Efficient unsteady aerodynamic loads prediction based on nonlinear system identification and proper orthogonal decomposition

In the present work, an efficient surrogate-based framework is developed for the prediction of motion-induced surface pressure fluctuations and integral force and moment coefficients. The model construction is realized by performing forced-motion computational fluid dynamics (CFD) simulations, while the result is processed via the proper orthogonal decomposition (POD) to obtain the predominant flow modes. Subsequently, a nonlinear system identification is carried out with respect to the applied excitation and the resulting POD coefficients. For the input/output model identification task, a recurrent local linear neuro-fuzzy approach is employed in order to capture the linear and nonlinear characteristics of the dynamic system. Once the reduced-order model (ROM) is trained, it can substitute the flow solver within unsteady aerodynamic or aeroelastic simulation frameworks for a given configuration at fixed freestream conditions. For demonstration purposes, the ROM approach is applied to the LANN wing in high subsonic and transonic flow. Due to the characteristic lambda-shock system, the unsteady aerodynamic surface pressure distribution is dominated by nonlinear effects. Numerical investigations show a good correlation between the results obtained by the ROM methodology in comparison to the full-order CFD solution. In addition, the surrogate approach yields a significant speed-up regarding unsteady aerodynamic calculations, which is beneficial for multidisciplinary computations.     

Unsteady aerodynamic reduced-order modeling of an aeroelastic wing using arbitrary mode shapes

Computational fluid dynamics (CFD) based unsteady aerodynamic reduced-order model (ROM) can offer significant improvements to the efficiency of transonic aeroelastic analysis. To construct a ROM based on mode shapes, one run of CFD solver is needed to compute aerodynamic responses corresponding to mode excitations. When mode shapes change with structure, another run of the CFD solver is required to construct the new ROM. The typically large computational cost associated with repeated runs of the CFD solver impedes the application of existing unsteady aerodynamic reduced-order modeling methods to transonic aeroelastic design optimization and aeroelastic uncertainty analysis. This paper demonstrates a method that can replace the CFD solver used in the process of existing unsteady aerodynamic reduced-order modeling. It can produce aerodynamic responses corresponding to mode excitations for arbitrary mode shapes within a few seconds. Computational cost can be reduced by two orders of magnitude using the mode excitations and the corresponding aerodynamic responses computed by the method to construct the ROMs used for flutter analyses in aeroelastic design optimization or aeroelastic uncertainty analysis in transonic regime compared with the existing unsteady aerodynamic reduced-order modeling methods. Results show that the method can accurately produce the aerodynamic responses corresponding to the mode excitations and predict the flutter characteristics of AGARD 445.6 wings root-attached in three different ways.     

Component Mode Synthesis Using Nonlinear Normal Modes

This paper describes a methodology for developing reduced-order dynamic models of structural systems that are composed of an assembly of nonlinear component structures. The approach is a nonlinear extension of the fixed-interface component mode synthesis (CMS) technique developed for linear structures by Hurty and modified by Craig and Bampton. Specifically, the case of nonlinear substructures is handled by using fixed-interface nonlinear normal modes (NNMs). These normal modes are constructed for the various substructures using an invariant manifold approach, and are then coupled through the traditional linear constraint modes (i.e., the static deformation shapes produced by unit interface displacements). A class of systems is used to demonstrate the concept and show the effectiveness of the proposed procedure. Simulation results show that the reduced-order model (ROM) obtained from the proposed procedure outperforms the ROM obtained from the classical fixed-interface linear CMS approach as applied to a nonlinear structure. The proposed method is readily applicable to large-scale nonlinear structural systems that are based on finite-element models.     

