Implementation and utilisation of a Grid-enabled problem solving environment in Matlab

• Published in: Future generation computer systems Vol. 21; no. 6; pp. 920 - 929
• Main Authors: Hakki Eres, M., Pound, Graeme E., Jiao, Zhouan, Wason, Jasmin L., Xu, Fenglian, Keane, Andy J., Cox, Simon J.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2005
• Subjects: Computational fluid dynamics; Engineering design optimisation; Grid computing; Matlab software; Problem solving environment; Grid computing; Engineering design optimisation; Computational fluid dynamics; Problem solving environment; Matlab software
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2003.12.016

In many areas of design search and optimisation one needs to utilise computational fluid dynamics (CFD) methods in order to obtain a numerical solution of the flow field in and/or around a proposed design. From this solution measures of quality for the design may be calculated, which are then used by the optimisation methods. In large models the processing time for the CFD computations can very well be many orders of magnitude larger than for the optimisation methods themselves; and the overall optimisation process usually demands a combination of computational and database resources; therefore this class of problems is well suited to Grid computing. The Geodise toolkit is a suite of tools for Grid-enabled parametric geometry generation, meshing, CFD analysis, design optimisation and search, databasing, Grid computing, and notification within the Matlab environment. These Grid services are presented to the design engineer as Matlab functions that conform to the usual syntax of Matlab. The use of the Geodise toolkit in Matlab introduces a flexible and Grid-enabled problem solving environment (PSE) for design search and optimisation. This PSE is illustrated here with two exemplar problems.