Trajectory driven multidisciplinary design optimization of a sub-orbital spaceplane using non-stationary Gaussian process

作者: Robin Dufour , Julien de Muelenaere , Ali Elham

DOI: 10.1007/S00158-015-1267-3

关键词:

摘要: This paper presents the multidisciplinary optimization of an aircraft carried sub-orbital spaceplane. The process focused on three disciplines: aerodynamics, structure and trajectory. spaceplane geometry was coupled with its structural weight estimated using empirical formulas. trajectory optimized a pseudo-spectral approach automated mesh refinement that allowed for increasing sparsity Jacobian constraints. aerodynamics computed Euler code results were used to create surrogate model based non-stationary Gaussian procedure specially developed this study.

参考文章(43)
Iñaki Inza, Endika Bengoetxea, Jose A. Lozano, Pedro Larrañaga, Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing) Springer-Verlag New York, Inc.. ,(2006)
Nikolaus Hansen, The CMA Evolution Strategy: A Comparing Review Towards a new evolutionary computation. ,vol. 192, pp. 75- 102 ,(2006) , 10.1007/3-540-32494-1_4
Christian Plagemann, Kristian Kersting, Wolfram Burgard, Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness european conference on machine learning. pp. 204- 219 ,(2008) , 10.1007/978-3-540-87481-2_14
Peter Kaletta, Klaus Wolf, Andreas Fischer, Structural Optimization in Aircraft Engineering using Support Vector Machines Springer, Berlin, Heidelberg. pp. 411- 418 ,(2004) , 10.1007/978-3-642-17022-5_53
T. Lang, C. Plagemann, W. Burgard, Adaptive Non-Stationary Kernel Regression for Terrain Modeling robotics science and systems. ,vol. 03, pp. 352- ,(2007) , 10.15607/RSS.2007.III.011
Christopher K I Williams, Carl Edward Rasmussen, Gaussian Processes for Machine Learning ,(2005)
Tarek Abudawood, Peter Flach, Evaluation Measures for Multi-class Subgroup Discovery european conference on machine learning. pp. 35- 50 ,(2009) , 10.1007/978-3-642-04180-8_20