基于气动力辨识的ASE模型降阶研究

CFD/CSD耦合计算能够准确预测跨音速段飞行器弹性振动的非定常气动力, 但其带来的巨大 计算量及高阶维数不利于气动弹性系统的分析与综合. 针对于此,采用系统辨识及 均衡截断技术对高阶气动伺服弹性模型进行降阶处理,并利用所得到的低阶模型进行系统综 合：(1) 基于Volterra级数气动力辨识技术,得到非定常气动力的时域降阶模型(ROM), 耦合结构动力学模型及控制机构动力学模型获得气动伺服弹性(ASE)状态空间方 程;(2) 利用均衡截段法对时域ASE模型进行进一步降阶,得到能够较真实反映所关心频域内系统响应 的低阶ASE模型;(3) 针对建模误差和降阶误差存在造成的系统不确定性问题,结合降阶模型 采用混合灵敏度$H_{\infty}$控制方法设计颤振主动抑制鲁棒控制律,保证其作用 于真实系统的有效性;对控制器进行 均衡阶段降阶并保持其鲁棒性,得到低阶鲁棒的颤振抑制控制器. 最后利用典型的BACT模型 进行气动伺服弹性的降阶及主动颤振抑制控制,仿真结果表明,基于ROM建立的低阶气动弹 性模型能够较真实地反应系统的颤振特性;而基于截断后的降阶模型所设计的低阶鲁棒控制 器能够有效应用于存在不确定性摄动的实际系统,并将系统颤振速度提高36%.     

A non-intrusive approach for the reconstruction of POD modal coefficients through active subspaces

Reduced order modeling (ROM) provides an efficient framework to compute solutions of parametric problems. Basically, it exploits a set of precomputed high-fidelity solutions—computed for properly chosen parameters, using a full-order model—in order to find the low dimensional space that contains the solution manifold. Using this space, an approximation of the numerical solution for new parameters can be computed in real-time response scenario, thanks to the reduced dimensionality of the problem. In a ROM framework, the most expensive part from the computational viewpoint is the calculation of the numerical solutions using the full-order model. Of course, the number of collected solutions is strictly related to the accuracy of the reduced order model. In this work, we aim at increasing the precision of the model also for few input solutions by coupling the proper orthogonal decomposition with interpolation (PODI)—a data-driven reduced order method—with the active subspace (AS) property, an emerging tool for reduction in parameter space. The enhanced ROM results in a reduced number of input solutions to reach the desired accuracy. In this contribution, we present the numerical results obtained by applying this method to a structural problem and in a fluid dynamics one.     

基于特征正交分解的非定常气动力建模技术

采用特征正交分解(proper orthogonal decomposition, POD)方法, 建立了基于状态空间的非定常气动力降阶模型, 并耦合结构方程, 建立了降阶的气动弹性系统, 开展了颤振分析的初步研究, 计算效率提高了2~3个数量级. 具体过程是:首先获取全阶系统的频域快照构成关联矩阵, 通过对关联矩阵进行奇异值分解提取流场模态(或流场基), 对低能量模态截断形成降阶子空间, 并将其映射到全阶系统, 从而形成基于状态空间的降阶非定常气动力模型. 对气动弹性标模AGARD445.6进行算例验证, 证明了降阶方法正确, 可以提供高效、高精度的气动弹性分析.      

大长细比导弹的气动弹性降阶模型

对于大长细比导弹,需要在设计阶段准确计算气动弹性/气动伺服弹性,但其复杂的气动力给计算带来困难,因此气动力降阶模型是突破大长细比导弹跨音速气动弹性分析与控制瓶颈的关键技术.虽然气动力模型降阶方法已在预测二维机翼结构的气动弹性方面取得重要进展,但几乎未见关于全机模型的气动力降阶模型研究报道.本文基于递归Wiener模型的气动力降阶方法,利用CFD计算的气动力作为模型辨识数据,用鲁棒子空间和Levenberg-Marquardt算法辨识降阶模型参数,建立了大长细比导弹气动力降阶模型.在此基础上与大长细比导弹有限元模型相结合,构造出气动弹性降阶模型,并在数值仿真中测试气动弹性降阶模型在不同马赫数下的适用性.数值仿真结果表明,该气动弹性降阶模型能够精确预测导弹模型在不同飞行条件下的非定常气动力和导弹模型的气动弹性频率响应特性.     

