Multiple linear system techniques for 3D finite element method modeling of direct current resistivity

The strategies that minimize the overall solution time of multiple linear systems in 3D finite element method (FEM) modeling of direct current (DC) resistivity were discussed. A global stiff matrix is assembled and stored in two parts separately. One part is associated with the volume integral and the other is associated with the subsurface boundary integral. The equivalent multiple linear systems with closer right-hand sides than the original systems were constructed. A recycling Krylov subspace technique was employed to solve the multiple linear systems. The solution of the seed system was used as an initial guess for the subsequent systems. The results of two numerical experiments show that the improved algorithm reduces the iterations and CPU time by almost 50%, compared with the classical preconditioned conjugate gradient method.

Simulation of a Laboratory Scale Ball Mill via Discrete Element Method Modelling

Discrete Element Method (DEM) is a powerful tool for simulating different types of mills. It also used for computing different types of particles such as rocks, grains, and molecules. DEM has been widely used in the field of rock mechanics. In the present work, DEM approach is applied to model the milling media (powder particles and balls) inside a planetary ball mill and to estimate the distribution of particles of a dry powder during milling. In fact, the efficiency of the DEM strongly depends on the input parameters. The DEM simulation results indicated that DEM is a promising tool for the simulation of the dynamic particles motion and interactions within planetary ball mill. These results could be utilized to further develop the synthesis performance, anticipate the reaction, and reduce the wear in the dry milling reactions.

Comparative Study of Probabilistic and Least-Squares Methods for Developing Predictive Models

This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.

Application of the Sub-Model Method in the Engine Strength Analysis

On the basis of introducing the fundamental theory and the basic analysis steps of the sub model method, the strength of the new engine complex assembly structure was analyzed according to the properties of the engine structures, some of the key parts of the engine were analyzed with refined mesh by sub model method and the error of the FEM solution was estimated by the extrapolation method. The example showed that the sub model can not only analyze the comlex structures without the restriction of the software and hardware of the computers, but get the more precise analysis result also. This method is more suitable for the strength analysis of the complex assembly structure.

Application of Elzaki Transform Method to Market Volatility Using the Black-Scholes Model

Black-Scholes Model (B-SM) simulates the dynamics of financial market and contains instruments such as options and puts which are major indices requiring solution. B-SM is known to estimate the correct prices of European Stock options and establish the theoretical foundation for Option pricing. Therefore, this paper evaluates the Black-Schole model in simulating the European call in a cash flow in the dependent drift and focuses on obtaining analytic and then approximate solution for the model. The work also examines Fokker Planck Equation (FPE) and extracts the link between FPE and B-SM for non equilibrium systems. The B-SM is then solved via the Elzaki transform method (ETM). The computational procedures were obtained using MAPLE 18 with the solution provided in the form of convergent series.

Numerical Method for Modeling the Constitutive Relationship of Sand under Different Stress Paths

A numerical method was used in order to establish the constitutive relationship of sands under different stress paths, Firstly, based on the numerical method modeling the constitutive law of sands, the elastoplastic constitutive relationship of sand was established for three paths： the constant proportion of principle stress path, the conventional triaxial compression （CTC） path, and the p=constant （TC） path. The yield lines of plastic volumetric strain and plastic generalized shear strain were given. Through visualization, the three dimensional surface of the stress-strain relationship in the whole stress field （p, q） obtained under the three paths was plotted. Also, by comparing the stress-strain surfaces and yield locus of the three stress paths, the differences were found to be obvious, which demonstrates that the influence of the stress paths on constitutive law was not neglected. The numerical modeling method overcame the difficulty of finding an analytical expression for plastic potential. The results simulated the experimental data with an accuracy of 90% on average, so the constitutive model established in this paper provides an effective constitutive equation for this kind of engineering, reflecting the effect of practical stress paths that occur in sands.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

3-D Geological Modeling-Concept,Methods and Key Techniques

3-D geological modeling plays an increasingly important role in Petroleum Geology, Mining Geology and Engineering Geology. The complexity of geological conditions requires different modeling methods in different situations. This paper summarizes the general concept of geological modeling; compares the characteristics of borehole-based modeling, cross-section based modeling and multi- source interactive modeling; analyses key techniques in 3-D geological modeling; and highlights the main difficulties and directions of future studies.

