Improved Modeling Method Research Articles

An Improved Modeling Method Combined With Mixed Finite-Element Method for Integrated Passive Devices

An Improved Modeling Method Combined With Mixed Finite-Element Method for Integrated Passive Devices

Crystal plasticity modeling fatigue behavior in bimodal Ti–6Al–4V: Effects of microdefect and lamellar orientation

AbstractInvestigating fatigue failure in titanium alloys is crucial for material design and engineering. Fatigue behavior in dual‐phase titanium alloys is strongly correlated with microstructural features and microdefects. This work formulates an improved modeling method to investigate fatigue behavior of bimodal Ti–6Al–4V, emphasizing the effects of lamellar orientation and microdefects. Using an improved Voronoi tessellation method, we establish representative volume element (RVE) models with various grain size distributions. Crystal plasticity finite element modeling (CPFEM) is used to analyze fatigue deformation in bimodal Ti–6Al–4V, considering microdefects and lamellar orientation. Fatigue indicator parameters are then incorporated into CPFEM to predict fatigue life and verified with experimental data. Numerical results highlight the significant influence of lamellar orientation and microdefects on fatigue behavior, with predicted life within the 3‐error band. This method efficiently overcomes challenges in quantitatively characterizing microstructural lamellae that experiments are short of, paving the way for designing fatigue‐resistant alloy materials with similar microstructures.

Improved algorithm of finding the next volume in direct CAD method for fusion neutronics analysis

The magnetic confinement fusion device, Tokamak, has a complex 3D geometry structure. As a result, the fusion neutronics geometry modeling process will be time-consuming and labor-intensive. Some researchers have improved modeling methods by using CAD (Computer Aided Design) models, but there is still a need to convert CAD models to CSG (Constructive Solid Geometry) or pre-process CAD models. Based on the existing direct CAD Monte Carlo approach, this paper creates a new algorithm for finding the next volume, which greatly simplifies or even omits the preprocessing process. By forming a grouping bounding box acceleration algorithm, the improved finding next volume performance is adequate to ensure precision and computation speed for complex 3D models. The results are verified by comparing them with a commercial Monte Carlo code, cosRMC, and an example of neutron fluence per particle on real CAD models of the HL-3 first wall and divertor shows a better applicability of this new algorithm.

THE METHOD OF HYDRODYNAMIC MODELING USING A CONVOLUTIONAL NEURAL NETWORK

Context. Solving hydrodynamic problems is associated with high computational complexity and therefore requires considerable computing resources and time. The proposed approach makes it possible to significantly reduce the time for solving such problems by applying a combination of two improved modeling methods.
 Objective. The goal is to create a comprehensive hydrodynamic modeling method that requires significantly less time to determine the dynamics of the velocity field by using the modified lattice Boltzmann method and the pressure distribution by using a convolutional neural network.
 Method. A method of hydrodynamic modeling is proposed, which realizes the synergistic effect arising from the combination of the improved lattice Boltzmann method and a convolutional neural network with a specially adapted structure. The essence of the method consists of implementing a sequence of iterations, each of which simulates the process of changing parameters when moving to the next time layer. Each iteration includes a predictor step and a corrector step. At the predictor step, the lattice Boltzmann method works, which allows us to obtain the field of fluid velocities in the working area at the next time layer using the field of velocities at the previous layer. At the corrector step, we apply an improved convolutional neural network trained on a previously created data set. Using a neural network allows us to determine the pressure distribution on a new time layer with a predetermined accuracy. After adding the fluid compressibility correction on the new time layer, we get a refined value of the velocity field, which can be used as initial data for applying the lattice Boltzmann method at the next iteration. Calculations stop when the specified number of iterations is reached.
 Results. The operation of the proposed method was studied on the example of modeling fluid movement in a fragment of the human gastrointestinal tract. The simulation results showed that the time spent implementing the simulation process was reduced by 6–7 times while maintaining acceptable accuracy for practical tasks.
 Conclusions. The proposed hydrodynamic modeling method with a convolutional neural network and the lattice Boltzmann method significantly reduces the time and computing resources required to implement the modeling process in areas with complex geometry. Further development of this method will make it possible to implement real-time hydrodynamic modeling in threedimensional domains.

