Proton Nuclear Magnetic Resonance-Based Method for the Quantification of Epoxidized Methyl Oleate

Epoxidized methyl esters (EMO) with their high oxirane ring reactivity, acts as a raw material in the synthesis of various industrial chemicals including polymers, stabilizers, plasticizers, glycols, polyols, carbonyl compounds, biolubricants etc. EMO has been generally quantified by the gas chromatography (GC) and high-performance liquid chromatography (HPLC) techniques. Taking into the account of the limitations of these techniques, two qHNMR-based equations have been proposed for the quantification of EMO in the mixture of EMO and methylesters (MO). The validity of the proposed method was determined using standard mixtures of MO and EMO having different molar concentrations. The developed equations have been applied on the samples of EMO prepared from oleic acid in two-step process viz., esterification followed by epoxidation. The qHNMR-based EMO quantification showed acceptable agreement with the results obtained from HPLC analysis.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

AHP FUZZY在残采工程方案优选中的应用

为探讨太白金矿回收原采场边部残余低品位矿体的方案，通过层次分析法（AHP）与模糊数学（FUZZY）理论，选取对残采工程影响较大的9个因素，建立优选模型。运用层次分析法确定了各评价指标的权重；采用模糊数学法分析计算各采矿方法的定性、定量指标，分别得到各评价指标的隶属度。最终计算出3种工程布置方案的综合优选度分别为68.1%、69.3%、58.1%，从而沿矿体走向双进路布置确定为最优开采方案。实践表明，采用该模型优选的工程布置方案具有一定的科学性、合理性，值得借鉴与推广。     

匹配机械弹性车轮的电子稳定控制器参数分析

为了提高匹配机械弹性车轮(MEW)的某越野车操纵稳定性，考虑MEW与传统子午线轮胎侧偏特性存在的不确定性摄动，基于Lyapunov稳定性理论为电子稳定控制(ESC)程序设计了鲁棒反馈控制器；引入轮胎侧偏刚度不确定性的范数有界模型，运用Schur补引理和线性矩阵不等式(LMI)求解反馈矩阵。设定不同的车速和路面附着系数，通过搭建CarSim/Simulink联合仿真平台对控制器展开鱼钩试验，仿真结果表明，匹配MEW的ESC控制器能够保证车辆行驶的稳定性，横摆角速度与质心侧偏角跟踪误差分别稳定在0.03~0.3 rad/s与0.06~0.1 rad之内，并且设计的控制算法对MEW在05倍普通充气车轮侧偏刚度变化范围内具有很好的鲁棒性，从而为匹配MEW的整车主动安全控制提供了理论参考。     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Stochastic free vibration analyses of composite shallow doubly curved shells – A Kriging model approach

This paper presents the Kriging model approach for stochastic free vibration analysis of composite shallow doubly curved shells. The finite element formulation is carried out considering rotary inertia and transverse shear deformation based on Mindlin’s theory. The stochastic natural frequencies are expressed in terms of Kriging surrogate models. The influence of random variation of different input parameters on the output natural frequencies is addressed. The sampling size and computational cost is reduced by employing the present method compared to direct Monte Carlo simulation. The convergence studies and error analysis are carried out to ensure the accuracy of present approach. The stochastic mode shapes and frequency response function are also depicted for a typical laminate configuration. Statistical analysis is presented to illustrate the results using Kriging model and its performance.     

基于支持向量机和磁记忆技术的管道缺陷深度的定量化反演研究

金属管道表面往往存在不同深度的腐蚀缺陷。金属磁记忆检测技术是目前唯一能对铁磁性构件的早期损伤进行诊断的无损检测技术,然而磁记忆原始信号本身并不能直接实现对管道腐蚀缺陷深度特征的定量化识别,进而无法实现对管道腐蚀程度的预警。针对该问题,采用支持向量机方法建立了管道缺陷深度的定量化反演模型,利用该模型对管道上深度为1~15mm的腐蚀缺陷进行了多次反演,反演结果的平均误差为2.398mm,平均均方根误差为3.205mm,结果表明,模型对管道腐蚀缺陷深度的定量化反演是可行的。研究结果可为该领域的研究提供一定的参考,且具有较高的实际应用价值。     

Degradation rate quantification of solid oxide fuel cell performance with and without Al2TiO5 addition

Degradation rates of electrical current during constant voltage operation of SOFCs with anodes made using NiO precursor powders from two different manufacturers with and without the addition of aluminum titanate (ALT) added by either mechanical mixing or anode infiltration have been quantified using a novel MATLAB algorithm. Because the algorithm has been used to quantify degradation rates for many different SOFC tests, it is thought that the method can be applied to most measured SOFC data to quantify the instantaneous cell degradation rate as a function of time for the entire SOFC performance measurement. Degradation rates determined at different times have been plotted against varying concentrations of ALT addition, facilitating the estimation of optimum ALT concentration for SOFC anodes made with NiO from a specific manufacturer. The algorithm used to determine degradation rates is available upon request to the corresponding author.     

Polynomial filtering for nonlinear stochastic systems with state‐ and disturbance‐dependent noises

This article is concerned with the polynomial filtering problem for a class of nonlinear stochastic systems governed by the Itô differential equation. The system under investigation involves polynomial nonlinearities, unknown‐but‐bounded disturbances, and state‐ and disturbance‐dependent noises ((x,d)‐dependent noises for short). By expanding the polynomial nonlinear functions in Taylor series around the state estimate, a new polynomial filter design method is developed with hope to reduce the conservatism of the existing results. In virtue of stochastic analysis and inequality technique, sufficient conditions in terms of parameter‐dependent linear matrix inequalities (PDLMIs) are derived to guarantee that the estimation error system is input‐to‐state stable in probability. Moreover, the desired polynomial matrix can be obtained by solving the PDLMIs via the sum‐of‐squares approach. The effectiveness and applicability of the proposed method are illustrated by two numerical examples with one concerning the permanent magnet synchronous motor.