International journal of computational fluid dynamics real-time prediction of unsteady flow based on POD reduced-order model and particle filter

An integrated method consisting of a proper orthogonal decomposition (POD)-based reduced-order model (ROM) and a particle filter (PF) is proposed for real-time prediction of an unsteady flow field. The proposed method is validated using identical twin experiments of an unsteady flow field around a circular cylinder for Reynolds numbers of 100 and 1000. In this study, a PF is employed (ROM-PF) to modify the temporal coefficient of the ROM based on observation data because the prediction capability of the ROM alone is limited due to the stability issue. The proposed method reproduces the unsteady flow field several orders faster than a reference numerical simulation based on Navier–Stokes equations. Furthermore, the effects of parameters, related to observation and simulation, on the prediction accuracy are studied. Most of the energy modes of the unsteady flow field are captured, and it is possible to stably predict the long-term evolution with ROM-PF.     

Low-order modelling of shallow water equations for sensitivity analysis using proper orthogonal decomposition

This article presents a reduced-order model (ROM) of the shallow water equations (SWEs) for use in sensitivity analyses and Monte-Carlo type applications. Since, in the real world, some of the physical parameters and initial conditions embedded in free-surface flow problems are difficult to calibrate accurately in practice, the results from numerical hydraulic models are almost always corrupted with uncertainties. The main objective of this work is to derive a ROM that ensures appreciable accuracy and a considerable acceleration in the calculations so that it can be used as a surrogate model for stochastic and sensitivity analyses in real free-surface flow problems. The ROM is derived using the proper orthogonal decomposition (POD) method coupled with Galerkin projections of the SWEs, which are discretised through a finite-volume method. The main difficulty of deriving an efficient ROM is the treatment of the nonlinearities involved in SWEs. Suitable approximations that provide rapid online computations of the nonlinear terms are proposed. The proposed ROM is applied to the simulation of hypothetical flood flows in the Bordeaux breakwater, a portion of the ‘Rivière des Prairies' located near Laval (a suburb of Montreal, Quebec). A series of sensitivity analyses are performed by varying the Manning roughness coefficient and the inflow discharge. The results are satisfactorily compared to those obtained by the full-order finite volume model.     

Model order reduction for dynamical systems: A geometric approach

The aim of this paper is to ask the question as whether it is possible, for a given dynamical system defined by a vector field over a finite dimensional inner product space, to construct a reduced-order model over a finite dimensional manifold. In order to give a positive answer to this question, we prove that if the manifold under consideration is an immersed submanifold of the vector space, considered as ambient manifold, then it is possible to construct explicitly a reduced-order vector field over this submanifold. In particular, we found that the reduced-order vector field satisfies the variational principle of Dirac–Frenkel and that we can formulate the Proper Orthogonal Decomposition under this framework. Finally, we propose a local-point estimator of the time-dependent error between the original vector field and the reduced-order one.     

Model reduction using Dynamic Mode Decomposition

Dynamic Mode Decomposition (DMD) is a recent post-processing technique that extracts from snapshots dynamic relevant information for the flow. Without explicit knowledge of the dynamical operator, the DMD algorithm determines eigenvalues and eigenvectors of an approximate linear model. The ability of DMD to extract dynamically relevant features of the flow predispose it for building a representative reduced-order subspace from the data and for deriving a reduced-order model. The use of the DMD for reduced-order modelling will be investigated in this paper on experimental flow data of a cylinder wake.     

A POD-based reduced-order model for uncertainty analyses in shallow water flows

ABSTRACT

This work presents an intrusive reduced-order model (IROM) for uncertainty propagation analyses for flood flows. The 2D shallow water equations are reduced using Galerkin’s projection onto bases obtained from the snapshot-based proper orthogonal decomposition technique. To speed up the computations, the non-polynomial and nonlinear momentum and friction terms are judiciously approximated and the time accuracy issues are addressed using the principal interval decomposition technique. The performance of the IROM is investigated in some test cases. Also, this model is applied to the study of uncertainty propagation for a hypothetical flood in a real river, to derive a probabilistic flood map. The upstream discharge and the Manning roughness coefficient are considered as the uncertain parameters. For relatively small variations around the mean of the inputs, the comparisons of the statistical moments (mean and standard deviation) of the water depth show errors, between the reduced and full models, less than 0.72%. These simulations were completed at up to 50 times faster using the proposed reduced model.     