Feasibility of applying graph theory to diagnosing generalized anxiety disorder using machine learning models

The aim of this study was to investigate whether the alterations of topological properties can facilitate the diagnosis of generalized anxiety disorder (GAD). Twenty first-episode drug-naive Chinese individuals with GAD and twenty age-sex-education-matched healthy controls (HCs) were included in the primary training set, and the results of which were validated using nineteen drug-free patients with GAD and nineteen unmatched HCs. Two 3 T scanners were used to acquire T1, diffusion tensor, and resting-state functional images. Topological properties were altered in the functional cerebral networks among patients with GAD, but not in the structural networks. Using the nodal topological properties in the anti-correlated functional networks, machine learning models distinguished drug-naive GADs from their matched HCs independent of the type of kernels and the amount of features. Although the models built with drug-naive GADs failed to distinguish drug-free GADs from HCs, the features selected for those models could be used to build new models for distinguishing drug-free GADs from HCs. Our findings suggested that it is feasible to utilize the topological characteristics of brain network to facilitate the diagnosis of GAD. However, further research with decent sample sizes, multimodal features, and improved modeling methods are needed to build more robust models.

Development of a Hepatic VX2 carcinoma model in rabbits using an improved minimally invasive method and evaluation with imaging examinations.

The hepatic VX2 carcinoma model in rabbits has been widely used in interventional diagnosis and treatment research for hepatocellular carcinoma (HCC). However, traditional methods for developing this model all have their shortcomings. To develop an improved method to construct an animal model of hepatic VX2 carcinoma. The puncture technique was used to obtain the VX2 tumor tissue. A tumor puncture-inoculation kit was designed and modified to implant the tumor tissue into the recipient rabbit's liver. 18 New Zealand white rabbits were implanted with VX2 tumor tissue using the improved tumor puncture-inoculation kit under ultrasound guidance. Ultrasonography, contrast-enhanced computerized tomography, magnetic resonance imaging, and digital subtraction angiography were performed to evaluate tumor formation and imaging characteristics. Statistical analysis was performed using SPSS software. Two groups were compared using Student's t-test analysis. All rabbits tolerated VX2 tumor tissue implantation successfully. 17 out of the 18 experimental rabbits developed liver tumors, and one rabbit had abdominal tumor metastasis. The average volume of tumors was 39.47 mm3 and 460.1 mm3 (P < 0.001) on the 7th and 14th days after modeling, respectively. Imageological diagnosis showed that all tumors had abundant blood supply and typical imaging characteristics. This improved modeling method is easy to operate and less traumatic, with a high tumor formation rate, low metastasis rate, prominent tumor imaging characteristics, and high detection rate, which is expected to become a promising method for constructing rabbit liver tumor model.

An improved method for predicting soluble solids content in apples by heterogeneous transfer learning and near-infrared spectroscopy

Predicting fruit soluble solids content (SSC) is a hot topic in non-destructive detection. Biological variability of fruit decreases the accuracy of the prediction model. Therefore, modeling methods that can reduce the negative effect of biological variability are necessary. In this paper, an improved modeling method based on the partial least squares (PLS) regression was proposed, using convolutional autoencoder and heterogeneous transfer learning for feature extraction. The dataset used for calibration and prediction contains spectra and corresponding SSC values of apples collected from 2012 to 2018. In comparison with the traditional PLS method, the proposed method performed better for long-term SSC prediction of apples with biological variability. The correlation coefficients calculated on validation set were 0.934, 0.940, 0.915, 0.899, 0.901, respectively. The root mean square errors calculated on validation set were 0.736 °Brix, 0.694 °Brix, 0.674 °Brix, 0.571 °Brix, 0.620 °Brix, respectively. Besides, the proposed method could still achieve relatively satisfactory results when the quantity of calibration samples was less.

