A novel approach for noise prediction using Neural network trained with an efficient optimization technique | International Journal for Simulation and Multidisciplinary Design Optimization (IJSMDO)

Advances in Modeling and Optimization of Manufacturing Processes

Open Access

Issue		Int. J. Simul. Multidisci. Des. Optim. Volume 14, 2023 Advances in Modeling and Optimization of Manufacturing Processes


Article Number		3
Number of page(s)		7
DOI		https://doi.org/10.1051/smdo/2023002
Published online		08 May 2023

Z. Sun, Z. Li, M. Fan, Airfoil shape optimization based on Non Uniform Rational Bspline and optimization algorithm, IOP Conf. Ser.: Earth Environ. Sci. 474, 052075 (2020) [CrossRef] [Google Scholar]
A. Agriss M. Agouzoul, A. Ettaouil, A. Mehdari Numerical study of new techniques drag reduction: application to aerodynamic devices, Int. J. Simul. Multidisci. Des. Optim. 12, 16 (2021) [CrossRef] [EDP Sciences] [Google Scholar]
H. Wen, S. Sang, C. Qiu, X. Du, X. Zhu, Q. Shi, A new optimization method of wind turbine aerofoil performance based on Bessel equation and GABP artificial neural network, Energy 187, 116106 (2019) [CrossRef] [Google Scholar]
P. Pal, R. Datta, D. Rajbansi, A. Segev, A neural net based prediction of sound pressure level for the design of the aerofoil, in Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing (Springer, Cham, 2019), pp. 105–112 [Google Scholar]
D.K. Choi, Multilevel-modeling interpretation of trailing-edge noise models for wind turbines with NACA 0012 airfoil, Int. J. Precis. Eng. Manufactur. Green Technol. 8, 1501–1514 (2021) [CrossRef] [Google Scholar]
K. Yang, T. Fan, T. Chen, Y. Shi, Q. Yang, A quasi-Newton method based vertical federated learning framework for logistic regression, ArXiv preprint arXiv:1912.00513 (2019) [Google Scholar]
B. Yu, L. Xie, F. Wang, An improved deep convolutional neural network to predict airfoil lift coefficient, in International Conference on Aerospace System Science and Engineering. (Springer, Singapore, 2019, July), pp. 275–286 [Google Scholar]
Y. Zhang, W.J. Sung, D.N. Mavris, Application of convolutional neural network to predict airfoil lift coefficient, in 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (2018), p. 1903 [Google Scholar]
R. El Maani, S. Elouardi, B. Radi, A. El Hami, Multiobjective aerodynamic shape optimization of NACA0012 airfoil based mesh morphing, Int. J. Simul. Multidisci. Des. Optim. 11, 11 (2020) [CrossRef] [EDP Sciences] [Google Scholar]
S. Oh, Comparison of a response surface method and artificial neural network in predicting the aerodynamic performance of a wind turbine airfoil and its optimization, Appl. Sci. 10, 6277 (2020) [CrossRef] [Google Scholar]
K.-N. Chen, P.-Y. Chen, Structural optimization of 3 MW wind turbine blades using a two-step procedure, Int. J. Simul. Multidiscipl. Des. Optim. 4, 159–165 (2012) [Google Scholar]
J. Luo, Y. Shi, W. Song, Finlet optimization for airfoil trailing edge noise minimization using ANN, in AIAA AVIATION 2020 FORUM (2020), p. 2537 [Google Scholar]
X. Du, P. He, J.R. Martins, Rapid airfoil design optimization via neural networks-based parameterization and surrogate modeling, Aerospace Sci. Technol. 113, 106701 (2021) [CrossRef] [Google Scholar]
X. Fang, Q. Ni, M. Zeng, A modified quasi-Newton method for nonlinear equations, J. Comput. Appl. Math. 328, 44–58 (2018) [CrossRef] [MathSciNet] [Google Scholar]
T. Brooks, D. Stuart Pope, Michael A. Marcolini, Airfoils self-noise and predictions, NASA Reference Publication 1218 (1989) [Google Scholar]

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