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.

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.

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 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.

Automatic discovery of stateful variables in network protocol software based on replay analysis

Network protocol software is usually characterized by complicated functions and a vast state space.In this type of program,a massive number of stateful variables that are used to represent the evolution of the states and store some information about the sessions are prone to potentialflaws caused by violations of protocol specification requirements and program logic.Discovering such variables is significant in discovering and exploiting vulnerabilities in protocol software,and still needs massive manual verifications.In this paper,we propose a novel method that could automatically discover the use of stateful variables in network protocol software.The core idea is that a stateful variable features information of the communication entities and the software states,so it will exist in the form of a global or static variable during program execution.Based on recording and replaying a protocol program’s execution,varieties of variables in the life cycle can be tracked with the technique of dynamic instrument.We draw up some rules from multiple dimensions by taking full advantage of the existing vulnerability knowledge to determine whether the data stored in critical memory areas have stateful characteristics.We also implement a prototype system that can discover stateful variables automatically and then perform it on nine programs in Pro FuzzBench and two complex real-world software programs.With the help of available open-source code,the evaluation results show that the average true positive rate(TPR)can reach 82%and the average precision can be approximately up to 96%.

A Comprehensive Review of Experience with the Application of the Mechanical Threshold Stress Model

Accurate prediction of stress-strain behavior of metals as a function of arbitrary temperature and strain rate paths has remained a challenge. The Mechanical Threshold Stress constitutive model is one formalism that has emerged following several decades of research. Vast experience has accumulated with the application of the Mechanical Threshold Stress model over a wide variety of pure metals and alloys. Out of this has arisen common trends across metal systems. The magnitude of activation energies presents one example of this, where these variables consistently increase in magnitude as the obstacle to dislocation motion transitions from short range to long range. Trends in strain hardening are also observed. In Face-Centered Cubic metals the magnitude of strain hardening scales with the stacking fault energy;trends in Body-Centered Cubic metals are less clear. Model parameters derived for over twenty metals and alloys are tabulated. Common trends should guide future application of the MTS model and further model development.

Minimal Realization of Linear Graph Models for Multi-physics Systems

An engineering system may consist of several different types of components,belonging to such physical"domains"as mechanical,electrical,fluid,and thermal.It is termed a multi-domain(or multi-physics)system.The present paper concerns the use of linear graphs(LGs)to generate a minimal model for a multi-physics system.A state-space model has to be a minimal realization.Specifically,the number of state variables in the model should be the minimum number that can completely represent the dynamic state of the system.This choice is not straightforward.Initially,state variables are assigned to all the energy-storage elements of the system.However,some of the energy storage elements may not be independent,and then some of the chosen state variables will be redundant.An approach is presented in the paper,with illustrative examples in the mixed fluid-mechanical domains,to illustrate a way to recognize dependent energy storage elements and thereby obtain a minimal state-space model.System analysis in the frequency domain is known to be more convenient than in the time domain,mainly because the relevant operations are algebraic rather than differential.For achieving this objective,the state space model has to be converted into a transfer function.The direct way is to first convert the state-space model into the input-output differential equation,and then substitute the time derivative by the Laplace variable.This approach is shown in the paper.The same result can be obtained through the transfer function linear graph(TF LG)of the system.In a multi-physics system,first the physical domains have to be converted into an equivalent single domain(preferably,the output domain of the system),when using the method of TFLG.This procedure is illustrated as well,in the present paper.

An Optimal Control Theory Based Analysis of Brushless DC Motor Drive

The application of BLDC motor drives in industries is becoming more popular nowadays. An error will occur in the drive that is originated by some disturbances which are the major problems to reduce the stability of the system. To obtain the minimum performance index, the optimal control signal is formulated, which is the main objective of this paper. Based on quadratic performance index, the optimal control system of BLDC motor drive is a design which spotlights in this paper. The complexity of the mathematical expressions has been reduced by using state space approach to the BLDC system. The burden to the control engineers has reduced based on tedious computation by using thus optimal design. To provide the desired operating performance, this optimal design helps to realize the BLDC system with practical components.

Time Discretized Variational Iteration Method for the Stochastic Volatility Process with Jumps

A model for both stochastic jumps and volatility for equity returns in the area of option pricing is the stochastic volatility process with jumps (SVPJ). A major advantage of this model lies in the area of mean reversion and volatility clustering between returns and volatility with uphill movements in price asserts. Thus, in this article, we propose to solve the SVPJ model numerically through a discretized variational iteration method (DVIM) to obtain sample paths for the state variable and variance process at various timesteps and replications in order to estimate the expected jump times at various iterates resulting from executing the DVIM as n increases. These jumps help in estimating the degree of randomness in the financial market. It was observed that the average computed expected jump times for the state variable and variance process is moderated by the parameters (variance process through mean reversion), Θ (long-run mean of the variance process), σ (volatility variance process) and λ (constant intensity of the Poisson process) at each iterate. For instance, when = 0.0, Θ = 0.0, σ = 0.0 and λ = 1.0, the state variable cluttered maximally compared to the variance process with less volatility cluttering with an average computed expected jump times of 52.40607869 as n increases in the DVIM scheme. Similarly, when = 3.99, Θ = 0.014, σ = 0.27 and λ = 0.11, the stochastic jumps for the state variable are less cluttered compared to the variance process with maximum volatility cluttering as n increases in the DVIM scheme. In terms of option pricing, the value 52.40607869 suggest a better bargain compared to the value 20.40344029 due to the fact that it yields less volatility rate. MAPLE 18 software was used for all computations in this research.

