Proper orthogonal decomposition based seismic source wavefield reconstruction for finite element reverse time migration

The large storage requirement is a critical issue in cross-correlation imaging-condition based reverse time migration(RTM),because it requires the operation of the source and receiver wavefields at the same time.The boundary value method(BVM),based on the finite difference method(FDM),can be used to reconstruct the source wavefield in the reverse time propagation in the same way as the receiver wavefield,which can reduce the storage burden of the RTM data.Considering that the FDM cannot well handle models with discontinuous material properties and rough interfaces,we develop a source wavefield reconstruction strategy based on the finite element method(FEM),using proper orthogonal decomposition(POD)to enhance computational efficiency.In this method,we divide the whole time period into several segments,and construct the POD basis functions to get a reduced order model(ROM)for the source wavefield reconstruction in each segment.We show the corresponding quantitative analysis of the storage requirement of the POD-FEM.Numerical tests on the homogeneous model show the effectiveness of the proposed method,while the layered model and part of the Marmousi model tests indicate that the POD-FEM can keep an excellent balance between computational efficiency and memory usage compared with the full-stored method(FSM)and the BVM,and can be effectively applied in imaging.

Coherent structures over riblets in turbulent boundary layer studied by combining time-resolved particle image velocimetry(TRPIV),proper orthogonal decomposition(POD),and finite-time Lyapunov exponent(FTLE)

Time-resolved particle image velocimetry（TRPIV） experiments are performed to investigate the coherent structure＇s performance of riblets in a turbulent boundary layer（TBL） at a friction Reynolds number of 185. To visualize the energetic large-scale coherent structures（CSs） over a smooth surface and riblets, the proper orthogonal decomposition（POD） and finite-time Lyapunov exponent（FTLE） are used to identify the CSs in the TBL. Spatial-temporal correlation is implemented to obtain the characters and transport properties of typical CSs in the FTLE fields. The results demonstrate that the generic flow structures, such as hairpin-like vortices, are also observed in the boundary layer flow over the riblets, consistent with its smooth counterpart. Low-order POD modes are more sensitive to the riblets in comparison with the high-order ones,and the wall-normal movement of the most energy-containing structures are suppressed over riblets. The spatial correlation analysis of the FTLE fields indicates that the evolution process of the hairpin vortex over riblets are inhibited. An apparent decrease of the convection velocity over riblets is noted, which is believed to reduce the ejection/sweep motions associated with high shear stress from the viscous sublayer. These reductions exhibit inhibition of momentum transfer among the structures near the wall in the TBL flows.

Model reduction for supersonic cavity flow using proper orthogonal decomposition(POD)and Galerkin projection

The reduced-order model （ROM） for the two-dimensional supersonic cavity flow based on proper orthogonal decomposition （POD） and Galerkin projection is investigated. Presently, popular ROMs in cavity flows are based on an isentropic assumption, valid only for flows at low or moderate Mach numbers. A new ROM is constructed involving primitive variables of the fully compressible Navier-Stokes （N-S） equations, which is suitable for flows at high Mach numbers. Compared with the direct numerical simulation （DNS） results, the proposed model predicts flow dynamics （e.g., dominant frequency and amplitude） accurately for supersonic cavity flows, and is robust. The comparison between the present transient flow fields and those of the DNS shows that the proposed ROM can capture self-sustained oscillations of a shear layer. In addition, the present model reduction method can be easily extended to other supersonic flows.

基于POD和Kriging的水滴收集量快速预测方法

数值模拟手段预测翼面处水滴撞击特性通常较为耗时,为快速准确计算结冰条件下的翼面处水滴收集量,提出了基于本征正交分解和代理模型的水滴收集量快速预测方法。首先对FAR 25部附录C中连续最大结冰条件进行优化拉丁超立方采样,通过数值模拟手段获得各采样点在翼面处水滴收集量分布,从而构造样本空间。在此基础上,利用本征正交分解(Proper orthogonal decompostion,POD)方法找到表达和重构水滴收集量的本征模态以及相应的拟合系数。最后,利用Kriging模型建立样本空间中各采样点与拟合系数间的代理模型,实现翼面处水滴收集量分布的快速预测。经多组工况验证表明:该方法可较为准确地预测翼面处水滴收集量分布,其计算成本较数值模拟方法大幅降低,能够为无人机防除冰设计提供有益参考。

Coherency matrix-based proper orthogonal decomposition with application to wind field simulation

Proper Orthogonal Decomposition （POD） provides a powerful modal transformation tool for stochastic dynamics. In this paper, coherency matrix-based proper orthogonal decomposition （CPOD） is presented as an innovative form of the POD based on cross power spectral density matrices. By introducing a discretizing scheme, the CPOD-based spectral representation method is obtained for use in stochastic simulation. Moreover, some criteria are proposed that allow the truncation order of CPOD to be conveniently determined. A numerical example to illustrate the application of the proposed method for the simulation of a wind velocity field is provided.

