基于MARC的混模法冲压回弹分析

针对有模法在成形后需再次计算接触、无模法添加的弹性地基约束无法限制工件纵向刚性位移的问题,提出在MARC中利用混模法对厚板回弹进行分析。首先分析了薄厚板成形的差异性,总结出厚板数值模拟的控制要点,梳理出基于MARC的厚板回弹模拟分析的一般流程;并依据相应分析流程和控制要点设置厚板成形过程。获得成形结果后,以有模法释放凸模与工件之间接触,降低凹模与工件之间的接触反力;其次,再以无模法并附加定位约束释放凹模。最后以某纵梁为例,对混模法进行验证,结果表明,混模法在降低计算成本的同时能够最大限度地降低人为分离拉应力的产生,且回弹值模拟结果与实测结果最为接近。

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Better Initial Placement Algorithm for Large-Scale Mixed-Mode Detailed Placement

An algorithm is presented for better legal solution in detailed placement of large scale mixed macros and standard cells IC design.Due to the limitation of computing complexity,an effective and efficient initial placement is very important for detailed placement.Novelty of this algorithm lies in a better solution at initial stage by using network flow method to satisfy row capacity constraint and the thought of linear placement problem(LPP) to resolve overlaps.Moreover,divide and conquer strategy and other simplified methods are adopted to minimize complexity.Experimental results show that the algorithm can get an average of 16% wire length improvement on PAFLO in reasonable CPU time.

Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model

Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.

Adaptive moving target detection algorithm based on Gaussian mixture model

In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.

Integrated logistics facilities network design for 3PLS under uncertainty

According to the operational characteristics of the logistics networks for the third party logistics supplier （3PLS）, the forward and reverse logistics networks together for 3PLS under the uncertain environment are designed. First, a fuzzy model is proposed by taking multiple customers, multiple commodities, capacitated facility location and integrated logistics facility layout into account. In the model, the fuzzy customer demands and transportation rates are illustrated by triangular fuzzy numbers. Secondly, the fuzzy model is converted into a crisp model by applying fuzzy chance constrained theory and possibility theory, and one hybrid genetic algorithm is designed for the crisp model. Finally, two different examples are designed to illustrate that the model and solution discussed are valid.

Optimization of mining method in subsea deep gold mines: A case study

The mining method optimization in subsea deep gold mines was studied. First, an index system for subsea mining method selection was established based on technical feasibility, security status, economic benefit, and management complexity. Next, an evaluation matrix containing crisp numbers and triangular fuzzy numbers(TFNs) was constructed to describe quantitative and qualitative information simultaneously. Then, a hybrid model combining fuzzy theory and the Tomada de Decis?o Interativa Multicritério(TODIM) method was proposed. Finally, the feasibility of the proposed approach was validated by an illustrative example of selecting the optimal mining method in the Sanshandao Gold Mine(China). The robustness of this approach was demonstrated through a sensitivity analysis. The results show that the proposed hybrid TODIM method is reliable and stable for choosing the optimal mining method in subsea deep gold mines and provides references for mining method optimization in other similar undersea mines.

Modeling and Optimization for Short-term Scheduling of Multipurpose Batch Plants

In the past two decades, short-term scheduling of multipurpose batch plants has received significant attention. Most scheduling problems are modeled using either state-task-network or resource-task-network(RTN) process representation. In this paper, an improved mixed integer linear programming model for short-term schedul-ing of multipurpose batch plants under maximization of profit is proposed based on RTN representation and unit-specific events. To solve the model, a hybrid algorithm based on line-up competition algorithm and linear programming is presented. The proposed model and hybrid algorithm are applied to two benchmark examples in literature. The simulation results show that the proposed model and hybrid algorithm are effective for short-term scheduling of multipurpose batch plants.

Time-series analysis with a hybrid Box-Jenkins ARIMA

Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.

Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.

Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization

Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simultaneous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization(AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.

Evaluation of mixed-mode stress intensity factors by extended finite element method

Extended finite element method （XFEM） implementation of the interaction integral methodology for evaluating the stress intensity factors （SIF） of the mixed-mode crack problem is presented. A discontinuous function and the near-tip asymptotic function are added to the classic finite element approximation to model the crack behavior. Two-state integral by the superposition of actual and auxiliary fields is derived to calculate the SIFs. Applications of the proposed technique to the inclined centre crack plate with inclined angle from 0° to 90° and slant edge crack plate with slant angle 45°, 67.5° and 90° are presented, and comparisons are made with closed form solutions. The results show that the proposed method is convenient, accurate and computationallv efficient.

Numerical Simulation of Separating Gas Mixtures via Hydrate Formation in Bubble Column

To develop a new technique for separating gas mixtures via hydrate formation,a set of medium-sized experimental bubble column reactor equipment was constructed.On the basis of the structure parameters of the ex- perimental bubble column reactor,assuming that the liquid phase was in the axial dispersion regime and the gas phase was in the plug flow regime,in the presence of hydrate promoter tetrahydrofuran(THF),the rate of hydrogen enrichment for CH4+H2 gas mixtures at different operational conditions(such as temperature,pressure,concentra- tion of gas components,gas flow rate,liquid flow rate)were simulated.The heat product of the hydrate reaction and its axial distribution under different operational conditions were also calculated.The results would be helpful not only to setting and optimizing operation conditions and design of multi-refrigeration equipment,but also to hydrate separation technique industrialization.

