The Diffusion Problem of Toxic Gases in a Tunnel. The Pump Absorption Model

In this work we study the diffusion of a toxic gas in a tunnel due to an explosion in order to realize a control system—via a wireless network of active sensors—for identification and immediate containment procedures. To this end an aspiration pump is turned on instantaneously at a distance of b meters from the explosion to mitigate the effects of the terroristic act. We exactly find the diffusion concentration in order to provide a solution useful for comparison to other models: numerical ones or models with many aspiration pumps. The model is described by the diffusion partial differential equation (PDE) with a non-homogeneous term which models the aspiration pump. A model with more suction pumps is outlined.     

Object-oriented modelling and simulation of heat exchangers with finite element methods

The paper describes the derivation of finite-element models of one-dimensional fluid flows with heat transfer in pipes, using the Galerkin/least-squares approach. The models are first derived for one-phase flows, and then extended to homogeneous two-phase flows. The resulting equations have then been embedded in the context of object-oriented system modelling; this allows one to combine the fluid flow model with a model for other phenomena such as heat transfer, as well as with models of other discrete components such as pumps or valves, to obtain complex models of heat exchangers. The models are then validated by simulating a typical heat exchanger plant.     

Control-oriented modeling of servo-pump driven injection molding machines in the filling and packing phase

Servo-valves or variable displacement pumps are typically used to control conventional hydraulic injection molding machines (IMMs). Recent developments in electrical drive technology allow to utilize servo-motor driven pumps instead, which is beneficial due to their higher energy efficiency. Their dynamic behavior, however, is significantly different compared to the conventional setup. Thus, currently used mathematical models and control concepts cannot be directly applied. This paper presents a computationally efficient and scalable mathematical model of the injection process for these servo-pump driven IMMs. A first-principles model of the injection machine is combined with a phenomenological model describing the injection process, i.e. the compression of the melt and the polymer flow into the mold. The proposed model is tailored to real-time applications and serves as an ideal basis for the design of model-based control strategies. The feasibility of the proposed model is demonstrated by a number of different experiments. They confirm a high model accuracy over the whole operating range for different mold geometries.     

Modelling and characterization of a pneumatically actuated peristaltic micropump

There is an emerging class of microfluidic bioreactors which possess long-term, closed circuit perfusion under sterile conditions with in vivo-like flow parameters. Integrated into microfluidics, peristaltic-like pneumatically actuated displacement micropumps are able to meet these requirements. We present both a theoretical and experimental characterization of such pumps. In order to examine volume flow rate, we have developed a mathematical model describing membrane motion under external pressure. The viscoelasticity of the membrane and hydrodynamic resistance of the microfluidic channel have been taken into account. Unlike other models, the developed model includes only the physical parameters of the pump and allows the estimation of their impact on the resulting flow. The model has been validated experimentally.     

A mathematical model for solute coupled water transport in the production of aqueous humor

A simple mathematical model for the transport of solute and water in the production of aqueous humor by ciliary epithelium in the eye has been developed. The model introduces the intercellular channel, caped with a leaky (porous) tight junction between the layers of non-pigmented ciliary epithelium, as a bisectional channel which consists of two sections: one representing the tight junction which constitutes the blood-aqueous barrier and the other intercellular space with the active solute transport pumps on its lateral surfaces near the junction. The intercellular space and porous tight junction are modeled as electroneutral, uniform, semi-permeable channels of unequal cross-sectional area. Both the cylindrical pore- and rectangular-slit models for the transport through the channels are simultaneously introduced. The approximate analytical solutions to the governing non-linear coupled equations are obtained in normalized forms by employing Segal’s “Isotonic Convection Approximation”. The computational results for the scaled variables are presented through the graphs. The effects of important parameters on the flow/transport produced by (1) the hydrostatic pressure difference alone, (2) the concentration difference alone, and (3) the active transport alone, are examined and discussed. The results of the model may contribute to the present understanding of the mechanisms governing transport processes involved in the aqueous production.     

