Performance influences on metamodelling for aerodynamic surrogate-based optimization of an aerofoil

References

• Andres, E., S. Salcedo-Sanz, F. Monge, and A. M. Perez-Bellido. 2012. “Efficient Aerodynamic Design Through Evolutionary Programming and Support Vector Regression Algorithms.” Expert Systems with Applications 39 (12): 10700–10708. doi: 10.1016/j.eswa.2012.02.197
   Web of Science ®Google Scholar
• Bengio, Y., and Y. Grandvalet. 2004. “No Unbiased Estimator of the Variance of K-Fold Cross-Validation.” Journal of Machine Learning Research 5 (1): 1089–1105.
   Google Scholar
• Berke, L., and P. Hajela. 1992. “Applications of Artificial Neural Nets in Structural Mechanics.” Structural Optimization 4 (2): 90–98. doi: 10.1007/BF01759922
   Web of Science ®Google Scholar
• Bollaerts, K., M. Snock, K. Simons, S. Fierens, A. Poffijn, L. Van Bladel, D. Geraets, et al. 2015. “Thyroid Cancer Incidence Around the Belgian Nuclear Sites: Surrogate Exposure Modelling.” Cancer Epidemiology, 39 (1): 48–54. doi: 10.1016/j.canep.2014.10.011
   PubMed Web of Science ®Google Scholar
• Bompard, M., J. Peter, and J. Desideri. 2010. “Surrogate Models Based on Function and Derivative Values for Aerodynamic Global Optimization.” In Proceedings of the Fifth European Conference on Computational Fluid Dynamics (ECCOMAS CFD 2010), edited by J. C. F. Pereira, A. Sequeira and J. M. C. Pereira, 1–17. Lisbon: ECCOMAS.
   Google Scholar
• Box, G. E. P., and K. B. Wilson. 1951. “On the Experimental Attainment of Optimum Conditions.” Journal of the Royal Statistical Society. Series B. 13 (1): 1–45.
   Google Scholar
• Burman, P. A. 1989. “A Comparative Study of Ordinary Cross-Validation, V-Fold Cross-Validation and the Repeated Learning-Testing Methods.” Biometrika 76 (3): 503–514. doi: 10.1093/biomet/76.3.503
   Web of Science ®Google Scholar
• Carpenter, W. C., and J. F. M. Barthelemy. 1992. “A Comparison of polynomial Approximations and Artificial Neural Nets as Response Surfaces.” In 33rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2474–2482. Washington, DC: American Institute of Aeronautics and Astronautics.
   Google Scholar
• Clarke, S. M., J. H. Griebsch, and T. W. Simpson. 2005. “Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses.” Transactions of ASME, Journal of Mechanical Design 127 (6): 1077–1087. doi: 10.1115/1.1897403
   Web of Science ®Google Scholar
• Cook, P. H., M. A. McDonald, and M. C. P. Firmin. 1979. “Aerofoil RAE 2822 - Pressure Distributions, and Boundary Layer and Wake Measurements.” Experimental Data Base for Computer Program Assessment, AGARD Report AR 138.
   Google Scholar
• Couckuyt, I., T. Dhaene, and P. Demeester. 2014. “ooDACE Toolbox: A Flexible Object-Oriented Kriging Implementation.” Journal of Machine Learning Research 15 (Oct): 3183–3186.
   Google Scholar
• Couckuyt, I., F. D. Turck, T. Dhaene, and D. Gorissen. 2011. “Automatic Surrogate Model Type Selection During the Optimization of Expensive Black-Box Problems.” In Proceedings : Winter Simulation Conference, edited by S. Jain, R. Creasey, and J. Himmelspach, 4269–4279. New York, NY: IEEE.
   Google Scholar
• DLR Institute of Aerodynamics and Flow Technology. 2004. “Technical Documentation of the DLR TAU-Code”. Braunschweig, Germany.
   Google Scholar
• Emmerich, M., and B. Naujoks. 2004. “Metamodel Assisted Multiobjective Optimisation Strategies and Their Application in Airfoil Design.” In Adaptive Computing in Design and Manufacture VI, edited by I. C. Parmee, 249–260. London: Springer.
