[pt] A representação matemática de comportamentos complexos em materiais exige formulações constitutivas sofisticada, como é o caso de modelos com múltiplas superfícies de plastificação. Assim, um modelo elastoplástico complexo demanda um procedimento robusto de integração das equações de evolução plástica. O desenvolvimento de esquemas de integração para modelos de plasticidade é um tópico de pesquisa importante, já que estes estão diretamente ligados à acurácia e eficiência de simulações numéricas de materiais como metais, concretos, solos e rochas. O desempenho da solução de elementos finitos é diretamente afetado pelas características de convergência do procedimento de atualização de estados. Dessa forma, este trabalho explora a implementação de modelos constitutivos complexos, focando em modelos genéricos com múltiplas superfícies de plastificação. Este estudo formula e avalia algoritmos de atualização de estado que formam uma estrutura robusta para a simulação de materiais regidos por múltiplas superfícies de plastificação. Algoritmos de integração implícita são desenvolvidos com ênfase na obtenção de robustez, abrangência e flexibilidade para lidar eficazmente com aplicações complexas de plasticidade. Os algoritmos de atualização de estado, baseados no método de Euler implícito e nos métodos de Newton-Raphson e Newton-Krylov, são formulados utilizando estratégias de busca unidimensional para melhorar suas características de convergência. Além disso, é implementado um esquema de subincrementação para proporcionar mais robustez ao procedimento de atualização de estado. A flexibilidade dos algoritmos é explorada, considerando várias condições de tensão, como os estados plano de tensões e plano de deformações, num esquema de integração único e versátil. Neste cenário, a robustez e o desempenho dos algoritmos são avaliados através de aplicações clássicas de elementos finitos. Além disso, o cenário desenvolvido no contexto de modelos com múltiplas superfícies de plastificação é aplicado para formular um modelo elastoplástico com dano acoplado, que é avaliado através de ensaios experimentais em estruturas de concreto. Os resultados obtidos evidenciam a eficácia dos algoritmos de atualização de estado propostos na integração de equações de modelos com múltiplas superfícies de plastificação e a sua capacidade para lidar com problemas desafiadores de elementos finitos. / [en] The mathematical representation of complex material behavior requires a sophisticated constitutive formulation, as it is the case of multisurface plasticity. Hence, a complex elastoplastic model demands a robust integration procedure for the plastic evolution equations. Developing integration schemes for plasticity models is an important research topic because these schemes are directly related to the accuracy and efficiency of numerical simulations for materials such as metals, concrete, soils and rocks. The performance of the finite element solution is directly influenced by the convergence characteristics of the state-update procedure. Therefore, this work explores the implementation of complex constitutive models, focusing on generic multisurface plasticity models. This study formulates and evaluates state-update algorithms that form a robust framework for simulating materials governed by multisurface plasticity. Implicit integration algorithms are developed with an emphasis on achieving robustness, comprehensiveness and flexibility to handle cumbersome plasticity applications effectively. The state-update algorithms, based on the backward Euler method and the Newton-Raphson and Newton-Krylov methods, are formulated using line search strategies to improve their convergence characteristics. Additionally, a substepping scheme is implemented to provide further robustness to the state-update procedure. The flexibility of the algorithms is explored, considering various stress conditions such as plane stress and plane strain states, within a single, versatile integration scheme. In this scenario, the robustness and performance of the algorithms are assessed through classical finite element applications. Furthermore, the developed multisurface plasticity background is applied to formulate a coupled elastoplastic-damage model, which is evaluated using experimental tests in concrete structures. The achieved results highlight the effectiveness of the proposed state-update algorithms in integrating multisurface plasticity equations and their ability to handle challenging finite element problems.

