Aerodynamic data modeling plays an important role in aerospace and industrial fluid engineering. Support vector machines (SVMs), as a novel type of learning machine based on statistical learning theory, can be used for regression problems and have been reported to perform well with promising results. The work presented here examines the feasibility of applying SVMs to the aerodynamic modeling field. Mainly, the empirical comparisons between SVMs and the commonly used neural network technique are carried out through two practical data modeling cases - performance-prediction of a new prototype mixer for engine combustors, calibration of a five-hole pressure probe. A CFD-based diffuser optimization design is also involved in the paper, in which an SVM is used to construct a response surface and hereby to make the optimization to be able to perform on an easily computable surrogate space. The obtained simulation results in all the application cases demonstrate that SVMs are potential options for the chosen modeling tasks.
