Algorithm Selection Method Based on Coupling Strength for Partitioned Analysis of Structure-Piezoelectric-Circuit Coupling

In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis ofstructure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectriccoupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulationsare used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely(1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weaklycoupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) a partially stronglycoupled and partially weakly coupled algorithm that uses an implicit formulation and an explicit formulation forthe two types of coupling, respectively.Numerical examples using a piezoelectric energy harvester,which is a typicalstructure-piezoelectric-circuit coupling problem, demonstrate that the proposed method selects the most costeffectivealgorithm.

Dynamic Coupled Analysis of the Floating Platform Using the Asynchronous Coupling Algorithm

This paper discusses the numerical modeling of the dynamic coupled analysis of the floating platform and mooring/risers using the asynchronous coupling algorithm with the purpose to improve the computational efficiency when multiple lines are connected to the platform. The numerical model of the platform motion simulation in wave is presented. Additionally, how the asynchronous coupling algorithm is implemented during the dynamic coupling analysis is introduced. Through a comparison of the numerical results of our developed model with commercial software for a SPAR platform, the developed numerical model is checked and validated.

Dynamic Optimization Method on Electromechanical Coupling System by Exponential Inertia Weight Particle Swarm Algorithm

Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm（EPSA）, is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimizati on. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.

Comparative study of kinetic modeling for the oxidative coupling of methane by genetic and marquardt algorithms

Overall kinetic studies on the oxidative coupling of methane,OCM,have been conducted in a tubular fixed bed reactor,using perovskite titanate as the reaction catalyst.The appropriate operating conditions were found to be：temperature 750-775 ℃,total feed flow rate of 160 ml/min,CH4 /O2 ratio of 2 and GHSV of 100·min-1 .Under these conditions,C 2 yield of 28% was achieved.Correlations of the kinetic data have been performed with lumped rate equations for C2 and COx formation as functions of temperature,O2 and CH4 partial pressures.Six models have been selected among the common lumped kinetic models.The selected models have been regressed with the experimental data which were obtained from the Catatest system by genetic algorithm in order to obtain optimized parameters.The kinetic coefficients in the overall reactions were optimized by different numerical optimization methods such as：the Levenberg-Marquardt and genetic algorithms and the results were compared with one another.It has been found that the Santamaria model is in good agreement with the experimental data.The Arrhenius parameters of this model have been obtained by linear regression.It should be noted that the Marquardt algorithm is sensitive to the first guesses and there is possibility to trap in the relative minimum.

Kinetic simulation of oxidative coupling of methane over perovskite catalyst by genetic algorithm:Mechanistic aspects

The reaction kinetics of oxidative coupling of methane catalyzed by perovskite was studied in a fixed bed flow reactor.At atmospheric pressure,the reactions were carried out at 725,750 and 775℃,inlet methane to oxygen ratios of 2 to 4.5 and gas hourly space velocity （GHSV） of 100 min^-1.Correlation of the kinetic data has been performed with the proposed mechanisms.The selected equations have been regressed with experimental data accompanied by genetic algorithm （GA） in order to obtain optimized parameters.After investigation the Langmuir-Hinshelwood mechanism was selected as the best mechanism,and Arrhenius and adsorption parameters of this model were obtained by linear regression.In this research the Marquardt algorithm was also used and its results were compared with those of genetic algorithm.It should be noted that the Marquardt algorithm is sensitive to the selection of initial values and there is possibility to trap in a local minimum.

Research and Application of Log Defect Detection and Visualization System Based on Dry Coupling Ultrasonic Method

In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.

Estimation of Mutual Coupling Coefficient of the Array by Simulated Annealing Algorithm

We propose a method for estimating the mutual coupling coefficient among antennas in this paper which is based on the principle of signal subspace and the simulated annealing （SA） algorithm. The computer simulation has been conducted to illustrate the＇ excellent performance of this method and to demonstrate that it is statistically efficient. The benefit of this new method is that calibration signals and unknown signals can be received simultaneously, during the course of calibration.

