| 高超声速风洞气动力/热试验数据天地相关性研究进展 |
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| 引用本文： | 罗长童,胡宗民,刘云峰,姜宗林.高超声速风洞气动力/热试验数据天地相关性研究进展[J].实验流体力学,2020,34(3):78-89. |
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| 作者姓名： | 罗长童 胡宗民 刘云峰 姜宗林 |
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| 作者单位： | 1.中国科学院力学研究所 高温气体动力学国家重点实验室, 北京 100190 |
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| 摘 要： | 高温真实气体效应、黏性干扰效应和尺度效应等高超声速流动特性突破了实验气体动力学传统的流动相似模拟准则，使得高超声速流动现象超出了经典气体动力学理论能够准确预测的范围。如何利用地面风洞试验数据预测天上的飞行状态，即天地相关性问题，成为制约新型高超声速飞行器研制与发展的关键性科学问题。本文概述了天地相关性的最新研究进展，并重点介绍了多空间相关理论与泛函智能优化关联方法。多空间相关理论认为，从更高维度空间视角看，不同风洞的试验结果都是内在相关的，而飞行试验可视为理想的风洞试验，所以地面风洞试验数据间的关联规律包含了天地相关性问题。泛函智能优化关联方法基于风洞群（能模拟不同参数区段的不同类型风洞）的试验数据，在泛函空间中利用专业化智能学习算法，从高维度的全参数空间出发，进行降维和自适应空间变换，自动推演出不同风洞共同遵守的不变规律，从而实现风洞试验数据的关联。验证实例和应用实践都表明，多空间相关理论与泛函智能优化关联方法是有效的，是高超声速气动力/热天地相关性研究的一个新方向。
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| 关 键 词： | 高超声速 天地相关 智能优化 不变规律 自适应空间变换 |
| 收稿时间： | 2019-12-17 |
Research progress on ground-to-flight correlation of aerodynamic force and heating data from hypersonic wind tunnels |
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| Affiliation: | 1.State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China2.Department of Aerospace Engineering Science, School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | Hypersonic flow characteristics such as high temperature real gas effect, viscous interference effect, Mach number effect and scale effect, do not follow the similarity simulation criterion of experimental gas dynamics, which makes the hypersonic flow phenomenon beyond the range that can be accurately predicted by the classical gas dynamics theory. How to use the ground experimental data to predict the flight state, that is, the problem of ground-to-flight (G2F) correlation, is the key scientific problem that restricts the development of new aerospace vehicles. This paper summarizes the latest research progress of G2F correlation, and focuses on the multi-space correlation theory and correlation method of intelligent functional optimization. According to the theory, from a high-dimensional point of view, the experimental results of different wind tunnels are intrinsically related, and the flight test can be regarded as an ideal wind tunnel experiment. Based on the experimental data of wind tunnel groups (different types of wind tunnels which can simulate different parameter sections), using a specialized intelligent learning algorithm in functional space, the correlation method is performed by starting from the high-dimensional full parameter space. And then by carrying out a series of dimension reduction and adaptive space transformation, the invariant law is automatically deduced that different wind tunnels abide by together, so as to get a formula for G2F correlation. The results of verification examples and preliminary applications show that the multi-space correlation theory and functional intelligent optimization correlation method are effective, which would be a new trend in the research of G2F correlation for hypersonic aerodynamic force and heating. |
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