Integrating Mach Number Prediction with Outlier Detection for Wind Tunnel Systems
Publication: Journal of Aerospace Engineering
Volume 32, Issue 5
Abstract
Mach number prediction plays a crucial role in wind tunnel systems. Due to the complicated system behavior, many existing predictors cannot obtain the desired level of accuracy. In addition, the presence of outliers in databases further negatively influences predictive accuracy. In this paper, we address these two problems in one scheme. In contrast to robust regression models, in this paper the problems of prediction and outlier detection are considered separately but are solved by one paradigm. We propose an ensemble model as a predictor, in which a Gaussian process model is used as the base learner. The motivation for using the Gaussian process is its superiority in solving complex nonlinear regression problems. The objective of the ensemble model is to further improve the predictive accuracy of the Gaussian process model. Our outlier detection model is also based on a Gaussian process. It is composed of two complementary components; one is based on Gaussian process regression, and the other is based on Gaussian process classification. We verify our predictor and outlier detection model with three data sets from a real-world wind tunnel system. The results not only verify the model’s predictive performance but also underline the superiority of the detection model.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 51634002 and 61702070) and the National Key R&D Program of China (Grant No. 2017YFB0304104).
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Information & Authors
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Published In
Journal of Aerospace Engineering
Volume 32 • Issue 5 • September 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Oct 23, 2018
Accepted: Feb 1, 2019
Published online: May 30, 2019
Published in print: Sep 1, 2019
Discussion open until: Oct 30, 2019
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