   Google Scholar
• Epstein, B., A. Jameson, S. Peigin, D. Roman, N. Harrison, and J. Vassberg. 2008. “Comparative Study of 3D Wing Drag Minimization by Different Optimization Techniques.” In 46th AIAA Aerospace Sciences Meeting and Exhibit, AIAA 2008–326. Reston, VA: AIAA.
   Google Scholar
• GARTEUR AD/AG-52. 2015. “Surrogate Based Global Optimization Methods in Preliminary Aerodynamic Design.” Garteur Group Annual Report.
   Google Scholar
• Giannakoglou, K. C., D. I. Papadimitriou, and I. C. Kampolis. 2006. “Aerodynamic Shape Design Using Evolutionary Algorithms and New Gradient-Assisted Metamodels.” Computer Methods in Applied Mechanics and Engineering 195 (1): 6312–6329. doi: 10.1016/j.cma.2005.12.008
   Web of Science ®Google Scholar
• Giunta, A., T. Watson, and J. Koehler. 1998. “A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models.” In 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis & Optimization, AIAA-98-4758. Reston, VA: AIAA.
   Google Scholar
• Içten, E., Z. K. Nagy, and G. V. Reklaitis. 2015. “Process Control of a Dropwise Additive Manufacturing System for Pharmaceuticals Using Polynomial Chaos Expansion Based Surrogate Model.” Computers & Chemical Engineering, 83 (1): 221–231. doi: 10.1016/j.compchemeng.2015.07.014
   Web of Science ®Google Scholar
• Jahangirian, A., and A. Shahrokhi. 2011. “Aerodynamic Shape Optimization Using Efficient Evolutionary Algorithms and Unstructured CFD Solver.” Computers & Fluids 46 (1): 270–276. doi: 10.1016/j.compfluid.2011.02.010
   Web of Science ®Google Scholar
• Jeong, S., M. Murayama, and K. Yamamoto. 2005. “Efficient Optimization Design Method Using Kriging Model.” Journal of Aircraft 42 (2): 413–420. doi: 10.2514/1.6386
   Web of Science ®Google Scholar
• Jeong, S., S. Obayashi, and K. Yamamoto. 2005. “Aerodynamic Optimization Design with Kriging Model.” Transactions of the Japan Society for Aeronautical and Space Sciences. 48(161): 161–168. doi: 10.2322/tjsass.48.161
   Web of Science ®Google Scholar
• Jin, R., W. Chen, and T. W. Simpson. 2000. “Comparative Studies of Metamodeling Techniques Under Multiple Modeling Criteria.” In 8th AIAA/NASA/USAF/ISSMO Symposium on Multi-disciplinary Analysis and Optimization, AIAA-2000-4801. Reston, VA: AIAA.
   Google Scholar
• Kashiwamura, T., M. Shiratori, and Q. Yu. 1997. “Statistical Optimization Method.” Computer Aided Optimum Design of Structures. V: 213–227.
   Google Scholar
• Kleijnen, J. P. C. 2009. “Kriging Metamodeling in Simulation: A Review.” European Journal of Operational Research 192 (3): 707–716. doi: 10.1016/j.ejor.2007.10.013
   Web of Science ®Google Scholar
• McKay, M. D., R. J. Beckman, and W. J. Conover. 1979. “A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code.” Technometrics, 21 (2): 239–245. doi:doi: 10.2307/1268522.
   Web of Science ®Google Scholar
• Nain, P. K., and K. Deb. 2003. “Computationally Effective Search and Optimization Procedure using Coarse to Fine Approximation.” In Proceedings of the Congress on Evolutionary Computation (CEC 2003), 2081–2088. Piscataway, NJ: IEEE.
   Google Scholar
• Ong, Y. S., P. B. Nair, and A. J. Keane. 2003. “Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling.” AIAA Journal 41 (4): 687–696. doi: 10.2514/2.1999
   Web of Science ®Google Scholar
• Ong, Y. S., P. B. Nair, A. J. Keane, and K. W. Wong. 2005. “Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems.” In Knowledge Incorporation in Evolutionary Computation, edited by Y. Jin, 307–331. Berlin: Springer.