基于RBF插值技术的CFD/CSD非线性耦合分析方法研究

基于非结构混合网格的N-S方程求解器和结构柔度影响系数法,发展了一种考虑气动、结构非线性的基于RBF插值技术CFD/CSD耦合分析方法,适用于解决现代大展弦比飞机的非线性静气动弹性问题。该方法采用时间相关法（即求解非定常方程组,用长时间的渐近解趋于定常状态）求解静气弹分析时的定常流动。考虑大展弦比飞机结构变形问题为大变形小应力问题,在利用柔度系数法求解结构方程时,假设每次求解结构方程时应力与应变为线性关系,整体静气弹分析过程为非线性关系,因此每次求解结构方程时要更新柔度影响系数矩阵。在非定常N-S方程每求解一个时间步耦合一次结构有限元分析,由于结构有限元分析的时间相对于气动分析时间是很短的,所以这种方法实际上近似使用了一次求解非定常气动力的时间完成了整个静气动弹性分析的过程。对于气动网格与结构有限元网格不一致性,本文采用径向基函数（RBF）插值方法中的TPS方法进行结构弹性变形和气动载荷插值,采用虚功原理完成气动载荷数据交换。为了节省气弹分析时间,采用动网格方法对气动网格进行更新,本文基于RBF插值方法发展一种适用于混合网格（四面体、三棱柱、金字塔和六面体）变形的动网格方法,可以保证附面层网格的质量与分布从而准确模拟其流动。利用该方法对M6机翼、DLR-F6翼身组合体和某大型客机机翼进行了静气动弹性特性分析,结果验证了本文开发的非线性CFD/CSD耦合分析方法的可行性、精确性和高效性。     

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.     

Model reduction for reacting flow applications

A model reduction approach based on Galerkin projection, proper orthogonal decomposition (POD), and the discrete empirical interpolation method (DEIM) is developed for chemically reacting flow applications. Such applications are challenging for model reduction due to the strong coupling between fluid dynamics and chemical kinetics, a wide range of temporal and spatial scales, highly nonlinear chemical kinetics, and long simulation run-times. In our approach, the POD technique combined with Galerkin projection reduces the dimension of the state (unknown chemical concentrations over the spatial domain), while the DEIM approximates the nonlinear chemical source term. The combined method provides an efficient offline–online solution strategy that enables rapid solution of the reduced-order models. Application of the approach to an ignition model of a premixed H₂/O₂/Ar mixture with 19 reversible chemical reactions and 9 species leads to reduced-order models with state dimension several orders of magnitude smaller than the original system. For example, a reduced-order model with state dimension of 60 accurately approximates a full model with a dimension of 91,809. This accelerates the simulation of the chemical kinetics by more than two orders of magnitude. When combined with the full-order flow solver, this results in a reduction of the overall computational time by a factor of approximately 10. The reduced-order models are used to analyse the sensitivity of outputs of interest with respect to uncertain input parameters describing the reaction kinetics.     

REDUCED-ORDER MODELS OF UNSTEADY VISCOUS FLOWS IN TURBOMACHINERY USING VISCOUS–INVISCID COUPLING

The proper orthogonal decomposition technique is applied in the frequency domain to obtain a reduced-order model of the flow in a turbomachinery cascade. The flow is described by an inviscid–viscous interaction model where the inviscid part is described by the full potential equation and the viscous part is described by an integral boundary layer model. The fully nonlinear steady flow is computed and the unsteady flow is linearized about the steady solution. A frequency-domain model is constructed and validated, showing to provide similar results when compared with previous computational and experimental data presented in the literature. The full model is used to obtain a reduced-order model in the frequency domain. A cascade of airfoils forming a slightly modified Tenth Standard Configuration is investigated to show that the reduced-order model with only 25 degrees of freedom accurately predicts the unsteady response of the full system with approximately 10 000 degrees of freedom.     