Aerodynamic shape optimization and multidisciplinary optimization algorithms have the potential not only to improve conventional aircraft, but also to enable the design of novel configurations. By their very nature, these algorithms generate and analyze a large number of unique shapes, resulting in high computational costs. In order to improve their efficiency and enable their use in the early stages of the design process, a fast and robust flow solution algorithm is necessary. This thesis presents an efficient parallel Newton-Krylov-Schur flow solution algorithm for the three-dimensional Navier-Stokes equations coupled with the Spalart-Allmaras one-equation turbulence model. The algorithm employs second-order summation-by-parts (SBP) operators on multi-block structured grids with simultaneous approximation terms (SATs) to enforce block interface coupling and boundary conditions. The discrete equations are solved iteratively with an inexact-Newton method, while the linear system at each Newton iteration is solved using the flexible Krylov subspace iterative method GMRES with an approximate-Schur parallel preconditioner. The algorithm is thoroughly verified and validated, highlighting the correspondence of the current algorithm with several established flow solvers. The solution for a transonic flow over a wing on a mesh of medium density (15 million nodes) shows good agreement with experimental results. Using 128 processors, deep convergence is obtained in under 90 minutes. The solution of transonic flow over the Common Research Model wing-body geometry with grids with up to 150 million nodes exhibits the expected grid convergence behavior. This case was completed as part of the Fifth AIAA Drag Prediction Workshop, with the algorithm producing solutions that compare favourably with several widely used flow solvers. The algorithm is shown to scale well on over 6000 processors. The results demonstrate the effectiveness of the SBP-SAT spatial discretization, which can be readily extended to high order, in combination with the Newton-Krylov-Schur iterative method to produce a powerful parallel algorithm for the numerical solution of the Reynolds-averaged Navier-Stokes equations. The algorithm can efficiently solve the flow over a range of clean geometries, making it suitable for use at the core of an optimization algorithm.

Aerodynamic shape optimization and multidisciplinary optimization algorithms have the potential not only to improve conventional aircraft, but also to enable the design of novel configurations. By their very nature, these algorithms generate and analyze a large number of unique shapes, resulting in high computational costs. In order to improve their efficiency and enable their use in the early stages of the design process, a fast and robust flow solution algorithm is necessary. This thesis presents an efficient parallel Newton-Krylov-Schur flow solution algorithm for the three-dimensional Navier-Stokes equations coupled with the Spalart-Allmaras one-equation turbulence model. The algorithm employs second-order summation-by-parts (SBP) operators on multi-block structured grids with simultaneous approximation terms (SATs) to enforce block interface coupling and boundary conditions. The discrete equations are solved iteratively with an inexact-Newton method, while the linear system at each Newton iteration is solved using the flexible Krylov subspace iterative method GMRES with an approximate-Schur parallel preconditioner. The algorithm is thoroughly verified and validated, highlighting the correspondence of the current algorithm with several established flow solvers. The solution for a transonic flow over a wing on a mesh of medium density (15 million nodes) shows good agreement with experimental results. Using 128 processors, deep convergence is obtained in under 90 minutes. The solution of transonic flow over the Common Research Model wing-body geometry with grids with up to 150 million nodes exhibits the expected grid convergence behavior. This case was completed as part of the Fifth AIAA Drag Prediction Workshop, with the algorithm producing solutions that compare favourably with several widely used flow solvers. The algorithm is shown to scale well on over 6000 processors. The results demonstrate the effectiveness of the SBP-SAT spatial discretization, which can be readily extended to high order, in combination with the Newton-Krylov-Schur iterative method to produce a powerful parallel algorithm for the numerical solution of the Reynolds-averaged Navier-Stokes equations. The algorithm can efficiently solve the flow over a range of clean geometries, making it suitable for use at the core of an optimization algorithm.

Advances in numerical optimization have raised the possibility that efficient and novel aircraft configurations may be ``discovered'' by an algorithm. To begin exploring this possibility, a fast and robust set of tools for aerodynamic shape optimization is developed. Parameterization and mesh-movement are integrated to accommodate large changes in the geometry. This integrated approach uses a coarse B-spline control grid to represent the geometry and move the computational mesh; consequently, the mesh-movement algorithm is two to three orders faster than a node-based linear elasticity approach, without compromising mesh quality. Aerodynamic analysis is performed using a flow solver for the Euler equations. The governing equations are discretized using summation-by-parts finite-difference operators and simultaneous approximation terms, which permit nonsmooth mesh continuity at block interfaces. The discretization results in a set of nonlinear algebraic equations, which are solved using an efficient parallel Newton-Krylov-Schur strategy. A gradient-based optimization algorithm is adopted. The gradient is evaluated using adjoint variables for the flow and mesh equations in a sequential approach. The flow adjoint equations are solved using a novel variant of the Krylov solver GCROT. This variant of GCROT is flexible to take advantage of non-stationary preconditioners and is shown to outperform restarted flexible GMRES. The aerodynamic optimizer is applied to several studies of induced-drag minimization. An elliptical lift distribution is recovered by varying spanwise twist, thereby validating the algorithm. Planform optimization based on the Euler equations produces a nonelliptical lift distribution, in contrast with the predictions of lifting-line theory. A study of spanwise vertical shape optimization confirms that a winglet-up configuration is more efficient than a winglet-down configuration. A split-tip geometry is used to explore nonlinear wake-wing interactions: the optimized split-tip demonstrates a significant reduction in induced drag relative to a single-tip wing. Finally, the optimal spanwise loading for a box-wing configuration is investigated.