   Google Scholar
• Osiceanu, A. M., L. E. Murao, D. Kollanur, J. Swinnen, A. R. De Vleeschuwer, D. J. Lefebvre, K. De Clerq, J. Neyts, and N. Goris. 2014. “In Vitro Surrogate Models to aid in the Development of Antivirals for the Containment of Foot-and-Mouth Disease Outbreaks.” Antiviral Research, 105 (1): 59–63. doi: 10.1016/j.antiviral.2014.02.009
   PubMed Web of Science ®Google Scholar
• Papila, N., W. Shyy, N. Fitz-Coy, and R. T. Haftka. 1999. “Assessment of Neural Net and Polynomial-Based Techniques for Aerodynamic Applications.” In AIAA 17th Applied Aerodynamics Conference, AIAA-99-3167. Reston, VA: IAAA.
   Google Scholar
• Pierson, J. C., A. Mortelliti, P. S. Barton, P. W. Lane, and D. B. Lindenmayer. 2016. “Evaluating the Effectiveness of Overstory Cover as a Surrogate for Bird Community Diversity and Population Trends.” Ecological Indicators 61 (2): 790–798. doi: 10.1016/j.ecolind.2015.10.031
   Web of Science ®Google Scholar
• Praveen, C., and R. Duvigneau. 2007. “Radial Basis Functions and Kriging Metamodels for Aerodynamic Optimization.” INRIA Technical report.
   Google Scholar
• Ratle, A. 2001. “Kriging as a Surrogate Fitness Landscape in Evolutionary Optimization.” Artificial Intelligence for Engineering Design Analysis and Manufacturing 15 (1): 37–49. doi: 10.1017/S0890060401151024
   Web of Science ®Google Scholar
• Sacks, J., S. B. Schiller, and W. J. Welch, 1989. Designs for Computer Experiments. Technometrics, 31(1): 41–47. doi: 10.1080/00401706.1989.10488474
   Web of Science ®Google Scholar
• Sacks, J., W. J. Welch, T. J. Mitchell, and H. P. Wynn. 1989. “Design and Analysis of Computer Experiments.” Statistical Science 4 (4): 409–423. doi: 10.1214/ss/1177012413
   Google Scholar
• Sakata, S., F. Ashida, and M. Zako. 2002. “Optimization of Laminated Composites for Eigenfrequency Problem Using K-CSSL Method.” In Proceedings of the Tenth US–Japan Conference on Composite Materials, edited by F.-K. Chang, 560–569. Stanford, CA: DEStech Publishers.
   Google Scholar
• Sakata, S., F. Ashida, and M. Zako. 2003a. “An Efficient Algorithm for Kriging Approximation and Optimization with Large-Scale Sampling Data.” Computer Methods in Applied Mechanics and Engineering 193 (3-5): 385–404. doi: 10.1016/j.cma.2003.10.006
   Web of Science ®Google Scholar
• Sakata, S., F. Ashida, and M. Zako. 2003b. “Structural Optimization Using Kriging Approximation.” Computer Methods in Applied Mechanics and Engineering 192 (7–8): 923–939. doi: 10.1016/S0045-7825(02)00617-5
   Web of Science ®Google Scholar
• Saltelli, A., M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola. 2008. Global Sensitivity Analysis: The Primer. Chichester: John Wiley & Sons.
   Google Scholar
• Saltelli, A., S. Tarantola, F. Campolongo, and M. Ratto. 2004. Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Chichester: John Wiley & Sons.
   Google Scholar
• Shahrokhi, A., and A. Jahangirian. 2010. “A Surrogate Assisted Evolutionary Optimization Method with Application to the Transonic Airfoil Design.” Engineering Optimization 42 (6): 497–515. doi: 10.1080/03052150903305468
   Web of Science ®Google Scholar
• Shan, S., and G. Wang. 2010. “Survey of Modeling and Optimization Strategies to Solve High-Dimensional Design Problems with Computationally-Expensive Black-Box Functions.” Structural and Multidisciplinary Optimization 41 (2): 219–241. doi: 10.1007/s00158-009-0420-2
   Web of Science ®Google Scholar
• Simpson, T. W., D. K. J. Lin, and W. Chen. 2001. “Sampling Strategies for Computer Experiments: Design and Analysis.” International Journal of Reliability and Applications, 2 (3): 209–240.
