An adaptive-order rational Arnoldi method for model-order reductions of linear time-invariant systems

AbstractThis work proposes a model reduction method, the adaptive-order rational Arnoldi (AORA) method, to be applied to large-scale linear systems. It is based on an extension of the classical multi-point Padé approximation (or the so-called multi-point moment matching), using the rational Arnoldi iteration approach. Given a set of predetermined expansion points, an exact expression for the error between the output moment of the original system and that of the reduced-order system, related to each expansion point, is derived first. In each iteration of the proposed adaptive-order rational Arnoldi algorithm, the expansion frequency corresponding to the maximum output moment error will be chosen. Hence, the corresponding reduced-order model yields the greatest improvement in output moments among all reduced-order models of the same order. A detailed theoretical study is described. The proposed method is very appropriate for large-scale electronic systems, including VLSI interconnect models and digital filter designs. Several examples are considered to demonstrate the effectiveness and efficiency of the proposed method

Adaptive rational interpolation: Arnoldi and Lanczos-like equations

Accepted versio

Model-order reduction techniques for the numerical solution of electromagnetic wave scattering problems

It is the aim of this work to contribute to the development of model-order reduction (MOR) techniques for the field of computational electromagnetics in relation to the electric field integral equation (EFIE) formulation. The ultimate goal is to enable a fast-sweep analysis. In a fast-sweep problem, some parameter on which the original problem depends is varying and the problem must be solved as the parameter changes over a desired parameter range. The complexity of the original model prohibits its direct use in simulation to compute the results at every required point. However, one can use MOR techniques to generate reduced-order models (ROMs), which can be rapidly solved to characterise the parameter-dependent behaviour of the system over the entire parameter range. This thesis focus is to implement robust, fast and accurate MOR techniques with strict error controls, for application with varying parameters, using the EFIE formulations. While these formulations result in matrices that are significantly smaller relative to differential equation-based formulations, the matrices resulting from discretising integral equations are very dense. Consequently, EFIEs pose a difficult proposition in the generation of low-order accurate reduced order models. The MOR techniques presented in this thesis are based on the theory of Krylov projections. They are widely accepted as being the most flexible and computationally efficient approaches in the generation of ROMs. There are three main contributions attributed to this work. ² The formulation of an approximate extension of the Arnoldi algorithm to produce a ROM for an inhomogeneous contrast-sweep and source-sweep analysis. ² Investigation of the application of the Well-Conditioned Asymptotic Waveform Evaluation (WCAWE) technique to problems in which the system matrix has a nonlinear parameter dependence for EFIE formulations. ² The development of a fast full-wave frequency sweep analysis using the WCAWE technique for materials with frequency-dependent dielectric properties

MODEL ORDER REDUCTION OF NONLINEAR DYNAMIC SYSTEMS USING MULTIPLE PROJECTION BASES AND OPTIMIZED STATE-SPACE SAMPLING

Model order reduction (MOR) is a very powerful technique that is used to deal with the increasing complexity of dynamic systems. It is a mature and well understood field of study that has been applied to large linear dynamic systems with great success. However, the continued scaling of integrated micro-systems, the use of new technologies, and aggressive mixed-signal design has forced designers to consider nonlinear effects for more accurate model representations. This has created the need for a methodology to generate compact models from nonlinear systems of high dimensionality, since only such a solution will give an accurate description for current and future complex systems.The goal of this research is to develop a methodology for the model order reduction of large multidimensional nonlinear systems. To address a broad range of nonlinear systems, which makes the task of generalizing a reduction technique difficult, we use the concept of transforming the nonlinear representation into a composite structure of well defined basic functions from multiple projection bases.We build upon the concept of a training phase from the trajectory piecewise-linear (TPWL) methodology as a practical strategy to reduce the state exploration required for a large nonlinear system. We improve upon this methodology in two important ways: First, with a new strategy for the use of multiple projection bases in the reduction process and their coalescence into a unified base that better captures the behavior of the overall system; and second, with a novel strategy for the optimization of the state locations chosen during training. This optimization technique is based on using the Hessian of the system as an error bound metric.Finally, in order to treat the overall linear/nonlinear reduction task, we introduce a hierarchical approach using a block projection base. These three strategies together offer us a new perspective to the problem of model order reduction of nonlinear systems and the tracking or preservation of physical parameters in the final compact model

Momenten-Abgleich-Verfahren in der Modellreduktion von elektromagnetischen Problemstellungen