Modeling Method for Flexible Energy Behaviors in CNC Machining Systems

CNC machining systems are inevitably confronted with frequent changes in energy behaviors because they are widely used to perform various machining tasks. It is a challenge to understand and analyze the flexible energy behaviors in CNC machining systems. A method to model flexible energy behaviors in CNC machining systems based on hierarchical objected-oriented Petri net(HOONet) is proposed. The structure of the HOONet is constructed of a high-level model and detail models. The former is used to model operational states for CNC machining systems, and the latter is used to analyze the component models for operational states. The machining parameters having great impacts on energy behaviors in CNC machining systems are declared with the data dictionary in HOONet models. A case study based on a CNC lathe is presented to demonstrate the proposed modeling method. The results show that it is effective for modeling flexible energy behaviors and providing a fine-grained description to quantitatively analyze the energy consumption of CNC machining systems.

Modeling Methods and Test Verification of Root Insert Contact Interface for Wind Turbine Blade

Two modeling methods of the root insert for wind turbine blade are presented,i.e.,the local mesh optimization method(LMOM)and the global modeling method(GMM).Based on the optimized mesh of the local model for the metal contact interface,LMOM is proposed to analyze the load path and stress distribution characteristics,while GMM is used to calculate and analyze the stress distribution characteristics of the resin layer established between the bushing and composite layers of root insert.To validate the GMM,a tension test is carried out.The result successfully shows that the shear strain expresses a similar strain distribution tendency with the GMM′s results.

The Complex System Modeling Method Based on Uniform Design and Neural Network

In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.

Decoupling Conditions for Elasto-plastic Consolidation Question Based on Numerical Modeling Method

Elasto-plastic consolidation is one of the classic coupling questions in geomechanics. To solve this problem, an elasto-plastic constitutive model is derived based on the numerical modeling method. The model is applied to Blot＇s consolidation theory. Incremental governing partial differential equations are established using this method. According to the stress path, the decoupling condition of these equations is discussed. Based on these conditions, an incremental diffusion equation and uncoupling governing equations are presented. The method is then applied to numerical analyses of three examples. The results show that （1） the effect of the stress path should be taken into account in the simulation of the soil consolidation question; （2） this decoupling method can predict the evolvement of pore water pressure; （3） the settlement using cam-clay model is less than that using numerical model because of dilatancy.

Local linearization modeling method of a multilayer wave-shaped steel-belt damper (MWSD)

To analyze the modeling methods of the dry friction rotor system,a local linearization model of the dry friction damping rotor system was built based on the simplified model of the wave-shaped steel-belt supporting rotor system.In this model,the linear stiffness of damper closed to pre-deformation was defined as the stiffness of damper,the maximum amplitude of the rotor was calculated according to the load and linear rotor,and the damper's parameters were defined on the basis of the energy dissipation parameters.The presented method can reflect the hysteresis characteristics and is easy to calculate.Experimental results demonstrate the accuracy and feasibility of this method.

A Numerical Modelling Method of Fractured Reservoirs with Embedded Meshes and Topological Fracture Projection Configurations

Projection-based embedded discrete fracture model(pEDFM)is an effective numerical model to handle the flow in fractured reservoirs,with high efficiency and strong generalization of flow models.However,this paper points out that pEDFM fails to handle flow barriers in most cases,and identifies the physical projection configuration of fractures is a key step in pEDFM.This paper presents and proves the equivalence theorem,which explains the geometric nature of physical projection configurations of fractures,that is,the projection configuration of a fracture being physical is equivalent to it being topologically homeomorphic to the fracture,by analyzing the essence of pEDFM.Physical projection configurations of fractures may be rigorously established based on this theorem,allowing pEDFM to obtain physical numerical results for many flow models,particularly those with flow barriers.Furthermore,a natural idea emerges of employing flow barriers to flexibly‘cut’the formation to quickly handle the flow problems in the formation with complex geological conditions,and several numerical examples are implemented to test this idea and application of the improved pEDFM.

