2339 TEU支线型集装箱船设计特点

针对一艘出口德国的2 339TEU支线型集装箱,介绍其基于CFD技术的低阻线型设计开发,基于MSC.Patran的高可靠性结构设计,大型左右不对称上层建筑整体吊装有限元强度分析及窄边舱技术等在船舶设计与制造中的应用;新技术、新设计手段在船舶设计与制造中应用使船舶各项性能显著提高,其对类似船舶的设计与制造具有积极的参考价值和意义。

支线型冷藏集装箱船振动特性研究

近年来,规范的更新和船东对舒适性要求的提升,船体振动问题愈来愈受到重视。本文采用有限元法从上层建筑整体纵向振动、船体梁总振动、局部结构振动3个方面对某型支线型冷藏集装箱船进行振动特性预报。针对不足的情况,提出改进方案,使振动水平能够满足相关标准的要求,探讨集装箱船早期设计阶段上层建筑整体纵向振动的预报方法。计算分析中获得的经验和结论,可供同类型船舶设计参考。

支线型机场高速公路城市区段立交设置研究

支线型机场高速公路既承担了过境交通,又承担着出入机场高速公路的主要交通,支线型机场高速公路在收费站至机场边界段,需解决部分城市规划区进出机场的交通需求。根据国内外机场高速公路设置案例,针对成都天府国际机场的此类需求,分析了立交设置前及立交设置过程中需解决的问题,立交设置前需解决穿过机场交通问题、道路等级等问题,立交设计过程中需解决工程可行性研究、与待检区结合的交通组织问题、与机场规划匝道的交织问题、城市规划区交通问题以及相邻区域的接入问题,综合分析得出推荐方案,为研究支线型机场高速公路及立交设置提供了相关案例经验及理论分析基础。

核安全1级设备线型支承分析方法研究

为了保证核1级设备线性支承的强度,介绍了ASME、RCC_M规范对核1级设备支承的要求。讨论线型支承分析过程中临界屈曲应力的确定方法,明确许用压应力放大系数的取值范围。阐述支承设计过程中输入载荷的完整性。以蒸汽发生器中部横向支承D级工况的分析为例,对其进行了分析与评价,为核1级设备及线型支承的设计提供了参考。

冷藏集装箱运输对支线箱船的设计影响

针对专用冷藏集装箱船载运冷藏箱数量对布置紧凑的支线型箱船设计影响的情况,通过研究冷藏集装箱的特点,结合规范和营运需求,对冷藏箱布置、通道布置、通风布置、电气系统等方面的设计要点进行研究。结果表明:通过采取冷藏箱操作平台、半开放式抗扭箱及货舱综合式通风系统等解决方法,可以大幅提高冷藏集装箱的装载量,对类似船型的设计具有借鉴意义。

Nonlinear Model Predictive Control Based on Support Vector Machine with Multi-kernel

Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.

Online residual useful life prediction of large-size slewing bearings A data fusion method

To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.

Linear Support Vector Machine Based Inter-harmonic Modeling and Parameter Estimation

There are often system. The high measure many inter-harmonics in power t accuracy of inter-harmonics order, amplitude and initial phase is needed. A new approach is presented for inter-harmonic modeling and parameter estimation based on linear support vector machine （SVM）. Firstly, parameter estimation of linear model is realized based on standard linear SVM. Then, interharmonic model is transformed to a linear model according to trigonometric functions. The approach obtains order of inter-harmonic model with windowed Blackman-Tukey （BT） spectrum analysis, and gets number and frequency of harmonics. Finally, the linear SVM is applied to estimate the inter-harmonic parameters, amplitude and initial phase. The simulation results show that the proposed approach has high precision and good antinoise. The accuracy of three parameters are all higher than 98%.

Initial pre-stress finding procedure and structural performance research for Levy cable dome based on linear adjustment theory

The cable-strut structural system is statically and kinematically indeterminate. The initial pre-stress is a key factor for determining the shape and load carrying capacity. A new numerical algorithm is presented herein for the initial pre-stress finding procedure of complete cable-strut assembly. This method is based on the linear adjustment theory and does not take into account the material behavior. By using this method,the initial pre-stress of the multi self-stress modes can be found easily and the cal-culation process is simplified and efficient also. Finally,the initial pre-stress and structural performances of a particular Levy cable dome are analyzed comprehensively. The algorithm has proven to be efficient and correct,and the numerical results are valuable for practical design of Levy cable dome.

Slope displacement prediction based on morphological filtering

Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.

Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy

The performance of data-driven models relies heavily on the amount and quality of training samples, so it might deteriorate significantly in the regions where samples are scarce. The objective of this paper is to develop an online SVR model updating strategy to track the change in the process characteristics efficiently with affordable computational burden. This is achieved by adding a new sample that violates the Karush–Kuhn–Tucker conditions of the existing SVR model and by deleting the old sample that has the maximum distance with respect to the newly added sample in feature space. The benefits offered by such an updating strategy are exploited to develop an adaptive model-based control scheme, where model updating and control task perform alternately.The effectiveness of the adaptive controller is demonstrated by simulation study on a continuous stirred tank reactor. The results reveal that the adaptive MPC scheme outperforms its non-adaptive counterpart for largemagnitude set point changes and variations in process parameters.

Nonlinear model predictive control based on support vector machine and genetic algorithm

This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.