Dans certains regimes hypersoniques, le rayonnement peut enormement modifier l'ecoulement aerodynamique. Pour de telles applications, il est important d'avoir un modele qui realise un couplage fort entre l'hydrodynamique et le transfert radiatif afin d'avoir un bon comportement de la solution. Cependant, le couplage avec l'equation du transfert radiatif est en general extremement couteux et donc peu raisonnable pour des simulations multidimensionnelles instationnaires. Notre choix est d'utiliser un modele aux moments pour la partie rayonnement, ce qui est bien moins couteux. Celui-ci est base sur une fermeture entropique a la Levermore qui permet de conserver les principales proprietes de la physique. On developpe une version multigroupe de ce modele afin de pouvoir traiter des cas realistes tres dependants de la frequence. Le systeme couple resultant est hyperbolique et possede des proprietes interessantes qui sont etudiees. Ce modele radiatif est couple avec les equations de Navier-Stokes avec une approche totalement implicite et fortement couplee. De plus, pour gagner de la place memoire, on choisit d'utiliser une methode sans Jacobienne, en pratique une methode de type GMRes preconditionne. Cette methode se revele assez rapide pour pouvoir simuler des applications realistes a un cout de calcul raisonnable, ce qui n'est pas le cas de la plupart des modeles courament utilises dans la litterature. Plusieurs applications sont donnees pour illustrer le bon comportement du modele a la fois dans des configurations academiques simplifiees ou l'on peut faire des comparaisons et dans des configurations realistes comme l'ecoulement lors de l'entree atmospherique de sondes superorbitales.

La simulation numérique de systèmes d'équations différentielles raides ordinaires ou algébriques est devenue partie intégrante dans le processus de conception des systèmes mécaniques à dynamiques complexes. L'objet de ce travail est de développer des méthodes numériques pour réduire les temps de calcul par le parallélisme en suivant deux axes : interne à l'intégrateur numérique, et au niveau de la décomposition de l'intervalle de temps. Nous montrons l'efficacité limitée au nombre d'étapes de la parallélisation à travers les méthodes de Runge-Kutta et DIMSIM. Nous développons alors une méthodologie pour appliquer le complément de Schur sur le système linéarisé intervenant dans les intégrateurs par l'introduction d'un masque de dépendance construit automatiquement lors de la mise en équations du modèle. Finalement, nous étendons le complément de Schur aux méthodes de type "Krylov Matrix Free". La décomposition en temps est d'abord vue par la résolution globale des pas de temps dont nous traitons la parallélisation du solveur non-linéaire (point fixe, Newton-Krylov et accélération de Steffensen). Nous introduisons les méthodes de tirs à deux niveaux, comme Parareal et Pita dont nous redéfinissons les finesses de grilles pour résoudre les problèmes raides pour lesquels leur efficacité parallèle est limitée. Les estimateurs de l'erreur globale, nous permettent de construire une extension parallèle de l'extrapolation de Richardson pour remplacer le premier niveau de calcul. Et nous proposons une parallélisation de la méthode de correction du résidu.

