[논문]이미지 데이터를 이용한 익형 매개변수화 및 공력계수 예측을 위한 인공지능 모델 연구

이승훈; 김보라; 이정훈; 김준영; 윤민

doi:10.5407/jksv.2023.21.2.083

이미지 데이터를 이용한 익형 매개변수화 및 공력계수 예측을 위한 인공지능 모델 연구
Study of an AI Model for Airfoil Parameterization and Aerodynamic Coefficient Prediction from Image Data 원문보기

한국가시화정보학회지= Journal of the Korean society of visualization, v.21 no.2, 2023년, pp.83 - 90

이승훈 (Department of Mechanical Engineering, Korea Maritime and Ocean University) , 김보라 (Department of Mechanical Engineering, Korea Maritime and Ocean University) , 이정훈 (Department of Mechanical Engineering, Korea Maritime and Ocean University) , 김준영 (Department of Ocean Advanced Materials Convergence Engineering, Korea Maritime and Ocean University) , 윤민 (Department of Mechanical Engineering, Korea Maritime and Ocean University)

Abstract ▼ AI-Helper

The shape of an airfoil is a critical factor in determining aerodynamic characteristics such as lift and drag. Aerodynamic properties of an airfoil have a decisive impact on the performance of various engineering applications, including airplane wings and wind turbine blades. Therefore, it is essential to analyze the aerodynamic characteristics of airfoils. Various analytical tools such as experiments, computational fluid dynamics, and Xfoil are used to perform these analyses, but each tool has its limitation. In this study, airfoil parameterization, image recognition, and artificial intelligence are combined to overcome these limitations. Image and coordinate data are collected from the UIUC airfoil database. Airfoil parameterization is performed by recognizing images from image data to build a database for deep learning. Trained model can predict the aerodynamic characteristics not only of airfoil images but also of sketches. The mean absolute error of untrained data is 0.0091.

주제어

표/그림 (9)

그림 Fig. 1. Airfoil parameterization
그림 Fig. 2. Multilayer perceptron
그림 Fig. 3. Training & Predicting flow
그림 Fig. 4. Test C_L data predict & ground truth
그림 Fig. 5. Test C_D data predict & ground truth
표 Table 1. Hyperopt's hyper parameter values
표 Table 2. Optimized model's hyperparameter
표 Table 3. Model's evaluation metrics
그림 Fig. 6. Progress of sketch airfoil prediction

참고문헌 (13)

Drela, M., 1989, "XFOIL: An analysis and？design system for low Reynolds number airfoils,"？In Low Reynolds Number Aerodynamics, Springer, Berlin, Heidelberg., pp.1~12.？
Xflr5., 2019, http://www.xflr5.tech/xflr5.html.？
Oliver, J. M., Kipouros, T., and Savill, A.M.,？2013, "A Self-adaptive Genetic Algorithm Applied？to Multi-Objective Optimization of an Airfoil," In？EVOLVE-A Bridge between Probability, Set？Oriented Numerics, and Evolutionary Computation？IV. Advances in Intelligent Systems and Computing, Springer Heidelberg., pp.261~ 276.？
Echavarria, C., Hoyos, J. D., Jimenez, J. H.,？Suarez, G., and Saldarriaga, A., 2022, "Optimal？airfoil design through particle swarm optimization？fed by CFD and XFOIL," J. Braz. Soc. Mech.？Sci. Eng., Vol. 44(11), p.561.？

상세보기
Kim, B. R., Lee, S. H., Jang, S. H., Hwang, G.？I., and Yoon, M., 2022, "Prediction of？aerodynamics using VGG16 and U-Net," J.？Korean Soc. Vis., Vol. 20(3), pp.109~116.？
Seo, J. H., Yoon, H. S., Kim, and M. I., 2022,？"Prediction of aerodynamic force coefficients and？flow fields of airfoils using CNN and EncoderDecoder models.," J. Korean Soc. Vis., Vol.？20(3), pp.94~101.？
Bradski, G., 2000, "The openCV library," Dr. Dobb's？Journal: Software Tools for the Professional？Programmer, Vol. 25(11), pp.120~ 123.？
Ramchoun, H., Ghanou, Y., Ettaouil, M., and？Janati Idrissi, M. A., 2016, "Multilayer perceptron:？Architecture optimization and training,", Proc.？IJIMAI, Vol. 4(1), pp.26~30.？
Pokhrel, P., 2023, "A Comparison of AutoML？Hyperparameter Optimization Tools for Tabular？Data," Master's thesis, Youngstown State University.？
Frazier, P. I., 2018, "A tutorial on Bayesian？optimization," arXiv preprint arXiv:1807.02811.？
Selig, M. S., 1996, "UIUC airfoil data site,"？Department of Aeronautical and Astronautical？Engineering University of Illinois at Urbana-Champaign.？
Chicco, D., Warrens, M. J., and Jurman, G.,？2021, "The coefficient of determination R-squared？is more informative than SMAPE, MAE, MAPE,？MSE and RMSE in regression analysis？evaluation," PeerJ Comput. Sci., Vol. 7, pp.623.？
Nagelkerke, N. J., 1991," A note on a general？definition of the coefficient of determination,"？Biometrika., Vol. 78(3), pp.691~ 692.？

상세보기

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증