Fourth-Order Predictive Modelling: II. 4th-BERRU-PM Methodology for Combining Measurements with Computations to Obtain Best-Estimate Results with Reduced Uncertainties

This work presents a comprehensive fourth-order predictive modeling (PM) methodology that uses the MaxEnt principle to incorporate fourth-order moments (means, covariances, skewness, kurtosis) of model parameters, computed and measured model responses, as well as fourth (and higher) order sensitivities of computed model responses to model parameters. This new methodology is designated by the acronym 4th-BERRU-PM, which stands for “fourth-order best-estimate results with reduced uncertainties.” The results predicted by the 4th-BERRU-PM incorporates, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizes the results produced by extant data assimilation and data adjustment procedures.

Adding-Point Strategy for Reduced-Order Hypersonic Aerothermodynamics Modeling Based on Fuzzy Clustering

Reduced order models（ROMs） based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.

Sliding Mode Control Design via Reduced Order Model Approach

This paper presents a design of continuous-time sliding mode control for the higher order systems via reduced order model. It is shown that a continuous-time sliding mode control designed for the reduced order model gives similar performance for thc higher order system. The method is illustrated by numerical examples. The paper also introduces a technique for design of a sliding surface such that the system satisfies a cost-optimality condition when on the sliding surface.

Second-Order MaxEnt Predictive Modelling Methodology. II: Probabilistically Incorporated Computational Model (2nd-BERRU-PMP)

This work presents a comprehensive second-order predictive modeling (PM) methodology based on the maximum entropy (MaxEnt) principle for obtaining best-estimate mean values and correlations for model responses and parameters. This methodology is designated by the acronym 2nd-BERRU-PMP, where the attribute “2nd” indicates that this methodology incorporates second- order uncertainties (means and covariances) and second (and higher) order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best-Estimate Results with Reduced Uncertainties” and the last letter (“P”) in the acronym indicates “probabilistic,” referring to the MaxEnt probabilistic inclusion of the computational model responses. This is in contradistinction to the 2nd-BERRU-PMD methodology, which deterministically combines the computed model responses with the experimental information, as presented in the accompanying work (Part I). Although both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies yield expressions that include second (and higher) order sensitivities of responses to model parameters, the respective expressions for the predicted responses, for the calibrated predicted parameters and for their predicted uncertainties (covariances), are not identical to each other. Nevertheless, the results predicted by both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies encompass, as particular cases, the results produced by the extant data assimilation and data adjustment procedures, which rely on the minimization, in a least-square sense, of a user-defined functional meant to represent the discrepancies between measured and computed model responses.

Second-Order MaxEnt Predictive Modelling Methodology. I: Deterministically Incorporated Computational Model (2nd-BERRU-PMD)

This work presents a comprehensive second-order predictive modeling (PM) methodology designated by the acronym 2nd-BERRU-PMD. The attribute “2nd” indicates that this methodology incorporates second-order uncertainties (means and covariances) and second-order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best- Estimate Results with Reduced Uncertainties” and the last letter (“D”) in the acronym indicates “deterministic,” referring to the deterministic inclusion of the computational model responses. The 2nd-BERRU-PMD methodology is fundamentally based on the maximum entropy (MaxEnt) principle. This principle is in contradistinction to the fundamental principle that underlies the extant data assimilation and/or adjustment procedures which minimize in a least-square sense a subjective user-defined functional which is meant to represent the discrepancies between measured and computed model responses. It is shown that the 2nd-BERRU-PMD methodology generalizes and extends current data assimilation and/or data adjustment procedures while overcoming the fundamental limitations of these procedures. In the accompanying work (Part II), the alternative framework for developing the “second- order MaxEnt predictive modelling methodology” is presented by incorporating probabilistically (as opposed to “deterministically”) the computed model responses.

