Optimal Engineering Design: Principles and Applications
Optimal Engineering Design: Principles and Applications
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Data management and transfer in high-performance computational grid environments
Parallel Computing - Parallel data-intensive algorithms and applications
Designing Grid-based Problem Solving Environments and Portals
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 9 - Volume 9
Grid Services in Action: Grid Enabled Optimisation and Design Search
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid?
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Review Article: Workflow based framework for life science informatics
Computational Biology and Chemistry
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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.