Numerical simulation of a patent technology for sealing of deep-sea oil wells using nonlinear finite element method

For over 50 years bridge plugs and cement have been used for well abandonment and work over and are still the material of choice. However the failures of cement abandonments using bridge plugs has been reported on many occasions, some of which have resulted in fatal consequences. A new patented product is designed to address the shortcomings associated with using bridge plugs and cement. The new developed tools use an alloy based on bismuth that is melted in situ using Thermite reaction. The tool uses the expansion properties of bismuth to seal the well. Testing the new technology in real field under more than 2 km deep sea water can be expensive. Virtual simulation of the new device under simulated thermal and mechanical environment can be achieved using nonlinear finite element method to validate the product and reduce cost. Experimental testing in the lab is performed to measure heat generated due to thermite reaction. Then, a sequential thermal mechanical explicit/implicit finite element solver is used to simulate the device under both testing lab and deep water conditions.

Simulation of three-dimension cutting force and tool deflection in the end milling operation based on finite element method

This paper presents a 3D simulation system which is employed in order to predict cutting forces and tool deflection during end-milling operation. In order to verify the accuracy of 3D simulation, results (cutting forces and tool deflection) were compared with those based on the theoretical relationships, in terms of agreement with experiments. The results obtained indicate that the simulation is capable of predicting the cutting forces and tool deflection.

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects.

A wide range of tests for heteroskedasticity have been proposed in the econometric and statistics literature. Although a few exact homoskedasticity tests are available, the commonly employed procedures are quite generally based on asymptotic approximations which may not provide good size control in finite samples. There has been a number of recent studies that seek to improve the reliability of common heteroskedasticity tests using Edgeworth, Bartlett, jackknife and bootstrap methods. Yet the latter remain approximate. In this paper, we describe a solution to the problem of controlling the size of homoskedasticity tests in linear regression contexts. We study procedures based on the standard test statistics [e.g., the Goldfeld-Quandt, Glejser, Bartlett, Cochran, Hartley, Breusch-Pagan-Godfrey, White and Szroeter criteria] as well as tests for autoregressive conditional heteroskedasticity (ARCH-type models). We also suggest several extensions of the existing procedures (sup-type of combined test statistics) to allow for unknown breakpoints in the error variance. We exploit the technique of Monte Carlo tests to obtain provably exact p-values, for both the standard and the new tests suggested. We show that the MC test procedure conveniently solves the intractable null distribution problem, in particular those raised by the sup-type and combined test statistics as well as (when relevant) unidentified nuisance parameter problems under the null hypothesis. The method proposed works in exactly the same way with both Gaussian and non-Gaussian disturbance distributions [such as heavy-tailed or stable distributions]. The performance of the procedures is examined by simulation. The Monte Carlo experiments conducted focus on : (1) ARCH, GARCH, and ARCH-in-mean alternatives; (2) the case where the variance increases monotonically with : (i) one exogenous variable, and (ii) the mean of the dependent variable; (3) grouped heteroskedasticity; (4) breaks in variance at unknown points. We find that the proposed tests achieve perfect size control and have good power.

Simulation-Based Finite-Sample Normality Tests in Linear Regressions

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed to perform the tests have been derived for the location-scale model; hence reliance on available significance points in the context of regression models may cause size distortions. We propose a general solution to the problem of controlling the size normality tests for the disturbances of standard linear regression, which is based on using the technique of Monte Carlo tests.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

Mechanical simulation of the endovascular repair of abdominal aortic aneurysms

Ce travail de thèse porte sur la simulation du déploiement des prothèses vasculaires de type stent-graft (SG) lors de la réparation endovasculaire (EVAR) des anévrismes de l’aorte abdominale (AAA). Cette étude se présente en trois parties: (i) tests mécaniques en flexion et compression de SG couramment utilisés (corps et jambage de marque Cook) ainsi que la simulation numérique desdits tests, (ii) développement d’un modèle numérique d’anévrisme, (iii) stratégie de simulation du déploiement des SG. La méthode numérique employée est celle des éléments finis. Dans un premier temps, une vérification du modèle éléments finis (MEF) des SG est realisée par comparaison des différents cas de charge avec leur pendant expérimental. Ensuite, le MEF vasculaire (AAA) est lui aussi vérifié lors d’une comparaison des niveaux de contraintes maximales principales dans la paroi avec des valeurs de la littérature. Enfin, le déploiement est abordé tout en intégrant les cathéters. Les tests mécaniques menés sur les SG ont été simulés avec une différence maximale de 5,93%, tout en tenant compte de la pré-charge des stents. Le MEF de la structure vasculaire a montré des contraintes maximales principales éloignées de 4,41% par rapport à un modèle similaire précédemment publié. Quant à la simulation du déploiement, un jeu complet de SG a pu être déployé avec un bon contrôle de la position relative et globale, dans un AAA spécifique pré-déformé, sans toutefois inclure de thrombus intra-luminal (TIL). La paroi du AAA a été modélisée avec une loi de comportement isotropique hyperélastique. Étant donné que la différence maximale tolérée en milieu clinique entre réalité et simulation est de 5%, notre approche semble acceptable et pourrait donner suite à de futurs développements. Cela dit, le petit nombre de SG testés justifie pleinement une vaste campagne de tests mécaniques et simulations supplémentaires à des fins de validation.

Numerical simulation of tunnel fires using preconditioned finite volume schemes

This article is concerned with the numerical simulation of flows at low Mach numbers which are subject to the gravitational force and strong heat sources. As a specific example for such flows, a fire event in a car tunnel will be considered in detail. The low Mach flow is treated with a preconditioning technique allowing the computation of unsteady flows, while the source terms for gravitation and heat are incorporated via operator splitting. It is shown that a first order discretization in space is not able to compute the buoyancy forces properly on reasonable grids. The feasibility of the method is demonstrated on several test cases.

Performance of balanced two-stage empirical predictors of realized cluster latent values from finite populations: A simulation study

Predictors of random effects are usually based on the popular mixed effects (ME) model developed under the assumption that the sample is obtained from a conceptual infinite population; such predictors are employed even when the actual population is finite. Two alternatives that incorporate the finite nature of the population are obtained from the superpopulation model proposed by Scott and Smith (1969. Estimation in multi-stage surveys. J. Amer. Statist. Assoc. 64, 830-840) or from the finite population mixed model recently proposed by Stanek and Singer (2004. Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 1119-1130). Predictors derived under the latter model with the additional assumptions that all variance components are known and that within-cluster variances are equal have smaller mean squared error (MSE) than the competitors based on either the ME or Scott and Smith`s models. As population variances are rarely known, we propose method of moment estimators to obtain empirical predictors and conduct a simulation study to evaluate their performance. The results suggest that the finite population mixed model empirical predictor is more stable than its competitors since, in terms of MSE, it is either the best or the second best and when second best, its performance lies within acceptable limits. When both cluster and unit intra-class correlation coefficients are very high (e.g., 0.95 or more), the performance of the empirical predictors derived under the three models is similar. (c) 2007 Elsevier B.V. All rights reserved.