3D finite element simulation of microstructure evolution in blade forging of Ti-6Al-4V alloy based on the internal state variable models

The physically-based internal state variable （ISV） models were used to describe the changes of dislocation density, grain size, and flow stress in the high temperature deformation of titanium alloys in this study. The constants of the present models could be identified based on experimental results, which were conducted at deformation temperatures ranging from 1093 K to 1303 K, height reductions ranging from 20% to 60%, and the strain rates of 0.001, 0.01, 0.1, 1.0, and 10.0 s-1. The physically-based internal state variable models were implemented into the commercial finite element （FE） code. Then, a three-dimensional （3D） FE simulation system coupling of deformation, heat transfer, and microstructure evolution was developed for the blade forging of Ti-6Al-4V alloy. FE analysis was carried out to simulate the microstructure evolution in the blade forging of Ti-6Al-4V alloy. Finally, the blade forging tests of Ti-6Al-4V alloy were performed to validate the results of FE simulation. According to the tensile tests, it is seen that the mechanical properties, such as tensile strength and elongation, satisfy the application requirements well. The maximum and minimum differences between the calculated and experimental grain size of primary α phase are 11.71% and 4.23%, respectively. Thus, the industrial trials show a good agreement with FE simulation of blade forging.

Modeling of microstructure evolution of AZ80 magnesium alloy during hot working process using a unified internal state variable method

In this paper, a unified internal state variable(ISV) model for predicting microstructure evolution during hot working process of AZ80 magnesium alloy was developed. A novel aspect of the proposed model is that the interactive effects of material hardening, recovery and dynamic recrystallization(DRX) on the characteristic deformation behavior were considered by incorporating the evolution laws of viscoplastic flow, dislocation activities, DRX nucleation and boundary migration in a coupled manner. The model parameters were calibrated based on the experimental data analysis and genetic algorithm(GA) based objective optimization. The predicted flow stress, DRX fraction and average grain size match well with experimental results. The proposed model was embedded in the finite element(FE) software DEFORM-3 D via user defined subroutine to simulate the hot compression and equal channel angular extrusion(ECAE) processes. The heterogeneous microstructure distributions at different deformation zones and the dislocation density evolution with competitive deformation mechanisms were captured.This study can provide a theoretical solution for the hot working problems of magnesium alloy.

Analytical criteria for local activity of CNN with five state variables and application to Hyper-chaos synchronization Chua's circuit

The analytic criteria for the local activity theory in one-port cellularneural network (CNN) with five local state variables are presented. The application to a Hyper-chaossynchronization Chua's circuit (HCSCC) CNN with 1125 variables is studied. The bifurcation diagramsof the HCSCC CNN show that they are slightly different from the smoothed CNN with one or two portsand four state variables calculated earlier. The evolution of the patterns of the state variables ofthe HCSCC CNN is stimulated. Oscillatory patterns, chaotic patterns, convergent or divergentpatterns may emerge if the selected cell parameters are located in the locally active domains butnearby or in the edge of chaos domain.

DYNAMIC RESPONSE OF ELASTIC RECTANGULAR PLATES BY SPLINE STATE VARIABLE METHOD

Application of spline element and state space method for analysis of dynamic response of elastic rectangular plates is presented. The spline element method is used for space domain and the state space method in control theory of system is used for time domain. A state variable recursive scheme is developed, then the dynamic response of structure can he calculated directly. Several numerical examples are given. The results which are presented to demonstrate the accuracy and efficiency of the present method are quite satisfactory.

Inferring interactions of time-delayed dynamic networks by random state variable resetting

Time delays exist widely in real systems, and time-delayed interactions can result in abundant dynamic behaviors and functions in dynamic networks. Inferring the time delays and interactions is challenging due to systematic nonlinearity,noises, a lack of information, and so on. Recently, Shi et al. proposed a random state variable resetting method to detect the interactions in a continuous-time dynamic network. By arbitrarily resetting the state variable of a driving node, the equivalent coupling functions of the driving node to any response node in the network can be reconstructed. In this paper,we introduce this method in time-delayed dynamic networks. To infer actual time delays, the nearest neighbor correlation(NNC) function for a given time delay is defined. The significant increments of NNC originate from the delayed effect.Based on the increments, the time delays can be reconstructed and the reconstruction errors depend on the sampling time interval. After time delays are accurately identified, the equivalent coupling functions can also be reconstructed. The numerical results have fully verified the validity of the theoretical analysis.