Fourth-Order Predictive Modelling: I. General-Purpose Closed-Form Fourth-Order Moments-Constrained MaxEnt Distribution

This work (in two parts) will present a novel predictive modeling methodology aimed at obtaining “best-estimate results with reduced uncertainties” for the first four moments (mean values, covariance, skewness and kurtosis) of the optimally predicted distribution of model results and calibrated model parameters, by combining fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters. Underlying the construction of this fourth-order predictive modeling methodology is the “maximum entropy principle” which is initially used to obtain a novel closed-form expression of the (moments-constrained) fourth-order Maximum Entropy (MaxEnt) probability distribution constructed from the first four moments (means, covariances, skewness, kurtosis), which are assumed to be known, of an otherwise unknown distribution of a high-dimensional multivariate uncertain quantity of interest. This fourth-order MaxEnt distribution provides optimal compatibility of the available information while simultaneously ensuring minimal spurious information content, yielding an estimate of a probability density with the highest uncertainty among all densities satisfying the known moment constraints. Since this novel generic fourth-order MaxEnt distribution is of interest in its own right for applications in addition to predictive modeling, its construction is presented separately, in this first part of a two-part work. The fourth-order predictive modeling methodology that will be constructed by particularizing this generic fourth-order MaxEnt distribution will be presented in the accompanying work (Part-2).

Second-Order MaxEnt Predictive Modelling Methodology. III: Illustrative Application to a Reactor Physics Benchmark

This work illustrates the innovative results obtained by applying the recently developed the 2nd-order predictive modeling methodology called “2nd- BERRU-PM”, where the acronym BERRU denotes “best-estimate results with reduced uncertainties” and “PM” denotes “predictive modeling.” The physical system selected for this illustrative application is a polyethylene-reflected plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation (involving 21,976 uncertain parameters), the solution of which is representative of “large-scale computations.” The results obtained in this work confirm the fact that the 2nd-BERRU-PM methodology predicts best-estimate results that fall in between the corresponding computed and measured values, while reducing the predicted standard deviations of the predicted results to values smaller than either the experimentally measured or the computed values of the respective standard deviations. The obtained results also indicate that 2nd-order response sensitivities must always be included to quantify the need for including (or not) the 3rd- and/or 4th-order sensitivities. When the parameters are known with high precision, the contributions of the higher-order sensitivities diminish with increasing order, so that the inclusion of the 1st- and 2nd-order sensitivities may suffice for obtaining accurate predicted best- estimate response values and best-estimate standard deviations. On the other hand, when the parameters’ standard deviations are sufficiently large to approach (or be outside of) the radius of convergence of the multivariate Taylor-series which represents the response in the phase-space of model parameters, the contributions stemming from the 3rd- and even 4th-order sensitivities are necessary to ensure consistency between the computed and measured response. In such cases, the use of only the 1st-order sensitivities erroneously indicates that the computed results are inconsistent with the respective measured response. Ongoing research aims at extending the 2nd-BERRU-PM methodology to fourth-order, thus enabling the computation of third-order response correlations (skewness) and fourth-order response correlations (kurtosis).

Assessment of a two-surface plasticity model for hexagonal materials

A computationally efficient two-surface plasticity model is assessed against crystal plasticity. Focus is laid on the mechanical behavior of magnesium alloys in the presence of ductility-limiting defects, such as voids. The two surfaces separately account for slip and twinning such that the constitutive formulation captures the evolving plastic anisotropy and evolving tension-compression asymmetry. For model identification, a procedure is proposed whereby the initial guess is based on a combination of experimental data and computationally intensive polycrystal calculations from the literature. In drawing direct comparisons with crystal plasticity, of which the proposed model constitutes a heuristically derived reduced-order model, the available crystal plasticity simulations are grouped in two datasets. A calibration set contains minimal data for both pristine and porous material subjected to one loading path. Then the two-surface model is assessed against a broader set of crystal plasticity simulations for voided unit cells under various stress states and two loading orientations. The assessment also includes microstructure evolution(rate of growth of porosity and void distortion). The ability of the two-surface model to capture essential features of crystal plasticity is analyzed along with an evaluation of computational cost. The prospects of using the model in guiding the development of physically sound damage models in Mg alloys are put forth in the context of high-throughput simulations.

