Model reduction for multidisciplinary optimization - application to a 2D wing

Multidisciplinary optimization (MDO) is a growing field in engineering, with various applications in aerospace, aeronautics, car industry, etc. However, the presence of multiple disciplines leads to specific issues, which prevent MDO to be fully integrated in industrial design methodology. In practice, the key issues in MDO lie in the management of the interconnections between disciplines, along with the high number of simulations required to find a feasible multidisciplinary (optimal) solution. Therefore, in this paper, a novel approach is proposed, combining proper orthogonal decomposition to decrease the amount of data exchanged between disciplines, with surrogate models based on moving least squares to reduce disciplines. This method is applied to an original 2D wing demonstrator involving two disciplines (fluid and structure). The numerical results obtained for an optimization task show its benefits in diminishing both the interfaces between disciplines and the overall computational time.     

中石化国际化经营人才英语培训的实践与思考

国际化经营人才英语培训有其自身的特点和规律。不断探索和总结其规律,形成符合实际的由能力模块、培训方式和教学管理构成的培训模式对增强培训效果十分重要。能力模块的构建必须以听说为主,满足受训者出国后工作和生活的需要。培训方式要满足个性发展需要,营造英语环境,帮助学员度过平台期。管理上应体现以人为本、厂校结合、适度施压的原则,打造中石化国际化经营人才英语培训品牌。     

On the propagation of uncertainty in inflow turbulence to wind turbine loads

When stochastic simulation of inflow turbulence random fields is employed in the analysis or design of wind turbines in normal operating states, it is common to use well-established standard spectral models represented in terms of parameters that are usually treated as fixed or deterministic values. Studies have suggested, though, that many of these spectral parameters can exhibit some degree of variability. It is not unreasonable to expect, then, that derived flow fields based on simulation with such spectral models can be in turn highly variable for different realizations. Turbine load and performance variability would also be expected to result if response simulations are carried out with these variable flow fields. The aim here is to assess the extent of variability in derived inflow turbulence fields that arises from the noted variability in spectral model parameters. Simulation of these parameters as random variables forms the basis of this study. A commercial-sized 1.5 MW concept wind turbine is considered in the numerical studies. Variability in turbulence power spectra at field points on the rotor plane and in turbulence coherence functions for separations on the order of a rotor diameter and smaller is studied. Using time domain simulations, variability in various wind turbine response measures is also studied where the focus is on statistics such as response root-mean-square and 10-min extreme estimates. It is seen that while variability in inflow turbulence spectra can be great, the variability in turbine loads is generally considerably lower. One exception is for turbine yaw loads whose larger variability arises due to sensitivity to a coherence decay parameter that is itself highly variable. Finally, because reduced-order representations of turbulence random fields using empirical orthogonal decomposition techniques allow useful physical insights into spatial patterns of flow, variability in the energy distribution and the shapes of such empirical eigenmodes is studied and a simplified model is proposed that retains key variability sources in a limited number of modes and that accurately preserves overall inflow turbulence field uncertainty.     

Analysis of dominant flow structures and their flow dynamics in chemical process equipment using snapshot proper orthogonal decomposition technique

Snapshot proper orthogonal decomposition (POD) technique has been applied to reveal the dominant flow structures, their dynamics and length scales in six widely used industrial equipments (stirred tank, bubble column, Taylor-Couette flow (annual contactor), ultrasonic reactor, jet reactor, and channel flow). The variation in length scale of structures within an equipment, with change in its operating conditions (Reynolds number and power input) or change in its geometric configuration (sparger and impeller designs), has been brought out in this work. The planar data set for POD analysis was obtained from particle image velocimetry (PIV) and large eddy simulation (LES) studies. The dominant spatial topology was analyzed by using the velocity and vorticity POD modes. The modes have revealed the following flow structures: the ascending streaks and bursts in channel flow, the vortex tube and leading edge vortices in jets, the irregular small chaotic vortices in Taylor-Couette flow, the variation in plume oscillation and flow structures in the vortical region of bubble column resulting from changes in sparger design, the high intensity vortices near the source of ultrasound in the ultrasonic reactor and the effect of impeller designs on dominant flow structures and near blade vortices in the stirred tank. The length scales of structures are obtained by applying image processing on the spatial modes. The dynamics of these flow structures in each of the items of equipment is captured by reconstructing the flow field using appropriate spatial and temporal modes that contribute to these structures. Further, a unique attempt has been made to correlate the length scale distribution with the mixing time.     

