A data-driven deep learning approach for predicting separation-induced transition of submarines-Reference-Cited by-同舟云学术

A data-driven deep learning approach for predicting separation-induced transition of submarines

Published:2022-02 Issue:2 Volume:34 Page:024101
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Xuan Yang¹²³^ORCID,Lyu Hongqiang⁴,An Wei⁴,Liu Jianhua⁵^ORCID,Liu Xuejun¹³^ORCID

Affiliation:

1. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

2. Key Laboratory of Aerodynamic Noise Control, State Key Laboratory of Aerodynamics, Mianyang, China

3. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China

4. College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

5. China Ship Scientific Research Center, National Key Laboratory on Ship Vibration and Noise, Wuxi 214082, China

Funder

the Key Laboratory of Aerodynamic Noise Control

the State Key Laboratory of Aerodynamics

Aerodynamic Science Foundation of China

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0079648

Reference37 articles.

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3. AERODYNAMICS OF SMALL VEHICLES

4. A. M. O. Smith , “ Transition, pressure gradient, and stability theory,” Report No. ES 26388 ( Douglas Aircraft Co. 1956).

5. J. L. Van Ingen , “ A suggested semi-empirical method for the calculation of the boundary-layer transition region,” Report No. VTH-74 ( Delft University of Technology, 1956).

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