Advances in numerical optimization have raised the possibility that efficient and novel aircraft configurations may be ``discovered'' by an algorithm. To begin exploring this possibility, a fast and robust set of tools for aerodynamic shape optimization is developed. Parameterization and mesh-movement are integrated to accommodate large changes in the geometry. This integrated approach uses a coarse B-spline control grid to represent the geometry and move the computational mesh; consequently, the mesh-movement algorithm is two to three orders faster than a node-based linear elasticity approach, without compromising mesh quality. Aerodynamic analysis is performed using a flow solver for the Euler equations. The governing equations are discretized using summation-by-parts finite-difference operators and simultaneous approximation terms, which permit nonsmooth mesh continuity at block interfaces. The discretization results in a set of nonlinear algebraic equations, which are solved using an efficient parallel Newton-Krylov-Schur strategy. A gradient-based optimization algorithm is adopted. The gradient is evaluated using adjoint variables for the flow and mesh equations in a sequential approach. The flow adjoint equations are solved using a novel variant of the Krylov solver GCROT. This variant of GCROT is flexible to take advantage of non-stationary preconditioners and is shown to outperform restarted flexible GMRES. The aerodynamic optimizer is applied to several studies of induced-drag minimization. An elliptical lift distribution is recovered by varying spanwise twist, thereby validating the algorithm. Planform optimization based on the Euler equations produces a nonelliptical lift distribution, in contrast with the predictions of lifting-line theory. A study of spanwise vertical shape optimization confirms that a winglet-up configuration is more efficient than a winglet-down configuration. A split-tip geometry is used to explore nonlinear wake-wing interactions: the optimized split-tip demonstrates a significant reduction in induced drag relative to a single-tip wing. Finally, the optimal spanwise loading for a box-wing configuration is investigated.

The objective of this PhD research programme is to investigate the effectiveness of a finite volume method incorporating radial basis functions for simulating nonlinear transport processes. The finite volume method is the favoured numerical technique for solving the advection-diffusion equations that arise in transport simulation. The method transforms the original problem into a system of nonlinear, algebraic equations through the process of discretisation. The accuracy of this discretisation determines to a large extent the accuracy of the final solution. A new method of discretisation is presented that employs radial basis functions (rbfs) as a means of local interpolation. When combined with Gaussian quadrature integration methods, the resulting finite volume discretisation leads to accurate numerical solutions without the need for very fine meshes, and the additional overheads they entail. The resulting nonlinear, algebraic system is solved efficiently using a Jacobian-free Newton-Krylov method. By employing the new method as an extension of existing shape function-based approaches, the number of nonlinear iterations required to obtain convergence can be reduced. Furthermore, information obtained from these iterations can be used to increase the efficiency of subsequent rbf-based iterations, as well as to construct an effective parallel reconditioner to further reduce the number of nonlinear iterations required. Results are presented that demonstrate the improved accuracy offered by the new method when applied to several test problems. By successively refining the meshes, it is also possible to demonstrate the increased order of the new method, when compared to a traditional shape function basedmethod. Comparing the resources required for both methods reveals that the new approach can be many times more efficient at producing a solution of a given accuracy.

Sea ice plays a key role in the global climate system. Indeed, through the albedo effect it reflects significant solar radiation away from the oceans, while it also plays a key role in the momentum and heat transfer between the atmosphere and ocean by acting as an insulating layer between the two. Furthermore, as more sea ice melts due to climate change, additional fresh water is released into the upper oceans, affecting the global circulation of the ocean as a whole. While there has been significant effort in recent decades, the ability to simulate sea ice has lagged behind other components of the climate system and most Earth System Models fail to capture the observed losses of Arctic sea ice, which is largely attributed to our inability to resolve sea ice dynamics. The most widely accepted model for sea ice dynamics is the Viscous- Plastic (VP) rheology, which leads to a very non-linear set of partial differential equations that are known to be intrinsically difficult to solve numerically. This work builds on recent advances in solving these equations with a Jacobian-Free Newton- Krylov (JFNK) solver. We present an improved JFNK solver, where a fully second order discretization is achieved via the Crank Nicolson scheme and consistency is improved via a novel approach to the rheology term. More importantly, we present a significant improvement to the Jacobian approximation used in the Newton iterations, and partially form the action of the matrix by expressing the linear and nearly linear terms in closed form and approximating the remaining highly non-linear term with a second order approximation of its Gateaux derivative. This is in contrast with the previous approach which used a first order approximation for the Gateaux derivative of the whole functional. Numerical tests on synthetic equations confirm the theoretical convergence rate and demonstrate the drastic improvements seen by using a second order approximation in the Gateaux derivative. To produce a fast and efficient solver for VP sea ice dynamics, the improved JFNK solver is then coupled with a non- oscillatory, central differencing scheme for transporting sea ice as well as a novel method for tracking the ice domain using a level set method. Two idealized test cases are then presented and simulation results discussed, demonstrating the solver’s ability to efficiently produce Viscous-Plastic, physically motivated solutions. / Graduate