A nonlinear wave coupling algorithm and its programing and application in plasma turbulences

The fully developed turbulence can be regarded as a nonlinear system,with wave coupling inside,which causes the nonlinear energy to transfer,and drives the turbulence to develop further or be suppressed.Spectral analysis is one of the most effective methods to study turbulence system.In order to apply it to the study of the nonlinear wave coupling process of edge plasma turbulence,an efficient algorithm based on spectral analysis technology is proposed to solve the nonlinear wave coupling equation.The algorithm is based on a mandatory temporal static condition with the nonideal spectra separated from the ideal spectra.The realization idea and programing flow are given.According to the characteristics of plasma turbulence,the simulation data are constructed and used to verify the algorithm and its implementation program.The simulation results and experimental results show the accuracy of the algorithm and the corresponding program,which can play a great role in the studying the energy transfer in edge plasma turbulences.As an application,the energy cascade analysis of typical edge plasma turbulence is carried out by using the results of a case calculation.Consequently,a physical picture of the energy transfer in a kind of fully developed turbulence is constructed,which confirms that the energy transfer in this turbulent system develops from lower-frequency region to higher-frequency region and from linear growing wave to damping wave.

Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.

Abrasive Waterjet Machining Simulation by Coupling Smoothed Particle Hydrodynamics /Finite Element Method

In dealing with abrasive waterjet machining（AWJM） simulation,most literatures apply finite element method（FEM） to build pure waterjet models or single abrasive particle erosion models.To overcome the mesh distortion caused by large deformation using FEM and to consider the effects of both water and abrasive,the smoothed particle hydrodynamics（SPH） coupled FEM modeling for AWJM simulation is presented,in which the abrasive waterjet is modeled by SPH particles and the target material is modeled by FEM.The two parts interact through contact algorithm.Utilizing this model,abrasive waterjet with high velocity penetrating the target materials is simulated and the mechanism of erosion is depicted.The relationships between the depth of penetration and jet parameters,including water pressure and traverse speed,etc,are analyzed based on the simulation.The simulation results agree well with the existed experimental data.The mixing multi-materials SPH particles,which contain abrasive and water,are adopted by means of the randomized algorithm and material model for the abrasive is presented.The study will not only provide a new powerful tool for the simulation of abrasive waterjet machining,but also be beneficial to understand its cutting mechanism and optimize the operating parameters.

Measurements of electron-phonon coupling factor and interfacial thermal resistance of metallic nano-films using a transient thermoreflectance technique

Using a transient thermoreflectance （TTR） technique, several Au films with different thicknesses on glass and SiC substrates are measured for thermal characterization of metMlic nano-films, including the electron phonon coupling factor G, interfazial thermal resistance R, and thermal conductivity Ks of the substrate. The rear heating-front detecting （RF） method is used to ensure the femtosecond temporal resolution. An intense laser beam is focused on the rear surface to heat the film, and another weak laser beam is focused on the very spot of the front surface to detect the change in the electron temperature. By varying the optical path delay between the two beams, a complete electron temperature profile can be scanned. Different from the normally used single-layer model, the double-layer model involving interfaciM thermal resistance is studied here. The electron temperature cooling profile can be affected by the electron energy transfer into the substrate or the electron-phonon interactions in the metallic films. For multiple-target optimization, the genetic algorithm （GA） is used to obtain both G and R. The experimental result gives a deep understanding of the mechanism of ultra-fast heat transfer in metals.

A Novel Beam Design Method for mm Wave Multi-Antenna Arrays with Mutual Coupling Reduction

To overcome the mutual coupling (MC) of multiple antennas in millimeter wave (mmWave) communication systems, a novel beam design method with low complexity is proposed in this paper. Firstly, an equivalent channel model incorporating the effect of MC is analyzed and established, and then an optimal precoding / combiner vector for beamforming is derived. On this basis, by using greedy geometric (GG) algorithm, a novel hybrid beam design method is proposed. Finally, the performance of proposed method is analyzed and compared with other traditional ones. The simulation results show that the proposed method has better suppression of the MC effect as well as lower complexity.

Cognitive radio resource allocation based on coupled chaotic genetic algorithm

A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

Novel Apriori-Based Multi-Label Learning Algorithm by Exploiting Coupled Label Relationship

It is a key challenge to exploit the label coupling relationship in multi-label classification（MLC）problems.Most previous work focused on label pairwise relations,in which generally only global statistical information is used to analyze the coupled label relationship.In this work,firstly Bayesian and hypothesis testing methods are applied to predict the label set size of testing samples within their k nearest neighbor samples,which combines global and local statistical information,and then apriori algorithm is used to mine the label coupling relationship among multiple labels rather than pairwise labels,which can exploit the label coupling relations more accurately and comprehensively.The experimental results on text,biology and audio datasets shown that,compared with the state-of-the-art algorithm,the proposed algorithm can obtain better performance on 5 common criteria.

