考虑动力学效应的结冰试验相似准则修正方法

结冰相似准则是将飞行条件转换为试验参数的重要理论方法,然而现有的相似准则多基于小水滴结冰过程而提出,其在应用于大水滴的相似转换时会出现较大偏差。针对这一现状,开展考虑水滴动力学特性的结冰相似准则研究。首先,基于ONERA相似准则,融入关于水滴变形破碎和飞溅动力学特性的相似参数,提出了修正的结冰相似准则;其次,基于这两种相似准则,采用数值模拟的方法计算获得了修正前后的收集系数,并对比验证了修正方法的有效性;最后,分析两种修正方法得到的试验参数随尺寸缩比的变化情况,给出了所修正的相似准则在结冰试验中的应用建议。结果表明,本文修正方法提高了局部收集系数的吻合度,降低了收集系数平均误差和撞击极限误差,且应用该准则得到的缩比试验参数在结冰风洞的设计范围之内。该修正方法可以为过冷大水滴结冰风洞试验相似变换提供指导。

基于机器学习的结冰风洞温度场均匀性分析

为提高结冰风洞的试验效率和流场品质,需建立温度快速预测方法以及提高风洞温度场均匀性。针对结冰风洞换热器出口气流温度建立了基于机器学习的预测模型,为解决真实场景下数据量小的问题,采用变分自编码器(VAE)进行数据增强,后采用遗传算法优化XGBoost方法建立最终的温度预测模型;在此基础上,利用已建立预测模型对风洞温度场均匀性及其影响因素进行了分析。结果表明:通过模型预测的换热器出口气流温度与真实值的平均绝对误差(MAE)约为0.60℃,R^(2)分数约为98.38%;试验风速、模拟高度以及回气压力等工况参数对温度场均匀性均有一定影响。

POD-BPNN预测模型及结冰条件不确定性量化

当前,数值模拟作为研究飞机结冰的主要手段之一,在计算结冰冰形时会引入大量参数不确定性,并影响数值模拟的精度和可信度。发展不确定性量化方法,科学量化这种不确定性对评估数值模拟结果具有重要意义。针对传统参数不确定性量化方法难以解决高维输入到输出的问题,基于本征正交分解和误差反向传播神经网络,提出了一种结冰冰形预测代理模型。以水滴中值粒径和温度为例,验证了代理模型单输入参数和双输入参数情况下的精度和泛化能力。最后,在代理模型计算的冰形基础上,结合蒙特卡洛采样,利用2σ准则确定结冰范围,发现水滴中值粒径不确定性主要影响明冰的冰角生长,而温度和水滴中值粒径不确定性的叠加主要作用于霜冰厚度。该研究为后续多结冰条件的影响分析和多维输入到输出的不确定性量化提供了思路。

基于BP神经网络的冰形特征参数预测

机翼结冰影响了飞机飞行的气动特性,严重时将会引起事故,对冰形特征参数进行预测对翼型气动特性研究以及后续防除冰措施具有重要的意义。本文利用BP神经网络,建立翼型冰形特征参数预测模型,并采用k折交叉验证进行网络结构选择,以气象与飞行条件作为输入,结冰极限、冰角高度和角度等冰形特征参数作为输出。结果表明:预测的冰形特征参数(除下冰角高度外)与数值结果相对误差低于5%,证明该方法具有较好的预测效果。

3D Ice Shape Description Method Based on BLSOM Neural Network

When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.

Prediction Model of Aircraft Icing Based on Deep Neural Network

Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions.Due to the complexity of the icing process,the rapid assessment of ice shape remains an important challenge.In this paper,an efficient prediction model of aircraft icing is established based on the deep belief network(DBN)and the stacked auto-encoder(SAE),which are all deep neural networks.The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation.After that the model is applied on the ice shape evaluation of NACA0012 airfoil.The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction.The model provides an important tool for aircraft icing analysis.

风洞侧壁干扰对机翼表面霜冰的影响

采用数值模拟的方法研究了不同阻塞度下侧壁干扰对NACA0012翼型表面霜冰生长的影响。通过对流场和水滴撞击特性的研究发现:侧壁干扰会压缩来流空气,致使更多水滴跟随气流向机翼前缘汇集,增大了翼面水滴收集系数,且翼面水滴总收集量与阻塞度成正比、与液滴雷诺数成反比;同时会改变水滴的撞击方向,使水滴撞击极限和最大水收集系数的位置发生偏移。采用多步法对结冰特性的研究发现:在基准计算工况下,当阻塞度为20%时,侧壁干扰效应带来结冰量的相对附加增量约为16%;在同一阻塞度下,各时间步长内的结冰相对附加增量基本相等。提出了一种表征侧壁干扰对水滴收集影响强弱的因子,该影响因子与水滴收集相对附加增量具有较高的线性关系。