UNIFAC-ZM活度系数模型预测聚合物溶液中溶剂扩散系数

根据Vrentas-Duds自由体积理论，结合基于UNIFAC活度系数模型，在较宽的浓度范围内预测了聚合物溶液中小分子的扩散行为。对某些聚合物体系，计算结果和实验数据基本一致。表明UNIFAC-ZM模型可代替Flory-Huggins方程用于预测聚合物溶液中小分子扩散系数。调节自由体积模型中的指前因子D0数值可改善预测结果。     

Anisotropic diffusion with generalized diffusion coefficient function for defect detection in low-contrast surface images

In this paper, an anisotropic diffusion model with a generalized diffusion coefficient function is presented for defect detection in low-contrast surface images and, especially, aims at material surfaces found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image is extremely difficult to detect, because the intensity difference between the unevenly illuminated background and the defective region is hardly observable and no clear edges are present between the defect and its surroundings.The proposed anisotropic diffusion model provides a generalized diffusion mechanism that can flexibly change the curve of the diffusion coefficient function. It adaptively carries out a smoothing process for faultless areas and performs a sharpening process for defect areas in an image. An entropy criterion is proposed as the performance measure of the diffused image and then a stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to automatically determine the best parameter values of the generalized diffusion coefficient function. Experimental results have shown that the proposed method can effectively and efficiently detect small defects in various low-contrast surface images.     

A comparison of traditional geometrical models and mass triangle model in calculating the surface tensions of ternary sulphide melts

Surface tension of the Ni₃S₂–FeS–Cu₂S ternary mattes has been calculated using a mass triangle model as well as six traditional geometrical models based on the same calculation data to investigate the difference between mass triangle model and other kinds of geometrical models. From the calculated results, it might be seen that, the mass triangle model, irrespective of the method of selection of the binary data, would give the best results compared with other traditional geometrical models. The mean square root errors of the mass triangle method only range from 1.09% to 2.8%, which are almost within the experimental error of 2.5%.     

A chaotic model for advertising diffusion problem with competition

In this article, the author extends Dawid and Feichtinger's chaotic advertising diffusion model into the duopoly case. A computer simulation system is used to test this enhanced model. Based on the analysis of simulation results, it is found that the best advertising strategy in duopoly is to increase the advertising investment to reach the best Win–Win situation where the oscillation of market portion will not occur. In order to effectively arrive at the best situation, we define a synthetic index and two thresholds. An estimation method for the parameters of the index and thresholds is proposed in this research. We can reach the Win–Win situation by simply selecting the control parameters to make the synthetic index close to the threshold of min-oscillation state. The numerical example and computational results indicated that the proposed chaotic model is useful to describe and analyse advertising diffusion process in duopoly, it is an efficient tool for the selection and optimisation of advertising strategy.     

A diffusion wavelet approach for 3-D model matching

This paper proposes a new 3D shape retrieval approach based on diffusion wavelets which generalize wavelet analysis and associated signal processing techniques to functions on manifolds and graphs. Unlike current works on 3D matching, which are based either on the topological information of the model or its scatter point distribution information, this approach uses both information for more effective matching. Diffusion wavelets enable both global and local analyses on graphs, and can capture the topology of a surface with the diffusion map of its mesh representation. As a result, both multi-scale properties of the 3D geometric model and the topology among the meshes can be extracted for use in 3D geometric model retrieval. Tests using 3D benchmarks demonstrate that the approach based on diffusion wavelets is effective and performs better than those by spherical wavelet and spherical harmonics in 3D model matching.     

Fast prediction of the quasi phase equilibrium in phase field model for multicomponent alloys based on machine learning method

The phase field model for multicomponent alloys are usually coupled with the thermodynamic database. But it will take much time to solve the quasi phase equilibrium equations. In order to reduce such time and keep the enough accuracy, we develop a new model to predict the quasi phase equilibrium based on the machine learning method. As an example, the quasi phase equilibrium during the isothermal solidification of Al-Cu-Mg alloy is studied in detail. A neural network model with 3 inputs, 4 outputs and a hidden layer of 150 nodes is constructed. The “training data” are prepared by solving the quasi phase equilibrium equations with least square method. The neural network model is trained by different amount of data set, which can fully cover the ranges of all the variables. The accuracy and performance of the neural network model are discussed in detail. Its high accuracy and fast speed demonstrated that this will be a convenient method to acquire the quasi phase equilibrium data in phase field model for multicomponent alloys.     

