Model of bidirectional reflectance distribution function for metallic materials

Based on the three-component assumption that the reflection is divided into specular reflection,directional diffuse reflection,and ideal diffuse reflection,a bidirectional reflectance distribution function（BRDF） model of metallic materials is presented.Compared with the two-component assumption that the reflection is composed of specular reflection and diffuse reflection,the three-component assumption divides the diffuse reflection into directional diffuse and ideal diffuse reflection.This model effectively resolves the problem that constant diffuse reflection leads to considerable error for metallic materials.Simulation and measurement results validate that this three-component BRDF model can improve the modeling accuracy significantly and describe the reflection properties in the hemisphere space precisely for the metallic materials.

Modeling and simulation of material distribution in the sequential co-injection molding process

In co-injection molding,the properties and distribution of polymers will affect the application of products.The focus of this work is to investigate the effect of molding parameters on the skin/core material distribution based on three-dimensional(3-D)flow and heat transfer model for the sequential coinjection molding process,and the flow behaviors and material distributions of skin and core melts inside a slightly complex cavity(dog-bone shaped cavity)are predicted numerically.The governing equations of fluids in mold are solved by finite volume method and Semi-Implicit Method for Pressure Linked Equations(SIMPLE)algorithm on collocated meshes,and the domain extension technique is employed in numerical method for this cavity to assure that the numerical algorithm is implemented successfully.The level set transport equation which is used to trace the free surfaces in co-injection molding is discretized and solved by the 5 th-order Weighted Essentially Non-Oscillatory(WENO)scheme in space and 3 rd-order Total Variation Diminishing Runger-Kutta(TVD-R-K)scheme in time respectively.Numerical simulations are conducted under various volume fraction of core melt,skin and core melt temperatures,skin and core melt flow rates.The predicted results of material distribution in length,width and thickness directions are in close agreement with the experimental results,which indicate that volume fraction of core melt,core melt temperature and core melt flow rate are principal factors that have a significant influence on material distribution.Numerical results demonstrate the effectiveness of the 3-D model and the corresponding numerical methods in this work,which can be used to predict the melt flow behaviors and material distribution in the process of sequential co-injection molding.

Degree of polarization based on the three-component pBRDF model for metallic materials

An expression of degree of polarization（DOP） for metallic material is presented based on the three-component polarized bidirectional reflectance distribution function（p BRDF） model with considering specular reflection, directional diffuse reflection and ideal diffuse reflection. The three-component p BRDF model with a detailed reflection assumption is validated by comparing simulations with measurements. The DOP expression presented in this paper is related to surface roughness, which makes it more reasonable in physics. Test results for two metallic samples show that the DOP based on the three-component p BRDF model accords well with the measurement and the error of existing DOP expression is significantly reduced by introducing the diffuse reflection. It indicates that our DOP expression describes the polarized reflection properties of metallic surfaces more accurately.

SEPARATION OF MATERIALS IN SWIRL SHAFT PREHEATER

On the basis of theoretical analysis and cold model test, the material motion locus and the particle size which can be separated in the swirl shaft preheater was stud-ied. A new calculation method for separation efficiency based on the meterial size distribution curve of charge and dis-charge stock from the shaft is discussed in this paper. The ex-perimental results show the material separation is due to in-teraction of both the centrifugation and the gravity. When the inlet gas velocity is a constant, the separation efficiency will be raised as the load of batch feeding inreases. If the load of batch feeding is a constant, the separation efficiency will be decreased together with the elevation of the inlet gas. The loss of shaft resistence is increased when the separation effi-ciency is raised. In our experiment the suitable separation ef-ficiency of the shaft is about 70 percent.

Relationships between femoral strength evaluated by nonlinear finite element analysis and BMD,material distribution and geometric morphology

Precisely quantifying the strength of the proximal femur and accurately assessing hip fracture risk would enable those at high risk to be identified so that preventive interventions could be taken.Development of better measures of femoral strength using the clinically

Kinetic model of vibration screening for granular materials based on biological neural network

The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation.The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface.The dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was established.Different screening parameters were randomly selected,and the corresponding relationships were established as a database.Then,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,respectively.The training results indicated that ANFIS models had good stability and accuracy.The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.