In this thesis, the application of moment matching based model order reduction techniques to first- and second-order model problems of Maxwell's equations in semiconductor structures is considered. Apart from preserving the specific structure of Maxwell's equations in the reduced order model, we provide a new Greedy-type expansion point selection strategy based on the adaptive-order rational Arnoldi method. Moreover, we give an overview on the appropriate treatment of the discrete divergence conditions for moment matching based model order reduction. With respect to the offline stage of model order reduction, we introduce a specific framework of moment matching methods allowing for the efficient computation of a reduced order model. In detail, we consider a modification of the adaptive-order rational Arnoldi method avoiding the complete recomputation of the orthonormal vector sequences for subsequently computed reduced order models. Apart from employing an algebraic two-level approach for solving sequences of shifted linear systems, we have also discussed the application of the recycling SQMR method in moment matching based model order reduction. In the latter case, we typically benefit from exploiting the fact that the discretized first- and second-order Maxwell's equations offer a specific J-symmetry.Die zugrundeliegende Arbeit beinhaltet die Anwendung der Modellreduktion mittels Momenten-Abgleich-Verfahren auf Maxwell-Gleichungen erster bzw. zweiter Ordnung aus dem Anwendungsgebiet der Halbleiterstrukturen. Abgesehen von der Erhaltung der speziellen Struktur der Maxwell-Gleichungen im reduzierten Modell, wird eine neue Greedy-artige Entwicklungspunktauswahl basierend auf dem adaptiven rationalen Arnoldi-Verfahren eingeführt. Darüber hinaus geben wir einen Überblick über die geeignete Behandlung der diskreten Divergenz-Bedingungen für Momenten-Abgleich-Verfahren in der Modellreduktion. Im Hinblick auf die Offline-Phase der Modellreduktion, werden wir im weiteren Verlauf ein effizientes Framework für Momenten-Abgleich-Verfahren einführen, die eine effiziente Berechnung einer Folge reduzierter Modelle erlaubt. Insbesondere werden wir dabei eine Modifikation des adaptiven rationalen Arnoldi-Verfahrens vorstellen, die eine vollständige, wiederholte Berechnung der Sequenzen orthonormaler Vektoren für aufeinanderfolgende reduzierte Modelle vermeidet. Abgesehen von der Anwendung eines algebraischen Zwei-Level-Verfahrens für die Lösung geshifteter linearer Gleichungssysteme, haben wir darüber hinaus die Anwendung des recycling SQMR Verfahrens innerhalb der Modellreduktion mittels Momenten-Abgleich-Verfahren betrachtet. Im letzteren Fall profitieren wir in der Regel von der Tatsache, dass die diskretisierten Maxwell-Gleichungen erster bzw. zweiter Ordnung eine spezielle J-Symmetrie aufweisen

Model Order Reduction

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science

Krylov-subspace based model reduction for simulation of machine tools

Die vorliegende Arbeit hat die Reduktion von Strukturmodellen, wie sie bei der Simulation von Werkzeugmaschinen zum Einsatz kommt, zum Thema. Dabei steht die Entwicklung neuer mathematischer Reduktionsverfahren, die auf Krylov-Unterräumen basieren im Fokus der Arbeit. Daneben wird auch die Bedeutung der Modellreduktion als wichtiges Hilfsmittel zur Gewährleistung einer effizienten Simulation im Gesamtentwicklungsprozess von Werkzeugmaschinen dargestellt. Für die Reduktion von Strukturmodellen werden bislang vorwiegend die sogenannten modalen Verfahren verwendet. Die mit diesen Verfahren reduzierten Modelle können im statischen Bereich einen erheblichen Fehler aufweisen. In der vorliegenden Arbeit werden neue Reduktionsverfahren, die auf der Grundlage mathematischer Methoden zur Modellreduktion aufbauen und eine Automatisierung des Modellreduktionsprozesses ermöglichen, entwickelt und validiert. Die neu entwickelten Verfahren basieren dabei auf einer angepassten iterativen und adaptiven Auswahl der für die mathematischen Reduktionsmethoden benötigten Parameter. Zudem basieren diese Verfahren auf globale Krylov-Unterräume und der Verwendung der globalen Arnoldi-Methode, die sich durch eine hohe Effizienz bei der Berechnung der Reduktion von Systemen mit mehreren Ein- und Ausgängen auszeichnet. Mit Hilfe einer geeigneten Methode zur Abschätzung des Approximationsfehlers des reduzierten Systems erlauben die neu entwickelten Verfahren eine automatische Modellreduktion ohne Benutzerinteraktion während des Reduktionsprozesses.The subject of the present work is the reduction of structural models, as used in the simulation of machine tools. The focus of the work is the development of new mathematical reduction procedures, based on Krylov-subspaces and distinguished by a feasible automation. In addition, however, the model reduction as an important tool for an efficient simulation in the overall development process of machine tools is presented. For the reduction of structure models usually the so-called modal method based on the solution of a eigenvalue problem is used. Reduced models obtained with the modal method can have considerable errors in the static area. In this work new reduction procedures, based on mathematical reduction methods, for automated reduction of finite element models are developed and validated. The new procedures are based on a adapted approach to choose optimal parameter for the mathematical reduction methods. Moreover the new procedures are based on global Krylov-subspaces and the global Arnoldi-method, characterized by high efficiency in the calculation of the reduction of systems with multiple inputs and outputs. By using a suitable method for estimation of the approximation error, a automated reduction process with any user interaction is suggested

Model Reduction for Vehicle Systems Modelling

The full model of a double-wishbone suspension has more than 30 differential-algebraic equations which takes a remarkably long time to simulate. By contrast, the look-up table for the same suspension is simulated much faster, but may not be very accurate. Therefore, developing reduced models that approximate complex systems is necessary because model reduction decreases the simulation time in comparison with the original model, enables real time applications, and produces acceptable accuracy. In this research, we focus on model reduction techniques for vehicle systems such as suspensions and how they are approximated by models having lower degrees of freedom. First, some existing model reduction techniques, such as irreducible realization procedures, balanced truncation, and activity-based reduction, are implemented to some vehicle suspensions. Based on the application of these techniques, their disadvantages are revealed. Then, two methods of model reduction for multi-body systems are proposed. The first proposed method is 2-norm power-based model reduction (2NPR) that combines 2-norm of power and genetic algorithms to derive reduced models having lower degrees of freedom and fewer number of components. In the 2NPR, some components such as mass, damper, and spring are removed from the original system. Afterward, the values of the remaining components are adjusted by the genetic algorithms. The most important advantage of the 2NPR is keeping the topology of multi-body systems which is useful for design purposes. The second method uses proper orthogonal decomposition. First, the equations of motion for a multi-body system are converted to explicit second-order differential equations. Second, the projection matrix is obtained from simulation or experimental data by proper orthogonal decomposition. Finally, the equations of motion are transferred to a lower-dimensional state coordinate system. The implementation of the 2NPR to two double-wishbone suspensions and the comparison with other techniques such as balanced truncation and activity-based model reduction also demonstrate the efficiency of the new reduction technique