   Google Scholar
• Simpson, T. W., T. M. Mauery, J. J. Korte, and F. Mistree. 1998. “Comparison of Response Surface and Kriging Models for Multidisciplinary Design Optimization.” In 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis & Optimization, AIAA-98-4755. Reston, VA: AIAA.
   Google Scholar
• Sobieski, I. P., V. M. Manning, and I. M. Kroo. 1998. “Response Surface Estimation and Refinement in Collaborative Optimization.” In 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, AIAA-98-4753, 4753–4770. Reston, VA: AIAA.
   Google Scholar
• Song, M. K., S. E. Ward, J. P. Fine, L. C. Hanson, F. C. Lin, G. A. Hladik, J. B. Hamilton, and J. C. Bridgman. 2015. “Advance Care Planning and End-of-Life Decision Making in Dialysis: A Randomized Controlled Trial Targeting Patients and Their Surrogates.” American Journal of Kidney Diseases 66 (5): 813–822. doi: 10.1053/j.ajkd.2015.05.018
   PubMed Web of Science ®Google Scholar
• Steuben, J., J. Micholopoulos, A. Iliopoilos, and C. Turner. 2015. “Inverse Characterization of Composite Materials via Surrogate Modeling.” Composite Structures, 132 (1): 694–708. doi: 10.1016/j.compstruct.2015.05.029
   Web of Science ®Google Scholar
• Stigler, S. M. 1999. Statistics on the Table: The History of Statistical Concepts and Methods. Cambridge, MA: Harvard University Press.
   Google Scholar
• Thamarai-Selvi, S., E. Mahendran, and S. Rama. 2009. “Neural Network-Based Interpolation and Extrapolation of Wind Tunnel Test Data.” International Journal of Computational Intelligence and Applications 8 (2): 225–235. doi:doi: 10.1142/S1469026809002564.
   Google Scholar
• Viúdez-Moreiras, D., E. Andrés, D. González, and M. J. Martin Burgos. 2016. “Performance Comparison of Kriging and SVR Surrogate Models Applied to the Objective Function Prediction within Aerodynamic Shape Optimization.” In Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (CFD 2016), edited by M. Papadrakakis, V. Papadopoulos, G. Stefanou, and V. Plevris, 4104–4116. Athens: National Technical University of Athens.
   Google Scholar
• Viúdez-Moreiras, D., M. J. Martin-Burgos, R. Abarca, E. Andrés-Pérez, J. Ponsín, and F. Monge. 2017. “Surrogate Modeling for the Main Landing Gear Doors of an Airbus Passenger Aircraft.” Aerospace Science and Technology 68 (1): 135–148. doi: 10.1016/j.ast.2017.04.021
   Web of Science ®Google Scholar
• Wu, X., Y. Zheng, B. Wu, Y. Tian, F. Han, and C. Zheng. 2016. “Optimizing Conjunctive Use of Surface Water and Groundwater for Irrigation to Address Human-Nature Water Conflicts: A Surrogate Modeling Approach.” Agricultural Water Management 163 (1): 380–392. doi: 10.1016/j.agwat.2015.08.022
   Web of Science ®Google Scholar
• Yin, Y., and P. Shang. 2015. “Modified Cross Sample Entropy and Surrogate Data Analysis Method for Financial Time Series.” Physica A: Statistical Mechanics and its Applications, 433 (1): 17–25. doi: 10.1016/j.physa.2015.03.055
   Web of Science ®Google Scholar
• Zhou, Z., Y. S. Ong, P. B. Nair, A. J. Keane, and K. Y. Lum. 2007. “Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization.” IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 37 (1): 66–76. doi: 10.1109/TSMCC.2005.855506
   Web of Science ®Google Scholar