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Pattern search algorithm for Blackboard-based Multidisciplinary Design Optimisation frameworks

Pattern search algorithm for Blackboard-based Multidisciplinary Design Optimisation frameworks
Pattern search algorithm for Blackboard-based Multidisciplinary Design Optimisation frameworks
Preliminary aircraft design necessitates the use of a range of analysis tools, which are often scattered among many departments in an organisation and require regular tuning from skilled operators. For this reason, a distributed Multidisciplinary Design Optimisation approach that permits individual organisational domains to use their preferred analysis and optimisation tools would be most suitable. This paper revisits a Blackboard framework, which uses simple heuristics to automatically guide organisational design domains to a single optimum by narrowing the bounds on the shared design variables. The authors present a newly developed rule base for this legacy framework, which has been given the title “Multidisciplinary Pattern Search”. Two examples, one of which is for conceptual transonic wing design, demonstrate the merit of the newly developed rule base, database and visualisation modules. They also serve as a means for comparisons with two established Multidisciplinary Design Optimisation architectures. The results indicate that the Blackboard performs better than the distributed Collaborative Optimisation approach, albeit worse than the monolithic Simultaneous Analysis and Design method that tends to be organisationally disruptive to implement
Aircraft Desing, Multidisciplinary design optimisation, Blackboard Framework, Multidisciplinary Pattern Search
0021-8669
121-136
Jelev, Nickolay
cb200afa-2590-41b0-99ff-8f8aa78b8517
Keane, Andy
26d7fa33-5415-4910-89d8-fb3620413def
Holden, Carren M.E.
411ab81a-6161-4eef-9d23-8ffc8972b572
Jelev, Nickolay
cb200afa-2590-41b0-99ff-8f8aa78b8517
Keane, Andy
26d7fa33-5415-4910-89d8-fb3620413def
Holden, Carren M.E.
411ab81a-6161-4eef-9d23-8ffc8972b572

Jelev, Nickolay, Keane, Andy and Holden, Carren M.E. (2019) Pattern search algorithm for Blackboard-based Multidisciplinary Design Optimisation frameworks. Journal of Aircraft, 56 (1), 121-136. (doi:10.2514/1.C034897).

Record type: Article

Abstract

Preliminary aircraft design necessitates the use of a range of analysis tools, which are often scattered among many departments in an organisation and require regular tuning from skilled operators. For this reason, a distributed Multidisciplinary Design Optimisation approach that permits individual organisational domains to use their preferred analysis and optimisation tools would be most suitable. This paper revisits a Blackboard framework, which uses simple heuristics to automatically guide organisational design domains to a single optimum by narrowing the bounds on the shared design variables. The authors present a newly developed rule base for this legacy framework, which has been given the title “Multidisciplinary Pattern Search”. Two examples, one of which is for conceptual transonic wing design, demonstrate the merit of the newly developed rule base, database and visualisation modules. They also serve as a means for comparisons with two established Multidisciplinary Design Optimisation architectures. The results indicate that the Blackboard performs better than the distributed Collaborative Optimisation approach, albeit worse than the monolithic Simultaneous Analysis and Design method that tends to be organisationally disruptive to implement

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Multidisciplinary Pattern Search AIAA - Accepted Manuscript
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Accepted/In Press date: 12 May 2018
e-pub ahead of print date: 6 September 2018
Published date: January 2019
Keywords: Aircraft Desing, Multidisciplinary design optimisation, Blackboard Framework, Multidisciplinary Pattern Search

Identifiers

Local EPrints ID: 421531
URI: https://eprints.soton.ac.uk/id/eprint/421531
ISSN: 0021-8669
PURE UUID: 01235bfa-ee10-4a49-b894-27f30ccf7be2

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Date deposited: 14 Jun 2018 16:30
Last modified: 20 Jul 2019 04:15

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Contributors

Author: Nickolay Jelev
Author: Andy Keane
Author: Carren M.E. Holden

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