Improved EMI Behavioral Modeling Method of Three-Phase Inverter Based on the Noise-Source Phase Alignment

The electromagnetic interference (EMI) behavioral model (BM), including active one-port networks, is widely used to guide the EMI filter design. To build BM, the series and shunt impedance insertion (SSII) based modeling method is normally adopted. During the SSII-based modeling, the time-domain measurements under different impedance insertion cases are introduced. However, the noise-source phases are time-varying, which means that the noise-source phases are different under multiple measurements. The different noise-source phases cause the errors of BM. To address this shortcoming, this article investigates the effect of the noise-source phases on BM for the first time. The improved BM method based on the noise-source phase alignment is proposed. The improved BM method effectively improves the EMI prediction accuracy of BM with an error of 6 dB or less. The effectiveness of the improved modeling method for the conducted emissions estimation is verified in the three-phase inverter test prototype.

Improved mechanical models and Ic estimation for the whole life cycle of high temperature superconducting coated conductors

To evaluate the effect of mechanical behaviors on the critical current (Ic) of superconducting conductors, this paper develops an improved modeling method at all the composite-layer, conductor, and cable levels, which provides a means to analyze the stress and strain for the whole manufacture process. First, a sandwich model based on composite shells was developed to calculate the residual thermal stress of coated conductors during the fabrication process. Then, the uniaxial tensile model was used to verify the loading method for applying the residual thermal stress to subsequent models as initial conditions. With the pre-stress loaded, mechanical behaviors of coated conductors under bending and torsion were analyzed. Further, an exponential function model was adopted to describe the relationship between the normalized critical current and longitudinal strain. Lastly, a series of numerical models were built to analyze the bending behavior of CORC (conductor on round core) cables. Combined with tests of a sample cable, the strain-dependent critical current predicted the Ic degradation under different bending radii. Calculation results show that the sandwich model is hundreds of times faster than other applicable finite element (FE) models. The loading method provides the initial stress for any geometry and software to precisely analyze the stress of coated conductors, especially the rare earth-barium-copper-oxide (ReBCO) layer with intrinsic compressive stress. The electromechanical behaviors of bending and torsion are in good agreement with experimental data, and the exponential mode obtained from conductor performance can estimate Ic degradation of the CORC cable within a relative error of 1.46 % in the reversible region.

Dynamics Analysis of Rotating Cantilever Beams with Free End Mass

An improved modeling method for the dynamics analysis of rotating cantilever beams with free end mass is introduced. The proposed improved modeling method is based on the nonlinear Green strain theory in this study. By using the Hamilton principle and the Galerkin method, the discrete dynamic equations of the axial and chordwise motions are obtained. Meanwhile, the equations for free vibrations analysis of the rotating cantilever beam with free end mass are derived by introducing the equilibrium axial deformation into both axial and chordwise motions. Numerical simulations are performed to validate the proposed model, and the results are compared and discussed with the other modeling methods. The comparison shows that even a small free end mass has an important influence on the dynamic characteristics of the rotating beam. Furthermore, the influences of various parameters on the vibration frequencies and axial deformations of the rotating cantilever beams are analyzed, such as the angular speed, the free end mass to beam mass ratio, and the center body radius to beam length ratio. Under the same conditions, the influence of the free end mass on the variation in the first- and high-order frequencies with the increasing angular speed are different.

An Improved Impedance Modeling Method of Grid-Tied Inverters With White-Box Property

Small-signal modeling of grid-tied inverters (GTIs) is an essential work in stability analysis of GTIs-weak grid coupling system. Different from the previous small-signal modeling methods of GTIs with black-box property, a reduced-order modeling method has been proposed in this article to provide an impedance model with white-box property. First, impedance and admittance are seamlessly incorporated into the modeling method, where concepts of partial admittances, partial impedance, active power admittance, and reactive power admittance are especially introduced. As a result, simplified expressions of impedance model of GTIs have been obtained with physical interpretations of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> impedances strengthened. After that, dominated relationships between terms of GTI's impedance matrix and its internal physical parts are explicitly revealed to represent white-box property of the proposed impedance model. Specifically, the influence and effect mechanism of feedforward voltage compensation, current controller, phase lock loop, and power points ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_d</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_q</i> ) are emphasized based on the proposed impedance model. Finally, perturbations generated independent with PLL are injected into a hardware in-loop experiment platform, and impedance measurement results are provided to verify the conclusions drawn from the improved modeling method.

