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International Journal of Aerospace Engineering
Volume 2014, Article ID 218710, 11 pages
Research Article

Use of Active Learning to Design Wind Tunnel Runs for Unsteady Cavity Pressure Measurements

Mechanical Engineering and Material Science Department, William Marsh Rice University, Houston, TX 77251-1892, USA

Received 23 December 2013; Accepted 13 May 2014; Published 19 June 2014

Academic Editor: Christopher J. Damaren

Copyright © 2014 Ankur Srivastava and Andrew J. Meade. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Wind tunnel tests to measure unsteady cavity flow pressure measurements can be expensive, lengthy, and tedious. In this work, the feasibility of an active machine learning technique to design wind tunnel runs using proxy data is tested. The proposed active learning scheme used scattered data approximation in conjunction with uncertainty sampling (US). We applied the proposed intelligent sampling strategy in characterizing cavity flow classes at subsonic and transonic speeds and demonstrated that the scheme has better classification accuracies, using fewer training points, than a passive Latin Hypercube Sampling (LHS) strategy.