On barrier and modified barrier multigrid methods for 3d topology optimization

One of the challenges encountered in optimization of mechanical structures, in particular in what is known as topology optimization, is the size of the problems, which can easily involve millions of variables. A basic example is the minimum compliance formulation of the variable thickness sheet (VTS) problem, which is equivalent to a convex problem. We propose to solve the VTS problem by the Penalty-Barrier Multiplier (PBM) method, introduced by R.\ Polyak and later studied by Ben-Tal and Zibulevsky and others. The most computationally expensive part of the algorithm is the solution of linear systems arising from the Newton method used to minimize a generalized augmented Lagrangian. We use a special structure of the Hessian of this Lagrangian to reduce the size of the linear system and to convert it to a form suitable for a standard multigrid method. This converted system is solved approximately by a multigrid preconditioned MINRES method. The proposed PBM algorithm is compared with the optimality criteria (OC) method and an interior point (IP) method, both using a similar iterative solver setup. We apply all three methods to different loading scenarios. In our experiments, the PBM method clearly outperforms the other methods in terms of computation time required to achieve a certain degree of accuracy

Implicit schemes and parallel computing in unstructured grid CFD

The development of implicit schemes for obtaining steady state solutions to the Euler and Navier-Stokes equations on unstructured grids is outlined. Applications are presented that compare the convergence characteristics of various implicit methods. Next, the development of explicit and implicit schemes to compute unsteady flows on unstructured grids is discussed. Next, the issues involved in parallelizing finite volume schemes on unstructured meshes in an MIMD (multiple instruction/multiple data stream) fashion are outlined. Techniques for partitioning unstructured grids among processors and for extracting parallelism in explicit and implicit solvers are discussed. Finally, some dynamic load balancing ideas, which are useful in adaptive transient computations, are presented

Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing

Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM

Parallel processing for nonlinear dynamics simulations of structures including rotating bladed-disk assemblies

The principal objective of this research is to develop, test, and implement coarse-grained, parallel-processing strategies for nonlinear dynamic simulations of practical structural problems. There are contributions to four main areas: finite element modeling and analysis of rotational dynamics, numerical algorithms for parallel nonlinear solutions, automatic partitioning techniques to effect load-balancing among processors, and an integrated parallel analysis system

Parallel computational strategies for modelling transient Stokes fluid flow.

The present work is centred on two main research areas; the development of finite element techniques for the modelling of transient Stokes flow and implementation of an effective parallel system on distributed memory platforms for solving realistic large-scale Lagrangian flow problems. The first part of the dissertation presents the space-time Galerkin / least-square finite element implicit formulation for solving incompressible or slightly compressible transient Stokes flow with moving boundaries. The formulation involves a time discontinuous Galerkin method and includes least-square terms in the variational formulation. Since the additional terms involve the residual of the Euler- Lagrangian equations evaluated over element interiors, it prevents numerical oscillation on the pressure field when equal lower order interpolation functions for velocity and pressure fields are used, without violating the Babuska-Brezzi stability condition. The space-time Galerkin / least-square formulation has been successfully extended into the finite element explicit analysis, in which the penalty based discrete element contact algorithm is adopted to simulate fiuid-structure or fluid-fluid particle contact. The second part of the dissertation focuses on the development of an effective parallel processing technique, using the natural algorithm concurrency of finite element formulations. A hybrid iterative direct parallel solver is implemented into the ELFEN/implicit commercial code. The solver is based on a non-overlapping domain decomposition and sub-structure approach. The modified Cholesky factorisation is used to eliminate the unknown variables of the internal nodes at each subdomain and the resulting interfacial equations are solved by a Krylov subspace iterative method. The parallelization of explicit fluid dynamics is based on overlapping domain decomposition and a Schwarz alternating procedure. Due to the dual nature of the overlapping domain decomposition a buffer zone between any two adjacent subdomains is introduced for handling the inter-processor communication. Both solvers are tested on a PC based interconnected network system and its performances are judged by the parallel speed-up and efficiency

Initialization and reformulation strategies for improved solving of nonlinear algebraic equation systems tested on chemical process models

Solving nonlinear algebraic systems of equations by numerical methods is often a time-consuming challenge in chemical engineering, especially when systems are ill-conditioned and/or no well estimated initial values for the numerical solver are available. In this work, a hybrid method is developed to solve such systems independently of an insufficient initialization. The hybrid method makes use of methods from interval arithmetic to exclude infeasible ranges of values of the unkown variables and to efficiently locate solutions in the remaining feasible region by Newton-based methods. The user only has to set the bounds of the unknown variables in advance. To ensure the independence of the approach from Newton-based methods, several of these are applied, namely: A self implemented Newton method, Scipy's SLSQP and Fsolve as well as Ipopt. The hybrid method is implemented in Python and tested on process engineering examples. These systems are all complex, but differ in dimension, condition, and nonlinearity. For all systems at least one physically feasible solution is found in a few minutes. All solutions in the unrestricted variable space can even be found for some systems. The interval arithmetic offers here the possibility to prove mathematically that there can be no further solutions. This is theoretically possible for all other test examples as well, but in the larger systems the interval arithmetic based reduction requires too many box reduction steps to get close to the real-valued solution(s). The effectiveness of the reduction of variable bounds is particularly dependent on the initialization of these and the formulation of the equations. As part of this work, a wide variety of initializations and formulations of the equations were examined and the most important findings were collected in the form of guidelines. Furthermore, a first classification of the investigated systems of equations was carried out, measured by their complexity. Based on this, it can be estimated which of the three solution strategies (interval arithmetic method, Newton-based method or hybrid approach) is most suitable in the individual case. The problem-independent applicability of the hybrid approach should be verified on further large, complex, nonlinear algebraic process models. Many steps within the procedure offer the possibility to be performed in parallel and could contribute significantly to its acceleration. Thus, the approach could also become interesting for solving optimization problems or discretized, differential algebraic equation systems