利用长短期记忆神经网络的改进POD-Galerkin降阶模型及其在流场预测中的应用

针对标准POD-Galerkin降阶模型在流场快速预测中存在误差而导致精度不高的问题,提出了一种利用长短期记忆神经网络的改进POD-Galerkin降阶模型。使用本征正交分解对流场进行降维,投影得到低维降阶模型,引入两个长短期记忆神经网络,建立从POD-Galerkin降阶模型到实际POD模态时间系数之间的修正映射、低阶模态时间系数与高阶模态时间系数之间的扩展映射,分别用于消除标准POD-Galerkin降阶模型的误差累积和扩展降阶模型的阶数,从而实现物理驱动与数据驱动混合的流动降阶模型的构建。将改进POD-Galerkin降阶模型应用于二维圆柱绕流的流场预测,通过与原始标准POD-Galerkin降阶模型的对比,分析了所提模型的精度和计算速度。结果表明:添加神经网络修正项后的降阶模型相较于标准POD-Galerkin降阶模型,有效提升了降阶模型的精度,预测各阶模态时间系数的均方根误差能够减小1~2个数量级,预测的流场更接近原始流场;在预测相同阶数的情况下,计算时间显著减小,基于4阶和6阶扩展的8阶改进降阶模型相较于原始8阶POD-Galerkin降阶模型预测速度分别提高了约56%和25%。

The proper orthogonal decomposition method for the Navier-Stokes equations

The proper orthogonal decomposition (POD) method for the instationary Navier-Stokes equations is considered. Several numerical approaches to evaluating the POD eigenfunctions are presented. The POD eigenfunctions are applied as a basis for a Galerkin projection of the instationary Navier-Stokes equations. And a low-dimensional ordinary differential models for fluid flows governed by the instationary Navier-Stokes equations are constructed. The numerical examples show that the method is feasible and efficient for optimal control of fluids.

风力机翼型S809绕流流动特性的POD和DMD对比分析

失速时的流动分离现象对风力机叶片的气动性能有重要影响,S809作为典型水平轴风力机翼型,在临界失速攻角下气动性能会大幅降低。基于流动特征提取的非定常流场降阶模型(reduced-order model,ROM)是进一步深入了解非定常流动的重要手段。本文通过计算流体力学方法得到轻、深失速攻角下翼型的流动特征,对时变速度场进行本征正交分解(proper orthogonal decomposition,POD)和动态模态分解(dynamic mode decomposition,DMD)分析,得到轻、深失速下翼型的非定常流场信息(能量占比、模态频率等)。通过两种方法的对比,结果表明,POD和DMD方法能够准确捕捉流动过程中的非定常结构和升力主频相同的典型模态,但是POD方法由于基于能量特征,在捕捉模态时会忽略与升力主频相近但能量较小的流动结构,而基于频率特征的DMD方法能够准确获得场的演化信息(增长率、频率等)。本文研究有利于针对主频结构发展相应的流动控制方法,从而改善翼型流场情况,提高气动性能。

Proper orthogonal decomposition analysis of coherent motions in a turbulent annular jet

A three-dimensional incompressible annular jet is simulated by the large eddy simulation(LES)method at a Reynolds number Re=8500.The time-averaged velocity field shows an asymmetric wake behind the central bluff-body although the flow geometry is symmetric.The proper orthogonal decomposition(POD)analysis of the velocity fluctuation vectors is conducted to study the flow dynamics of the wake flow.The distribution of turbulent kinetic energy across the three-dimensional POD modes shows that the first four eigenmodes each capture more than 1%of the turbulent kinetic energy,and hence their impact on the wake dynamics is studied.The results demonstrate that the asymmetric mean flow in the near-field of the annular jet is related to the first two POD modes which correspond to a radial shift of the stagnation point.The modes 3 and 4 involve the stretching or squeezing effects of the recirculation region in the radial direction.In addition,the spatial structure of these four POD eigenmodes also shows the counter-rotating vortices in the streamwise direction downstream of the flow reversal region.