纬编间隔床品面料的结构及性能分析

采用3.3 tex涤纶单丝为间隔纱,8.3 tex涤纶和2.2 tex氨纶交织为表层,制备4种不同密度、厚度及间隔纱连接方式的纬编间隔织物。分析织物组织结构和编织工艺,并测试力学性能和热湿舒适性能,分析面料厚度、密度与各项性能关系,用数学模糊分析法对面料进行综合分析。结果表明,间隔纱连接方式对面料拉伸断裂性和顶破性有一定影响,对透气透湿性无明显影响;厚度、密度较小且间隔纱交叉连接的织物综合性能最好;选择床垫面料时考虑厚度、密度及间隔纱连接方式对面料性能影响。     

Proper orthogonal decomposition of two vertical profiles of full-scale nonstationary downburst wind speeds[lzcl]

The paper proposes a novel analysis framework for nonstationary wind speeds, from which accumulated results can potentially lead to or enhance empirical nonstationary wind speed models such as the hybrid one for downbursts in (Eng. Struct. 26 (2004) 619). This framework is motivated by the proper orthogonal decomposition (POD) technique and consists of four major steps: separation, POD, approximation and property inference. In the first step, the original wind speed time histories are separated into their time-varying mean speeds and fluctuating speeds through wavelet shrinkage, which is a promising new tool for smoothing; fluctuating speeds are further expressed as the product of their time-varying standard deviations and normalized fluctuating speeds with unit variance. In the second step, the POD is applied to the time-varying means, standard deviations and normalized fluctuation. In the third step, by properly selecting the number of retained modes, the original time series can be approximated to a numerical model. Finally, wind properties such as velocity vertical profiles and turbulence vertical profiles can be calculated from the model. The POD employed herein is a specific POD for arbitrary multivariate data representation. The normalized fluctuations are characterized by both their power spectral densities (PSD) and evolutionary power spectral densities (EPSD). The concept of EPSD for nonstationary processes is briefly presented and one EPSD estimator is given. Specifically, the PSD/EPSD of an original process is related to the PSD/EPSD of the principal coordinate process obtained by the POD. This framework is applied to two sets of nonstationary full-scale thunderstorm downburst wind speed time series. Many appealing downburst properties are obtained and the benefit of the POD is demonstrated.     

Off-line model reduction for on-line linear MPC of nonlinear large-scale distributed systems

Model predictive control (MPC) is an efficient method for the controller design of a large number of processes. However, linear MPC is often inappropriate for controlling nonlinear large-scale systems, while non-linear MPC can be computationally costly. The resulting optimization-based procedure can lead to local minima due to the, non-convexities that non-linear systems can exhibit. To overcome the excessive computational cost of MPC application for large-scale nonlinear systems, model reduction methodology in conjunction with efficient system linearizations have been exploited to enable the efficient application of linear MPC for nonlinear distributed parameter systems (DPS). An off-line model reduction technique, the proper orthogonal decomposition (POD) method, combined with a finite element Galerkin projection is first used to extract accurate non-linear low-order models from the large-scale ones. Trajectory Piecewise-Linear (TPWL) methodologies are subsequently developed to construct a piecewise linear representation of the reduced nonlinear model, both in a static and in a dynamic fashion. Linear MPC, based on quadratic programming, can then be efficiently performed on the resulting low-order, piece-wise affine system. Our combined methodology is readily applicable in combination with advanced MPC methodologies such as multi-parametric MPC (MP-MPC) (Pistikopoulos, 2009). The stabilisation of the oscillatory behaviour of a tubular reactor with recycle is used as an illustrative example to demonstrate our methodology.     

消波墩在正槽式溢洪道急流冲击波中的应用

溢洪道的控制堰下一般接陡坡泄槽,为使陡槽内水流平顺,平面上陡槽以等宽直线布置为佳,但受地形、地质等条件的限制,会采用急流段边墙转折,这就将导致冲击波的产生.消波墩作为陡槽局部抬高的变体,可以有效的克服产生的急流冲击波,有良好的工程应用效果.     

Dynamic models for bird population—A parameter-varying partial differential equation identification approach

In order to study the global decline of biodiversity, accurate models of animal population dynamics are required. In this paper, this challenging problem of biodiversity decline analysis is tackled by modelling the dynamics of bird populations. More specifically, a new data-driven modelling of bird population dynamics is suggested which resorts to a parameter-varying partial differential equation (PDE) model, the Galerkin method and the proper orthogonal decomposition. The parameter-varying formulation allows us to introduce in models prior information such as temperatures or landscape patterns. Hence, such models can be used to study the impact on biodiversity of the global warming or the agricultural intensification, for instance. In order to deal with the specific conditions of ecological applications, a specific attention is paid to the initialization as well as the implementation of an iterative identification procedure based on 3D partial moments and a Levenberg–Marquardt algorithm. These tools are tested on the data-driven modelling of the population of European Stonechat Saxicola torquatus, a European common bird species, by using data from the national French Breeding Bird Survey and the CORINE Land Cover. The perspectives are to model specific community of birds in order to evaluate the effect of global changes in population trends and to develop tools to help decision makers take into account biodiversity goals into public policies.