基于Twin Builder的110kV油浸式变压器3维磁场降阶模型及损耗分析

为解决3维变压器磁场有限元仿真计算效率低的问题,提出基于本征正交分解法的磁场降阶模型与基于二次多项式的损耗响应面模型。以1台110 kV变压器为算例,首先建立了考虑绕组涡流损耗的变压器磁场仿真模型,通过仿真分析了其在额定工况下的磁场及损耗分布;其次,通过仿真试验设计获取了多组工况下的磁场与损耗数据,在Twin Builder中训练得到变压器磁场的各降阶模块;最后,搭建面向数字孪生应用的变压器3维磁场降阶模型,实现了变压器磁场损耗的快速计算与云图分布的可视化。与计算单数据相比,仿真结果考虑了涡流效应的绕组损耗在靠近端部时有明显升高,最大值分别出现在高、低压绕组两端,约为绕组中部损耗的1.3~1.65倍。基于该磁场结果建立的降阶模型在计算效率方面较全阶模型有显著提升,将实际仿真时间缩短至0.76s,可满足数字孪生应用场景下的磁场快速计算需求。

基于POD和DMD的60°交叉管绕流分析

本文利用本征正交分解(Proper Orthogonal Decomposition,POD)和动力学模态分解(Dynamic Mode Decom⁃position,DMD)分析雷诺数Re=200时三维60°交叉管在间隙比G=4下的涡量数据,并探究尾涡的演变规律。分析结果表明:尾流场中的涡流尺度及重要程度随频率增加而减小,少数低频模态便可主导大尺度流动现象,而高频模态主要丰富尾流场中的小尺度湍流细节;上下游圆柱的涡结构以0.19 Hz的频率从两圆柱脱落,并在相同的频率下以平行形态向下游演变;上游圆柱脱落涡与下游圆柱的相互作用导致下游圆柱涡激振动明显,并产生多个高频升力频谱峰值。

并网型直流微电网的非线性降阶建模及其估计吸引域的优化计算

面对“双高”电力系统的高维度、高随机性和强非线性,现有的建模和稳定性分析方法受限于维度、难以求解且准确度低。针对此问题,该文提出一个面向“双高”电力系统,包含非线性降阶建模和估计吸引域优化计算的稳定性分析框架。首先,考虑分布式光伏和恒功率负载的地理环境因素,应用Pioncáre规范理论,将并网型直流微电网的二次状态偏差模型依次进行分块降维、解耦和降阶变换,建立一阶二次微分方程形式的非线性降阶模型。然后,基于李雅普诺夫稳定判据,结合构造含辅助变量的最优化模型思想,并利用克罗内克积性质,提出估计吸引域的优化计算方法,构建优化的估计吸引域(optimalestimatedregionofattraction,OEROA)。最后,以分布式光伏云层遮蔽和恒功率负载扰动下的微电网系统作为算例,与基于LaSalle定理的李雅普诺夫法、Takagi-Sugeno(T-S)模糊模型法对比,验证所提方法构建的估计吸引域具有更低的保守性,以及所提分析框架的有效性。

多换流器直流系统的虚拟惯性评估与控制参数协调设计

针对基于类虚拟同步发电机(AVSG)的多换流器直流系统遇到的非预期虚拟惯性问题,提出了一种多换流器直流系统虚拟惯性通用分析方法,有效修正了系统级等效AVSG外特性。首先,将多个AVSG控制回路等效为一个等值AVSG控制回路,建立了直流系统的等值单台AVSG模型。其次,提出了一种系统级虚拟惯性评估方法,从等值单台AVSG模型的输出阻抗频域响应特性角度对系统级虚拟惯性进行评估。然后,讨论了电压比例-积分(PI)控制参数对虚拟惯性特性的影响,并开展了电压PI控制参数和虚拟惯性系数间的协调设计。最后,在PLECS仿真软件及RT-BOX硬件在环实验平台上搭建了多换流器直流系统的开关模型,通过多种实验工况验证了等值单台AVSG模型和控制参数协调设计的有效性和可行性。

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%。

基于全阶模型的直驱风电机组多时间尺度等效惯量机理建模

构建风电机组的等效惯量模型是开展风电场暂态支撑定量分析与优化控制工作的关键基础,在转动惯量降低、频率特性劣化的高比例可再生能源电网场景下尤其关键。然而,现有模型研究往往结合分析目标侧重若干环节而未能建立全环节的完整模型,也未能揭示不同时间尺度降阶模型与计算精度的关系。首先,以直驱风电机组为对象,在对比风电等效惯量与同步机惯量在机理和实现方式等方面差异的基础上,分析了机械、控制、电气等环节对风电等效惯量的动态影响,建立了风电机组的频率响应全阶模型。然后,基于奇异摄动理论推导了考虑不同时间尺度动态的等效惯量降阶模型,应用瓦西里耶娃理论推导了误差与模型时间尺度之间的关系。最后,采用电磁暂态仿真算例验证了全阶机理模型的有效性及不同降阶模型的精度水平。

