Journal article 379 views 47 downloads
Enhanced flow visualisation of complex aerodynamic phenomena using automatic stream surface seeding with application to the BLOODHOUND SSC Land Speed Record vehicle / M. Edmunds; B. Evans; I. Masters; R. S. Laramee
The Aeronautical Journal, Volume: 120, Issue: 1226, Pages: 547 - 571
Swansea University Author: Evans, Ben
PDF | Version of Record
Copyright © Royal Aeronautical Society 2016 This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Download (1.39MB)
This application paper describes a novel, cluster-based, semi-automatic, stream surface placement strategy for structured and unstructured computational fluid dynamics (CFD) data, tailored towards a specific application: The BLOODHOUND jet and rocket propelled land speed record vehicle. An existing...
|Published in:||The Aeronautical Journal|
Check full text
No Tags, Be the first to tag this record!
This application paper describes a novel, cluster-based, semi-automatic, stream surface placement strategy for structured and unstructured computational fluid dynamics (CFD) data, tailored towards a specific application: The BLOODHOUND jet and rocket propelled land speed record vehicle. An existing automatic stream surface placement algorithm(8), is extensively modified to cater for large unstructured CFD simulation data. The existing algorithm uses hierarchical clustering of velocity and distance vectors to find potential stream surface seeding locations. This work replaces the hierarchical clustering algorithm, designed to work with small regular grids, with a K-means clustering approach suitable for large unstructured grids. Modifications are made to the seeding curve construction algorithm, improving the smoothness and distribution of the discretised curve in complex cases. A new distance function is described which allows the user to target particular characteristics of simulation data. The proposed algorithm reduces the required memory footprint and computational requirement compared to previous work(8). The performance and effectiveness of the proposed algorithm is demonstrated, and CFD domain expert evaluation is provided describing the value of this approach.
College of Engineering