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This paper aims to present a novel numerical technique for solving the incompressible multiphase mixture model.

The multiphase mixture model contains a set of momentum and continuity equations for the mixture phase, a second phase continuity equation and the algebraic equation for the relative velocity. For solving continuity equation for the second phase and advection term of momentum, an improved approach fine grid advection-multiphase mixture flow (FGA-MMF) is developed. In the FGA-MMF method, the continuity equation for the second phase is solved with higher-order schemes in a two times finer grid. To solve the advection term of the momentum equation, the advection fluxes of the volume fraction in the continuity equation for the second phase are used.

This approach has been used in various tests to simulate unsteady flow problems. Comparison between numerical results and experimental data demonstrates a satisfactory performance. Numerical examples show that this approach increases the accuracy and stability of the solution and decreases non-monotonic results.

The solver for the multi-phase mixture model can only be adopted to solve the incompressible fluid flow.

The paper developed an innovative solution (FGA-MMF) to find multi-phase flow field value in the multi-phase mixture model. Advantages of the FGA-MMF technique are the ability to accurately determine the phases interpenetrating, decreasing the numerical diffusion of the interface and preventing instability and non-monotonicity in solution of large density variation problems.

Asphalt mixture is widely used in road engineering, and its performance research is particularly important. But the study of asphalt mixture performance needs a lot of tests, such as bending test, splitting test and so on. It also needs a lot of time and material resources. The purpose of this paper is to obtain test results through finite element numerical simulation, and show that this saves a lot of manpower and material resources.

The mechanical parameters of the material are obtained through uniaxial compression tests. The true stress and plastic strain are calculated according to nominal stress and nominal strain. A constitutive model is established. Then a finite element model of asphalt mixture is established. The numerical simulation and performance study of asphalt mixture bending test is carried out. At the same time, according to the above method, the asphalt mixture is subjected to freeze-thaw cycles and ultraviolet aging, and the mechanical parameters are obtained by a uniaxial compression test. A numerical model is established to simulate the bending characteristics of asphalt mixture after freeze-thaw cycles and ultraviolet aging.

A uniaxial compression test of the asphalt mixture is conducted to obtain nominal stress and nominal strain. The true stress and plastic strain are calculated and the elastic modulus is established with Poisson’s ratio as the elastic part, and the true stress and plastic strain as the plastic part. The model is constructed, the finite element model is established and the bending test is numerically simulated. The verified trend is consistent, and the method is feasible. According to the above method, the concrete is subjected to freeze-thaw cycle and ultraviolet aging, and the finite element model is established by using uniaxial compression test to obtain parameters. The bending test is simulated and the verification method is feasible. With the increase of the number of freeze-thaw cycles and the increase of UV aging time, the maximum bending strain of SBS modified asphalt mixture and matrix asphalt mixture is decreased .The low-temperature performance of SBS modified asphalt mixture is better than that of matrix asphalt mixture.

A method of simulating asphalt mixture test by finite element method numerical simulation is established. By using this method, the performance of asphalt mixture is studied, which saves a lot of manpower and material resources. At the same time, this method can be used to study the characteristics of asphalt mixture under complex conditions.

The purpose of this paper is to address some of the fundamental issues surrounding crop insurance ratemaking, from the perspective of the reinsurer, through the development of a scientific pricing framework.

The generating process of the historical loss cost ratio's (LCR's) are reviewed, and the Erlang mixture distribution is proposed. A modified credibility approach is developed based on the Erlang mixture distribution and the liability weighted LCR, and information from the observed data of the individual region/province is integrated with the collective experience of the entire crop reinsurance program in Canada.

A comprehensive data set representing the entire crop insurance sector in Canada is used to show that the Erlang mixture distribution captures the tails of the data more accurately compared to conventional distributions. Further, the heterogeneous credibility premium based on the liability weighted LCR's is more conservative, and provides a more scientific approach to enhance the reinsurance pricing.

Credibility models are in the early stages of application in the area of agriculture insurance, therefore, the credibility models presented in this paper could be verified with data from other geographical regions.

