A constitutive model for the flow stress behavior and microstructure evolution in aluminum alloys under hot working conditions – with application to AA6099

A constitutive model for aluminum alloys under hot working conditions is proposed. The elastic-viscoplastic model is implemented in a finite strain continuum mechanical framework. The model accounts for the interplay between dynamic recovery and recrystallization during hot working of aluminum alloys and central aspects of microstructure evolution such as grain/subgrain size and dislocation density. The proposed model is generic in the sense that it can be used for arbitrary aluminum alloys, but in order to demonstrate its capabilities, the model is calibrated to a newly developed AA6099 alloy in the present study. The model is thoroughly discussed and details on the numerical implementation as well as on the calibration of the model against experimental data are provided.     

Exploratory Analysis and Modeling of Stock Returns

In this article, novel joint semiparametric spline-based modeling of conditional mean and volatility of financial time series is proposed and evaluated on daily stock return data. The modeling includes functions of lagged response variables and time as predictors. The latter can be viewed as a proxy for omitted economic variables contributing to the underlying dynamics. The conditional mean model is additive. The conditional volatility model is multiplicative and linearized with a logarithmic transformation. In addition, a cube-root power transformation is employed to symmetrize the lagged response variables. Using cubic splines, the model can be written as a multiple linear regression, thereby allowing predictions to be obtained in a simple manner. As outliers are often present in financial data, reliable estimation of the model parameters is achieved by trimmed least-square (TLS) estimation for which a reasonable amount of trimming is suggested. To obtain a parsimonious specification of the model, a new model selection criterion corresponding to TLS is derived. Moreover, the (three-parameter) generalized gamma distribution is identified as suitable for the absolute multiplicative errors and shown to work well for predictions and also for the calculation of quantiles, which is important to determine the value at risk. All model choices are motivated by a detailed analysis of IBM, HP, and SAP daily returns. The prediction performance is compared to the classical generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric power GARCH (APGARCH) models as well as to a nonstationary time-trend volatility model. The results suggest that the proposed model may possess a high predictive power for future conditional volatility. Supplementary materials for this article are available online.     

The Development of a Mathematical Model of the Operations of a National Glass Manufacturer

In this paper, a mathematical model of the entire operations of a national glass manufacturer is developed. This includes the float glass manufacture, distribution, storage operations and the technical considerations dictated by the plant as well as the operating procedures. The model is initially for a planning year, and is generated from a ‘monthly model’. This monthly model interconnects with other monthly models primarily via stock flows. The mathematical model is formulated in a unique way that allows certain production aspects to be modelled using a ‘pseudo-continuous’ time frame, rather than a discrete one. The generation of the overall model (as a mixed integer linear programming problem) and its solution is also discussed.     

GM(1,1)模型参数的神经网络算法

GM(1,1)模型的实质是小样本、贫信息下的预测模型,其目的是得到误差尽可能小的预测值.在分析GM(1,1)模型建模机理的基础上,提出了GM(1,1)模型中参数a,b的一种新算法——神经网络算法.把神经网络中的BP算法应用于GM(1,1)模型的建模过程,实例表明可使预测精度得到提高.     

变系数广义线性模型及其估计

本文以经典广义线性模型为基础,通过假定其中的回归变量的系数是某一度量空间中点的任意函数,提出了一类有广泛应用背景的变系数广义线性模型,增加了模型的灵活性和适应性,同时也适用于空间数据的统计分析。基于局部加权最大似然估计方法,文章讨论了变系数广义线性模型的拟合与统计推断,以及与之相关的局部权系统和其中光滑参数的确定。     

A tactical model for planning the production and distribution of fresh produce

We present an integrated tactical planning model for the production and distribution of fresh produce. The main objective of the model is to maximize the revenues of a producer that has some control over the logistics decisions associated with the distribution of the crop. The model is used for making planning decisions for a large fresh produce grower in Northwestern Mexico. The decisions obtained are based on traditional factors such as price estimation and resource availability, but also on factors that are usually neglected in traditional planning models such as price dynamics, product decay, transportation and inventory costs. The model considers the perishability of the crops in two different ways, as a loss function in its objective function, and as a constraint for the storage of products. The paper presents a mixed integer programming model used to implement the problem as wells as the computational results obtained from it.     

