16 research outputs found

    The JOREK non-linear extended MHD code and applications to large-scale instabilities and their control in magnetically confined fusion plasmas

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    JOREK is a massively parallel fully implicit non-linear extended magneto-hydrodynamic (MHD) code for realistic tokamak X-point plasmas. It has become a widely used versatile simulation code for studying large-scale plasma instabilities and their control and is continuously developed in an international community with strong involvements in the European fusion research programme and ITER organization. This article gives a comprehensive overview of the physics models implemented, numerical methods applied for solving the equations and physics studies performed with the code. A dedicated section highlights some of the verification work done for the code. A hierarchy of different physics models is available including a free boundary and resistive wall extension and hybrid kinetic-fluid models. The code allows for flux-surface aligned iso-parametric finite element grids in single and double X-point plasmas which can be extended to the true physical walls and uses a robust fully implicit time stepping. Particular focus is laid on plasma edge and scrape-off layer (SOL) physics as well as disruption related phenomena. Among the key results obtained with JOREK regarding plasma edge and SOL, are deep insights into the dynamics of edge localized modes (ELMs), ELM cycles, and ELM control by resonant magnetic perturbations, pellet injection, as well as by vertical magnetic kicks. Also ELM free regimes, detachment physics, the generation and transport of impurities during an ELM, and electrostatic turbulence in the pedestal region are investigated. Regarding disruptions, the focus is on the dynamics of the thermal quench (TQ) and current quench triggered by massive gas injection and shattered pellet injection, runaway electron (RE) dynamics as well as the RE interaction with MHD modes, and vertical displacement events. Also the seeding and suppression of tearing modes (TMs), the dynamics of naturally occurring TQs triggered by locked modes, and radiative collapses are being studied.Peer ReviewedPostprint (published version

    Numerical models for the large-scale simulation of fault and fracture mechanics

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    The possible activation of pre-existing faults and the generation of new fractures in the subsurface may play a critical role in several fields of great social interest, such as the management and the exploitation of groundwater resources, especially in arid areas, the hydrocarbon recovery and storage, and the monitoring of the seismic activity in the Earth’s crust. The sliding and/or opening of a fault can create preferential leakage paths for the pore fluid escape, causing a matter of great concern in the process of storing fluids and hydrocarbons underground. The most challenging effect connected to a fault activation is the possible earthquake triggering. Many earthquakes associated with the production and injection of fluids have been recently reported. Similar issues arise also in the development of unconventional hydrocarbon reservoirs, that has recently experienced a dramatic increase thanks to the deployment of the “fracking” technology, which is based on the massive generation of fractures through the injection of fluids at high pressures. The use of this technique in densely populated areas has raised a large scientific debate on the possible connected environmental risks. The over-exploitation of fresh aquifers in arid regions has caused the generation of significant ground fissures. In this thesis, a novel formulation based on the use of Lagrange multipliers has been developed for the stable and robust numerical modeling of fault mechanics. A fault or fracture is simulated as a pair of inner surfaces included in a 3D geological formation where Lagrange multipliers are used to prescribe the contact constraints. The standard variational formulation of the contact problem with Lagrange multipliers is modified to take into account the energy dissipated by the frictional work along the activated fault portion. This term is computed by making use of the principle of maximum plastic dissipation, whose application defines the direction of the limiting shear stress vector. The novel approach has been verified against analytical solutions and applied in a number of real-world problems. In particular, we test the novel approach in four cases: (i) mechanics of two adjacent blocks, to investigate the numerical properties of the algorithm; (ii-iii) ground fractures due to groundwater withdrawal, with different geometries; (iv) fault reactivation in an underground reservoir subject to primary production and Underground Gas Storage cycles. The results are analyzed and commented. In the fourth case, the possible magnitude of the seismic events triggered by fault reactivation is computed, in order to evaluate whether underground human activities may generate seismicity. The application of the fault model to large-scale problems gives rise to a set of sparse discrete systems of linearized equations with a generalized non-symmetric saddle point structure. The second part of this thesis is devoted to the development of efficient algorithms for the iterative solution of this kind of system. We focus on a preconditioning technique, denoted as “constraint preconditioning”, which exploits the native block structure of the Jacobian. The quality and performance of the preconditioner relies on two steps: (i) the preconditioning of the leading block and (ii) the Schur complement computation. In this work, novel preconditioning techniques for the leading block based on a multilevel framework are developed and tested. The main idea behind the multilevel preconditioner is to improve the quality of the factorized approximate inverses borrowing the scheme of incomplete factorizations, thus introducing some sequentially in perfectly parallelizable algorithms. The proposed approach is robust, from a theoretical point of view, and very efficient in parallel environment. As to the latter point, i.e. the Schur complement computation, it can be done with the aid of different approximations. The main difference is whether the Jacobian is symmetrized or not. The computation can be founded on the FSAI approximation of the leading block inverse or on a physically-based block diagonal block algorithm. The Schur complement must be inverted, thus other possibilities come in. The approximate Schur complement can be inverted through FSAI, if symmetric, or an incomplete factorization, if non-symmetric, but it can also be solved exactly, thanks to a direct solver. The performances of the proposed algorithms are finally investigated and discussed in a set of real-world numerical examples

