A surrogate model for thermal characteristics of stratospheric airship. Issue 12 (15th June 2018)

• Record Type: Journal Article
• Authors: Zhao, Da; Liu, Dongxu; Zhu, Ming
• Is Part Of: Advances in space research. Volume 61:Issue 12(2018)
• Page Start: 2989
• Page End: 3001
• Publication Date: 2018-06-15
• Subjects: Stratospheric airship -- Surrogate model -- Thermal characteristics -- Least squares support vector regression -- Gravitational search algorithm -- Learning algorithm; Space sciences -- Periodicals; Astronautics -- Periodicals; Geophysics -- Periodicals; 500.505
• DOI: 10.1016/j.asr.2018.03.036
• Languages: English
• ISSNs: 0273-1177

Abstract

Highlights: A surrogate model for thermal characteristic stratospheric aerostat is proposed. The surrogate model based on LSSVR-GSA has great fitting and generalization ability. Pre-treated classification strategy plays a major role in improving model accuracy. Abstract: A simple and accurate surrogate model is extremely needed to reduce the analysis complexity of thermal characteristics for a stratospheric airship. In this paper, a surrogate model based on the Least Squares Support Vector Regression (LSSVR) is proposed. The Gravitational Search Algorithm (GSA) is used to optimize hyper parameters. A novel framework consisting of a preprocessing classifier and two regression models is designed to train the surrogate model. Various temperature datasets of the airship envelope and the internal gas are obtained by a three-dimensional transient model for thermal characteristics. Using these thermal datasets, two-factor and multi-factor surrogate models are trained and several comparison simulations are conducted. Results illustrate that the surrogate models based on LSSVR-GSA have good fitting and generalization abilities. The pre-treated classification strategy proposed in this paper plays a significant role in improving the accuracy of the surrogate model.