Abstract
Wind measurement from shipborne anemometer is susceptible to the airflow distortion due to ship hull and superstructure. The measurement bias needs to be minimized with regard to various meteorological and navigation applications. To address this problem, this study illustrates the feasibility to correct the measurement bias due to airflow distortion by applying Least Squares Support Vector Machine with Particle Swarm Optimization (PSO-LSSVM) method. The airflow field around hull and superstructure of an experimental ship is simulated by computational fluid dynamics (CFD) techniques. And then the nonlinear relationship between the airflow through conventional anemometer mounting sites on the main mast and the airflow through the reference point above bridge is implicitly obtained using the PSO-LSSVM regression. The dataset of relative wind observation taken during a sea trial is used to validate the effectiveness of this method. The results show that the established model efficiently eliminates most of the speed bias and reduces half of the direction bias of the mean relative wind, which indicates this method could be extended to estimate the undisturbed freestream on the open sea surface.
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Acknowledgement
The study was supported by the National Natural Science Foundation of China under Grant 41606112 and 41705046.
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Hu, T., Qi, S., Qiu, Z., Zou, J., Wang, D. (2019). Application of PSO-LSSVM in Bias Correction of Shipborne Anemometer Measurement. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1005. Springer, Singapore. https://doi.org/10.1007/978-981-13-7983-3_31
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DOI: https://doi.org/10.1007/978-981-13-7983-3_31
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