Study of elastic wave propagation in two phase anisotropic media by numerical modeling of pseudospectral method

When there exists anisotropy in underground media elastic parameters of the observed coordinate possibly do not coincide with that of the natural coordinate. According to the theory that the density of potential energy, dissipating energy is independent of the coordinate, the relationship of elastic parameters between two coordinates is derived for two-phase anisotropic media. Then, pseudospectral method to solve wave equations of two-phase anisotropic media is derived. At last, we use this method to simulate wave propagation in two-phase anisotropic media four types of waves are observed in the snapshots, i.e., fast P wave and slow P wave, fast S wave and slow S wave. Shear wave splitting, SV wave cusps and elastic wave reflection and transmission are also observed.

A novel method of rapidly modeling optical properties of actual photonic crystal fibres

The flexible structure of photonic crystal fibre not only offers novel optical properties but also brings some difficulties in keeping the fibre structure in the fabrication process which inevitably cause the optical properties of the resulting fibre to deviate from the designed properties. Therefore, a method of evaluating the optical properties of the actual fibre is necessary for the purpose of application. Up to now, the methods employed to measure the properties of the actual photonic crystal fibre often require long fibre samples or complex expensive equipments. To our knowledge, there are few studies of modeling an actual photonic crystal fibre and evaluating its properties rapidly. In this paper, a novel method, based on the combination model of digital image processing and the finite element method, is proposed to rapidly model the optical properties of the actual photonic crystal fibre. Two kinds of photonic crystal fibres made by Crystal Fiber A/S are modeled. It is confirmed from numerical results that the proposed method is simple, rapid and accurate for evaluating the optical properties of the actual photonic crystal fibre without requiring complex equipment.

Uncertain optimal model and solving method to platform scheduling problem in battlefield

The platform scheduling problem in battlefield is one of the important problems in military operational research.It needs to minimize mission completing time and meanwhile maximize the mission completing accuracy with a limited number of platforms.Though the traditional certain models obtain some good results,uncertain model is still needed to be introduced since the battlefield environment is complex and unstable.An uncertain model is prposed for the platform scheduling problem.Related parameters in this model are set to be fuzzy or stochastic.Due to the inherent disadvantage of the solving methods for traditional models,a new method is proposed to solve the uncertain model.Finally,the practicability and availability of the proposed method are demonstrated with a case of joint campaign.

Interval analysis method and convex models for impulsive response of structures with uncertain-but-bounded external loads

Two non-probabilistic, set-theoretical methods for determining the maximum and minimum impulsive responses of structures to uncertain-but-bounded impulses are presented. They are, respectively, based on the theories of interval mathematics and convex models. The uncertain-but-bounded impulses are assumed to be a convex set, hyper-rectangle or ellipsoid. For the two non-probabilistic methods, less prior information is required about the uncertain nature of impulses than the probabilistic model. Comparisons between the interval analysis method and the convex model, which are developed as an anti-optimization problem of finding the least favorable impulsive response and the most favorable impulsive response, are made through mathematical analyses and numerical calculations. The results of this study indicate that under the condition of the interval vector being determined from an ellipsoid containing the uncertain impulses, the width of the impulsive responses predicted by the interval analysis method is larger than that by the convex model; under the condition of the ellipsoid being determined from an interval vector containing the uncertain impulses, the width of the interval impulsive responses obtained by the interval analysis method is smaller than that by the convex model.

Static output feedback stabilization for second-order singular systems using model reduction methods

In this paper,the static output feedback stabilization for large-scale unstable second-order singular systems is investigated.First,the upper bound of all unstable eigenvalues of second-order singular systems is derived.Then,by using the argument principle,a computable stability criterion is proposed to check the stability of secondorder singular systems.Furthermore,by applying model reduction methods to original systems,a static output feedback design algorithm for stabilizing second-order singular systems is presented.A simulation example is provided to illustrate the effectiveness of the design algorithm.

Validation methodology for distribution-based degradation model

Distribution-based degradation models （or graphical approach in some literature） occur in a wide range of applications. However, few of existing methods have taken the validation of the built model into consideration. A validation methodology for distribution-based models is proposed in this paper. Since the model can be expressed as consisting of assumptions of model structures and embedded model parameters, the proposed methodology carries out the validation from these two aspects. By using appropriate statistical techniques, the rationality of degradation distributions, suitability of fitted models and validity of degradation models are validated respectively. A new statistical technique based on control limits is also proposed, which can be implemented in the validation of degradation models＇ validity. The case study on degradation modeling of an actual accelerometer shows that the proposed methodology is an effective solution to the validation problem of distribution-based de qradation models.