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.

Resource reduction for simultaneous generation of two types of continuous variable nonclassical states

We demonstrate experimentally the simultaneous generation and detection of two types of continuous variable nonclassical states from one type-0 phase-matching optical parametric armplification(OPA)and subsequent two ring filter cavities(RFCs).The output field of the OPA includes the baseband wo and sideband modes ω0±nωf subjects to the cavity resonance condition,which are separated by two cascaded RFCs.The first RFC resonates with half the pump wavelength wo and the transmitted baseband component is a squeezed state.The relcted fields of the first RFC,including the sideband modes ω0±wf,are separated by the second RFC,construct Einstein Podolsky-Rosen entangled state.All freedoms,including the filter cavities for sideband separation and relative phases for the measure-ments of these sidebands,are actively stabilized.The noise variance of squeezed states is 10.2 dB below the shot noise limit(SNL),the correlation variances of both quadrature amplitude-sum and quadrature phase diference for the entanglement state are 10.0 dB below the corresponding SNL.

A hygro-thermo-mechanical constitutive model for shape memory polymers filled with nano-carbon powder

The nano-carbon powders are often used as fillers to endow the shape memory polymers(SMPs)with electroconductivity.It has been found that the shape memory effects(SMEs)of SMPs filled with nano-carbon powder can be triggered both by temperature and by water.To reveal the driving mechanism of SMEs,a constitutive model for describing the thermally activated and moisture activated SMEs of these shape memory polymer composite(SMPCs)is developed here.Because both of the SMEs share the same driving mechanism,the variable moisture is incorporated into the framework of a thermo-mechanical modeling approach to disclose the effect of moisture on the thermoviscoelastic properties.The SMPCs are regarded as isotropic materials and the effect of carbon powder on the mechanical properties of the matrix is also considered in the paper.Because the complete recovery may not be reached even they are exposed to the stimulus environment long enough,the blocking mechanism is also considered here.This is the mainly new contribution compared to the early work.Using the method of parameter determination presented here,the effectiveness of the proposed hygro-thermo-mechanical constitutive model is confirmed by comparing the model results with the test data of uniaxial deformation from the literature.

Calibration methodology for microscopic traffic simulation models of land ports of entry along the US-Mexico border

Non-commercial Land Ports of Entry(LPOEs)are unique transportation facilities controlling the ingress and egress of passenger vehicles from Mexico to the United States and vice versa.The calibration of microscopic traffic simulation models of non-commercial LPOEs requires a deep understanding of operational processes and driving behavior at these facilities.This paper provides a methodology to guide modelers in calibrating microscopic traffic simulation models of non-commercial LPOEs.The methodology establishes a common framework for modeling operational processes and traffic operations.Moreover,the methodology includes the value of four state variables that characterizes operations of passenger vehicles at non-commercial LPOEs.These variables are speed,acceleration,deceleration,and headway.The authors evaluated this methodology using the Otay Mesa non-commercial LPOE as a case study.Results showed that this methodology could be potentially used to model non-commercial LPOEs along the US-Mexico border and other border regions worldwide.

Model parameters estimation of aero-engine based on hybrid optimization algorithm

A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.

Investigating the Head Impact Force-Induced Evolution of Hyperphosphorylated Tau Proteins in Brain Tissue Through Mechanical Mesoscale Finite Element Simulation

For human heads that experienced repetitive subconcussive impacts,abnormal accumulation of hyperphosphorylated tau(p-tau)proteins was found in the postmortem brain tissue.To numerically understand the cause–effect relationship between the external force and the microscopic volume change of the p-tau protein,we created a mesoscale finite element model of the multilayer brain tissue containing microscopic voids representing the p-tau proteins.The model was applied under the loading boundary conditions obtained from a larger length scale simulation.A formerly developed internal state variable elastoplasticity model was implemented to describe the constitutive behaviors of gray and white matters,while the cerebrospinal fluid was assumed to be purely elastic.The effects of the initial sizes and distances of p-tau proteins located at four different brain regions(frontal,parietal,temporal and occipital lobes)on their volumetric evolutions were studied.It is concluded that both the initial sizes and distances of the proteins have more or less(depending on the specific brain region)influential effects on the growth or contraction rate of the p-tau protein.The p-tau proteins located within the brain tissue at the frontal and occipital lobes are more heavily affected by the frontal impact load compared with those at the parietal and temporal lobes.In summary,the modeling approach presented in this paper provides a strategy for mechanically studying the evolution of p-tau proteins in the brain tissue and gives insight into understanding the correlation between macroscopic force and microstructure change of the brain tissue.