A Unifying Theory for Nonlinear Additively and Multiplicatively Preconditioned Globalization Strategies : Convergence Results and Examples From the Field of Nonlinear Elastostatics and Elastodynamics

Nonlinear right preconditioned globalization strategies for the solution of nonlinear programming problems of the following kind $u \in \mathcal B \subset \mathbb R^n: J(u) = \min!$ where $\mathcal B$ is a convex set of admissible solutions, $n\in \mathbb N$, and $J: \mathbb R^n \to \mathbb R$, sufficiently smooth, are presented. Preconditioned globalization strategies are traditional Linesearch or Trust-Region strategies in combination with a nonlinear update operator which results from a nonlinear solution process for smaller, but related, nonlinear programming problems. We will formulate conditions on this abstract operator, in order to ensure global convergence, i.e., convergence to first-order critical points, of the resulting method. In addition, we introduce particular implementations of this abstract operator, i.e., nonlinear multiplicatively preconditioned Trust-Region (MPTS) and Linesearch strategies (MPLS), as well as nonlinear additively preconditioned Trust-Region (APTS) and Linesearch (APLS) strategies. As it turns out, these additive strategies are novel parallel, locally adaptive and robust solution methods for nonlinear programming problems. Moreover, the MPTS strategy generalizes the RMTR concepts in [GK08] in order to allow also for the application of alternating nonlinear domain decomposition methods. On the other hand, the MPLS method simplifies and generalizes the concepts in [WG08] giving rise to a novel solution strategy for pointwise constrained nonlinear programming problems. The respective nonlinear solution strategies are analyzed and global convergence is shown. In addition, global convergence is also shown for combined nonlinear additively and multiplicatively preconditioned Trust-Region and Linesearch strategies. Moreover, we show the efficiency and reliability of these methods in the context of problems arising from the field of nonlinear elasticity in 3d. Particular emphasis has been placed on the formulation and analysis of the resulting minimization problems. Here, we show that these problems satisfy the assumptions stated to show convergence of the respective preconditioned globalization strategies. Moreover, various elasto-static and elasto-dynamic examples are presented in order to compare the convergence rates and runtimes of the different strategies

Krylov's methods in function space for waveform relaxation.

by Wai-Shing Luk.Thesis (Ph.D.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 104-113).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Functional Extension of Iterative Methods --- p.2Chapter 1.2 --- Applications in Circuit Simulation --- p.2Chapter 1.3 --- Multigrid Acceleration --- p.3Chapter 1.4 --- Why Hilbert Space? --- p.4Chapter 1.5 --- Parallel Implementation --- p.5Chapter 1.6 --- Domain Decomposition --- p.5Chapter 1.7 --- Contributions of This Thesis --- p.6Chapter 1.8 --- Outlines of the Thesis --- p.7Chapter 2 --- Waveform Relaxation Methods --- p.9Chapter 2.1 --- Basic Idea --- p.10Chapter 2.2 --- Linear Operators between Banach Spaces --- p.14Chapter 2.3 --- Waveform Relaxation Operators for ODE's --- p.16Chapter 2.4 --- Convergence Analysis --- p.19Chapter 2.4.1 --- Continuous-time Convergence Analysis --- p.20Chapter 2.4.2 --- Discrete-time Convergence Analysis --- p.21Chapter 2.5 --- Further references --- p.24Chapter 3 --- Waveform Krylov Subspace Methods --- p.25Chapter 3.1 --- Overview of Krylov Subspace Methods --- p.26Chapter 3.2 --- Krylov Subspace methods in Hilbert Space --- p.30Chapter 3.3 --- Waveform Krylov Subspace Methods --- p.31Chapter 3.4 --- Adjoint Operator for WBiCG and WQMR --- p.33Chapter 3.5 --- Numerical Experiments --- p.35Chapter 3.5.1 --- Test Circuits --- p.36Chapter 3.5.2 --- Unstructured Grid Problem --- p.39Chapter 4 --- Parallel Implementation Issues --- p.50Chapter 4.1 --- DECmpp 12000/Sx Computer and HPF --- p.50Chapter 4.2 --- Data Mapping Strategy --- p.55Chapter 4.3 --- Sparse Matrix Format --- p.55Chapter 4.4 --- Graph Coloring for Unstructured Grid Problems --- p.57Chapter 5 --- The Use of Inexact ODE Solver in Waveform Methods --- p.61Chapter 5.1 --- Inexact ODE Solver for Waveform Relaxation --- p.62Chapter 5.1.1 --- Convergence Analysis --- p.63Chapter 5.2 --- Inexact ODE Solver for Waveform Krylov Subspace Methods --- p.65Chapter 5.3 --- Experimental Results --- p.68Chapter 5.4 --- Concluding Remarks --- p.72Chapter 6 --- Domain Decomposition Technique --- p.80Chapter 6.1 --- Introduction --- p.80Chapter 6.2 --- Overlapped Schwarz Methods --- p.81Chapter 6.3 --- Numerical Experiments --- p.83Chapter 6.3.1 --- Delay Circuit --- p.83Chapter 6.3.2 --- Unstructured Grid Problem --- p.86Chapter 7 --- Conclusions --- p.90Chapter 7.1 --- Summary --- p.90Chapter 7.2 --- Future Works --- p.92Chapter A --- Pseudo Codes for Waveform Krylov Subspace Methods --- p.94Chapter B --- Overview of Recursive Spectral Bisection Method --- p.101Bibliography --- p.10