A specific state variable for a class of 3D continuous fractional-order chaotic systems

A specific state variable in a class of 3D continuous fractional-order chaotic systems is presented. All state variables of fractional-order chaotic systems of this class can be obtained via a specific state variable and its （q-order and 2q-order） time derivatives. This idea is demonstrated by using several well-known fractional-order chaotic systems. Finally, a synchronization scheme is investigated for this fractional-order chaotic system via a specific state variable and its （q-order and 2q-order） time derivatives. Some examples are used to illustrate the effectiveness of the proposed synchronization method.

THE ACTIVATION METHOD FOR DISCRETIZED CONSERVATIVE NONLINEAR STABILITY PROBLEMS WITH MULTIPLE PARAMETER AND STATE VARIABLES

For nonlinear stability problems of discretized conservative systems with multiple parameter variables and multiple state variables, the activation method is put forward, by which activated potential functions and activated equilibrium equations are derived. The activation method is the improvement and enhancement of Liapunov-Schmidt method in elastic stability theory. It is more generalized and more normalized than conventional perturbation methods. The activated potential functions may be transformed into normalized catastrophe potential functions. The activated equilibrium equations may be treated as bifurcation equations. The researches in this paper will motivate the combination of elastic stability theory with catastrophe theory and bifurcation theory

Direct assimilation of simulated radar reflectivity for typhoon In-fa using EnKF:Issue with state variables updating

Using a convective scale WRF-GSI system and a reflectivity observation operator based on the double-moment microphysics(Thompson)scheme,simulated radar reflectivity data are produced and then directly assimilated with EnKF through Observing System Simulation Experi-ments(OSSEs)for the case of typhoon In-Fa(2021).We examined the ability of the EnKF to simultaneously estimate state variables and conducted sensitivity tests to evaluate the impact of updating different state variables.The results show that updating a full set of analysis variables can help obtain highly precise initialfields in the model and improve typhoon forecast skills.Excluding the horizontal wind update will affect the adjustment of the temperaturefield and the sea level pressurefield during the cyclic assimilation process.Updating the variables directly related to the reflectivity operator alone could adjust hydrometers well,but the positive impact arising from the assimilation quickly vanishes during the forecast.In addition,this study also includes a quantitative RMSE analysis for each variable during the assimilation cycle and compares the effect of each schemes on different variables.

Cascade model for continuous prediction of silicon content of molten iron with coupled state variable nodes

With the goal of achieving advanced and multi-step prediction of silicon content of molten iron in the blast furnace ironmaking process,a path adaptive optimization seeking strategy coupled with simulated annealing algorithm and genetic algorithm was proposed from the perspective of innovative intelligent algorithm application.It was further coupled with wavelet neural network algorithm to deeply explore the nonlinear and strong coupling relationship between the information of big data samples and construct a cascade model for continuous prediction of silicon content of molten iron with the intelligent research results of state variables such as permeability index as the node and silicon content forecast as the output.In the model construction process,the 3r criterion was used for non-anomaly estimation of abnormal data to build a time-aligned sample set for multi-step forecasting of iron content,the normalization method was used to eliminate the influence of dimensionality of sample information,and the spearman correlation analysis algorithm was used to eliminate the time delay between state variables,control variables,and silicon content of molten iron in the blast furnace smelting process.The results show that permeability and theoretical combustion temperature as the key state variable nodes have real-time correlation with the silicon content of molten iron,and there are accurate forecasting results on the optimal path with the endpoint of molten iron silicon content prediction.The path finding based on the improved genetic algorithm of simulated annealing has good effect on the downscaling and depth characterization of sample data and improves the data ecology for the application of wavelet neural network algorithm.The accuracy of the real-time continuous forecasting model for the silicon content of molten iron reaches 95.24%;the hit rate of continuous forecasting one step ahead reaches 91.16%,and the hit rate of continuous forecasting five steps ahead is 87.41%.This model,which can realize the nodal dynamics of state variables,has better promotion value.

Modeling of microstructural evolution and flow behavior of superalloy IN718 using physically based internal state variables

Microstructural evolution and flow behavior greatly affect the hot forming process of IN718.In this research,hot deformation behaviors of IN718 were investigated by performing hot compression tests at temperature range of 1000-1100℃with strain rates of 0.1-20.0 s^(-1).By incorporating physically based internal state variables such as dislocation density,volume fraction of dynamic recrystallization,and grain size,a set of unified viscoplastic constitutive equations were developed to predict the microstructural evolution and flow behavior of IN718.The material constants were determined using a genetic algorithm(G A)-based optimization method.Comparisons of the computed and experimental results indicate that the constitutive equations established in this study can accurately describe the hot deformation behavior and microstructural evolution of IN718.