    A rational QZ method

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    We propose a rational QZ method for the solution of the dense, unsymmetric generalized eigenvalue problem. This generalization of the classical QZ method operates implicitly on a Hessenberg, Hessenberg pencil instead of on a Hessenberg, triangular pencil. Whereas the QZ method performs nested subspace iteration driven by a polynomial, the rational QZ method allows for nested subspace iteration driven by a rational function, this creates the additional freedom of selecting poles. In this article we study Hessenberg, Hessenberg pencils, link them to rational Krylov subspaces, propose a direct reduction method to such a pencil, and introduce the implicit rational QZ step. The link with rational Krylov subspaces allows us to prove essential uniqueness (implicit Q theorem) of the rational QZ iterates as well as convergence of the proposed method. In the proofs, we operate directly on the pencil instead of rephrasing it all in terms of a single matrix. Numerical experiments are included to illustrate competitiveness in terms of speed and accuracy with the classical approach. Two other types of experiments exemplify new possibilities. First we illustrate that good pole selection can be used to deflate the original problem during the reduction phase, and second we use the rational QZ method to implicitly filter a rational Krylov subspace in an iterative method

    Fiedler matrices: numerical and structural properties

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    The first and second Frobenius companion matrices appear frequently in numerical application, but it is well known that they possess many properties that are undesirable numerically, which limit their use in applications. Fiedler companion matrices, or Fiedler matrices for brevity, introduced in 2003, is a family of matrices which includes the two Frobenius matrices. The main goal of this work is to study whether or not Fiedler companion matrices can be used with more reliability than the Frobenius ones in the numerical applications where Frobenius matrices are used. For this reason, in this work we present a thorough study of Fiedler matrices: their structure and numerical properties, where we mean by numerical properties those properties that are interesting for applying these matrices in numerical computations, and some of their applications in the field on numerical linear algebra. The introduction of Fiedler companion matrices is an example of a simple idea that has been very influential in the development of several lines of research in the numerical linear algebra field. This family of matrices has important connections with a number of topics of current interest, including: polynomial root finding algorithms, linearizations of matrix polynomials, unitary Hessenberg matrices, CMV matrices, Green’s matrices, orthogonal polynomials, rank structured matrices, quasiseparable and semiseparable matrices, etc.Programa Oficial de Doctorado en Ingeniería MatemáticaPresidente: Paul Van Dooren.- Secretario: Juan Bernardo Zaballa Tejada.- Vocal: Françoise Tisseu

    STK /WST 795 Research Reports

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    These documents contain the honours research reports for each year for the Department of Statistics.Honours Research Reports - University of Pretoria 20XXStatisticsBSs (Hons) Mathematical Statistics, BCom (Hons) Statistics, BCom (Hons) Mathematical StatisticsUnrestricte

    Efficient Solvers for the Phase-Field Crystal Equation: Development and Analysis of a Block-Preconditioner

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    A preconditioner to improve the convergence properties of Krylov subspace solvers is derived and analyzed in this work. This method is adapted to linear systems arising from a finite-element discretization of a phase-field crystal equation

    Row Compression and Nested Product Decomposition of a Hierarchical Representation of a Quasiseparable Matrix

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    This research introduces a row compression and nested product decomposition of an nxn hierarchical representation of a rank structured matrix A, which extends the compression and nested product decomposition of a quasiseparable matrix. The hierarchical parameter extraction algorithm of a quasiseparable matrix is efficient, requiring only O(nlog(n))operations, and is proven backward stable. The row compression is comprised of a sequence of small Householder transformations that are formed from the low-rank, lower triangular, off-diagonal blocks of the hierarchical representation. The row compression forms a factorization of matrix A, where A = QC, Q is the product of the Householder transformations, and C preserves the low-rank structure in both the lower and upper triangular parts of matrix A. The nested product decomposition is accomplished by applying a sequence of orthogonal transformations to the low-rank, upper triangular, off-diagonal blocks of the compressed matrix C. Both the compression and decomposition algorithms are stable, and require O(nlog(n)) operations. At this point, the matrix-vector product and solver algorithms are the only ones fully proven to be backward stable for quasiseparable matrices. By combining the fast matrix-vector product and system solver, linear systems involving the hierarchical representation to nested product decomposition are directly solved with linear complexity and unconditional stability. Applications in image deblurring and compression, that capitalize on the concepts from the row compression and nested product decomposition algorithms, will be shown

    Novel Approach to Real Polynomial Root-finding and Matrix Eigen-solving

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    Univariate polynomial root-finding is both classical and important for modern computing. Frequently one seeks just the real roots of a polynomial with real coefficients. They can be approximated at a low computational cost if the polynomial has no nonreal roots, but typically nonreal roots are much more numerous than the real ones. We dramatically accelerate the known algorithms in this case by exploiting the correlation between the computations with matrices and polynomials, extending the techniques of the matrix sign iteration, and exploiting the structure of the companion matrix of the input polynomial. We extend some of the proposed techniques to the approximation of the real eigenvalues of a real nonsymmetric matrix.Comment: 17 pages, added algorithm
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