Simulation of fluid transients,for nonlinear fluids,with a lumped parameter model

This paper deals with the modeling of transients in low pressure transmission lines. Modeling of low pressure lines becomes more and more important for increasing efficiency of fast switching applications and performance of pumps e.g. common rail diesel injection systems and suction pipes of pumps. One simulation method is the lumped parameter model. For example a straight pipe can be modeled as a cascade of inertia, friction and compressibility blocks. In this paper, the idea of cascades is adopted for transient simulation of nonlinear fluids. The model includes a nonlinear fluid law of an oil-air mixture and the balance equations i.e. the compressibility and the inertia of the fluid. Friction is modeled by the frequency dependent friction model of Kagawa et.al. Comparison of simulation results with measurements from a test rig shows good correlation. Finally the scope of this simulation model is discussed and compared with measurements. (© 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Combining an Additive and Tree-Based Regression Model Simultaneously: STIMA

Additive models and tree-based regression models are two main classes of statistical models used to predict the scores on a continuous response variable. It is known that additive models become very complex in the presence of higher order interaction effects, whereas some tree-based models, such as CART, have problems capturing linear main effects of continuous predictors. To overcome these drawbacks, the regression trunk model has been proposed: a multiple regression model with main effects and a parsimonious amount of higher order interaction effects. The interaction effects can be represented by a small tree: a regression trunk. This article proposes a new algorithm—Simultaneous Threshold Interaction Modeling Algorithm (STIMA)—to estimate a regression trunk model that is more general and more efficient than the initial one (RTA) and is implemented in the R-package stima. Results from a simulation study show that the performance of STIMA is satisfactory for sample sizes of 200 or higher. For sample sizes of 300 or higher, the 0.50 SE rule is the best pruning rule for a regression trunk in terms of power and Type I error. For sample sizes of 200, the 0.80 SE rule is recommended. Results from a comparative study of eight regression methods applied to ten benchmark datasets suggest that STIMA and GUIDE are the best performers in terms of cross-validated prediction error. STIMA appeared to be the best method for datasets containing many categorical variables. The characteristics of a regression trunk model are illustrated using the Boston house price dataset.

Supplemental materials for this article, including the R-package stima, are available online.     

On the application of a model of condition-based maintenance

In this paper the modelling of condition monitoring information for three critical water pumps at a large soft-drinks manufacturing plant is described. The purpose of the model is to predict the distribution of the residual lifetimes of the individual pumps. This information is used to aid maintenance management decision-making, principally relating to overhaul. We describe a simple decision rule to determine whether maintenance action is necessary given monitoring information to date.     

Modelling,simulation and identification of a mobile concrete pump

Due to the light-weight construction of modern large-scale manipulators used, e.g., in mobile concrete pumps, the elasticity of the construction elements plays a significant role in the dynamic behaviour of the system. Therefore, current research is concerned with control strategies for active damping of elastic vibrations and trajectory planning. For this purpose, tailored mathematical models are required. Apart from the mathematical modelling, the identification of the model parameters constitutes a challenging task. This is mainly due to the large number of parameters to be identified and, considering the large scale, due to the fact that the boom movement cannot be measured by means of standard sensors. This paper presents a systematic approach for the mathematical modelling and identification of hydraulically actuated large-scale manipulators. The feasibility of the overall approach is demonstrated by means of measurement results of a mobile concrete pump.     

Numerical modelling and analysis of external gear pumps by applying generalized control volumes

Components in gear pumps usually involve complex geometrical arrangements in order to achieve the desired performance. The use of lumped parametric models is considered the most accurate and effective method for investigation of the associated design issues. In this study, the numerical modelling approach based on the lumped parameters and control volume concepts is reviewed, especially for gear teeth within the meshing zone. To apply the approach to the entire gear pump, control volume concepts are generalized to all gear pockets and flow orifices with some reasonable assumptions. The assumptions include instantaneous angular positions, orifice transitions and imagined control volumes with internal flows. The fluid dynamics and pump performance, which even have the measurement difficulties, can be estimated to investigate and optimize the design parameters of gears by the model. A simulation example and its experimental results of a gear machine are presented to validate the proposed approach in this article.     