Optimum Weight Design of Functionally Graded Material Gears

Traditional gear weight optimization methods consider gear tooth number, module, face width or other dimension parameters of gear as design variables. However, due to the complicated form and geometric features peculiar to the gear, there will be large amounts of design parameters in gear design, and the influences of gear parameters changing on gear trains, transmission system and the whole equipment have to be taken into account, which increases the complexity of optimization problem. This paper puts forward to apply functionally graded materials（FGMs） to gears and then conduct the optimization. According to the force situation of gears, the material distribution form of FGM gears is determined. Then based on the performance parameters analysis of FGMs and the practical working demands for gears, a multi-objective optimization model is formed. Finally by using the goal driven optimization（GDO） method, the optimal material distribution is achieved, which makes gear weight and the maximum deformation be minimum and the maximum bending stress do not exceed the allowable stress. As an example, the applying of FGM to automotive transmission gear is conducted to illustrate the optimization design process and the result shows that under the condition of keeping the normal working performance of gear, the method achieves in greatly reducing the gear weight. This research proposes a FGM gears design method that is able to largely reduce the weight of gears by optimizing the microscopic material parameters instead of changing the macroscopic dimension parameters of gears, which reduces the complexity of gear weight optimization problem.

Spreading of a Multicomponent Drop in Water: Solutions and Suspensions

The distribution of material resulting from the impact of a freely falling drop with a target liquid has been studied by photo and video registration methods.Different cases have been investigated by considering drops made of aqueous solutions(ink,salt,acid)and including fine solid particles(i.e.,suspensions).New features have been observed in terms of flow dynamics and thin components produced as a result of the impact(such as banded elements,ligaments,and vortices at the surface of the liquid).In particular,the characteristics of emerging netlike structures have been found to depend on the size of the suspension clusters.For the case of acetic acid,a different patterning behavior has been obtained:sequences of concentric arcs are produced in that case.However,linear scaling laws and the discrete nature of the distribution of the droplet material seem to be features common to all the cases considered.

Topology Optimization Design of Automotive Engine Bracket

According to the structural characteristics of the automobile engine bracket, the finite element model of the bracket is established. As the connecting part between the engine and the body, the performance requirements of the automobile engine bracket affect the comfort and the safety of the vehicle directly. Using the RADIOSS solver, the dangerous point of the bracket is analyzed. Under the premise of ensuring its reliability, with the help of OptiStruct software to carry out the topology optimization design, to get the optimal material distribution of the bracket and the final design will meet the performance requirements.

BP neural network model for material distribution prediction based on variable amplitude anti-blocking screening DEM simulations

The material feeding changing of combine harvester is easy to cause accumulation and blockage of the vibrating screen,which seriously affects the harvest operation.In order to alleviate such accumulation and blockages on the vibrating screen surface,the guide chute rotation angle of the improved variable amplitude screening mechanism was selected as the target variable,and EDEM-RecurDyn was employed to simulate the anti-blocking process of the variable amplitude under a changing feeding quantity(0.5 kg/s abnormal,0.2 kg/s normal)of materials(rice grain and stem mixture).A BP(an error back propagation algorithm)neural network was designed and the prediction model of the material distribution was subsequently constructed on the variable screening surface under different chute angles during abnormal feeding.The results revealed a continuous decrease in the quality and time of the material blockage at the front end of the screen surface with the increasing guide chute angle.At the guide chute angle of 20°-45°and adjustment time of 3-6 s,the blocked and accumulated materials at the front-end screen surface was be moved back to Grid 6 for screening.However,overtime,the screen surface materials continued to move back under the chute angle of 40°-45°,which had a great impact on the screening performance.At the guide chute angle of 30°-35°and adjustment time of 4 s,the materials on the screen surface were evenly distributed in Grid 1-6.This was able to alleviate the accumulation and blockage of the screen surface materials.The R of the material distribution prediction model(BP neural network)on the screen surface was determined as 0.97,indicating the high reliability and accuracy of the material distribution model on the screen surface based on the BP neural network.This work provides an important reference for the variable amplitude intelligent control of screen surface material anti-blocking.