Multi-fidelity surrogate reduced-order modeling of steady flow estimation

A multi-fidelity reduced-order model (ROM), which incorporates low-fidelity data to improve the prediction of high-fidelity results, is proposed for the reconstruction of steady flow field at different conditions. The spatial basis functions of low-fidelity and high-fidelity data, which are generated for all training sets are extracted separately by proper orthogonal decomposition. Then a surrogate model is used to construct mappings between the mode coefficients obtained from low-fidelity and the high-fidelity data. In the online stage, both the low-fidelity flow at the predicted state and the surrogate model are needed to predict the mode coefficients of the high-fidelity flow, and the high-fidelity flow field is subsequently reconstructed. This method differs from existing surrogate-based reduced-order modeling method because it allows the use of partial physical information for flow estimation, which is coming from the low-fidelity data instead of adopting a black-box mapping between system state and the projection coefficients. Numerical studies are presented for a lid-driven cavity problem and transonic flow past a NACA0012 airfoil. Two low-fidelity models, with either a coarse mesh or a lower numerical order, are considered respectively. Results show that the proposed multi-fidelity ROM predicts the flow field accurately and outperforms the traditional methods in both interpolated and extrapolated conditions.     

Nonlinear Modal Analysis of Structural Systems Using Multi-Mode Invariant Manifolds

In this paper, an invariant manifold approach is introduced for the generationof reduced-order models for nonlinear vibrations of multi-degrees-of-freedomsystems. In particular, the invariant manifold approach for defining andconstructing nonlinear normal modes of vibration is extended to the case ofmulti-mode manifolds. The dynamic models obtained from this technique capture the essential coupling between modes of interest, while avoiding coupling fromother modes. Such an approach is useful for modeling complex systemresponses, and is essential when internal resonances exist between modes.The basic theory and a general, constructive methodology for the method arepresented. It is then applied to two example problems, one analytical andthe other finite-element based. Numerical simulation results are obtainedfor the full model and various types of reduced-order models, including theusual projection onto a set of linear modes, and the invariant manifoldapproach developed herein. The results show that the method is capable ofaccurately representing the nonlinear system dynamics with relatively fewdegrees of freedom over a range of vibration amplitudes.     

An efficient reduced-order modelling approach based on fluid eigenmodes and boundary element method

This paper presents an efficient reduced-order modelling approach based on the boundary element method. In this approach, the eigenvalue problem of the unsteady flows is defined based on the unknown wake singularities. By constructing this reduced-order model, the body quasi-static eigenmodes are removed from the eigensystem and it is possible to obtain satisfactory results without using the static correction technique when enough eigenmodes are used. In addition to the conventional method, eigenanalysis and reduced-order modelling of unsteady flows over a NACA 0012 airfoil, a wing with NACA 0012 section and a wing–body combination are performed using the proposed reduced order modelling (ROM) method. Numerical examples are presented that demonstrate the accuracy and computational efficiency of the present method.     

基于ROM技术的阵风响应分析方法

阵风响应分析是大型飞机设计过程中必不可少的环节. 现有的阵风响应分析主要采用基于线化升力面理论的气动力模型,不能考虑到各种非线性效应,不适合于跨音速气动弹性的分析. 基于CFD技术,采用系统辨识方法,在状态空间内建立了降阶的非定常气动力模型(reduced order model, ROM). 耦合结构运动方程、非定常气动力模型(结构运动)、外激阵风的气动力模型,建立了基于CFD技术的阵风响应分析模型.算例研究了某一典型机翼在方波激励下的阵风响应问题,对比了各阶模态位移的响应以及翼根弯矩的响应. 基于ROM技术的计算结果与CFD/CSD直接耦合仿真结果吻合,证明了该方法的正确性和精度.