Brain MRI Segmentation Using KFCM and Chan-Vese Model

To extract region of interests （ROI） in brain magnetic resonance imaging （MRI） with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means （KFCM） clustering algorithm and Chan-Vese （CV） model for brain MRI segmentation is proposed. The approach consists of two succes- sive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clus- tering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means （FCM） clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation.

Optimization of multi-revolution low-thrust transfer based on modified direct method

A modified direct optimization method is proposed to solve the optimal multi-revolution transfer with low-thrust between Earth-orbits. First, through parameterizing the control steering angles by costate variables, the search space of free parameters has been decreased. Then, in order to obtain the global optimal solution effectively and robustly, the simulated annealing and penalty function strategies were used to handle the constraints, and a GA/SQP hybrid optimization algorithm was utilized to solve the parameter optimization problem, in which, a feasible suboptimal solution obtained by GA was submitted as an initial parameter set to SQP for refinement. Comparing to the classical direct method, this novel method has fewer free parameters, needs not initial guesses, and has higher computation precision. An optimal-fuel transfer problem from LEO to GEO was taken as an example to validate the proposed approach. The results of simulation indicate that our approach is available to solve the problem of optimal muhi-revolution transfer between Earth-orbits.

Simulation of the mixing process in a straight tube with sudden changed cross-section

In this work, we revised the expression of mixing intensity to describe the mixing output through a cross section in a flow system by considering heterogeneity of flow field, and carefully investigated the mixing process along a straight tube with expanding/contracting cross section by simulation method. The simulation results show that a sudden expansion of cross section has remarkable mixing intensification effect within a limited period(on the sub-second scale) or tube-length(on the millimeter scale), corresponding to the generation of considerable local vortices determined by both the flow capacity and the ratio of cross section change; a sudden contraction of cross section has instantaneous but weak mixing intensification effect; through introducing a local expansion structure with proper length, as the combination of sudden expansion and sudden contraction, their mixing intensification effects could be superposed. Besides, the rationality and importance are experimentally verified to explore the time profile of mixing intensity and carry out the vortex analysis by simulation for enhancing the selectivity of a complicated reaction system. These progresses may lead to more meaningful quantitative description of mixing process in a flow microreactor for some specific chemical processes.

Modeling and Generating Realistic Background Traffic by Hybrid Approach

One of the key challenges in largescale network simulation is the huge computation demand in fine-grained traffic simulation.Apart from using high-performance computing facilities and parallelism techniques,an alternative is to replace the background traffic by simplified abstract models such as fluid flows.This paper suggests a hybrid modeling approach for background traffic,which combines ON/OFF model with TCP activities.The ON/OFF model is to characterize the application activities,and the ordinary differential equations(ODEs) based on fluid flows is to describe the TCP congestion avoidance functionality.The apparent merits of this approach are(1) to accurately capture the traffic self-similarity at source level,(2) properly reflect the network dynamics,and(3) efficiently decrease the computational complexity.The experimental results show that the approach perfectly makes a proper trade-off between accuracy and complexity in background traffic simulation.

In-Plane Creep Buckling of Concrete-Filled Steel Tubular Arches

The creep-induced deformation of the arch rib of concrete-filled steel tubular （CFST） arches under a sustained load can increase the bending moment, which may lead to earlier stability failure called creep buckling. To investigate the influences of concrete creep on the buckling strength of arches, a theoretical analysis for the creep buckling of CFST circular arches under distributed radial load is performed. The simplified Arutyunyan-Maslov （AM） creep law is used to model the creep behavior of concrete core, and the creep integral operator is introduced. The analytical solutions of the time-dependent buckling strength under the sustained load are achieved and compared with the existing formula based on the age-adjusted effective modulus method （AEMM）. Then the solutions are used to determine the influences of the steel ratio and the first loading age on the creep buckling of CFST arches. The results show that the analytical solutions are of good accuracy and applicability. For CFST arches, the steel ratio and the first loading age have significant influences on creep buckling. An approximate log-linear relationship between the decreased degrees of the creep buckling strength and the first loading age is found. For the commonly used parameters, the maximum loss of the buckling strength induced bv concrete creen is close to 40%

Morphodynamic evolution of the Xiaoqing River mouth:a Huanghe River-derived mixed energy estuary

In an estuary,tidal,wave and other marine powers interact with the coast in different ways and affect estuary morphology as well as its evolution.In the Huanghe(Yellow) River estuaries and nearby delta,there are many small sediment-affected estuaries with a unique morphology,such as the Xiaoqing River estuary.In this study,we investigated the special evolution and genetic mechanism of the Xiaoqing River estuary by analyzing graphic and image data with a numerical simulation method.The results show that NE and NE-E tide waves are the main driving force for sandbar formation.Sediment shoals have originated from huge amounts of sediment from the Huanghe River,with consequent deposition at the Xiaoqing River mouth.The lateral suspended sediments beyond the river mouth move landward.Siltation takes place on the northern shoreline near the river mouth whereas erosion occurs in the south.The deposits come mainly from scouring of the shallow seabed on the northern side of the estuary.Storm surges speed up deposition in the estuary.Development of the sediment shoals has occurred in two steps involving the processes of growth and further southward extension.Although the southward shift increases the river curvature and length,the general eastward orientation of the estuary is unlikely to change.Processes on the adjacent shorelines do not affect the development of the sediment shoals.The study presents a morphodynamic evolutionary model for the Xiaoqing River estuary,with a long-term series cycle,within which a relatively short cycle occurs.

Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents

According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.