Performance analysis and material distribution optimization for sound barriers using a semianalytical meshless method

With the increase in car ownership,traffic noise pollution has increased considerably and is one of the most severe types of noise pollution that affects living standards.Noise reduction by sound barriers is a common protective measure used in this country and abroad.The acoustic performance of a sound barrier is highly dependent on its shape and material.In this paper,a semianalytical meshless Burton-Miller‐type singular boundary method is proposed to analyze the acoustic performance of various shapes of sound barriers,and the distribution of sound‐absorbing materials on the surface of sound barriers is optimized by combining a solid isotropic material with a penalization method.The acoustic effect of the sound‐absorbing material is simplified as the acoustical impedance boundary condition.The objective of optimization is to minimize the sound pressure in a given reference plane.The volume of the sound‐absorbing material is used as a constraint.The density of the nodes covered with the sound‐absorbing material is used as the design variable.The method of moving asymptotes was used to update the design variables.This model completely avoids the mesh discretization process in the finite element method and requires only boundary nodes.In addition,the approach also does not require the singular integral calculation in the boundary element method.The method is illustrated and validated using numerical examples to demonstrate its accuracy and efficiency.

Bibliometric analysis and system review of vehicle routing optimization for emergency material distribution

Determining the optimal vehicle routing of emergency material distribution(VREMD)is one of the core issues of emergency management,which is strategically important to improve the effectiveness of emergency response and thus reduce the negative impact of large-scale emergency events.To summarize the latest research progress,we collected 511VREMD-related articles published from 2010 to the present from the Scopus database and conducted a bibliometric analysis using VOSviewer software.Subsequently,we cautiously selected 49 articles from these publications for system review;sorted out the latest research progress in model construction and solution algorithms;and summarized the evolution trend of keywords,research gaps,and future works.The results show that domestic scholars and research organizations held an unqualified advantage regarding the number of published papers.However,these organizations with the most publications performed poorly regarding the number of literature citations.China and the US have contributed the vast majority of the literature,and there are close collaborations between researchers from both countries.The optimization model of VREMD can be divided into single-,multi-,and joint-objective models.The shortest travel time is the most common optimization objective in the single-objective optimization model.Several scholars focus on multiobjective optimization models to consider conflicting objectives simultaneously.In recent literature,scholars have focused on the impact of uncertainty and special events(e.g.,COVID-19)on VREMD.Moreover,some scholars focus on joint optimization models to optimize vehicle routes and central locations(or material allocation)simultaneously.Solution algorithms can be divided into two primary categories,i.e.,mathematical planning methods and intelligent evolutionary algorithms.The branch and bound algorithm is the most dominant mathematical planning algorithm,while genetic algorithms and their enhancements are the most commonly used intelligent evolutionary algorithms.It is shown that the nondominated sorting genetic algorithmⅡ(NSGA-Ⅱ)can effectively solve the multiobjective model of VREMD.To further improve the algorithm’s performance,researchers have proposed improved hybrid intelligent algorithms that combine the advantages of NSGA-Ⅱand certain other algorithms.Scholars have also proposed a series of optimization algorithms for specific scenarios.With the development of new technologies and computation methods,it will be exciting to construct optimization models that consider uncertainty,heterogeneity,and temporality for large-scale real-world issues and develop generalized solution approaches rather than those applicable to specific scenarios.

A PIECEWISE LINEAR MODEL FOR ANALYZING THIN FILM/SUBSTRATE STRUCTURE IN FLEXIBLE ELECTRONICS

The conventional analytical method of predicting strain in a thin film under bending is restricted to the uniform material assumption, while in flexible electronics, the film/substrate structure is widely used with mismatched material properties taken into account. In this paper,a piecewise model is proposed to analyze the axial strain in a thin film of flexible electronics with the shear modification factor and principle of virtual work. The excellent agreement between analytical prediction and finite element results indicates that the model is capable of predicting the strain of the film/substrate structure in flexible electronics, whose mechanical stability and electrical performance is dependent on the strain state in the thin film.