Multiscale Nonlinear Thermo-Mechanical Coupling Analysis of Composite Structures with Quasi-Periodic Properties

This paper reports a multiscale analysis method to predict the thermomechanical coupling performance of composite structures with quasi-periodic properties.In these material structures,the configurations are periodic,and the material coefficients are quasi-periodic,i.e.,they depend not only on the microscale information but also on the macro location.Also,a mutual interaction between displacement and temperature fields is considered in the problem,which is our particular interest in this study.The multiscale asymptotic expansions of the temperature and displacement fields are constructed and associated error estimation in nearly pointwise sense is presented.Then,a finite element-difference algorithm based on the multiscale analysis method is brought forward in detail.Finally,some numerical examples are given.And the numerical results show that the multiscale method presented in this paper is effective and reliable to study the nonlinear thermo-mechanical coupling problem of composite structures with quasiperiodic properties.

Coupled Cross-correlation Neural Network Algorithm for Principal Singular Triplet Extraction of a Cross-covariance Matrix

This paper proposes a novel coupled neural network learning algorithm to extract the principal singular triplet(PST)of a cross-correlation matrix between two high-dimensional data streams. We firstly introduce a novel information criterion(NIC),in which the stationary points are singular triplet of the crosscorrelation matrix. Then, based on Newton's method, we obtain a coupled system of ordinary differential equations(ODEs) from the NIC. The ODEs have the same equilibria as the gradient of NIC, however, only the first PST of the system is stable(which is also the desired solution), and all others are(unstable)saddle points. Based on the system, we finally obtain a fast and stable algorithm for PST extraction. The proposed algorithm can solve the speed-stability problem that plagues most noncoupled learning rules. Moreover, the proposed algorithm can also be used to extract multiple PSTs effectively by using sequential method.

Simulation and Optimization for Thermally Coupled Distillation Using Artificial Neural Network and Genetic Algorithm

In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neural network based on the simulation results with ASPEN PLUS. Modified genetic algorithm was used to optimize the model. With the proposed model and optimization arithmetic, mathematical model can be calculated, decision variables and target value can be reached automatically and quickly. A practical example is used to demonstrate the algorithm.

Modeling and Decoupling of Coupling Tasks in Collaborative Development Process of Complicated Electronic Products

It is important to improve the development efficiency of decoupling a coupling task package according to the information relevancy relation between development tasks in the collaborative development process of complicated electronic products.In order to define the task coupling model in the development process,the weighted directed graph based on the information relevancy is established,and the correspondence between weighted directed graph model and numerical design structure matrix model of coupling tasks is introduced.The task coupling model is quantized,thereby the interactivity matrix of task package is built.A multi-goal task decoupling method based on improved genetic algorithm is proposed to decouple the task coupling model,which transforms the decoupling of task package into a multi-goal optimization issue.Then the improved genetic algorithm is used to solve the interactivity matrix of coupling tasks.Finally,the effectiveness of this decomposition method is proved by using the example of task package decoupling of collaborative development of a radar’s phased array antenna.

Kinetic study of oxidative coupling of methane over Mn and/or W promoted Na_2SO_4/SiO_2 catalysts

A comprehensive kinetic model for oxidative coupling of methane（OCM）over Mn and/or W promoted Na2SO4/SiO2 catalysts was developed based on a micro-catalytic reactor data.The methane conversion and ethylene,ethane,carbon monoxide and carbon dioxide selectivities were obtained in a wide operating condition range of 750 - 825-C,CH4/O2=2.5 - 10 and contact time=267 - 472 kg s m-3.Reaction networks of six models with different rate equation types were compared together.The kinetic rate parameters of each reaction network were estimated using linear regression or genetic algorithm optimization method（GA）.A reaction network suggested by Stansch et al.for OCM was found to be the best one and was further used in this work.The suggested model could predict the experimental results of OCM reaction within a deviation range of ± 20%.

Application of Wavelet Random Coupling Model in Monthly Rainfall Prediction

A Trous algorithm of wavelet transform was used to decompose wavelet signal, and the cross-correlation analysis was used to analyze the sequence of each wavelet transform, and then the mathematical model correspond with wavelet transform sequence was established, finally wavelet random coupling model was obtained by wavelet reconstruction algorithm. Then, according to the rainfall data in crop growth period of Farm Chahayang from 1956 to 2008, the wavelet random coupling model was established to fit the model prediction test. The results showed that the prediction and fitting accuracy of the model was high, the model could reflect the rainfall variation regulation in the region, and it was a practical prediction model. It was very important for us to determine reasonably irrigation schedule and to use efficiency coefficient of precipitation resource.