Artificial neural network surrogate modeling with its economic computational consumption and accurate generalization capabilities offers a feasible approach to aerodynamic design in the field of rapid investigation of design space and optimal solution searching. This paper reviews the basic principle of artificial neural network surrogate modeling in terms of data treatment and configuration setup. A discussion of artificial neural network surrogate modeling is held on different objectives in aerodynamic design applications, various patterns of realization via cutting-edge data technique in numerous optimizations, selection of network topology and types, and other measures for improving modeling. Then, new frontiers of modern artificial neural network surrogate modeling are reviewed with regard to exploiting the hidden information for bringing new perspectives to optimization by exploring new data form and patterns, e.g. quick provision of candidates of better aerodynamic performance via accumulated database instead of random seeding, and envisions of more physical understanding being injected to the data manipulation.
Botez RM. Green aircraft minimum fuel consumption methodologies. In: Oral or poster sessions, during the GLOREP 2018 conference, Timisoara, Romania, 15–17 November 2018.
3.
ACARE. Aeronautics and air transport beyond vision 2020 (towards 2050), 2010.
4.
Sky C. Clean sky. European Union Funding for Research and Innovation, 2018.
5.
GreenBEWhitesidesJLCampbellRL, et al.Method for the constrained design of natural laminar flow airfoils. J Aircraft1997; 34: 706–712.
6.
WangSSunGLiC. Natural laminar flow optimization of transonic nacelle based on differential evolution algorithm. J Aerosp Eng 2019.. DOI: 10.1061/(ASCE)AS.1943-5525.0001028.
7.
Kani JN and Elsheikh AH. Dr-rnn: a deep residual recurrent neural network for model reduction. arXiv preprint arXiv:170900939, 2017.
Eismann S, Bartzsch S and Ermon S. Shape optimization in laminar flow with a label-guided variational autoencoder. arXiv preprint arXiv:171203599, 2017.
10.
XuZSalehJHYangV. Optimization of supercritical airfoil design with buffet effect. AIAA J2018.
11.
SekarVZhangMShuC, et al.Inverse design of airfoil using a deep convolutional neural network. AIAA J2019.
12.
Van NguyenNLeeJTyanM, et al.Repetitively enhanced neural networks method for complex engineering design optimisation problems. Aeronaut J2015; 119: 1253–1270.
13.
Samareh JA. A survey of shape parameterization techniques. ntrsnasagov 1999.
14.
AndradóttirSPrudiusAA. Balanced explorative and exploitative search with estimation for simulation optimization. Informs J Comput2009; 21: 193–208.
15.
Barton RR. Simulation optimization using metamodels. In: Proceedings of the 2009 winter simulation conference (WSC), 2009, pp.230–238. New York: IEEE.
16.
SunGSunYWangS. Artificial neural network based inverse design airfoils and wings. Aerosp Sci Technol2015; 42: 415–428.
SeccoNRMattosBSd. Artificial neural networks to predict aerodynamic coefficients of transport airplanes. Aircraft Eng Aerosp Technol2017; 89: 211–230.
29.
KocukBAltinelİKArasN. Approximating the objective function's gradient using perceptrons for constrained minimization with application in drag reduction. Comput Oper Res2015; 64: 139–158.
30.
MazharFKhanAMChaudhryIA, et al.On using neural networks in uav structural design for CFD data fitting and classification. Aerosp Sci Technol2013; 30: 210–225.
31.
Jean R, Miroslav K and Atle J. Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control. arXiv preprint arXiv:180807664, 2018.
32.
JuYZhangCMaL. Artificial intelligence metamodel comparison and application to wind turbine airfoil uncertainty analysis. Adv Mech Eng2016; 8: 1687814016647317.
33.
SinhaMKuttieriRAGhoshAK, et al.High angle of attack parameter estimation of cascaded fins using neural network. J Aircraft2013; 50: 272–291.
34.
Koziel S and Leifsson L. Knowledge-based airfoil shape optimization using space mapping. In: 30th AIAA applied aerodynamics conference, New Orleans, Louisiana, 2012, p.3016.
35.
Chen J, Sun G and Jin X. Intelligent aerodynamic design for airfoil based on artificial neural network method. In: 2nd international conference on computer and automation engineering, ICCAE, Singapore, 2010, pp.428–436.
36.
BoutemedjetASamardžićMRebhiL, et al.Uav aerodynamic design involving genetic algorithm and artificial neural network for wing preliminary computation. Aerosp Sci Technol2019; 84: 464–483.
37.
Guo X, Li W and Iorio F. Convolutional neural networks for steady flow approximation. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, San Francisco, California, 2016, pp.481–490.
38.
ChenHDengKLiR. Utilization of machine learning technology in aerodynamic optimization. Acta Aeronaut Astronaut Sin2019, pp. 40.
39.
WangXWangSTaoJ, et al.A pca–ann-based inverse design model of stall lift robustness for high-lift device. Aerosp Sci Technol2018; 81: 272–283.
40.
SafikhaniHJamalinasabM. Pareto optimization of two-element wing models with morphing flap using computational fluid dynamics, grouped method of data handling artificial neural networks and genetic algorithms. Int J Eng-Trans A2018; 31: 666–672.
41.
TimnakNJahangirianASeyyedsalehibS. An optimum neural network for evolutionary aerodynamic shape design. Scient Iran Trans B2017; 24: 2490–2500.
42.
Koziel S and Leifsson L. Multi-level surrogate-based airfoil shape optimization. In: 51st AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, Dallas, Texas, 2013, p.778.
43.
RamRKGCooperYNBhatiaV, et al.Design optimization and analysis of naca 0012 airfoil using computational fluid dynamics and genetic algorithm. Appl Mech Mater2014; 664: 111–116.
44.
Ganesh RamRCooperYNBhatiaV, et al.Design optimization and analysis of naca 0012 airfoil using computational fluid dynamics and genetic algorithm. Appl Mech Mater2014; 664: 111–116.
45.
WangSSunGChenW, et al.Database self-expansion based on artificial neural network: an approach in aircraft design. Aerosp Sci Technol2018; 72: 77–83.
46.
HaryantoIUtomoTSSinagaN, et al.Optimization of maximum lift to drag ratio on airfoil design based on artificial neural network utilizing genetic algorithm. Appl Mech Mater2014; 493: 123–128.
47.
DuvigneauRVisonneauM. Hybrid genetic algorithms and artificial neural networks for complex design optimization in CFD. Int J Numer Methods Fluids2004; 44: 1257–1278.
48.
Guang O, Jun L and Ping Z. Aircraft unsteady aerodynamic hybrid modeling based on state-space representation and neural network. In: International conference on pattern recognition applications and methods, vol. 2, Porto, Portugal, 2017, pp.232–239.
49.
LeCunYBottouLBengioY, et al.Gradient-based learning applied to document recognition. Proc IEEE1998; 86: 2278–2324.
50.
Zhang Y, Sung WJ and Mavris DN. Application of convolutional neural network to predict airfoil lift coefficient. In: 2018 AIAA/ASCE/AHS/ASC structures, structural dynamics, and materials conference, Kissimmee, Florida, 2018, p.1903.
51.
Yilmaz E and German B. A convolutional neural network approach to training predictors for airfoil performance. In: 18th AIAA/ISSMO multidisciplinary analysis and optimization conference, Denver, Colorado, 2017, p.3660.
52.
Farimani AB, Gomes J and Pande VS. Deep learning the physics of transport phenomena. arXiv preprint arXiv:170902432, 2017.