POD算法在飞机RAT舱温度预测中的应用研究

冲压空气涡轮(RAT)系统是飞机的应急发电系统,在飞机失去主交流电源后为飞机供电,RAT平时回收在非气密区的RAT舱内,在应急工况下释放到气流中。RAT舱温度很大程度上决定了RAT的释放时间,进而影响飞机在应急工况下的安全性,RAT舱的温度预测对于应急供电下的安全性具有重要意义。通过本征正交分解(POD)将RAT舱温度与高度、大气静温、飞行速度、RAT舱周围舱温度作为模型整体进行统一分析,并通过RAT舱温度简化模型及简化模型与完整模型的映射关系,选取试验数据作为样本数据,利用POD算法进行RAT舱温度预测。结果表明:1阶模态占据所有模态的比重超过99%,归一化处理后预测结果的平均绝对误差最小可达到0.063,均方根误差最小达到0.07;POD算法可以在样本数据基础上,通过模态重构有效预测RAT舱的温度信息。

An Economical Approach to Flow-Adaptive Moderation of Spurious Ensemble Correlations and Its Application in the Proper Orthogonal Decomposition-Based Ensemble Four Dimensional Variational Assimilation Method

The purpose of this study is to describe an economical approach to an existing adaptive localization technique and its implementation in the proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar). Owing to the applications of the sparse processing and EOF decomposition techniques, the computational costs of this proposed sparse flow-adaptive moderation(SFAM) localization scheme are significantly reduced. The effectiveness of PODEn4 DVar with SFAM localization is demonstrated by using the Lorenz-96 model in comparison with the Smoothed ENsemble Correlations Raised to a Power(SENCORP) and static localization schemes, separately. The performance of PODEn4 DVar with SFAM localization shows a moderate improvement over the schemes with SENCORP and static localization, with low computational costs under the imperfect model.

Review of Proper Orthogonal Decomposition Method Applied to Computational Aeroelasticity

Over the recent years there has been an increased trend in the use of Reduced Order Models （ROM） for modeling the coupled aeroelastic system. Of all the ROM models, the Proper Orthogonal Decomposition Method （POD） has been the most widely used, reason being the relative simplicity of implementation and the physical insight that it offers towards the physical problem. In this paper we begin by briefly recalling the recent work using POD for the computational aeroelasticity followed by the mathematical formulation. Mathematical formulation is important as it provides understanding of how POD method works. Implementation issues related to the POD method are presented next. Since POD is an empirical technique therefore, it is marred by the robustness issues as is the case with all the ROMs. In the end the variations of POD method, developed over the years are presented along with the most recent trend of using hybrid ROM.

基于POD和代理模型的静气动弹性分析方法

静气动弹性问题考虑弹性结构与定常气动力间的相互耦合作用,对飞行器的性能和安全具有显著的影响.在现代飞行器设计阶段,计算流体力学(CFD)/计算结构力学(CSD)直接耦合方法是精确考察静气动弹性影响的重要手段.然而,基于CFD技术的气动力仿真手段在耦合过程中计算量大且耗时长,难以满足设计阶段的需求.因此,为了兼顾计算精度与效率,文章采用本征正交分解(POD)和Kriging代理模型相结合的模型降阶方法,替代CFD求解过程并耦合有限元分析(FEA)方法,建立了高效、准确的静气动弹性分析框架.相较于传统的以模态法为主的静气动弹性分析方法,该方法能够解决更为复杂的静气动弹性问题以及提供静气动弹性变形过程中的气动分布载荷.针对典型三维跨声速HIRENASD机翼模型开展的马赫数、迎角变化的算例验证表明:由建立的静气动弹性分析方法与CFD/CSD直接耦合方法计算得到机翼翼梢处的静变形量间的相对误差在5%以内;同时该方法预测静平衡位置处的气动分布载荷的误差在5%以内,静气动弹性分析的计算效率至少提升了6倍.

