TRAIN OF ARTIFICIAL INTELLIGENCE MODEL AND APPLICATION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR SHOCK WAVE PREDICTION OF AIRFOIL

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2023 Vol.28, Issue 4 Preview Page Next Page

TRAIN OF ARTIFICIAL INTELLIGENCE MODEL AND APPLICATION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR SHOCK WAVE PREDICTION OF AIRFOIL 익형의 충격파 예측을 위한 인공지능 모델 학습 및 설명가능한 인공지능 적용: 박현빈,¹ 홍윤표,² 강유업,² 이상아^*1
H. Park,¹ Y. Hong,² Y.E. Kang² and S. Lee^*1; ¹한국생산기술연구원 디지털전환연구부문, ²서울대학교 항공우주공학과

¹Digital Transformation R&D Department, Korea Institute of Industrial Technology, ²Dept. of Aerospace Engineering, Seoul National Univ.

31 December 2023. pp. 16-26

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Abstract

Recently, there have been attempts to predict shock waves using high performance neural networks. Those studies have a limitation in that it is difficult to explain the internal operation process by using the deep learning model only as a black box model, making it difficult to understand. In this study, artificial intelligence was used to predict the type of the shock wave from the shape of the airfoil. To this end, a total of 500 airfoils were generated by changing eight variables using the Class/Shape-function Transformation(CST) parametrization method, and flow was solved using KFLOW. Using the analysis results, Multi-Layer Perceptron(MLP) was trained to predict the type of the shock wave from the airfoil geometry. Using eXplainable Artificial Intelligence(XAI) techniques Local Interpretable Model-agnostic Explanation(LIME), SHapley Additive exPlanation(SHAP), and Explainable Boosting Machine(EBM), the prediction results of the model were presented in an interpretable form. The analysis results obtained by applying LIME and SHAP to the MLP model were compared with the analysis results of EBM.. As a result of global and local explanation, it was confirmed that the influence of the variable in charge of the upper middle of the airfoil was the greatest on shock wave prediction.

Keywords

딥러닝(Deep Learning)

라임(LIME)

샵(SHAP)

설명가능한 부스팅머신(EBM)

설명가능한 인공지능(XAI)

충격파(Shock Wave)

References

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Information

Publisher :Korean Society for Computational Fluids Engineering
Publisher(Ko) :한국전산유체공학회
Journal Title :Journal of Computational Fluids Engineering
Journal Title(Ko) :한국전산유체공학회지
Volume : 28
No :4
Pages :16-26

[1] 2019, Kim B.S., Vinicius C.A., Nils T., Theodore K., Markus G. and Barbara S., "Deep Fluids: A Generative Network for Parameterized Fluid Simulations," Journal of Computer graphics forum, Vol.38, No.2, pp.59-70.https://doi.org/10.1111/cgf.13619

[2] 2021, "Deep Learning for Flow Prediction in Complex Engineering Applications." (George Em Karniadakis, 2018)

[3] 2019, Raissi, M., Perdikaris, P. and Karniadakis, G.E., "Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations," Journal of Computational Physics, Vol.378, pp.686-707.https://doi.org/10.1016/j.jcp.2018.10.045

[4] 2023, Park H.S., Hwang W.T., Shin J.H., Cho K.W., Cho S.H. and Choi S.I., "Prediction of an airfoil flow field using artificial intelligence technique and a reduced-order model," KSCFE, Vol.28, No.1, pp.25-34.https://doi.org/10.6112/kscfe.2023.28.1.025

[5] 2022, Cihat D., Handle A. and Ozgur U.B., "A deep learning approach for the transonic flow field predictions around airfoils," Journal of Computer & Fluids, Vol236, pp.1-32.

[6] 2004, Lent, M.V., Fisher, W.C. and Mancuso, M., "An Explainable Artificial Intelligence System for Small-unit Tactical Behavior," IAAI 04, pp.900-907.

[7] 2019, Gunning. D. and Aha. D., DARPA’s Explainable Artificial Intelligence (XAI) Program. AI Magazine, Vol.40, No.2, pp.44-58.https://doi.org/10.1609/aimag.v40i2.2850

[8] 2020, Alejandro B.A., Natalia D.R., Javier D.S., Adrien B., Siham T., Alberto B., Salvador G., Sergio G.L., Daniel M., Richard B., Raja C. and Francisco H., "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," Journal of Information Fusion, Vol.58, pp.82-115.https://doi.org/10.1016/j.inffus.2019.12.012

[9] 2020, Chen, H.-Y. and Lee, C.-H., "Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis," IEEE Access, Vol.8, pp.134246-134256.https://doi.org/10.1109/ACCESS.2020.3006491

[10] 2018, Adadi, A. and Berrada, M., "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)," IEEE Access, Vol.6, pp.52138-52160.https://doi.org/10.1109/ACCESS.2018.2870052

[11] 2016, Marco T.R., Sameer S. and Carlos G., ""Why Should I Trust You?": Explaining the Predictions of Any Classifier," NAACL 2016, pp.97-101.

[12] 2017, Scott L. and Lee S.I., "A Unified Approach to Interpreting Model Predictions," Advances in neural information processing systems (2017), Vol.30.

[13] 2019, Harsha N., Samuel J., Paul K. and Rich C., "InterpretML: A unified framework for machine learning interpretability."

[14] 2022, Hong, Y., Lee, D., Yee, K., and Park, S.H., "Enhanced high-order scheme for high-resolution rotorcraft flowfield analysis," AIAA Journal, Vol.60(1), pp.144-159.https://doi.org/10.2514/1.J060803

[15] 2004, Park, S.H. and Kwon, J.H., "Implementation of kw turbulence models in an implicit multigrid method," AIAA journal, Vol.42(7), pp.1348-1357.https://doi.org/10.2514/1.2461

[16] 2008, Kulfan, B.M., "Universal parametric geometry representation method. Journal of aircraft," Vol.45, No.1, pp.142-158.https://doi.org/10.2514/1.29958

[17] 1979, Cook, P.H., McDonald, M.A., Firmin, M.C.P., "Aerofoil RAE 2822 - Pressure Distributions, and Boundary Layer and Wake Measurements," Experimental Data Base for Computer Program Assessment, AGARD Report AR 13.

[18] 2017, Jameson A., "Origins and further development of the Jameson–Schmidt–Turkel scheme," AIAA Journal, Vol.55.5, pp.1487-1510.https://doi.org/10.2514/1.J055493

[19] 1984, Sobieczky, H. and Seebass, A.R., "Supercritical airfoil and wing design," Annual Review of Fluid Mechanics, Vol.16.1, pp.337-363.https://doi.org/10.1146/annurev.fl.16.010184.002005

[20] 2001, Ahn, J.W., Kim H.J., Lee D.H. and Rho O.H., "Response surface method for airfoil design in transonic flow," Journal of aircraft, Vol.38.2, pp.231-238.https://doi.org/10.2514/2.2780

[21] 2005, Michael H. and Antony J., "Exploring the limits of shock-free transonic airfoil design," 44rd AIAA Aerospace Sciences Meeting and Exhibit.

[22] 2023, Fu, Junjie, et al., "The Bump Suppression Strategy for the Transonic Buffet of the Supercritical Airfoil," AIAA Journal, Vol.66(2), pp.808-830.

Journal of Computational Fluids Engineering ISSN:1598-6071(Print) 3022-6252(Online) 한국전산유체공학회지

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