Name
Abstract | ||
|---|---|---|
Traditional CAD tools generate a static solution to a design problem. Parametric systems allow the user to explore many variations on that design theme. Such systems make the computer a generative design tool and are already used extensively as a rapid prototyping technique in architecture and aeronautics. Combining a design generation tool with an analysis software and an evolutionary algorithm provides a methodology for optimising designs. This work combines [email protected]?s parametric aircraft design tool (OpenVSP) with a fluid dynamics solver (OpenFOAM) to create and analyse aircraft. An evolutionary algorithm is then used to generate a range of aircraft that maximise lift and reduce drag while remaining within the framework of the original design. Our approach allows the designer to automatically optimise their chosen design and to generate models with improved aerodynamic efficiency. Different components on three aircraft models are varied to highlight the ease and effectiveness of the parametric model optimisation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neucom.2014.04.004 | Neurocomputing |
Keywords | Field | DocType |
computational fluid dynamics,evolutionary algorithms,optimisation,parametric design | Rapid prototyping,Evolutionary algorithm,Control engineering,Artificial intelligence,Generative Design,Parametric model,Simulation,Design tool,Parametric statistics,Parametric design,Solver,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
142 | 1 | 0925-2312 |
Citations | PageRank | References |
5 | 0.48 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan Byrne | 1 | 49 | 5.87 |
| Philip Cardiff | 2 | 5 | 1.49 |
| Anthony Brabazon | 3 | 918 | 98.60 |
| Michael O'Neill | 4 | 876 | 69.58 |