VLES turbulence model for an Eulerian–Lagrangian modeling concept for bubble plumes

Traditional Reynolds-averaged Navier–Stokes (RANS) approaches to turbulence modeling, such as the k-ϵ model, have some well-known shortcomings when modeling transient flow phenomena. To mitigate this, a filtered URANS model has been derived where turbulent structures larger than a given filter size (typically grid size) is captured by the flow equations and smaller structures are modeled according to a modified k-ϵ model. This modeling approach is also known as a VLES model (Very Large Eddy Scale model), and provides more details of the transient turbulence than the k-ϵ model at little extra computational cost.In this study a two-phase extension to the VLES model is described. A modeling concept for bubble plumes has been developed in which the bubbles are tracked as particles and the flow of liquid is solved by the Navier–Stokes equations in a traditional mesh based approach. The flow of bubbles and liquid is coupled in an Eulerian–Lagrangian model. Turbulent dispersion of the bubbles is treated by a random walk model. The random walk model depends on an estimation of the eddy life time. The eddy life time for the VLES model differs from a k-ϵ model, and its mathematical expression is derived.The model is applied to ocean plumes emanating from discharge of gas at the ocean floor. Validation with experiments and comparison with k-ϵ model are shown.     

A simulation model for aircraft sequencing in the near terminal area

The objective of this study is the development of a simulation model to assist in aircraft sequencing operations in the terminal area. After the definition of the main characteristics of the model, a general structure of a terminal area is considered with a variable number of feeder fixes and alternative paths from the fixes to the runways. The model is designed so as to evaluate different operating policies. A discrete events simulation philosophy, using Fortran as simulation language, is employed. Finally a model application to the Rome terminal area is illustrated. The results obtained show that the model, here presented, is general enough to simulate the terminal area behaviour of any airport.     

First Steps Towards an Institution of Algebra Replacement Systems

Algebra replacement systems are introduced as formal models of state dependent and state transforming systems. The first part of an institution of algebra replacement systems is developed, that is, a model theoretic and logical framework that can be used to describe and reason about such systems. The usual operational understanding of a replacement system as a labeled transition system is then considered as one particular model in the model category. Under appropriate conditions such a constructed replacement system is initial.     

A two-objective mathematical model without cutting patterns for one-dimensional assortment problems

This paper considers a one-dimensional cutting stock and assortment problem. One of the main difficulties in formulating and solving these kinds of problems is the use of the set of cutting patterns as a parameter set in the mathematical model. Since the total number of cutting patterns to be generated may be very huge, both the generation and the use of such a set lead to computational difficulties in solution process. The purpose of this paper is therefore to develop a mathematical model without the use of cutting patterns as model parameters. We propose a new, two-objective linear integer programming model in the form of simultaneous minimization of two contradicting objectives related to the total trim loss amount and the total number of different lengths of stock rolls to be maintained as inventory, in order to fulfill a given set of cutting orders. The model does not require pre-specification of cutting patterns. We suggest a special heuristic algorithm for solving the presented model. The superiority of both the mathematical model and the solution approach is demonstrated on test problems.     

Functional-coefficient partially linear regression model

In this paper, the functional-coefficient partially linear regression (FCPLR) model is proposed by combining nonparametric and functional-coefficient regression (FCR) model. It includes the FCR model and the nonparametric regression (NPR) model as its special cases. It is also a generalization of the partially linear regression (PLR) model obtained by replacing the parameters in the PLR model with some functions of the covariates. The local linear technique and the integrated method are employed to give initial estimators of all functions in the FCPLR model. These initial estimators are asymptotically normal. The initial estimator of the constant part function shares the same bias as the local linear estimator of this function in the univariate nonparametric model, but the variance of the former is bigger than that of the latter. Similarly, initial estimators of every coefficient function share the same bias as the local linear estimates in the univariate FCR model, but the variance of the former is bigger than that of the latter. To decrease the variance of the initial estimates, a one-step back-fitting technique is used to obtain the improved estimators of all functions. The improved estimator of the constant part function has the same asymptotic normality property as the local linear nonparametric regression for univariate data. The improved estimators of the coefficient functions have the same asymptotic normality properties as the local linear estimates in FCR model. The bandwidths and the smoothing variables are selected by a data-driven method. Both simulated and real data examples related to nonlinear time series modeling are used to illustrate the applications of the FCPLR model.     

A disruption recovery model for a single stage production-inventory system

This paper presents a newly developed disruption recovery model for a single stage production and inventory system, where the production is disrupted for a given period of time during the production up time. The model is categorized as a constrained non-linear optimization program which we have solved using an efficient heuristic developed in this paper. The model was also solved using an evolutionary algorithm and a comparison of the results from both methods was performed. The heuristic was able to accurately solve the model with significantly less time compared to the evolutionary algorithm. It can be shown that the optimal recovery schedule is dependent on the shortage cost parameters, as well as the extent of the disruption. The proposed model offers a potentially useful tool to help manufacturers decide on the optimal recovery plan in real time whenever the production system experiences a sudden disruption.     