Developing a two-step improved damage creep constitutive model based on soft rock saturation-loss cycle triaxial creep test

The study of the time-dependent deformation behavior of soft rock under water–rock interaction is of great significance to reveal slope creep deformation behavior. In this study, the saturation-loss cycle's triaxial creep test was performed, and the creep deformation, creep rate, and long-term strength of silty mudstone were analyzed. A two-step improved modeling method was also proposed. Firstly, by introducing fractional Abel viscous pot and aging factor, a one-dimensional creep constitutive model was established based on the Burgers model. And then the three-dimensional creep damage constitutive was derived through Laplace transform. Finally, the Levenberg–Marquardt (L–M) algorithm method was used to identify the creep curve and calibrate the model parameters. The new constitutive model could reflect the soft rock's creep characteristics during saturation-loss cycle.

Thermal Error Model of Linear Motor Feed System Based on Bayesian Neural Network

The linear motor feed system has been in service in complex working conditions for a long time, thus causing the nonuniform distribution of the temperature field distribution. Thus, the thermal error has become a key factor affecting system motion accuracy. In order to maximize the accuracy and efficiency of thermal error compensation for linear motor feed systems, an improved modeling method for the thermal error of the linear motor feed system based on Bayesian neural networks is proposed in combination with the strong generalization performance and avoidance of overfitting of Bayesian neural networks. And the specific modeling ideas are as follows: Firstly, the X-Y cross-type two-axis linear motor feed system is taken as the test object. Due to the traditional neural network’s slow convergence, overfitting, and underfitting problems, the Bayesian neural network is used to model the thermal error of the linear motor feed system. Secondly, to avoid the influence of multicollinearity data on the final results, the grey relation analysis method is used to screen the temperature measuring points. The data with a large relation degree is selected for modeling to ensure the prediction accuracy of the neural network. Thirdly, the temperature variables of sensitive points and thermal positioning errors are taken as data input samples. Fourthly, a Bayesian neural network model is established. Fifthly, the hyperparameters of the Bayesian neural network are determined by a calculating method of Hessian matrix by Gauss-Newton approximation. And finally, a thermal error prediction model is established. The comparison and analysis with the neural network constructed by the ordinary Levenberg-Marquardt algorithm after a series of experimental demonstrations see that the prediction accuracy of the proposed method can be enhanced by up to 10%. It also shows that the prediction model has the advantages of high precision, strong generalization ability, anti-disturbance solid ability, and strong robustness, etc. Therefore, the prediction model is expected to be widely used in predicting and compensating thermal error of the feed system of high-speed CNC machine tools in practical machining occasions.

Modeling of rotating machinery: A novel frequency sweep system identification approach

In this study, the dynamic modeling of rotating machinery, which is a harmonic excitation system, is investigated based on a nonlinear autoregressive (NARX) model with external inputs. Generally, NARX model-based techniques require Gaussian (white) noise, and thus these methods are not suitable for rotating machinery. Although there have been some reports on the modeling of harmonic excitation systems, the existing methods cannot establish a single-input single-output (SISO) NARX model to represent the rotating machinery over a wide range of rotational speeds. An improved modeling method called the frequency sweep system identification approach is proposed in this study to solve this issue. A discrete-time Fourier transform (DTFT) is performed on the system input and output datasets over a wide speed range to obtain the resulting spectra, and the amplitudes corresponding to the rotational frequencies are extracted and spliced together to convert multiple time-domain signals into one input data set and one output data set composed of frequency-domain data. Then, the modeling process can be carried out using the orthogonal forward search algorithm. Moreover, the effect of the data sequence on the identification results is discussed theoretically. A key feature of the proposed method is that the model structure detection and coefficient calculation are conducted with spliced frequency-domain vectors. The feasibility of the proposed modeling approach is validated through numerical and experimental cases. This work is a supplement to existing modeling methods based on the NARX model and provides a modeling basis for the analysis and design of rotating machinery in combination with the NARX model.