Modeling clustered binary data with nonparametric unobserved heterogeneity: An application to stock crash analysis

Various random effects models have been developed for clustered binary data; however, traditional approaches to these models generally rely heavily on the specification of a continuous random effect distribution such as Gaussian or beta distribution. In this article, we introduce a new model that incorporates nonparametric unobserved random effects on unit interval (0,1) into logistic regression multiplicatively with fixed effects. This new multiplicative model setup facilitates prediction of our nonparametric random effects and corresponding model interpretations. A distinctive feature of our approach is that a closed-form expression has been derived for the predictor of nonparametric random effects on unit interval (0,1) in terms of known covariates and responses. A quasi-likelihood approach has been developed in the estimation of our model. Our results are robust against random effects distributions from very discrete binary to continuous beta distributions. We illustrate our method by analyzing recent large stock crash data in China. The performance of our method is also evaluated through simulation studies.     

Posterior Simulation in the Generalized Linear Mixed Model With Semiparametric Random Effects

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP) model with normal base measure, Gibbs samplingalgorithms based on the Pólya urn scheme are often used to simulate posterior draws in conjugate models (essentially, linear regression models and models for binary outcomes). In the nonconjugate case, some common problems associated with existing simulation algorithms include convergence and mixing difficulties.

This article proposes an algorithm for MDP models with exponential family likelihoods and normal base measures. The algorithm proceeds by making a Laplace approximation to the likelihood function, thereby matching the proposal with that of the Gibbs sampler. The proposal is accepted or rejected via a Metropolis-Hastings step. For conjugate MDP models, the algorithm is identical to the Gibbs sampler. The performance of the technique is investigated using a Poisson regression model with semi-parametric random effects. The algorithm performs efficiently and reliably, even in problems where large-sample results do not guarantee the success of the Laplace approximation. This is demonstrated by a simulation study where most of the count data consist of small numbers. The technique is associated with substantial benefits relative to existing methods, both in terms of convergence properties and computational cost.     

A dynamic parameterization modeling for the age-period-cohort mortality

An extended version of Hatzopoulos and Haberman (2009) dynamic parametric model is proposed for analyzing mortality structures, incorporating the cohort effect. A one-factor parameterized exponential polynomial in age effects within the generalized linear models (GLM) framework is used. Sparse principal component analysis (SPCA) is then applied to time-dependent GLM parameter estimates and provides (marginal) estimates for a two-factor principal component (PC) approach structure. Modeling the two-factor residuals in the same way, in age-cohort effects, provides estimates for the (conditional) three-factor age-period-cohort model. The age-time and cohort related components are extrapolated using dynamic linear regression (DLR) models. An application is presented for England & Wales males (1841-2006).     

Interaction Model and Model Selection for Function-on-Function Regression

Regression models with interaction effects have been widely used in multivariate analysis to improve model flexibility and prediction accuracy. In functional data analysis, however, due to the challenges of estimating three-dimensional coefficient functions, interaction effects have not been considered for function-on-function linear regression. In this article, we propose function-on-function regression models with interaction and quadratic effects. For a model with specified main and interaction effects, we propose an efficient estimation method that enjoys a minimum prediction error property and has good predictive performance in practice. Moreover, converting the estimation of three-dimensional coefficient functions of the interaction effects to the estimation of two- and one-dimensional functions separately, our method is computationally efficient. We also propose adaptive penalties to account for varying magnitudes and roughness levels of coefficient functions. In practice, the forms of the models are usually unspecified. We propose a stepwise procedure for model selection based on a predictive criterion. This method is implemented in our R package FRegSigComp. Supplemental materials are available online.     

Bayesian prediction of crack growth based on a hierarchical diffusion model

A general Bayesian approach for stochastic versions of deterministic growth models is presented to provide predictions for crack propagation in an early stage of the growth process. To improve the prediction, the information of other crack growth processes is used in a hierarchical (mixed‐effects) model. Two stochastic versions of a deterministic growth model are compared. One is a nonlinear regression setup where the trajectory is assumed to be the solution of an ordinary differential equation with additive errors. The other is a diffusion model defined by a stochastic differential equation where increments have additive errors. While Bayesian prediction is known for hierarchical models based on nonlinear regression, we propose a new Bayesian prediction method for hierarchical diffusion models. Six growth models for each of the two approaches are compared with respect to their ability to predict the crack propagation in a large data example. Surprisingly, the stochastic differential equation approach has no advantage concerning the prediction compared with the nonlinear regression setup, although the diffusion model seems more appropriate for crack growth. Copyright © 2016 John Wiley & Sons, Ltd.     