A predictive model of impurity diffusion coefficients in face-centered-cubic metallic systems based on machine-learning

This paper develops models for diffusion coefficient prediction to provide parameters for atomic mobility databases and to assist material design in a multi-scale simulation framework for face-centered-cubic (fcc) alloys. Models of impurity-diffusion activation energy (Q_I) and self-diffusion activation energy (Q_s) are trained using machine-learning with experimental diffusion data and basic physical properties. The values of Q_s in body-centered cubic (bcc), fcc and hexagonal close-packed (hcp) can be well-predicted using melting temperature, electronic configuration, atomic properties and elasticity parameters. Estimates of Q_I in fcc metallic systems calculated using a model with six features agreed well with experimental data. Compared with previous models of Q_s and Q_I, the newly developed models exhibit higher coefficients of determination (R²) and significantly lower mean absolute errors. The self- and impurity-diffusion coefficients in fcc metallic systems can be simulated by these models. The models are also successfully applied during the assessment process of the Ni–Ti binary atomic mobility database. Thus, the developed models provide an easy and reliable method for estimating the self- or impurity-diffusion coefficients of fcc alloys when they are unavailable.     

伴有二级不可逆反应管式反应器轴向扩散模型的近似计算

根据管式反应器返混程度不大这一事实,应用带大参数微分方程理论,对进行二级不可逆反应等温管式反应器的轴向扩散模型作了近似处理,提出了计算出口转化率的近似表达式。在较宽的参数范围内,用近似表达式对反应器出口转化率进行了计算;将近似表达式的计算结果与用打靶法计算的结果比较,二者吻合程度满意。通过计算还表明,利用该表达式进行近似计算,较之其它任何求解轴向扩散模型的数值计算方法具有计算简便的优点。     

A mollification regularization method for unknown source in time-fractional diffusion equation

In the present paper, we consider an inverse source problem for a fractional diffusion equation. This problem is ill-posed, i.e. the solution (if it exists) does not depend continuously on the data. We give the mollification regularization method to solve this problem. An a priori error estimate between the exact solution and its regularized approximation is obtained. Moreover, a new a posteriori parameter choice rule is also proposed and a good error estimate is also obtained. Numerical examples are presented to illustrate the validity and effectiveness of this method.     

A new hyperbolic model and an experimental study for the flow of polymer melts in Multi-Pass Rheometer

A new mathematical model and an experimental study are presented for polymeric liquids. The main advantage of the proposed model over the existing models is its hyperbolic nature, which overcomes some of the drawbacks of the available models. One of the advantages of the proposed model is that the boundary conditions can be determined without ambiguity for all variables including the stresses, which may not be the situation for other mixed type systems. The modified Tait equation is used to describe the polymer compressibility and to keep a real set of characteristics for the resulting system of equations. The Giesekus model is used to model the polymer viscoelasticity. A hybrid finite element/finite difference scheme coupled with Newton–Raphson’s linearization scheme is used to solve the governing system of equations. Numerical and experimental studies for the flow of polymer melts in a Multi-Pass Rheometer (MPR) are presented. Despite using a one-mode Giesekus model, the predicted shear-rate dependent viscosity curve is in good agreement with the experimental results.     

A context-awareness model for activity recognition in robot-assisted scenarios

Context awareness in ambient assisted living programmes for the elderly is a cornerstone in the current scenario of noncustomized service robots distributed around the world. This research proposes a context-awareness system for a human–robot scene interpretation based on seven primary contexts and the American Occupational Therapy Association. The context-awareness system defined here proposes an inference mechanism for the activity recognition supported on hierarchical Bayesian networks. However, when the information from sensors increases, the computational cost associated also increases. Thus, an evaluation of different Bayesian network models is necessary for decreasing its impact over the robot performance. Two topological models have been modelled and tested using OpenMarkov application: a two-level approach of an input–observations layer and the activity recognition layer, and a three-layer model setting apart a primary contexts layer, the input–observations layer, and the activity recognition layer. The qualitative and quantitative results presented here show better performance in terms of memory and memory in a three-layer model. Besides, its effect on a hybrid architecture of a robotic platform is presented.     