Kinematic Analysis of the Robot Having Closed Chain Mechanisms Based on an Improved Modeling Method and Lie Group Theory

The robot kinematic model is the basis of motion control, calibration, error analysis, etc. Considering these factors, the kinematic model needs to meet the requirements of completeness, model continuity, and minimality. DH model as the most widely used method to build robot kinematic model still has problems in completeness, model continuity, and calculation, especially for robots with complex mechanisms such as closed chain mechanism and branch mechanism. In this paper, an improved kinematic modeling method is proposed based on the cooperation of the DH model and the Hayati and Mirmirani model and considering the Lie group concept. The improved model is complete and continuous, and when combining with Lie group to calculate, it avoids numbers of trigonometric functions and antitrigonometric functions in the process so as to optimize the algorithm. With this method, the kinematic model of the closed chain cascade manipulator developed in our laboratory is established, and a working process of it is numerically calculated. The results of the numerical calculation are basically consistent with those of virtual prototype simulation, which means the established kinematic model is correct and the numerical calculation method can solve the problem correctly. The kinematic model and the results of the kinematic analysis provide a theoretical basis for the subsequent motion control, calibration, and error analysis of the robot.

Research on Modeling of a Micro Variable-Pitch Turboprop Engine Based on Rig Test Data

Exact component characteristics are required for establishing an accurate component level aeroengine model. When component characteristics is lacking, the dynamic coefficient method based on test data, is suitable for establishing a single-input and single-output aeroengine model. When it is applied to build multiple-input, multiple-output aeroengine models, some parameters are assumed to be unchanged, which causes large error. An improved modeling method based on rig data is proposed to establish a double-input, double-output model for a micro variable-pitch turboprop engine. The input variables are fuel flow and pitch angle, and the output variables are rotational speeds of the core engine and the propeller. First, in order to gather modeling data, a test bench is designed and rig tests are carried out. Then, two conclusions are obtained by analyzing the rig data, based on which, the power turbine output is taken as the function of the core speed and the propeller speed. The established model has the property that the input variables can vary arbitrarily within the defined domain, without any restriction to the output variables. Simulation results showed that the model has a high dynamic and steady-state accuracy. The maximum error was less than 8%. The real-time performance was greatly improved, compared to the component level model.

Ask the Experts: Better Foresight Comes With Strong Flowback Philosophies

The final act of completing a horizontal well and bringing it on line represents a special period for unconventional oil and gas producers. Typically done weeks or months after drilling and hydraulic fracturing operations, this stage—known as flowback—is akin to the moment of birth. Like a newborn baby, as each new well takes its first breath and exhales, it is customary to evaluate its health and start projecting how it will grow up. For a shale producer, this moment weighs heavily upon its future since flowback analysis feeds into long-term recovery estimates and economic models. Flowback data are also cheap compared to many other reservoir diagnostics. The challenge lies in the fact that there is no single flowback analysis method that operators can use to land on the “right” conclusion, while locking into the wrong one can be costly. The good news is that there are plenty of ways to narrow the band of uncertainty to paint a clearer picture of what early-time flow is really saying about how a well was completed, and what it will deliver going forward. Several of the latest industry insights and best practices on this topic were shared last winter at a conference organized by the Calgary-based completions-focused training startup SAGA Wisdom. The firm’s first annual meeting in St. Augustine, Florida, featured a panel of reservoir engineering experts that offered candid and practical advice on how to run a flowback analysis program. The discussion reflected the shale sector’s need for data management and improved modeling methods to illuminate what really drives tight-rock production. Real-Time Flowback for Long-Term Recovery Shale producers, service companies, and petroleum academia have delivered many flowback studies over the years. However, as far as the bottom line is concerned, flowback analysis is not merely an academic exercise. The goal for operators both large and small is to maximize rates, while minimizing the damage to the reservoir and conductive fracture networks. James Tucker was an engineer with Devon Energy when he coauthored one of OnePetro’s most-downloaded technical papers (SPE 174831) on flowback analysis and choke management. The paper described the operator’s use of rate transient analysis (RTA) and real-time data to double 30-day initial production (IP) rates from a field in the Eagle Ford Shale in Texas. Tucker, now a senior reservoir engineer with Austin-based Venado Oil &amp; Gas, highlighted when the paper was written in 2014 that oil prices were significantly higher than today. That offered more leeway for engineers to test ideas with a wider margin of error than can be afforded today. “Yes, there is value to be had by pulling the rigs forward and increasing your IP,” he said. “But as oil prices have declined, and we’ve moved to more mature plays and the second-tier areas, the range of degradation is much more sensitive.”