Rheological properties of surrounding rock in deep hard rock tunnels and its reasonable support form

Second lining stability, which is the last protection in tunnel engineering, is critically important. The theological properties of the surrounding rock heavily affect second lining stability. In this work, we used laboratory triaxial compressive rheological limestone tests to study nonlinear creep damage characteristics of surrounding rock mass in construction projects. We established a nonlinear creep damage constitutive model for the rock mass, as well as a constitutive model numerical implementation made by programming. Second, we introduced a new foam concrete with higher compression performance and good ductility and studied its mechanical properties through uniaxial and triaxial tests. This concrete was used as the filling material for the reserved deformation layer between the primary support and second lining. Finally, we proposed a high efficiency and accuracy staged optimization method. The minimum reserved deformation layer thickness was established as the optimization goal, and the presence of plastic strain in the second lining after 100 years of surrounding rock creep was used as an evaluation index. Reserved deformation layer thickness optimization analysis reveals no plastic strain in the second lining when the reserved deformation minimum thickness layer is 28.50 cm. The results show that the new foam concrete used as a reserved deformation layer filling material can absorb creep deformation of surrounding rock mass, reduce second lining deformation that leads to plastic strain, and ensure long-term second lining stability.

Full-scale model tests and nonlinear analysis of prestressed concrete simply supported box girders

Full-scale model tests were carried out on a 30 m span prestressed concrete box girder and a 20 m span prestressed concrete hollow slab. Failure models were prestressed reinforcement tensile failure and crashing of roof concrete, respectively. The ductility indexes of the box girder and hollow slab were 1.99 and 1.23, respectively, according to the energy viewpoint. Based on the horizontal section hypothesis, the nonlinear computation procedure was established using the limited banding law, and it could carry out the entire performance analysis including the unloading, mainly focusing on the ways to achieve the unloading curves computation through stress-strain, moment-curvature and load-displacement curves. Through the procedure, parameters that influence on the bearing capacity, deformation performance and ductility of the structures were analyzed. Those parameters were quantity of prestressed reinforcement and tension coefficients of prestressed reinforcement. From the analysis, some useful conclusions can be obtained.

Improved Crude Oil Price Forecasting With Statistical Learning Methods

Reliable forecasts of the price of oil are of interest for a wide range of applications. For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroeconomic projections and in assessing macroeconomic risks. Of particular interest is the question of the extent to which the price of oil is helpful in predicting recessions. This paper presents a statistical learning method （SLM） based on combined fuzzy system （FS）, artificial neural network （ANN）, and support vector regression （SVR） to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. A number of quantitative factors were discovered from this model and used as the input. For verification and testing, the West Texas Intermediate （WT1） crude oil spot price is used to test the effectiveness of the proposed learning methodology. Empirical results reveal that the proposed SLM-based forecasting can model the nonlinear relationship between the input variables and price very well. Furthermore, in-sample and out-of-sample prediction performance also demonstrates that the proposed SLM model can produce more accurate prediction results than other nonlinear models.

Multi-objective optimization of the streamlined head of high-speed trains based on the Kriging model

As the running speed of high-speed trains increases, aerodynamic drag becomes the key factor which limits the further increase of the running speed and energy consumption. Aerodynamic lift of the trailing car also becomes the key force which affects the amenity and safety of the train. In the present paper, a simplified CRH380A high-speed train with three carriages is chosen as the model in order to optimize aerodynamic drag of the total train and aerodynamic lift of the trailing car. A constrained mul- ti-objective optimization design of the aerodynamic head shape of high-speed trains based on adaptive non-dominated sorting genetic algorithm is also developed combining local function three-dimensional parametric approach and central Latin hypercube sampling method with maximin criteria based on the iterative local search algorithm. The results show that local function parametric approach can be well applied to optimal design of complex three-dimensional aerodynamic shape, and the adaptive non-dominated sorting genetic algorithm can be more accurate and efficient to find the Pareto front. After optimization the aerodynamic drag of the simplified train with three carriages is reduced by 3.2%, and the lift coefficient of the trailing car by 8.24%, the volume of the streamlined head by 2.16%; the aerodynamic drag of the real prototype CRH380A is reduced by 2.26%, lift coefficient of the trailing car by 19.67%. The variation of aerodynamic performance between the simplified train and the true train is mainly concentrated in the deformation region of the nose cone and tail cone. The optimization approach proposed in the present paper is simple yet efficient, and sheds lights on the constrained multi-objective engineering optimization design of aerodynamic shape of high-speed trains.

Application of Support Vector Machine to Ship Steering

System identification is an effective way for modeling ship manoeuvring motion and ship manoeuvrability prediction. Support vector machine is proposed to identify the manoeuvring indices in four different response models of ship steering motion, including the first order linear, the first order nonlinear, the second order linear and the second order nonlinear models. Predictions of manoeuvres including trained samples by using the identified parameters are compared with the results of free-running model tests. It is discussed that the different four categories are consistent with each other both analytically and numerically. The generalization of the identified model is verified by predicting different untrained manoeuvres. The simulations and comparisons demonstrate the validity of the proposed method.

Zero-Hopf bifurcation for an infection-age structured epidemic model with a nonlinear incidence rate

An infection-age structured epidemic model with a nonlinear incidence rate is investigated.We formulate the model as an abstract non-densely defined Cauchy problem and derive the condition which guarantees the existence and uniqueness for positive age-dependent equilibrium of the model.By analyzing the associated characteristic transcendental equation and applying the normal form theory presented recently for non-densely defined semilinear equations,we show that the SIR(susceptible-infected-recovered)epidemic model undergoes Zero-Hopf bifurcation at the positive equilibrium which is the main result of this paper.