基于POD-ATS-HTS混合变步长方法的油浸式电力变压器绕组瞬态温升计算方法研究

针对当前油浸式电力变压器绕组瞬态温升仿真中,采用固定时间步长效率低的问题,提出一种混合变步长方法。首先,采用初始解优化算法,有效减少计算过程中方程的迭代次数;其次,结合本征正交分解算法(properorthogonal decomposition,POD),改善大规模有限元方程组存在的阶数过高、条件数过大的问题,提高了方程的求解效率和数值稳定性;第三,提出自适应(adaptivetimestepping,ATS)-启发式(heuristic time stepping,HTS)混合变步长算法,通过对时间步长的自适应与启发式调整,有效解决瞬态计算中计算效率与计算精度的对立问题;最后,建立油浸式电力变压器绕组二维单分区分匝的流热耦合仿真模型,以验证所提算法的正确性与高效性。数值计算结果表明:在流场中,与固定步长的计算结果相比,混合变步长算法的误差小于0.46%,计算效率提升了18.45倍;在温度场中,与固定步长的计算结果相比,所提算法的误差小于0.04%,计算效率提升了6倍。同时,通过与传统变步长算法的计算结果对比,说明所提混合变步长算法在计算精度、计算效率及变步长效果方面均具有一定优势。此外,还探讨混合变步长计算中,不同的参数设置对瞬态计算结果及状态变化过程的影响,为其工程应用奠定了一定基础。

基于POD-RBF代理模型和特征点KNN校正的电力舱温度反演方法

为解决数值模拟方法计算不同工况下电力舱整体温度时算力需求大、适应性差等问题,本文提出了一种基于POD-RBF代理模型和特征点KNN校正的电力舱温度反演方法。该方法基于仿真计算得到的不同工况下电力舱温度场数据,利用本征正交分解和径向基函数方法构建电力舱的温度反演代理模型,以避免重复计算,从而快速得到仿真模型的近似解。同时,使用K最近邻算法将特征温度点引入反演模型中,以此校正温度反演误差,提高反演的准确性和适应能力。以实际电力舱为例,对指定工况下的电力舱进行了温度反演。结果表明,该方法可以在电力舱内电缆通流情况以及特征温度点温度已知的情况下实现电力舱的实时温度反演,其反演温度与仿真计算温度的最大相对误差为0.96%,满足工程运用标准。

基于格子Boltzmann和POD方法的非定常流场重建

以二维圆柱雷诺数Re=100绕流为研究对象,在格子Boltzmann方法模拟非定常流动的基础上,利用本征正交分解(proper orthogonal decomposition, POD)算法获取二维圆柱绕流周期性稳定脱落阶段POD基函数及对应的系数,用以实现非定常流场的重建,并研究不同POD模态阶数对重建效果的影响。结果表明:前5阶POD模态占总能量的99%,可以准确地重构流场,流场重构误差最大绝对值为8×10^(-4);随着模态阶数的增加,流场主要特征表达得越细致,且流场重建误差由大幅度降低,缓慢减低到趋于稳定几乎保持不变。

POD降阶方法在含局部非线性悬臂梁中的适应性分析

在非线性结构的振动控制设计中结构模型的阶数不宜过高,为此,本研究以含局部非线性的悬臂梁为研究对象,开展影响POD降阶方法所得低阶模型精度的研究。着重分析了非线性强弱、降阶模型的阶数、POD模态获取源信号的激振类型、响应信号的采样频率和响应信号采样时长等因素对降阶模型响应预测精度的影响。结果表明:对于强非线性的局部非线性悬臂梁系统,POD方法同样适用;在选取源信号的激振类型时,应避免选取脉冲激励信号;响应的采样频率与时长不一定要选取过大。最后,提出了一种针对含有噪声信号应用POD方法的解决方案,可为工程应用提供有益的参考。

基于双加权POD的建筑物风压场重构

本征正交分解法(Proper Orthogonal Decomposition,POD)是一种基于2阶统计量的降阶方法,通过寻找一组正交单位基使得随机场在新坐标下能有更简洁的描述。本文提出了面积和均方根双加权POD,将其应用于建筑物风压场重构。从均方值角度对POD进行推导,证明POD是均方值意义上的最佳展开方式;在新的推导框架下对POD进行双加权优化,使之能较好地捕捉风压场中能量较低的相干结构;对5∶1矩形风压场进行重构,初步验证了双加权POD的可行性。结果表明:双加权POD可以较好地重构5∶1矩形风压场,重构风压场各空间点的重构精度一致,且能够基本还原所有空间点的时程和功率谱密度。与面积加权POD相比,双加权POD能够显著提高风压场低能量区域的降阶模型重构精度。

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。