An Improved Parallel MDBN With AVMD for Nonlinear System Modeling

Nonlinear system modeling using Deep Belief Network (DBN) is currently a research hotspot. However, the training process of DBN needs large amount of data to guarantee accuracy, and the traditional DBN may not meet the requirement of high-precision modeling. In this paper, we first improve the original DBN and Variational Mode Decomposition (VMD) algorithms, and on this basis, we then proposed a parallel Momentum Deep Belief Networks (MDBN) with Adaptive Variational Mode Decomposition (AVMD). Parallel AVMD-MDBN is an improved modeling method based on the deep learning model DBN. Firstly, a single raw dataset is decomposed into a specific number of sub-datasets using AVMD. Then these sub-datasets are distributed among a number of improved MDBNs. A single raw dataset learning model and algorithm is extended to multiple feature extraction nodes to learn the characteristics of multiple sub-datasets in parallel. Finally, the results of the multiple nodes are transmitted to the main feature extraction node to complete the regression calculation. In order to verify the effectiveness of the model, the proposed parallel AVMD-MDBN model is tested on a nonlinear dynamic system modeling, a Mackey-Glass time-series prediction and a financial stock prediction. Our experimental results show that the proposed parallel AVMD-MDBN has better performances in terms of improving feature learning ability than that of other methods.

Research of Virtual and Real Occlusion Technology and Experimental Verification

For the wrong occlusion relationship between the virtual and real objects is generated due to the spatial relationship in augmented reality, which makes it difficult for the user to distinguish the real relationship between the virtual and real objects, the influence of immersion and interaction. The background contour of the occlusion object is extracted by the improved background modeling method and the improved shadow removal method. The occlusion relationship between the virtual and real objects is determined by depth information recovery and pixel traversal. The occlusion recovery is completed by using OpenGL and OpenCV as graphics drawing and handling tools. Experimental verification shows that the virtual and real occlusion handling method can effectively recovery the correct virtual and real occlusion relationship.

Model and robust gain‐scheduled PID control of a bio‐inspired morphing UAV based on LPV method

AbstractIn this paper, a linear parameter‐varying (LPV)‐based model and robust gain‐scheduled structural proportion integral and derivative (PID) control design solution are proposed and applied on a bio‐inspired morphing wing unmanned aerial vehicle (UAV) for the morphing process. In the LPV model method, the authors propose an improved modeling method for LPV systems. The method combines partial linearization and function substitution. Using the proposed method, we can choose the varying parameters simply, thus creating a model that is more flexible and applicable. Then, a robust gain‐scheduled structural PID control design method is given by introducing a structural matrix to design a structural PID controller, which is more consistent with the structure of the PID controller used in practice and has a simpler structure than representative ones in the existing literature. The simulation results show that the developed LPV morphing UAV model is able to catch the response of the original nonlinear model with a smaller error than the existing Jacobian linearization method and the designed controller can maintain stable flights in practice with satisfactory robustness and performance.