The credibility-based Erlang mixture model proposed in this paper should be useful for crop insurers and reinsurers to enhance their ratemaking frameworks.

This is the first paper to introduce the Erlang mixture model in the context of agricultural risk modeling. Two modified versions of the Bühlmann-Straub credibility model are also presented based on the liability weighted LCR to enhance the reinsurance pricing framework.

Develops a method of forecasting foreign exchange rate by normal mixture model (NMM). Initially establishes a set of exchange rate models and switches from one model to another probabilistically, depending on supply shocks or government policy changes. By assuming that the population distribution of foreign exchange rate is a mixture of normal distributions, these models can then be estimated simultaneously. Uses the estimated parameters of the model to forecast foreign exchange rate, and then four foreign exchange rate models are used to estimate the NMM. The out‐of‐sample forecasting results obtained show that we can decrease the mean squared error (MSE) of forecast error dramatically by using the NMM, compared with the MSE of the best forecast of each separate model.

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

In order to give full play to the function of noise reduction of asphalt pavement, it is necessary to understand its internal sound absorption mechanism. Therefore, the purpose of this study is to establish a micro model of the pore structure of asphalt mixture with the help of finite element method (FEM), discuss the noise reduction mechanism of asphalt pavement from the micro perspective and analyze and evaluate the noise attenuation law of the pore structure.

The FEM was used to establish the microscopic model of the pore structure of asphalt mixture. Based on the principle of acoustics, the noise reduction characteristics of asphalt pavement were simulated. The influence of gradation and pore characteristics on the noise reduction performance of asphalt pavement was analyzed.

The results show that the open graded friction course-13 (OGFC-13) has excellent performance in noise reduction. The resonant sound absorption structure composed of its large porosity can effectively reduce the pavement noise. For asphalt concrete-13 (AC-13) and stone matrix asphalt-13 (SMA-13), the less resonant sound absorption structure makes them have poor sound absorption effect. In addition, the variation rules of noise transmission loss (TL) curve and sound absorption coefficient curve of three graded asphalt mixtures were obtained. At the same time, the peak noise reduction values of OGFC-13, AC-13 and SMA-13 were obtained, which were 650Hz, 1000Hz and 800Hz, respectively.

The results show that the simulation results can well reflect and express the experimental results. This will provide a reference for further exploring the sound absorption mechanism and its variation rule of porous asphalt pavement. It also has some positive significance for the application of low noise asphalt pavement.

The purpose of this paper is to focus on compressive strength modelling of cementitious mixtures like mortar and Roller-compacted concrete (RCC) containing rubber aggregates from shredded worn tires and filler using adaptive neuro fuzzy inference systems (ANFIS).

The volume substitution contains a ratio of rubber aggregates vs sand in mortar and with crushed sand in RCC and ranges from 0 to 50 per cent. As for the filler, they are substituted with sand by 5 per cent in mortar mixture. The methodology consists of optimizing the percentage of substitution in cementitious mixtures to ensure better mechanical properties of materials like compressive strength. The prediction of compressive strength and the optimization of cementitious mixtures encourage their uses in such construction pavements, in area games or in other special constructions. These cementitious materials are considered as friendly to the environment by focussing on their improved deformability.

The results of this paper show that the performance of the constructed fuzzy method was measured by correlation of experimental and model results of mortar and RCC mixtures containing both rubber aggregates and filler. The comparison between elaborated models through the error and the accuracy calculations confirms the reliability of the ANFIS method.

The purpose of this paper is to assess the performance of the constructed fuzzy model by the ANFIS method for two types of cementitious materials like mortar and RCC containing rubber aggregates and filler. The fuzzy method could predict the compressive strength based on the limited measurement values in the mechanical experiment. Furthermore, the comparison between the elaborated models confirms the reliability of the ANFIS method through the error and the accuracy calculations for the best cementitious material mixtures.

This paper aims to express a mathematical model that predicts the effect of mineral additives on the physical–mechanical properties of high-performance sand concrete (HPSC), using SAS's JMP7 statistical analysis software.

