A simplified shape optimization strategy for blended-wing-body underwater gliders
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Peng Wang | Xinjing Wang | Huachao Dong | Chengshan Li | Peng Wang | Huachao Dong | Xinjing Wang | Chengshan Li
[1] Jian Cao,et al. Hull shape optimization for autonomous underwater vehicles using CFD , 2016 .
[2] Mohammad Reza Banan,et al. A new PSO-based algorithm for multi-objective optimization with continuous and discrete design variables , 2018 .
[3] Abdel-Karim S.O. Hassan,et al. A novel surrogate-based approach for optimal design of electromagnetic-based circuits , 2016 .
[4] D. Paley,et al. Underwater gliders: recent developments and future applications , 2004, Proceedings of the 2004 International Symposium on Underwater Technology (IEEE Cat. No.04EX869).
[5] Paul A. Brandner,et al. Experimental study of the steady fluid-structure interaction of flexible hydrofoils , 2014 .
[6] H. Sobieczky. Parametric Airfoils and Wings , 1999 .
[7] D. Krige. A statistical approach to some basic mine valuation problems on the Witwatersrand, by D.G. Krige, published in the Journal, December 1951 : introduction by the author , 1951 .
[8] R. M. Hicks,et al. Wing Design by Numerical Optimization , 1977 .
[9] M. A. Luersen,et al. Multiobjective optimization of laminated composite parts with curvilinear fibers using Kriging-based approaches , 2018 .
[10] Martin Solan,et al. Imaging Deep-Sea Life Beyond the Abyssal Zone , 2009 .
[11] Joshua Grady Graver,et al. UNDERWATER GLIDERS: DYNAMICS, CONTROL AND DESIGN , 2005 .
[12] D. C. Webb,et al. SLOCUM: an underwater glider propelled by environmental energy , 2001 .
[13] George E. P. Box,et al. Empirical Model‐Building and Response Surfaces , 1988 .
[14] Kwang-Ting Cheng,et al. AN APPROACH TO STRUCTURAL OPTIMIZATION—SEQUENTIAL QUADRATIC PROGRAMMING, SQP , 1984 .
[15] Ruichen Jin,et al. On Sequential Sampling for Global Metamodeling in Engineering Design , 2002, DAC 2002.
[16] Donald W. Mueller,et al. Growth of Binary Alloyed Semiconductor Crystals by the Vertical Bridgman-Stockbarger Process with a Strong Magnetic Field , 2005 .
[17] Tapabrata Ray,et al. Design and construction of an autonomous underwater vehicle , 2014, Neurocomputing.
[18] Michael B. Porter,et al. Glider-Based Passive Acoustic Monitoring Techniques in the Southern California Region , 2009 .
[19] Nan Zhang,et al. Study on Energy and Hydrodynamic Performance of the Underwater Glider , 2006 .
[20] Haili Liao,et al. An adaptive surrogate model based on support vector regression and its application to the optimization of railway wind barriers , 2017 .
[21] Baowei Song,et al. Multi-start Space Reduction (MSSR) surrogate-based global optimization method , 2016 .
[22] Boussad Abbès,et al. Damage Prediction in Metal Forming Process Modeling and Optimization: Simplified Approaches , 2014 .
[23] Thomas J. Santner,et al. The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.
[24] Stefan Görtz,et al. Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling , 2012 .
[25] Donald R. Jones,et al. Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..
[26] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[27] John E. Bussoletti,et al. "Fundamental" Parameteric Geometry Representations for Aircraft Component Shapes , 2006 .
[28] Yung C. Shin,et al. Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems , 1994, IEEE Trans. Neural Networks.
[29] Aleksei Romanenko,et al. Simplified Simultaneous Perturbation Stochastic Approximation for the Optimization of Free Decoding Parameters , 2014, SPECOM.
[30] Paula Varandas Ferreira,et al. A simplified optimization model to short-term electricity planning , 2015 .
[31] Achille Messac,et al. Extended Radial Basis Functions: More Flexible and Effective Metamodeling , 2004 .
[32] H. Stommel. The Slocum Mission , 1989 .
[33] M. Furlong,et al. AUV design – shape, drag and practical issues , 2009 .
[34] Julien Marzat,et al. Worst-case global optimization of black-box functions through Kriging and relaxation , 2012, Journal of Global Optimization.
[35] Jooyoung Park,et al. Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.
[36] R. Davis,et al. The autonomous underwater glider "Spray" , 2001 .
[37] N. M. Nouri,et al. AUV hull shape design based on desired pressure distribution , 2016 .
[38] A. Jahangirian,et al. Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm , 2017 .
[39] C. C. Eriksen,et al. Seaglider: a long-range autonomous underwater vehicle for oceanographic research , 2001 .
[40] Liping Chen,et al. J Glob Optim Table 1 Constraint handling in auxiliary problem for Kriging and radial basis function Surrogate model Constraint handling in auxiliary problem Via penalty Via explicit constraint Kriging Schonlau , 2016 .
[41] Andy J. Keane,et al. Recent advances in surrogate-based optimization , 2009 .
[42] Qiqi Wang,et al. A high order multivariate approximation scheme for scattered data sets , 2010, J. Comput. Phys..
[43] D. Xiu. Numerical Methods for Stochastic Computations: A Spectral Method Approach , 2010 .
[44] José A. García-Rodríguez,et al. An efficient implementation of parallel simulated annealing algorithm in GPUs , 2012, Journal of Global Optimization.
[45] Jian Du,et al. Coach simplified structure modeling and optimization study based on the PBM method , 2016 .
[46] Alexander I. J. Forrester,et al. Multi-fidelity optimization via surrogate modelling , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[47] H. Sobieczky. Computational Methods for the Design of Adaptive Airfoils and Wings , 1980 .