Surface pressure prediction of convolutional vehicle based on machine learning algorithm

Surface pressure is an important index in the evaluation of the attitude control and motion characteristics of the conwolutional vehicle. In order to determine the full-domain surface pressure distribution of the vehicle during navigation, a surface pressure reconstruction algorithm based on machine learning is proposed. The surface pressure distribution of the convolutional vehicle may vary under different sailing environments. By arranging pressure observation points on the surface of the convolutional vehicle and obtaining the distribution of these pressure date, the corresponding navigational conditions can be characterized. In this paper, the pressure obtained from a finite number of observation points on the surface, as well as their coordinates, are used as input information of the model. Then we can obtain a mapping model from discrete pressure date to the full domain pressure distribution. To investigate the performance of the model, full-domain surface pressure prediction experiments are conducted on several different test datasets. The results demonstrate that our machine learning-based model can achieve high-precision surface pressure reconstruction, and the relative error of the predicted value can be reduced to within 10%.