A mixture design modeling approach is applied to sand concrete (SC) for optimizing mixtures without being obliged to do a lot of experiments, where the cement is partially replaced with two mineral additives silica fume (SF) and blast furnace slag (BFS) in proportions as high as 20% of the mass. A total of 15 mixtures of sand concrete is prepared in the laboratory using this analytical technique in combinations with binary and ternary systems to estimate the workability and the compressive strength (CS) of sand concrete at 7 and 28 days.

The results obtained showed that the use of derived models based on the experimental design approach greatly assisted in understanding the interactions between the various parameters of the studied mixtures; the mathematical models present excellent correlation coefficients (R² = 0.96 for CS7 days, R² = 0.93 for CS28 days and R² = 0.95 for slump) for all studied responses. Moreover, it was also found that the inclusion of additives (SF and BFS) in binary mixture SC12 and ternary mixtures SC8 leads to a significant improvement in mechanical strength compared to reference sand concrete SC15. These results give the possibility to obtain a formulation of HPSC.

This paper shows the possibility of manufacturing high-performance sand-concrete with good compressive strength; the developed mathematical model by using SAS's JMP7 statistical analysis software allowed us to reach a strength compression value of about 60 MPa, in 28 days, by replacing 10% of the cement weight with silica fume. Furthermore, with partial replacement of the cement weight (15%) with two additions such as silica fume (10%) and blast furnace slag (5%), a 58 MPa of compressive strength can be achieved, without overlooking the fact that this can be a key economic and environmental alternative.

This paper aims to analyze the accuracy of the single and two-phase numerical methods for calculation of ferrofluid convective heat transfer in the presence of a magnetic field. The findings of current study are compared with previous single-phase numerical results and experimental data. Accordingly, the effect of various parameters including nanoparticles concentration, Reynolds number and magnetic field strength on the performance of the single and two-phase models are evaluated.

A two-phase mixture numerical study is carried out to investigate the influence of four U-shaped electromagnets on the hydrodynamic and thermal characteristics of Fe₃O₄/Water ferrofluid flowing inside a heated channel.

It is observed that the applied external magnetic field signifies the convective heat transfer from the channel surface, despite local reduction at a few locations. The maximum heat transfer enhancement is predicted as 23% and 25% using single and two-phase models, respectively. The difference between the results of the two models is mainly attributed to the slip velocity effect which is accounted for in the two-phase model. The magnetic field gradient leads to a significant increase in the slip velocity which in turn causes a slight difference in velocity and temperature profiles obtained by the single and two-phase models in the magnetic field region. According to percentage error calculation, the two-phase method is generally more accurate than the single-phase method. However, the percentage error of both models improves by decreasing either magnetic field intensity or Reynolds number.

For the first time in the literature, to the best of the authors’ knowledge, the current work analyzes the accuracy of the single and two phase numerical methods for calculation of ferrofluid convective heat transfer in the presence of a magnetic field.

The aim of the study is to present a piecewise parabolic method (PPM) for numerical simulation of barotropic and nonbarotropic two‐fluid flows in more than one space dimension.

In transition layers of two components, a fluid mixture model system is introduced. Besides, conserving the mass, momentum and energy for the mixture, the model is supplemented with an advection equation for the volume fraction of one of the two fluid components to recover the pressure and track interfaces. The Tait and stiffened gas equations of state are used to describe thermodynamic properties of the barotropic and nonbarotropic components, respectively. To close the model system, a mixture equation of state is derived. The classical third‐order PPM is extended to the two‐fluid case and used to solve the model system.

The feasibility of this method has been demonstrated by good results of sample applications. Each of the material interfaces is resolved with two grid cells and there is no any pressure oscillation on the interfaces.

With the mixture model system, there may be energy gain or loss for the nonbarotropic component on the material interfaces.

The method can be applied to a wide range of practical problems.

The method is simple. It not only has the advantage of Lagrangian‐type schemes but also keeps the robustness of Eulerian schemes.