A stochastic model for thermal transport of nanofluid in porous media: Derivation and applications

In this paper, a stochastic thermal transport model is developed for nanofluid flowing through porous media. This model incorporates the influences of nanoparticle migration on convective heat transfer of the colloidal solution. We show that Lévy flight movement patterns of nanoparticles result in the derived model using fractional derivative for the diffusion term. The new thermal transport model is then applied to the mixed convective problem which is solved using finite difference method. Numerical results show that the smaller values of Lévy index $\gamma$ lead to larger Nusselt numbers, thus the occurrence of long jumps for nanoparticles increases the heat transport of nanofluids. The effects of other involved physical parameters are also presented and discussed.     

基于晶格理论的聚合物溶液活度模型

根据Gibbs-Helmholtz热力学基本关系式,结合溶液晶格模型推导出适合聚合物体系的活度系数模型,该模型方程含有结合项、剩余项和自由体积项。将其与Entropic-FV(EFV)、UNIFAC-FV及UNIFAC模型,同时用于预测14个由PIB、PS和PPO等聚合物与苯、甲苯和丁烷等溶剂组成的聚合物-溶剂体系中溶剂的活度,并与实验数据进行比较,活度的平均偏差分别为3．98%、6．61%、4．92%、19．19%。计算结果显示该模型的预测精度高于其它3种常用的聚合物、溶剂体系的溶剂活度模型。     

A mathematical model on EPQ for stochastic demand in an imperfect production system

In the paper, we develop an EPQ (economic production quantity) inventory model to determine the optimal buffer inventory for stochastic demand in the market during preventive maintenance or repair of a manufacturing facility with an EPQ (economic production quantity) model in an imperfect production system. Preventive maintenance, an essential element of the just-in-time structure, may cause shortage which is reduced by buffer inventory. The products are sold with the free minimal repair warranty (FRW) policy. The production system may undergo “out-of-control” state from “in-control” state, after a certain time that follows a probability density function. The defective (non-conforming) items in “in-control” or “out-of-control” state are reworked at a cost just after the regular production time. Finally, an expected cost function regarding the inventory cost, unit production cost, preventive maintenance cost and shortage cost is minimized analytically. We develop another case where the buffer inventory as well as the production rate are decision variables and the expected unit cost considering the above cost functions is optimized also. The numerical examples are provided to illustrate the behaviour and application of the model. Sensitivity analysis of the model with respect to key parameters of the system is carried out.     

A continuous-time integrated model for discrete control of production flows in a multi-level bills of material environment

An integrated dynamic model for discrete production scheduling and continuous capacity expansion is presented in this paper. The modeled manufacturing system, based on multi-level bills of materials, is characterized by flexible machines with negligible setups and production rates fixed for the current capacity. Make-to-stock formulation of the problem is studied and optimal behavior of the system is determined with the help of the maximum principle. A fast time-decomposition algorithm is suggested to locate the optimal solution.     

A joint economic-lot-size model for purchaser and vendor in fuzzy sense

This paper investigates a group of computing schemas for joint economic lot size as fuzzy values of the economic lot size model for purchaser and vendor. We express the fuzzy order quantity/production lot size for the purchaser/vendor as the normal triangular fuzzy number (q₁, q₀, q₂) and then we solve the aforementioned optimization problem under the condition 0 < q₁ < q₀ < q₂. We find that, after defuzzification, the joint total relevant cost is slightly higher than in the crisp model.     

A novel model for a manufacturing system with joint production lines in terms of prior-set

A novel model integrating transformation and decomposition techniques is proposed to construct a manufacturing system with joint production lines as a multi-state manufacturing network (MMN). Reworking actions and different defect rates of workstations are both taken into account in the MMN model. The capacity analysis and performance evaluation are implemented accordingly. In particular, a technique in terms of ‘prior-set’ is developed to deal with multiple reworking actions. Subsequently, two simple algorithms are proposed to generate all minimal capacity vectors that workstations should provide to satisfy a given demand. In terms of such vectors, the probability of demand satisfaction can be derived. Such a probability is referred to as the system reliability, which is a performance indicator to state the capability of the MMN. According to each specific minimal capacity vector, the production manager may further determine a better strategy to produce products.