A stiffness parameter and truncation error criterion for adaptive path following in structural mechanics

We explore a truncation error criterion to steer adaptive step length refinement and coarsening in incremental-iterative path following procedures, applied to problems in large-deformation structural mechanics. Elaborating on ideas proposed by Bergan and collaborators in the 1970s, we first describe an easily computable scalar stiffness parameter whose sign and rate of change provide reliable information on the local behavior and complexity of the equilibrium path. We then derive a simple scaling law that adaptively adjusts the length of the next step based on the rate of change of the stiffness parameter at previous points on the path. We show that this scaling is equivalent to keeping a local truncation error constant in each step. We demonstrate with numerical examples that our adaptive method follows a path with a significantly reduced number of points compared to an analysis with uniform step length of the same fidelity level. A comparison with Abaqus illustrates that the truncation error criterion effectively concentrates points around the smallest-scale features of the path, which is generally not possible with automatic incrementation solely based on local convergence properties.     

Precise Extraction of Charge Carrier Mobility for Organic Transistors

Unreliable mobility values, and particularly greatly overestimated values and severely distorted temperature dependences, have recently hampered the development of the organic transistor field. Given that organic field‐effect transistors (OFETs) have been routinely used to evaluate mobility, precise parameter extraction using the electrical properties of OFETs is thus of primary importance. This review examines the origins of the various mobilities that must be determined for OFET applications, the relevant extraction methods, and the data selection limitations, which help in avoiding conceptual errors during mobility extraction. For increased precision, the review also discusses device fabrication considerations, calibration of both the specific gate‐dielectric capacitance and the threshold voltage, the contact effects, and the bias and temperature dependences, which must actually be handled with great care but have mostly been overlooked to date. This review serves as a systematic overview of the OFET mobility extraction process to ensure high precision and will also aid in improving future research.     

基于多项式变换的参数估计方法

本文给出一种采用DPT估计SNCK信号时宽—带宽积的方法,并通过仿真该估计方法的性能与其它估计方法进行比较.首先给出SNCK信号参数估计的一般过程.为了便于计算和理论推导,根据估计出的中心频率将接收到的SNCK信号搬移到零频,从而进一步估计其它参数,如采用DPT估计SNCK信号时宽带宽积.本文将重点研究采用DPT算法估计SNCK信号值的方法.     

Models for circular data from time series spectra

Circular data are those for which the natural support is the unit circle and its toroidal extensions. Numerous constructions have been proposed which can be used to generate models for such data. We propose a new, very general, one based on the normalization of the spectra of complex-valued stationary processes. As illustrations of the new construction's application, we study models for univariate circular data obtained from the spectra of autoregressive moving average models and relate them to existing models in the literature. We also propose and investigate multivariate circular models obtained from the high-order spectra of stationary stochastic processes generated using linear filtering with an autoregressive moving average response function. A new family of distributions for a Markov process on the circle is also introduced. Results for asymptotically optimal inference for dependent observations on the circle are presented which provide a new paradigm for inference with circular models. The application of one of the new families of spectra-generated models is illustrated in an analysis of wind direction data.     

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

The ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the petroleum industry has not benefited enough from the promises of this machine learning technology. The petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of petroleum reservoir properties could have huge positive impact on the success of exploration, drilling and the overall reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of petroleum reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in petroleum reservoir characterization to improve the accuracy of reservoir properties predictions for more successful explorations and increased production of petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation.     

马尔可夫跳变线性系统最优控制的研究现状与进展

马尔可夫跳变线性系统（MJLS）是一种具有多个模态的随机系统，系统在各个模态之间的跳变转移由一组马尔可夫链来决定。MJLS模型因其在表示过程中可以产生突变而更能精确的描述实际工程应用中的系统。近年来，MJLS的最优控制问题成为了研究的热点，动态规划、极大值原理以及线性矩阵不等式等成为了解决此类问题的主流方法。本文对MJLS最优控制领域的研究现状进行了综述。分别对一般情况下、带有噪声的情况下、带有时滞的情况下以及某些特定情况下的MLJS最优控制问题的国内外研究现状进行论述。最后进行了总结并提出MJLS最优控制领域未来值得关注的研究方向。     

Effects of structural parameters on water film properties of transpiring wall reactor

Corrosion and salt deposition problems severely restrict the industrialization of supercritical water oxidation. Transpiring wall reactor can effectively weaken these two problems by a protective water film. In this work, methanol was selected as organic matter, and the influences of vital structural parameters on water film properties and organic matter removal were studied via numerical simulation. The results indicate that higher than 99% of methanol conversion could be obtained and hardly affected by transpiration water layer, transpiring wall porosity and inner diameter. Increasing layer and porosity reduced reactor center temperature, but inner diameter's influence was lower relatively. Water film temperature reduced but coverage rate raised as layer, porosity, and inner diameter increased. Notably, the whole reactor was in supercritical state and coverage rate was only approximately 85% in the case of one layer. Increasing reactor length affected slightly the volume of the upper supercritical zone but enlarged the subcritical zone.     

基于(火用)效率目标的环冷机余热回收系统操作参数优化

摘要：针对烧结环冷机余热回收利用率不高的难题，采用分析法建立了评价某钢铁厂烧结环冷机余热回收系统运行效率的效率模型。基于多孔介质模型、局部非热平衡方程、真实气体SRK方程建立环冷机内气固两相换热模型。通过CFD仿真模拟，探究料层高度、循环风机输入烟气温度、烧结矿底部入口风速三项可控环冷机运行工艺参数对系统效率的影响规律。结果表明，料层厚度在1～1.5 m区间每增加0.1 m，效率增加0.8%～1.1%；循环风温在100～140℃之间每增加10℃，效率增加1.4%～1.5%；烧结矿底部入口风速在0.9～1.9 m/s之间每增加0.1 m/s，效率降低0.18%～0.24%。在此基础上，基于工业运行数据建立效率正交试验优化模型，提高了该余热回收系统3.42%的效率。