Modelling homeorhesis by ordinary differential equations

Homeorhesis is a necessary feature of any living system. If a system does not perform homeorhesis, it is nonliving. The present work develops the sufficient conditions for the ODE model to describe homeorhesis and suggests the structure of the model. The proposed homeorhesis model is fairly general. It treats homeorhesis as piecewise homeostasis. The model can be specified in different ways depending on the specific system and specific purposes of this analysis. An example of the specification is the PhasTraM model, the homeorhesis-aware nonlinear reaction–diffusion model for hyperplastic oncogeny in the previous works of the author. The qualitative agreement of the developed homeorhesis model with the living-system experimental results is noted. The work also shows that the basic mathematical models (such as the active-particle generalized kinetic theory) are substantially more important for the living-matter studies than in the case of nonliving matter. A few directions for future research are suggested as well.     

Bayesian Inference via Filtering for a Class of Counting Processes: Application to the Micromovement of Asset Price

This paper highlights recent developments in a rich class of counting process models for the micromovement of asset price and in the Bayesian inference (estimation and model selection) via filtering for the class of models. A specific micromovement model built upon linear Brownian motion with jumping stochastic volatility is used to demonstrate the procedure to develop a micromovement model with specific tick-level sample characteristics. The model is further used to demonstrate the procedure to implement Bayes estimation via filtering, namely, to construct a recursive algorithm for computing the trade-by-trade Bayes parameter estimates, especially for the stochastic volatility. The consistency of the recursive algorithm model is proven. Simulation and real-data examples are provided as well as a brief example of Bayesian model selection via filtering.     

An efficient statistically equivalent reduced method on stochastic model updating

The demand for computational efficiency and reduced cost presents a big challenge for the development of more applicable and practical approaches in the field of uncertainty model updating. In this article, a computationally efficient approach, which is a combination of Stochastic Response Surface Method (SRSM) and Monte Carlo inverse error propagation, for stochastic model updating is developed based on a surrogate model. This stochastic surrogate model is determined using the Hermite polynomial chaos expansion and regression-based efficient collocation method. This paper addresses the critical issue of effectiveness and efficiency of the presented method. The efficiency of this method is demonstrated as a large number of computationally demanding full model simulations are no longer essential, and instead, the updating of parameter mean values and variances is implemented on the stochastic surrogate model expressed as an explicit mathematical expression. A three degree-of-freedom numerical model and a double-hat structure formed by a number of bolted joints are employed to illustrate the implementation of the method. Using the Monte Carlo-based method as the benchmark, the effectiveness and efficiency of the proposed method is verified.     

The volcano effect in bacterial chemotaxis

A population-level model of bacterial chemotaxis is derived from a simple bacterial-level model of behavior. This model, to be contrasted with the Keller–Segel equations, exhibits behavior we refer to as the “volcano effect”: steady-state bacterial aggregation forming a ring of higher density some distance away from an optimal environment. The model is derived, as in Erban and Othmer (2004) [1], from a transport equation in a state space including the internal biochemical variables of the bacteria and then simplified with a truncation at low moments with respect to these variables. We compare the solutions of the model to stochastic simulations of many bacteria, as well as the classic Keller–Segel model. This model captures behavior that the Keller–Segel model is unable to resolve, and sheds light on two different mechanisms that can cause a volcano effect.     

THE PERIODIC SOLUTIONS FOR TIMEDEPENDENT AGE-STRUCTURED POPULATION MODELS

1IntroductionTl1eage-structuredpopulationmodelhasbeensuccessfullyusedinpopulationdynamics['f'].Tl1egeneralage-structuredpopuIationmodelisdescribedbythefollowingpartialdifferentialequatiolls{3]:whereu(a,f)istheage-specificdensityofasingleage-structuredspeciesattimet,p(a,f)andb(a,t)aretheage-specificpercapitaldeathrateandbirthrateofthespeciesattimet,respectively.TherealAisthemaxinlulllageforanyindividuali11thepopulation,and[A1,A2listhefecundityperiodoffelllales.Thefunctiolluo(a)istheinitialdel…     

Productivity and customer satisfaction in Swedish pharmacies: A DEA network model

This paper presents a Data Envelopment Analysis (DEA) network model that allows inclusion of customer satisfaction in efficiency and productivity measures. The network consists of a production node and a consumption node and offers flexibility in modelling the production and consumption process where a firm-specific allocation of input resources to production and customer oriented activities is allowed. The proposed model is applied on a sample of Swedish pharmacies with organizational objectives that necessitates a monitoring of efficiency and productivity as well as customer satisfaction. Estimation results from the network model and a direct productivity model (without customer satisfaction) are compared and indicate that the technical efficiency is lower under the network model. The productivity results indicate productivity progress under both models, albeit with a slower rate of change under the network model.