Predictive flow-field estimation

We are considering the problem of real-time prediction of 3D turbulent velocity fields based on a small number of scalar measurements. The method of proper orthogonal decomposition (POD) allows for the decomposition of an ensemble of velocity fields into a set of spatial basis functions and a set of temporal coefficients. The computation of the temporal coefficients is by no means a trivial matter, especially when one is faced with a large number of modes. In this paper we discuss the use of radial basis function (RBF) models to capture the discrete time evolution and nonlinear dynamics of the POD coefficients. Further, we propose the use of regularized regression techniques to generate models that provide mappings between the POD coefficients and scalar measurements. As a final step towards real-time prediction, the state-space RBF models and regression measurement models are combined using unscented Kalman filters to produce optimal solutions such that a balance between the state models and measurement models is achieved.The proposed methods are tested for two specific cases. The classical Lorenz model is chosen to demonstrate the use and effectiveness of RBF models as a potential candidate for state models. Flow around a wall-mounted cube in a channel at Re=20,000 is considered as the second case. The aim for the second case is to be able to accurately predict the POD coefficients outside the ensemble. It is shown that a large number of POD coefficients is required to approximate the velocity fields with sufficient accuracy. The RBF models are created based on only the temporal information available from the initial ensemble, and it is shown that the RBF model is able to correctly approximate the high-dimensional phase space. Combined with the unscented Kalman filter it is indeed possible to track the evolution of the POD coefficients for a long time. The robustness of the filter is demonstrated by considering the presence of noise in measurements and using measurement information at time steps greater than the evolution time step.     

基于本征正交分解函数的波前动态高分辨率重构

 应用Hartmann波前传感器测量了准直平行光通过低速热射流后的畸变波前时间序列,对该时间序列进行本征正交分解得到系列本征函数和时间系数,对本征函数进行双线性插值处理得到未布置探测器处的本征函数值,新本征函数与时间系数相乘从而获得未布置探测器处的波前数据。在时域和频域比较了波前预测值与实际值,结果表明了该插值方法的有效性,从而实现对波前的高时间、高空间分辨率测量。     

基于自适应本征正交分解混合模型的跨音速流场分析

采用非线性模型替代线性回归模型响应本征正交分解(POD)基函数的系数,并采用自适应抽样方法确定快照集合,实现了基于自适应POD混合模型的跨音速叶片复杂流动分析及流场拟合.首先通过比较基于线性回归模型和非线性回归模型的基函数系数响应精度,验证非线性回归模型的收敛性和精确性;之后通过与静态抽样方法进行对比,研究分析自适应抽样技术的优越性;最后开展基于自适应POD混合模型的全三维跨音速流场分析及流动拟合.结果表明,采用自适应POD混合模型,不仅能够清晰地识别三维跨音速流场中的敏感流动特征,还能精确地拟合设计空间内任意状态的流场及出口气动参数.     

对称槽道涡波流场流动特征的POD分析

为深入分析层流状态下对称槽道内涡波流场的流动特性及其变化规律,对流场进行了二维粒子图像测速(2DPIV)测量获取瞬态速度矢量数据,利用本征正交分解(POD)技术进行模态分解以及涡波流场的重构,然后根据重构的流场对对称槽道内涡波流场进行了平均速度剖面、流场脉动强度以及特征点的速度和频谱分布等方面的分析。结果表明：POD的前15阶模态能够表征涡波流场的主导结构,第1,3阶模态主要表现为一对旋向相反的涡对特征,第2阶模态具有涡旋和波状主流的特征;提取了5个涡旋涡核的位置作为流场流动特性的特征点;根据POD重构流场分析发现流向平均速度呈抛物线形状分布,法向平均速度呈对称分布特征;流向脉动强度受壁面的影响较大,法向脉动强度呈现抛物线形状分布;距离中心主流较近的1^#,4^#,5^#特征点的速度脉动程度受主流的脉动强度影响较大,速度的脉动主频0.15 Hz与次频、流场的自然频率0.35 Hz共同影响特征点的速度分布;2^#,3^#特征点的流向速度呈衰减趋势,法向速度在初期幅度变化较大。     

变系数瞬态热传导问题边界元格式的特征正交分解降阶方法

提出了一种基于边界元法求解变系数瞬态热传导问题的特征正交分解(POD)降阶方法,重组并推导出变系数瞬态热传导问题适合降阶的边界元离散积分方程,建立了变系数瞬态热传导问题边界元格式的POD降阶模型,并用常数边界条件下建立的瞬态热传导问题的POD降阶模态,对光滑时变边界条件瞬态热传导问题进行降阶分析.首先,对一个变系数瞬态热传导问题,建立其边界域积分方程,并将域积分转换成边界积分;其次,离散并重组积分方程,获得可用于降阶分析的矩阵形式的时间微分方程组;最后,用POD模态矩阵对该时间微分方程组进行降阶处理,建立降阶模型并对其求解.数值算例验证了本文方法的正确性和有效性.研究表明:1)常数边界条件下建立的低阶POD模态矩阵,能够用来准确预测复杂光滑时变边界条件下的温度场结果;2)低阶模型的建立,解决了边界元法中采用时间差分推进技术求解大型时间微分方程组时求解速度慢、算法稳定性差的问题.