基于参数化降阶模型的非线性气动弹性高效分析

针对非线性气动弹性分析时需要在时域求解多个流场参数条件下结构运动方程而造成的计算消耗过大的问题,提出了一种适用于参数变化时非定常流场高效计算的参数化降阶模型,不仅可以应用于计算机翼等结构的总体气动力还可以得到每个时刻的结构表面的流场数据分布,并成功应用于典型机翼的跨声速颤振边界的计算,大大地提高了计算效率.结果显示,单流场条件时降阶模型的计算速度比直接使用时域分析方法提高了3倍;在计算多流场参数条件下,参数化降阶模型相比于使用单流场降阶模型计算速度提高了3.5倍,相较于时域分析方法提高了10倍.

不确定转子系统动力学降阶模型构建与模型散度参数辨识

在航空、航天、船舶等领域的实际工程转子系统中,广泛存在高维复杂的非线性系统。在航空发动机转子系统、燃气轮机转子系统等重点研究领域,通常还难以对高维复杂非线性系统进行直接的数据处理和分析统计。针对不确定性转子系统的模型维度较高等问题,提出了一种模型不确定性动力学降阶计算模型构建和模型散度参数辨识方法。首先,根据确定性动力学模型和静态矩阵降阶方法,完善了确定性动力学降阶模型;然后,基于随机矩阵理论和非参数动力学建模方法,提出了不确定性动力学降阶模型;最后,利用系统确定性模型的一阶临界转速、振型和实验数据,对不确定性动力学模型的散度参数进行了辨识;为了验证散度参数辨识方法的有效性,笔者又在转子实验平台上进行了实验验证。研究结果表明:实验结果与降阶之后振动响应均值的差异性较小,且与不确定性动力学模型相差不超过10%,表明所采用的理论模型在描述转子系统行为方面具备了较高的准确性和可靠性,该模型可以为深入研究模型不确定性转子系统提供参考。

基于数字孪生的磁流变液延时机构延迟解除隔离时间降阶模型

为建立满足实时性要求的磁流变液延时机构数字孪生体,针对传统流场仿真计算比较复杂耗时的问题,构建了该机构流体域降阶模型以实现其延时性能的快速计算。利用磁流变液延时机构流体域有限元全阶模型的参数化仿真作为样本数据,在DOE实验设计的基础上采用响应面法完成了流体域降阶模型建立,并结合有限元全阶模型仿真及高速旋转等效试验验证了降阶模型的精度及其可行性。结果表明传统流场与降阶模型仿真结果趋势相同,在检验工况中两者误差在5%以内,但降阶模型的仿真速度是流场仿真的100倍,即降阶模型可以在保证磁流变液延时机构模型求解精度的同时提高求解效率。

基于降阶模型的热过程快速预测方法研究

为了对热过程控制进行设计,常采用“热流耦合”的计算流体力学(CFD)方法对热过程进行模拟,为了得到合理的热控制方案,常需要反复的CFD分析,成本较高,而且不方便实时调整控制方案。为解决这一问题,基于Volterra级数降阶模型,提出了一种热过程快速分析预测的方法,为热过程控制策略制定及控制参数确定提供了一种工具。该方法根据在阶跃温度加热条件下热过程系统的响应,辨识Volterra级数降阶模型的核函数,构建系统温度与控制条件之间关系的降阶模型,进而快速计算不同控制过程下系统的温度响应。该降阶模型可以用来实时预测系统的加热过程和优化系统的热过程控制方法。算例的结果表明:采用Volterra级数降阶模型得到的系统温度响应与采用CFD方法得到的响应一致,且采用降阶模型方法进行热过程预测和控制优化的效率非常高。

基于双积分滑模控制的DAB电压控制研究

提出一种基于双积分滑模控制的双向有源全桥(DAB)DC-DC变换器电压控制策略,该策略在模型精度要求低、控制器设计流程简单的情况下,可以获得良好的控制效果。首先,基于单移相调制方式建立变换器的降阶模型。然后,采用双积分滑模控制理论设计变换器的输出电压控制器。设计过程通过引入时域分析法进行滑模面系数的选取分析。最后,仿真和实验结果表明了所提控制策略的有效性。

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。