Use of the Probability Integral Transformation to Fit Nonlinear Mixed-Effects Models With Nonnormal Random Effects

This article describes a simple computational method for obtaining the maximum likelihood estimates (MLE) in nonlinear mixed-effects models when the random effects are assumed to have a nonnormal distribution. Many computer programs for fitting nonlinear mixed-effects models, such as PROC NLMIXED in SAS, require that the random effects have a normal distribution. However, there is often interest in either fitting models with nonnormal random effects or assessing the sensitivity of inferences to departures from the normality assumption for the random effects. When the random effects are assumed to have a nonnormal distribution, we show how the probability integral transform can be used, in conjunction with standard statistical software for fitting nonlinear mixed-effects models (e.g., PROC NLMIXED in SAS), to obtain the MLEs. Specifically, the probability integral transform is used to transform a normal random effect to a nonnormal random effect. The method is illustrated using a gamma frailty model for clustered survival data and a beta-binomial model for clustered binary data. Finally, the results of a simulation study, examining the impact of misspecification of the distribution of the random effects, are presented.     

On estimation and inference in a partially linear hazard model with varying coefficients

We study estimation and inference in a marginal proportional hazards model that can handle (1) linear effects, (2) non-linear effects and (3) interactions between covariates. The model under consideration is an amalgamation of three existing marginal proportional hazards models studied in the literature. Developing an estimation and inference procedure with desirable properties for the amalgamated model is rather challenging due to the co-existence of all three effects listed above. Much of the existing literature has avoided the problem by considering narrow versions of the model. The object of this paper is to show that an estimation and inference procedure that accommodates all three effects is within reach. We present a profile partial-likelihood approach for estimating the unknowns in the amalgamated model with the resultant estimators of the unknown parameters being root- \(n\) consistent and the estimated functions achieving optimal convergence rates. Asymptotic normality is also established for the estimators.     

Reliability of repairable redundant systems in nuclear generating stations

Markov models are presented to assess the reliability performance of redundant standby systems in nuclear generating stations. These systems are inactive during the normal station operation. However, they are required to operate for a specified period after the loss of normal power supply during emergency. The estimated probabilities of system failure are useful in deciding on the best combination of standby units and repair facilities. The proposed models are applicable to such systems as combustion turbine units in emergency service (Class III power system, emergency power supply system), and pumps in emergency coolant injection system.     

Sarmanov family of multivariate distributions for bivariate dynamic claim counts model

To predict future claims, it is well-known that the most recent claims are more predictive than older ones. However, classic panel data models for claim counts, such as the multivariate negative binomial distribution, do not put any time weight on past claims. More complex models can be used to consider this property, but often need numerical procedures to estimate parameters. When we want to add a dependence between different claim count types, the task would be even more difficult to handle. In this paper, we propose a bivariate dynamic model for claim counts, where past claims experience of a given claim type is used to better predict the other type of claims. This new bivariate dynamic distribution for claim counts is based on random effects that come from the Sarmanov family of multivariate distributions. To obtain a proper dynamic distribution based on this kind of bivariate priors, an approximation of the posterior distribution of the random effects is proposed. The resulting model can be seen as an extension of the dynamic heterogeneity model described in Bolancé et al. (2007). We apply this model to two samples of data from a major Canadian insurance company, where we show that the proposed model is one of the best models to adjust the data. We also show that the proposed model allows more flexibility in computing predictive premiums because closed-form expressions can be easily derived for the predictive distribution, the moments and the predictive moments.     

Propagation effects of model-calculated probability values in Bayesian networks

Probabilistic causal interaction models have become quite popular among Bayesian-network engineers as elicitation of all probabilities required often proves the main bottleneck in building a real-world network with domain experts. The best-known interaction models are the noisy-OR model and its generalisations. These models in essence are parameterised conditional probability tables for which just a limited number of parameter probabilities are required. The models assume specific properties of intercausal interaction and cannot be applied uncritically. Given their clear engineering advantages however, they are subject to ill-considered use. This paper demonstrates that such ill-considered use can result in poorly calibrated output probabilities from a Bayesian network. By studying, in an analytical way, the propagation effects of noisy-OR calculated probability values, we identify conditions under which use of the model can be harmful for a network's performance. These conditions demonstrate that use of the noisy-OR model for mere pragmatic reasons is sometimes warranted, even when the model's underlying assumptions are not met in reality.