ESTIMATE ACCURACY OF NONLINEAR COEFFICIENTS OF SQUEEZEFILM DAMPER USING STATE VARIABLE FILTER METHOD

The estimate model for a nonlinear system of squeeze film damper (SFD) is described.The method of state variable filter (SVF) is used to estimate the coefficients of SFD.The factors which are critical to the estimate accuracy are discussed

Teleportation of a Kind of Three-Mode Entangled States of Continuous Variables

A quantum teleportation scheme to teleport a kind of tripartite entangled states of continuous variables by using a quantum channel composed of three bipartite entangled states is proposed. The joint Bell measurement is feasible because the bipartite entangled states are complete and the squeezed state has a natural representation in the entangled state basis. The calculation is greatly simplified by using the Schmidt decomposition of the entangled states.

Two-Variable Hermite Polynomial Excitation of Two-Mode Squeezed Vacuum State as Squeezed Two-Mode Number State

We find that the squeezed two-mode number state is just a two-variable Hermite polynomial excitation of thetwo-mode squeezed vacuum state (THPES).We find that the Wigner function of THPES and its marginal distributionsare just related to two-variable Hermite polynomials (or Laguerre polynomials) and that the tomogram of THPES canbe expressed by one-mode Hermite polynomial.

Estimation Method of State-of-Charge For Lithium-ion Battery Used in Hybrid Electric Vehicles Based on Variable Structure Extended Kalman Filter

Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.

Implementing Classical Hadamard Transform Algorithm by Continuous Variable Cluster State

Measurement-based one-way quantum computation, which uses cluster states as resources, provides an efficient model to perforrn computation. However, few of the continuous variable （CV） quantum algorithms and classical algorithms based on one-way quantum computation were proposed. In this work, we propose a method to implement the classical Hadamard transform algorithm utilizing the CV cluster state. Compared with classical computation, only half operations are required when it is operated in the one-way CV quantum computer. As an example, we present a concrete scheme of four-mode classical Hadamard transform algorithm with a four-partite CV cluster state. This method connects the quantum computer and the classical algorithms, which shows the feasibility of running classical algorithms in a quantum computer efficiently.

Variable dimensional state space based global path planning for mobile robot

A variable dimensional state space（VDSS） has been proposed to improve the re-planning time when the robotic systems operate in large unknown environments.VDSS is constructed by uniforming lattice state space and grid state space.In VDSS,the lattice state space is only used to construct search space in the local area which is a small circle area near the robot,and grid state space elsewhere.We have tested VDSS with up to 80 indoor and outdoor maps in simulation and on segbot robot platform.Through the simulation and segbot robot experiments,it shows that exploring on VDSS is significantly faster than exploring on lattice state space by Anytime Dynamic A＊（AD＊） planner and VDSS is feasible to be used on robotic systems.

Is Terzaghi's effective stress a stress variable under seepage conditions?

From the continuum mechanics perspective, an attempt was made to clarify the role of Terzaghi's effective stress in the theoretical analysis of saturated soil subjected to seepage. The necessity of performing a coupled hydromechanical analysis to solve the seepage-deformation interaction problem was illustrated by examining the equations of static equilibrium among the effective stress, seepage force, pore-water pressure and total stress. The conceptual definition of stress variable that satisfies the principles of continuum mechanics is applied in the coupled hydromechanical analysis. It is shown that Terzaghi's effective stress is in fact not a stress variable under seepage conditions, and the seepage force acting on the soil skeleton cannot be viewed as a body force. This offers a clue to the underlying cause of a paradox between the real Pascal's hydrostatic state and the hydrostatic state predicted by a class of continuum hydromechanical theories.

Transition State and Catastrophe Set

The study on transition states is one of the most important pathways to understand the essence of chemical reactions. With the development of femtoseeond pulse technique, Zewail et al. have carried out a number of experimental studies on the transition states, while the theoretical chemists has a great interest in the topic. In the previous

Squeeze-swapping by Bell measurement studied in terms of the entangled state representation

By virtue of the entangled state representation （Hong-Yi Fan and J R Klauder 1994 Phys. Rev. A 49 704） and the two-mode squeezing operator＇s natural representation （Hong-Yi Fan and Yue Fan 1996 Phys. Rev. A 54 958） we propose the squeeze-swapping mechanism which can generate quantum entanglement and new squeezed states of continuum variables.

A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system.Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view.However,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating expenses.This paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter.It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point.The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian.The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model.The results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.