摘要
结冰风洞云雾参数控制和测量2方面的技术瓶颈,导致结冰试验中的云雾条件存在较大误差,这会降低实验结果的精度。针对这一问题,从空气动力学的角度分析了冰形修正的关键要素,建立了采用人工神经网络技术对云雾参数与冰形典型几何特征量之间复杂非线性关系进行近似模拟的方法,并基于无限插值方法建立了一种冰形修正方法。以NACA0012翼型为例,对液态水含量和水滴粒径这2个云雾参数所带来的冰形误差进行了修正,修正后的冰形与目标冰形的吻合度有明显的改进,验证结果表明该方法可以应用于结冰风洞试验,能为实验结果的修正提供依据。
Precise control of icing wind tunnel cloud parameters and the accuracy of measured parameters are two technical problems of icing wind tunnel experiments.They affect the accuracy of the experiment and bring errors to results.To solve the problems,this paper proposes an ice shape correction method based on cloud parameters.The key element of the ice shape correction is analyzed with the aerodynamic point of view,and the relationship between the cloud parameters and the typical geometric characteristic parameters of the ice is simulated by using artificial neural network technology.A TFI ice shape correction method is established.The NACA0012 airfoil is selected as the research object.The ice shape has been corrected to account for the error caused by the cloud parameters of liquid water content and droplet diameter.The method has significantly improved the agreement between the corrected ice shape and the target shape.It is thus verified that our method is correct and it can be used for icing wind tunnel test.
出处
《实验流体力学》
CAS
CSCD
北大核心
2016年第3期8-13,共6页
Journal of Experiments in Fluid Mechanics
基金
国家自然科学基金(11172314
11472296)
国家重点基础研究发展计划(2015CB755800)
关键词
飞机结冰
冰形修正
几何特征量
结冰风洞
人工神经网络
云雾参数
aircraft icing
ice shape correction method
geometric characteristics
icing wind tunnel
artificial neural networks
cloud parameters
